diff --git a/.gitattributes b/.gitattributes index 5b74547c164407125c935bf4c1ac1f66ee3b967b..c2033a5d1bae32f85cc6a51fbc2fe63184f1b222 100644 --- a/.gitattributes +++ b/.gitattributes @@ -783,3 +783,6 @@ openflamingo/lib/python3.10/site-packages/scipy/stats/__pycache__/_multivariate. openflamingo/lib/python3.10/site-packages/scipy/stats/_rcont/rcont.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text openflamingo/lib/python3.10/site-packages/scipy/stats/__pycache__/_morestats.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text phi4/lib/python3.10/site-packages/numpy/_core/lib/libnpymath.a filter=lfs diff=lfs merge=lfs -text +phi4/lib/python3.10/site-packages/numpy/_core/__pycache__/fromnumeric.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text +openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_mio5_utils.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/__init__.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1350eae3df18d4c3a20bace39aa55e649928ab24 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/__init__.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_bsplines.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_bsplines.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d4df6bb2b20ccc4a0f55568cacebbf6b8b97cd6f Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_bsplines.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_fitpack_impl.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_fitpack_impl.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f6d1624e448f8204d614517fecca3c31de134442 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_fitpack_impl.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_pade.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_pade.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..da9187b8e5ccbe2648d48a914f9a22364ff06262 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_pade.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_rbf.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_rbf.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7adb98278efc5905c8cb664af0da4785da61fe53 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_rbf.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_rbfinterp.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_rbfinterp.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0be4c676228fb9080a03b94d58d9e7b1bdffbb9b Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/_rbfinterp.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/fitpack.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/fitpack.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..21c46721b8de8ab406950e2be4509ba8acda9bcb Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/fitpack.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/fitpack2.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/fitpack2.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5ff3a637fd9e7d762dbfc4d189453a0896ede2be Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/fitpack2.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/rbf.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/rbf.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..898b93725935b140bf0e159e8f8c42350e992686 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/interpolate/__pycache__/rbf.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_mio5_utils.cpython-310-x86_64-linux-gnu.so b/openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_mio5_utils.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..865422d179080bcbdd6407bad3dd589ee8999018 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_mio5_utils.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f6db2c15f3aab0d2b55bbb7cd6fdcd0dfe4e8ebab20ad5f48f2e715493c6f684 +size 264600 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so b/openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..b8d409824c2d991781c57c5203be09d0a7054f17 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82c90081d32a4102eeda9b5d02babb6a96631bb43e462cf4c56776bfcd44617f +size 147488 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/_ckdtree.pyi b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_ckdtree.pyi new file mode 100644 index 0000000000000000000000000000000000000000..42670067a38c3021d20aeb39bb04920ab9ccde24 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_ckdtree.pyi @@ -0,0 +1,214 @@ +from __future__ import annotations +from typing import ( + Any, + Generic, + overload, + TypeVar, +) + +import numpy as np +import numpy.typing as npt +from scipy.sparse import coo_matrix, dok_matrix + +from typing import Literal + +# TODO: Replace `ndarray` with a 1D float64 array when possible +_BoxType = TypeVar("_BoxType", None, npt.NDArray[np.float64]) + +# Copied from `numpy.typing._scalar_like._ScalarLike` +# TODO: Expand with 0D arrays once we have shape support +_ArrayLike0D = bool | int | float | complex | str | bytes | np.generic + +_WeightType = npt.ArrayLike | tuple[npt.ArrayLike | None, npt.ArrayLike | None] + +class cKDTreeNode: + @property + def data_points(self) -> npt.NDArray[np.float64]: ... + @property + def indices(self) -> npt.NDArray[np.intp]: ... + + # These are read-only attributes in cython, which behave like properties + @property + def level(self) -> int: ... + @property + def split_dim(self) -> int: ... + @property + def children(self) -> int: ... + @property + def start_idx(self) -> int: ... + @property + def end_idx(self) -> int: ... + @property + def split(self) -> float: ... + @property + def lesser(self) -> cKDTreeNode | None: ... + @property + def greater(self) -> cKDTreeNode | None: ... + +class cKDTree(Generic[_BoxType]): + @property + def n(self) -> int: ... + @property + def m(self) -> int: ... + @property + def leafsize(self) -> int: ... + @property + def size(self) -> int: ... + @property + def tree(self) -> cKDTreeNode: ... + + # These are read-only attributes in cython, which behave like properties + @property + def data(self) -> npt.NDArray[np.float64]: ... + @property + def maxes(self) -> npt.NDArray[np.float64]: ... + @property + def mins(self) -> npt.NDArray[np.float64]: ... + @property + def indices(self) -> npt.NDArray[np.float64]: ... + @property + def boxsize(self) -> _BoxType: ... + + # NOTE: In practice `__init__` is used as constructor, not `__new__`. + # The latter gives us more flexibility in setting the generic parameter + # though. + @overload + def __new__( # type: ignore[misc] + cls, + data: npt.ArrayLike, + leafsize: int = ..., + compact_nodes: bool = ..., + copy_data: bool = ..., + balanced_tree: bool = ..., + boxsize: None = ..., + ) -> cKDTree[None]: ... + @overload + def __new__( + cls, + data: npt.ArrayLike, + leafsize: int = ..., + compact_nodes: bool = ..., + copy_data: bool = ..., + balanced_tree: bool = ..., + boxsize: npt.ArrayLike = ..., + ) -> cKDTree[npt.NDArray[np.float64]]: ... + + # TODO: returns a 2-tuple of scalars if `x.ndim == 1` and `k == 1`, + # returns a 2-tuple of arrays otherwise + def query( + self, + x: npt.ArrayLike, + k: npt.ArrayLike = ..., + eps: float = ..., + p: float = ..., + distance_upper_bound: float = ..., + workers: int | None = ..., + ) -> tuple[Any, Any]: ... + + # TODO: returns a list scalars if `x.ndim <= 1`, + # returns an object array of lists otherwise + def query_ball_point( + self, + x: npt.ArrayLike, + r: npt.ArrayLike, + p: float, + eps: float = ..., + workers: int | None = ..., + return_sorted: bool | None = ..., + return_length: bool = ... + ) -> Any: ... + + def query_ball_tree( + self, + other: cKDTree, + r: float, + p: float, + eps: float = ..., + ) -> list[list[int]]: ... + + @overload + def query_pairs( # type: ignore[misc] + self, + r: float, + p: float = ..., + eps: float = ..., + output_type: Literal["set"] = ..., + ) -> set[tuple[int, int]]: ... + @overload + def query_pairs( + self, + r: float, + p: float = ..., + eps: float = ..., + output_type: Literal["ndarray"] = ..., + ) -> npt.NDArray[np.intp]: ... + + @overload + def count_neighbors( # type: ignore[misc] + self, + other: cKDTree, + r: _ArrayLike0D, + p: float = ..., + weights: None | tuple[None, None] = ..., + cumulative: bool = ..., + ) -> int: ... + @overload + def count_neighbors( # type: ignore[misc] + self, + other: cKDTree, + r: _ArrayLike0D, + p: float = ..., + weights: _WeightType = ..., + cumulative: bool = ..., + ) -> np.float64: ... + @overload + def count_neighbors( # type: ignore[misc] + self, + other: cKDTree, + r: npt.ArrayLike, + p: float = ..., + weights: None | tuple[None, None] = ..., + cumulative: bool = ..., + ) -> npt.NDArray[np.intp]: ... + @overload + def count_neighbors( + self, + other: cKDTree, + r: npt.ArrayLike, + p: float = ..., + weights: _WeightType = ..., + cumulative: bool = ..., + ) -> npt.NDArray[np.float64]: ... + + @overload + def sparse_distance_matrix( # type: ignore[misc] + self, + other: cKDTree, + max_distance: float, + p: float = ..., + output_type: Literal["dok_matrix"] = ..., + ) -> dok_matrix: ... + @overload + def sparse_distance_matrix( # type: ignore[misc] + self, + other: cKDTree, + max_distance: float, + p: float = ..., + output_type: Literal["coo_matrix"] = ..., + ) -> coo_matrix: ... + @overload + def sparse_distance_matrix( # type: ignore[misc] + self, + other: cKDTree, + max_distance: float, + p: float = ..., + output_type: Literal["dict"] = ..., + ) -> dict[tuple[int, int], float]: ... + @overload + def sparse_distance_matrix( + self, + other: cKDTree, + max_distance: float, + p: float = ..., + output_type: Literal["ndarray"] = ..., + ) -> npt.NDArray[np.void]: ... diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/_kdtree.py b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_kdtree.py new file mode 100644 index 0000000000000000000000000000000000000000..f65412e9e4ad3a0e0da3876c861430d678fe858c --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_kdtree.py @@ -0,0 +1,920 @@ +# Copyright Anne M. Archibald 2008 +# Released under the scipy license +import numpy as np +from ._ckdtree import cKDTree, cKDTreeNode + +__all__ = ['minkowski_distance_p', 'minkowski_distance', + 'distance_matrix', + 'Rectangle', 'KDTree'] + + +def minkowski_distance_p(x, y, p=2): + """Compute the pth power of the L**p distance between two arrays. + + For efficiency, this function computes the L**p distance but does + not extract the pth root. If `p` is 1 or infinity, this is equal to + the actual L**p distance. + + The last dimensions of `x` and `y` must be the same length. Any + other dimensions must be compatible for broadcasting. + + Parameters + ---------- + x : (..., K) array_like + Input array. + y : (..., K) array_like + Input array. + p : float, 1 <= p <= infinity + Which Minkowski p-norm to use. + + Returns + ------- + dist : ndarray + pth power of the distance between the input arrays. + + Examples + -------- + >>> from scipy.spatial import minkowski_distance_p + >>> minkowski_distance_p([[0, 0], [0, 0]], [[1, 1], [0, 1]]) + array([2, 1]) + + """ + x = np.asarray(x) + y = np.asarray(y) + + # Find smallest common datatype with float64 (return type of this + # function) - addresses #10262. + # Don't just cast to float64 for complex input case. + common_datatype = np.promote_types(np.promote_types(x.dtype, y.dtype), + 'float64') + + # Make sure x and y are NumPy arrays of correct datatype. + x = x.astype(common_datatype) + y = y.astype(common_datatype) + + if p == np.inf: + return np.amax(np.abs(y-x), axis=-1) + elif p == 1: + return np.sum(np.abs(y-x), axis=-1) + else: + return np.sum(np.abs(y-x)**p, axis=-1) + + +def minkowski_distance(x, y, p=2): + """Compute the L**p distance between two arrays. + + The last dimensions of `x` and `y` must be the same length. Any + other dimensions must be compatible for broadcasting. + + Parameters + ---------- + x : (..., K) array_like + Input array. + y : (..., K) array_like + Input array. + p : float, 1 <= p <= infinity + Which Minkowski p-norm to use. + + Returns + ------- + dist : ndarray + Distance between the input arrays. + + Examples + -------- + >>> from scipy.spatial import minkowski_distance + >>> minkowski_distance([[0, 0], [0, 0]], [[1, 1], [0, 1]]) + array([ 1.41421356, 1. ]) + + """ + x = np.asarray(x) + y = np.asarray(y) + if p == np.inf or p == 1: + return minkowski_distance_p(x, y, p) + else: + return minkowski_distance_p(x, y, p)**(1./p) + + +class Rectangle: + """Hyperrectangle class. + + Represents a Cartesian product of intervals. + """ + def __init__(self, maxes, mins): + """Construct a hyperrectangle.""" + self.maxes = np.maximum(maxes,mins).astype(float) + self.mins = np.minimum(maxes,mins).astype(float) + self.m, = self.maxes.shape + + def __repr__(self): + return "" % list(zip(self.mins, self.maxes)) + + def volume(self): + """Total volume.""" + return np.prod(self.maxes-self.mins) + + def split(self, d, split): + """Produce two hyperrectangles by splitting. + + In general, if you need to compute maximum and minimum + distances to the children, it can be done more efficiently + by updating the maximum and minimum distances to the parent. + + Parameters + ---------- + d : int + Axis to split hyperrectangle along. + split : float + Position along axis `d` to split at. + + """ + mid = np.copy(self.maxes) + mid[d] = split + less = Rectangle(self.mins, mid) + mid = np.copy(self.mins) + mid[d] = split + greater = Rectangle(mid, self.maxes) + return less, greater + + def min_distance_point(self, x, p=2.): + """ + Return the minimum distance between input and points in the + hyperrectangle. + + Parameters + ---------- + x : array_like + Input. + p : float, optional + Input. + + """ + return minkowski_distance( + 0, np.maximum(0, np.maximum(self.mins-x, x-self.maxes)), + p + ) + + def max_distance_point(self, x, p=2.): + """ + Return the maximum distance between input and points in the hyperrectangle. + + Parameters + ---------- + x : array_like + Input array. + p : float, optional + Input. + + """ + return minkowski_distance(0, np.maximum(self.maxes-x, x-self.mins), p) + + def min_distance_rectangle(self, other, p=2.): + """ + Compute the minimum distance between points in the two hyperrectangles. + + Parameters + ---------- + other : hyperrectangle + Input. + p : float + Input. + + """ + return minkowski_distance( + 0, + np.maximum(0, np.maximum(self.mins-other.maxes, + other.mins-self.maxes)), + p + ) + + def max_distance_rectangle(self, other, p=2.): + """ + Compute the maximum distance between points in the two hyperrectangles. + + Parameters + ---------- + other : hyperrectangle + Input. + p : float, optional + Input. + + """ + return minkowski_distance( + 0, np.maximum(self.maxes-other.mins, other.maxes-self.mins), p) + + +class KDTree(cKDTree): + """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. + + 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: 10. + 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 a 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 Maneewongvatana and Mount 1999. + 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. + + 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. + size : int + The number of nodes in the tree. + + """ + + class node: + @staticmethod + def _create(ckdtree_node=None): + """Create either an inner or leaf node, wrapping a cKDTreeNode instance""" + if ckdtree_node is None: + return KDTree.node(ckdtree_node) + elif ckdtree_node.split_dim == -1: + return KDTree.leafnode(ckdtree_node) + else: + return KDTree.innernode(ckdtree_node) + + def __init__(self, ckdtree_node=None): + if ckdtree_node is None: + ckdtree_node = cKDTreeNode() + self._node = ckdtree_node + + def __lt__(self, other): + return id(self) < id(other) + + def __gt__(self, other): + return id(self) > id(other) + + def __le__(self, other): + return id(self) <= id(other) + + def __ge__(self, other): + return id(self) >= id(other) + + def __eq__(self, other): + return id(self) == id(other) + + class leafnode(node): + @property + def idx(self): + return self._node.indices + + @property + def children(self): + return self._node.children + + class innernode(node): + def __init__(self, ckdtreenode): + assert isinstance(ckdtreenode, cKDTreeNode) + super().__init__(ckdtreenode) + self.less = KDTree.node._create(ckdtreenode.lesser) + self.greater = KDTree.node._create(ckdtreenode.greater) + + @property + def split_dim(self): + return self._node.split_dim + + @property + def split(self): + return self._node.split + + @property + def children(self): + return self._node.children + + @property + def tree(self): + if not hasattr(self, "_tree"): + self._tree = KDTree.node._create(super().tree) + + return self._tree + + def __init__(self, data, leafsize=10, compact_nodes=True, copy_data=False, + balanced_tree=True, boxsize=None): + data = np.asarray(data) + if data.dtype.kind == 'c': + raise TypeError("KDTree does not work with complex data") + + # Note KDTree has different default leafsize from cKDTree + super().__init__(data, leafsize, compact_nodes, copy_data, + balanced_tree, boxsize) + + def query( + self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf, workers=1): + r"""Query the kd-tree for nearest neighbors. + + Parameters + ---------- + x : array_like, last dimension self.m + An array of points to query. + k : int or Sequence[int], optional + Either the number of nearest neighbors to return, or a list of the + k-th nearest neighbors to return, starting from 1. + eps : nonnegative float, optional + Return approximate nearest neighbors; the kth returned value + is guaranteed to be no further than (1+eps) times the + distance to the real kth nearest neighbor. + p : float, 1<=p<=infinity, optional + Which Minkowski p-norm to use. + 1 is the sum-of-absolute-values distance ("Manhattan" distance). + 2 is the usual Euclidean distance. + infinity is the maximum-coordinate-difference distance. + A large, finite p may cause a ValueError if overflow can occur. + distance_upper_bound : nonnegative float, optional + 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. + + .. versionadded:: 1.6.0 + + Returns + ------- + d : float or 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. + Hits are sorted by distance (nearest first). + + .. versionchanged:: 1.9.0 + Previously if ``k=None``, then `d` was an object array of + shape ``tuple``, containing lists of distances. This behavior + has been removed, use `query_ball_point` instead. + + i : integer or array of integers + The index of each neighbor in ``self.data``. + ``i`` is the same shape as d. + Missing neighbors are indicated with ``self.n``. + + Examples + -------- + + >>> import numpy as np + >>> from scipy.spatial import KDTree + >>> x, y = np.mgrid[0:5, 2:8] + >>> tree = KDTree(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]] + + """ + x = np.asarray(x) + if x.dtype.kind == 'c': + raise TypeError("KDTree does not work with complex data") + + if k is None: + raise ValueError("k must be an integer or a sequence of integers") + + d, i = super().query(x, k, eps, p, distance_upper_bound, workers) + if isinstance(i, int): + i = np.intp(i) + return d, i + + def query_ball_point(self, x, r, p=2., eps=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, must 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. + + .. versionadded:: 1.6.0 + 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:: 1.6.0 + return_length : bool, optional + Return the number of points inside the radius instead of a list + of the indices. + + .. versionadded:: 1.6.0 + + 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 KDTree and using + query_ball_tree. + + Examples + -------- + >>> import numpy as np + >>> from scipy import spatial + >>> x, y = np.mgrid[0:5, 0:5] + >>> points = np.c_[x.ravel(), y.ravel()] + >>> tree = spatial.KDTree(points) + >>> sorted(tree.query_ball_point([2, 0], 1)) + [5, 10, 11, 15] + + 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() + + """ + x = np.asarray(x) + if x.dtype.kind == 'c': + raise TypeError("KDTree does not work with complex data") + return super().query_ball_point( + x, r, p, eps, workers, return_sorted, return_length) + + def query_ball_tree(self, other, r, p=2., eps=0): + """ + Find all pairs of points between `self` and `other` whose distance is + at most r. + + Parameters + ---------- + other : KDTree 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``. + 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 KDTree + >>> 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 = KDTree(points1) + >>> kd_tree2 = KDTree(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() + + """ + return super().query_ball_tree(other, r, p, eps) + + def query_pairs(self, r, p=2., eps=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``. + 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' + + .. versionadded:: 1.6.0 + + 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 ndarry 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 KDTree + >>> 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 = KDTree(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() + + """ + return super().query_pairs(r, p, eps, output_type) + + def count_neighbors(self, other, r, p=2., 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 : KDTree + 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 + + .. versionadded:: 1.6.0 + 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 + + .. versionadded:: 1.6.0 + + 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, + https://arxiv.org/abs/astro-ph/0012333 + + .. [2] Landy and Szalay, + "Bias and variance of angular correlation functions", + The Astrophysical Journal, 1993, + http://adsabs.harvard.edu/abs/1993ApJ...412...64L + + .. [3] Sheth, Connolly and Skibba, + "Marked correlations in galaxy formation models", + Arxiv e-print, 2005, + https://arxiv.org/abs/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, + http://adsabs.harvard.edu/abs/2003MNRAS.346...78H + + .. [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 KDTree + >>> rng = np.random.default_rng() + >>> points1 = rng.random((5, 2)) + >>> points2 = rng.random((5, 2)) + >>> kd_tree1 = KDTree(points1) + >>> kd_tree2 = KDTree(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 + + """ + return super().count_neighbors(other, r, p, weights, cumulative) + + def sparse_distance_matrix( + self, other, max_distance, p=2., output_type='dok_matrix'): + """Compute a sparse distance matrix. + + Computes a distance matrix between two KDTrees, leaving as zero + any distance greater than max_distance. + + Parameters + ---------- + other : KDTree + + 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'. + + .. versionadded:: 1.6.0 + + 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 KDTree + >>> rng = np.random.default_rng() + >>> points1 = rng.random((5, 2)) + >>> points2 = rng.random((5, 2)) + >>> kd_tree1 = KDTree(points1) + >>> kd_tree2 = KDTree(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 ]]) + + """ + return super().sparse_distance_matrix( + other, max_distance, p, output_type) + + +def distance_matrix(x, y, p=2, threshold=1000000): + """Compute the distance matrix. + + Returns the matrix of all pair-wise distances. + + Parameters + ---------- + x : (M, K) array_like + Matrix of M vectors in K dimensions. + y : (N, K) array_like + Matrix of N vectors in K dimensions. + p : float, 1 <= p <= infinity + Which Minkowski p-norm to use. + threshold : positive int + If ``M * N * K`` > `threshold`, algorithm uses a Python loop instead + of large temporary arrays. + + Returns + ------- + result : (M, N) ndarray + Matrix containing the distance from every vector in `x` to every vector + in `y`. + + Examples + -------- + >>> from scipy.spatial import distance_matrix + >>> distance_matrix([[0,0],[0,1]], [[1,0],[1,1]]) + array([[ 1. , 1.41421356], + [ 1.41421356, 1. ]]) + + """ + + x = np.asarray(x) + m, k = x.shape + y = np.asarray(y) + n, kk = y.shape + + if k != kk: + raise ValueError(f"x contains {k}-dimensional vectors but y contains " + f"{kk}-dimensional vectors") + + if m*n*k <= threshold: + return minkowski_distance(x[:,np.newaxis,:],y[np.newaxis,:,:],p) + else: + result = np.empty((m,n),dtype=float) # FIXME: figure out the best dtype + if m < n: + for i in range(m): + result[i,:] = minkowski_distance(x[i],y,p) + else: + for j in range(n): + result[:,j] = minkowski_distance(x,y[j],p) + return result diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/_procrustes.py b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_procrustes.py new file mode 100644 index 0000000000000000000000000000000000000000..ec460056ca06ebdff007423f91577cbd8f22e24c --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_procrustes.py @@ -0,0 +1,132 @@ +""" +This module provides functions to perform full Procrustes analysis. + +This code was originally written by Justin Kucynski and ported over from +scikit-bio by Yoshiki Vazquez-Baeza. +""" + +import numpy as np +from scipy.linalg import orthogonal_procrustes + + +__all__ = ['procrustes'] + + +def procrustes(data1, data2): + r"""Procrustes analysis, a similarity test for two data sets. + + Each input matrix is a set of points or vectors (the rows of the matrix). + The dimension of the space is the number of columns of each matrix. Given + two identically sized matrices, procrustes standardizes both such that: + + - :math:`tr(AA^{T}) = 1`. + + - Both sets of points are centered around the origin. + + Procrustes ([1]_, [2]_) then applies the optimal transform to the second + matrix (including scaling/dilation, rotations, and reflections) to minimize + :math:`M^{2}=\sum(data1-data2)^{2}`, or the sum of the squares of the + pointwise differences between the two input datasets. + + This function was not designed to handle datasets with different numbers of + datapoints (rows). If two data sets have different dimensionality + (different number of columns), simply add columns of zeros to the smaller + of the two. + + Parameters + ---------- + data1 : array_like + Matrix, n rows represent points in k (columns) space `data1` is the + reference data, after it is standardised, the data from `data2` will be + transformed to fit the pattern in `data1` (must have >1 unique points). + data2 : array_like + n rows of data in k space to be fit to `data1`. Must be the same + shape ``(numrows, numcols)`` as data1 (must have >1 unique points). + + Returns + ------- + mtx1 : array_like + A standardized version of `data1`. + mtx2 : array_like + The orientation of `data2` that best fits `data1`. Centered, but not + necessarily :math:`tr(AA^{T}) = 1`. + disparity : float + :math:`M^{2}` as defined above. + + Raises + ------ + ValueError + If the input arrays are not two-dimensional. + If the shape of the input arrays is different. + If the input arrays have zero columns or zero rows. + + See Also + -------- + scipy.linalg.orthogonal_procrustes + scipy.spatial.distance.directed_hausdorff : Another similarity test + for two data sets + + Notes + ----- + - The disparity should not depend on the order of the input matrices, but + the output matrices will, as only the first output matrix is guaranteed + to be scaled such that :math:`tr(AA^{T}) = 1`. + + - Duplicate data points are generally ok, duplicating a data point will + increase its effect on the procrustes fit. + + - The disparity scales as the number of points per input matrix. + + References + ---------- + .. [1] Krzanowski, W. J. (2000). "Principles of Multivariate analysis". + .. [2] Gower, J. C. (1975). "Generalized procrustes analysis". + + Examples + -------- + >>> import numpy as np + >>> from scipy.spatial import procrustes + + The matrix ``b`` is a rotated, shifted, scaled and mirrored version of + ``a`` here: + + >>> a = np.array([[1, 3], [1, 2], [1, 1], [2, 1]], 'd') + >>> b = np.array([[4, -2], [4, -4], [4, -6], [2, -6]], 'd') + >>> mtx1, mtx2, disparity = procrustes(a, b) + >>> round(disparity) + 0.0 + + """ + mtx1 = np.array(data1, dtype=np.float64, copy=True) + mtx2 = np.array(data2, dtype=np.float64, copy=True) + + if mtx1.ndim != 2 or mtx2.ndim != 2: + raise ValueError("Input matrices must be two-dimensional") + if mtx1.shape != mtx2.shape: + raise ValueError("Input matrices must be of same shape") + if mtx1.size == 0: + raise ValueError("Input matrices must be >0 rows and >0 cols") + + # translate all the data to the origin + mtx1 -= np.mean(mtx1, 0) + mtx2 -= np.mean(mtx2, 0) + + norm1 = np.linalg.norm(mtx1) + norm2 = np.linalg.norm(mtx2) + + if norm1 == 0 or norm2 == 0: + raise ValueError("Input matrices must contain >1 unique points") + + # change scaling of data (in rows) such that trace(mtx*mtx') = 1 + mtx1 /= norm1 + mtx2 /= norm2 + + # transform mtx2 to minimize disparity + R, s = orthogonal_procrustes(mtx1, mtx2) + mtx2 = np.dot(mtx2, R.T) * s + + # measure the dissimilarity between the two datasets + disparity = np.sum(np.square(mtx1 - mtx2)) + + return mtx1, mtx2, disparity + diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/_qhull.pyi b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_qhull.pyi new file mode 100644 index 0000000000000000000000000000000000000000..416128eab5f53cdc8f45c422bcc140451223bd3f --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_qhull.pyi @@ -0,0 +1,213 @@ +''' +Static type checking stub file for scipy/spatial/qhull.pyx +''' + + +import numpy as np +from numpy.typing import ArrayLike, NDArray +from typing_extensions import final + +class QhullError(RuntimeError): + ... + +@final +class _Qhull: + # Read-only cython attribute that behaves, more or less, like a property + @property + def ndim(self) -> int: ... + mode_option: bytes + options: bytes + furthest_site: bool + + def __init__( + self, + mode_option: bytes, + points: NDArray[np.float64], + options: None | bytes = ..., + required_options: None | bytes = ..., + furthest_site: bool = ..., + incremental: bool = ..., + interior_point: None | NDArray[np.float64] = ..., + ) -> None: ... + def check_active(self) -> None: ... + def close(self) -> None: ... + def get_points(self) -> NDArray[np.float64]: ... + def add_points( + self, + points: ArrayLike, + interior_point: ArrayLike = ... + ) -> None: ... + def get_paraboloid_shift_scale(self) -> tuple[float, float]: ... + def volume_area(self) -> tuple[float, float]: ... + def triangulate(self) -> None: ... + def get_simplex_facet_array(self) -> tuple[ + NDArray[np.intc], + NDArray[np.intc], + NDArray[np.float64], + NDArray[np.intc], + NDArray[np.intc], + ]: ... + def get_hull_points(self) -> NDArray[np.float64]: ... + def get_hull_facets(self) -> tuple[ + list[list[int]], + NDArray[np.float64], + ]: ... + def get_voronoi_diagram(self) -> tuple[ + NDArray[np.float64], + NDArray[np.intc], + list[list[int]], + list[list[int]], + NDArray[np.intp], + ]: ... + def get_extremes_2d(self) -> NDArray[np.intc]: ... + +def _get_barycentric_transforms( + points: NDArray[np.float64], + simplices: NDArray[np.intc], + eps: float +) -> NDArray[np.float64]: ... + +class _QhullUser: + ndim: int + npoints: int + min_bound: NDArray[np.float64] + max_bound: NDArray[np.float64] + + def __init__(self, qhull: _Qhull, incremental: bool = ...) -> None: ... + def close(self) -> None: ... + def _update(self, qhull: _Qhull) -> None: ... + def _add_points( + self, + points: ArrayLike, + restart: bool = ..., + interior_point: ArrayLike = ... + ) -> None: ... + +class Delaunay(_QhullUser): + furthest_site: bool + paraboloid_scale: float + paraboloid_shift: float + simplices: NDArray[np.intc] + neighbors: NDArray[np.intc] + equations: NDArray[np.float64] + coplanar: NDArray[np.intc] + good: NDArray[np.intc] + nsimplex: int + vertices: NDArray[np.intc] + + def __init__( + self, + points: ArrayLike, + furthest_site: bool = ..., + incremental: bool = ..., + qhull_options: None | str = ... + ) -> None: ... + def _update(self, qhull: _Qhull) -> None: ... + def add_points( + self, + points: ArrayLike, + restart: bool = ... + ) -> None: ... + @property + def points(self) -> NDArray[np.float64]: ... + @property + def transform(self) -> NDArray[np.float64]: ... + @property + def vertex_to_simplex(self) -> NDArray[np.intc]: ... + @property + def vertex_neighbor_vertices(self) -> tuple[ + NDArray[np.intc], + NDArray[np.intc], + ]: ... + @property + def convex_hull(self) -> NDArray[np.intc]: ... + def find_simplex( + self, + xi: ArrayLike, + bruteforce: bool = ..., + tol: float = ... + ) -> NDArray[np.intc]: ... + def plane_distance(self, xi: ArrayLike) -> NDArray[np.float64]: ... + def lift_points(self, x: ArrayLike) -> NDArray[np.float64]: ... + +def tsearch(tri: Delaunay, xi: ArrayLike) -> NDArray[np.intc]: ... +def _copy_docstr(dst: object, src: object) -> None: ... + +class ConvexHull(_QhullUser): + simplices: NDArray[np.intc] + neighbors: NDArray[np.intc] + equations: NDArray[np.float64] + coplanar: NDArray[np.intc] + good: None | NDArray[np.bool_] + volume: float + area: float + nsimplex: int + + def __init__( + self, + points: ArrayLike, + incremental: bool = ..., + qhull_options: None | str = ... + ) -> None: ... + def _update(self, qhull: _Qhull) -> None: ... + def add_points(self, points: ArrayLike, + restart: bool = ...) -> None: ... + @property + def points(self) -> NDArray[np.float64]: ... + @property + def vertices(self) -> NDArray[np.intc]: ... + +class Voronoi(_QhullUser): + vertices: NDArray[np.float64] + ridge_points: NDArray[np.intc] + ridge_vertices: list[list[int]] + regions: list[list[int]] + point_region: NDArray[np.intp] + furthest_site: bool + + def __init__( + self, + points: ArrayLike, + furthest_site: bool = ..., + incremental: bool = ..., + qhull_options: None | str = ... + ) -> None: ... + def _update(self, qhull: _Qhull) -> None: ... + def add_points( + self, + points: ArrayLike, + restart: bool = ... + ) -> None: ... + @property + def points(self) -> NDArray[np.float64]: ... + @property + def ridge_dict(self) -> dict[tuple[int, int], list[int]]: ... + +class HalfspaceIntersection(_QhullUser): + interior_point: NDArray[np.float64] + dual_facets: list[list[int]] + dual_equations: NDArray[np.float64] + dual_points: NDArray[np.float64] + dual_volume: float + dual_area: float + intersections: NDArray[np.float64] + ndim: int + nineq: int + + def __init__( + self, + halfspaces: ArrayLike, + interior_point: ArrayLike, + incremental: bool = ..., + qhull_options: None | str = ... + ) -> None: ... + def _update(self, qhull: _Qhull) -> None: ... + def add_halfspaces( + self, + halfspaces: ArrayLike, + restart: bool = ... + ) -> None: ... + @property + def halfspaces(self) -> NDArray[np.float64]: ... + @property + def dual_vertices(self) -> NDArray[np.integer]: ... diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/_voronoi.pyi b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_voronoi.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c7c361ff69414d50a6ebcfe2c837025b60083940 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/_voronoi.pyi @@ -0,0 +1,4 @@ + +import numpy as np + +def sort_vertices_of_regions(simplices: np.ndarray, regions: list[list[int]]) -> None: ... # noqa: E501 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/distance.pyi b/openflamingo/lib/python3.10/site-packages/scipy/spatial/distance.pyi new file mode 100644 index 0000000000000000000000000000000000000000..0e231b46dd3b2d248e54d1259b384b9e48facd9a --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/distance.pyi @@ -0,0 +1,211 @@ +from __future__ import annotations +from typing import (overload, Any, SupportsFloat, Literal, Protocol, SupportsIndex) + +import numpy as np +from numpy.typing import ArrayLike, NDArray + +# Anything that can be parsed by `np.float64.__init__` and is thus +# compatible with `ndarray.__setitem__` (for a float64 array) +_FloatValue = None | str | bytes | SupportsFloat | SupportsIndex + +class _MetricCallback1(Protocol): + def __call__( + self, __XA: NDArray[Any], __XB: NDArray[Any] + ) -> _FloatValue: ... + +class _MetricCallback2(Protocol): + def __call__( + self, __XA: NDArray[Any], __XB: NDArray[Any], **kwargs: Any + ) -> _FloatValue: ... + +# TODO: Use a single protocol with a parameter specification variable +# once available (PEP 612) +_MetricCallback = _MetricCallback1 | _MetricCallback2 + +_MetricKind = Literal[ + 'braycurtis', + 'canberra', + 'chebychev', 'chebyshev', 'cheby', 'cheb', 'ch', + 'cityblock', 'cblock', 'cb', 'c', + 'correlation', 'co', + 'cosine', 'cos', + 'dice', + 'euclidean', 'euclid', 'eu', 'e', + 'hamming', 'hamm', 'ha', 'h', + 'minkowski', 'mi', 'm', 'pnorm', + 'jaccard', 'jacc', 'ja', 'j', + 'jensenshannon', 'js', + 'kulczynski1', + 'mahalanobis', 'mahal', 'mah', + 'rogerstanimoto', + 'russellrao', + 'seuclidean', 'se', 's', + 'sokalmichener', + 'sokalsneath', + 'sqeuclidean', 'sqe', 'sqeuclid', + 'yule', +] + +# Function annotations + +def braycurtis( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +def canberra( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +# TODO: Add `metric`-specific overloads +# Returns a float64 or float128 array, depending on the input dtype +@overload +def cdist( + XA: ArrayLike, + XB: ArrayLike, + metric: _MetricKind = ..., + *, + out: None | NDArray[np.floating[Any]] = ..., + p: float = ..., + w: ArrayLike | None = ..., + V: ArrayLike | None = ..., + VI: ArrayLike | None = ..., +) -> NDArray[np.floating[Any]]: ... +@overload +def cdist( + XA: ArrayLike, + XB: ArrayLike, + metric: _MetricCallback, + *, + out: None | NDArray[np.floating[Any]] = ..., + **kwargs: Any, +) -> NDArray[np.floating[Any]]: ... + +# TODO: Wait for dtype support; the return type is +# dependent on the input arrays dtype +def chebyshev( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> Any: ... + +# TODO: Wait for dtype support; the return type is +# dependent on the input arrays dtype +def cityblock( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> Any: ... + +def correlation( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ..., centered: bool = ... +) -> np.float64: ... + +def cosine( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +def dice( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> float: ... + +def directed_hausdorff( + u: ArrayLike, v: ArrayLike, seed: int | None = ... +) -> tuple[float, int, int]: ... + +def euclidean( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> float: ... + +def hamming( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +def is_valid_dm( + D: ArrayLike, + tol: float = ..., + throw: bool = ..., + name: str | None = ..., + warning: bool = ..., +) -> bool: ... + +def is_valid_y( + y: ArrayLike, + warning: bool = ..., + throw: bool = ..., + name: str | None = ..., +) -> bool: ... + +def jaccard( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +def jensenshannon( + p: ArrayLike, q: ArrayLike, base: float | None = ... +) -> np.float64: ... + +def kulczynski1( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +def mahalanobis( + u: ArrayLike, v: ArrayLike, VI: ArrayLike +) -> np.float64: ... + +def minkowski( + u: ArrayLike, v: ArrayLike, p: float = ..., w: ArrayLike | None = ... +) -> float: ... + +def num_obs_dm(d: ArrayLike) -> int: ... + +def num_obs_y(Y: ArrayLike) -> int: ... + +# TODO: Add `metric`-specific overloads +@overload +def pdist( + X: ArrayLike, + metric: _MetricKind = ..., + *, + out: None | NDArray[np.floating[Any]] = ..., + p: float = ..., + w: ArrayLike | None = ..., + V: ArrayLike | None = ..., + VI: ArrayLike | None = ..., +) -> NDArray[np.floating[Any]]: ... +@overload +def pdist( + X: ArrayLike, + metric: _MetricCallback, + *, + out: None | NDArray[np.floating[Any]] = ..., + **kwargs: Any, +) -> NDArray[np.floating[Any]]: ... + +def seuclidean( + u: ArrayLike, v: ArrayLike, V: ArrayLike +) -> float: ... + +def sokalmichener( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> float: ... + +def sokalsneath( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +def sqeuclidean( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> np.float64: ... + +def squareform( + X: ArrayLike, + force: Literal["no", "tomatrix", "tovector"] = ..., + checks: bool = ..., +) -> NDArray[Any]: ... + +def rogerstanimoto( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> float: ... + +def russellrao( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> float: ... + +def yule( + u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ... +) -> float: ... diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/qhull.py b/openflamingo/lib/python3.10/site-packages/scipy/spatial/qhull.py new file mode 100644 index 0000000000000000000000000000000000000000..a8d51bf239bfe48077e66a36bcbf59f6dbadaf95 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/qhull.py @@ -0,0 +1,25 @@ +# This file is not meant for public use and will be removed in SciPy v2.0.0. +# Use the `scipy.spatial` namespace for importing the functions +# included below. + +from scipy._lib.deprecation import _sub_module_deprecation + + +__all__ = [ # noqa: F822 + 'ConvexHull', + 'Delaunay', + 'HalfspaceIntersection', + 'QhullError', + 'Voronoi', + 'tsearch', +] + + +def __dir__(): + return __all__ + + +def __getattr__(name): + return _sub_module_deprecation(sub_package="spatial", module="qhull", + private_modules=["_qhull"], all=__all__, + attribute=name) diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/__init__.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..949fc2985ee18468ee00ae20653abb44086cce5c Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/__init__.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test__plotutils.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test__plotutils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc82bdf197d38061046bb544d7fd8d42ccbac861 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test__plotutils.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_distance.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_distance.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1e85e1d68a01b674bd9cb2af95cb7f7874c71b29 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_distance.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_qhull.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_qhull.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0b34fd5d36f6c7ca4428d85b8b7045efaf7e5c11 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_qhull.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_slerp.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_slerp.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9dffbee9493276b70a1a12ed4e24ea16ab9be257 Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_slerp.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_spherical_voronoi.cpython-310.pyc b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_spherical_voronoi.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f0e006055853fe7f6350cf7d395da018522dba5b Binary files /dev/null and b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/__pycache__/test_spherical_voronoi.cpython-310.pyc differ diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/cdist-X2.txt b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/cdist-X2.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc3ea19674ee36856446c75df98b8c17c53ca51f --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/cdist-X2.txt @@ -0,0 +1,20 @@ +7.680465556300619667e-02 4.675022344069014180e-01 8.955498989131543963e-01 3.816236071436276411e-01 1.109030077070989329e-01 2.318928815459808668e-02 7.477394240984251983e-01 1.202289789304434864e-01 8.007290497575981769e-01 6.795195698871731027e-01 6.568225762396605605e-01 2.231475263228478445e-01 7.064624077661341151e-02 1.081656666815267176e-02 1.592069359090128033e-01 1.363392203645097389e-01 9.277020735447568667e-01 8.103136564528209407e-01 5.229467676276455812e-02 7.708020259874025504e-01 6.527954747473352359e-02 5.516397414886525796e-01 3.653371861367954443e-01 +8.144399106025798085e-01 7.731852525462976633e-01 6.909477620673205589e-01 9.696063817000286633e-01 4.297887511677249694e-01 6.989600553425188156e-01 7.310201335033380543e-01 3.135256147868910048e-01 5.715578037275241829e-01 3.935000744675094531e-01 2.057715781268398825e-01 5.892508589665171881e-01 8.512951599236765476e-01 9.569808799061578775e-01 6.164885878024699561e-01 4.714185430004367294e-01 6.128831737628155363e-01 6.641799309623502845e-01 6.001985185338730711e-01 4.231922889723856995e-01 7.605249308075449077e-01 1.064530958018087281e-01 6.306470691957204444e-01 +4.265470127256254518e-01 5.933766716280767239e-01 3.698589270536845053e-02 2.173799740537294412e-01 3.032679325475639009e-01 4.271831790058847611e-01 1.828944535901013690e-01 4.772333422710156592e-01 2.564773455194128138e-01 7.120329875362141347e-01 8.952243430110462530e-01 1.808777012183288013e-01 3.612151871458374464e-01 3.960999167923041631e-01 1.821669970670747318e-02 8.835474857189200559e-01 1.353104648821573663e-01 3.457291739160937016e-01 1.126467375304566199e-01 4.107293162402323450e-01 4.051719311053743056e-01 4.007382985250427243e-01 1.286905671428811848e-01 +2.910657003883979632e-01 9.616259180685315933e-03 2.033032441536681834e-01 1.096599110293863255e-01 4.191101704605176836e-01 5.462131536027151624e-01 8.393047907010142694e-01 9.046805198676335369e-01 7.009863472176891541e-01 2.508215985039629059e-01 6.754410796667598138e-01 6.740895474032024826e-01 1.358993708621679675e-01 8.219861775211464439e-01 6.322220445623235596e-01 2.766813559002430090e-01 6.575983861590951607e-01 9.515869708336625044e-01 8.654526462353933081e-01 3.450245117834797037e-01 5.649032890631299209e-01 4.717687914789682191e-01 3.296483580510030098e-01 +9.172477457635394016e-01 3.057396583041891436e-01 7.335332344225760082e-01 8.370236206345178509e-01 3.765464253115927695e-01 5.089680319287778199e-01 1.202325719268168003e-01 9.717771065272349240e-01 5.907820104019682050e-01 9.809211614977710880e-01 9.064285003671219698e-01 8.848841466121748489e-01 2.043407730734815297e-01 9.157600394927275511e-01 4.532260315147775831e-01 4.241077335005828397e-01 1.751730149568804240e-01 4.090412146081819911e-01 3.632197861847064058e-02 5.832539334970230360e-01 4.041848151536805434e-01 3.603643989086504629e-01 1.838411383882069261e-01 +2.508806403290032572e-01 4.381403985282813496e-01 4.694787405018008286e-02 6.353900562024634713e-01 1.200813444244532846e-01 6.072397042913001419e-01 9.937255904754030977e-01 4.916670237677555066e-01 3.473845913923001572e-01 3.526875922864345370e-01 5.448595548197197047e-01 2.245096010156972799e-01 9.003258279804994269e-01 3.534560469735994470e-01 2.989266066346342177e-01 4.621024982808636938e-01 9.626538866576676012e-01 9.791401720716153001e-01 7.138514287330390840e-01 9.832862333928654719e-01 3.233999591031431198e-01 5.406467224926423398e-01 9.581890295057201579e-01 +5.210583601680578436e-01 4.598159993059653949e-01 2.111497132057748027e-01 5.949977700916546652e-01 6.342618461422359077e-01 9.888228769705599275e-01 6.096770711536318998e-01 7.548431368960863974e-01 7.490858664860100546e-01 3.186213496546415058e-01 7.895687083231245351e-01 4.178326793268141159e-01 8.095818334534051752e-01 7.886271673523481684e-01 4.038905626506847923e-01 3.652649247094948981e-01 8.267205959224892542e-01 6.433617243328785262e-01 3.117681563249452559e-01 9.675995575054980868e-01 3.675673836358472890e-01 5.863757289184046151e-01 9.099029857959717305e-02 +4.024573981231733821e-01 3.578997554002771864e-01 3.519299868071553705e-01 7.417747693762357653e-01 2.963713903285800644e-01 9.602967989298948348e-01 3.811392331739601458e-01 5.493237898295448840e-01 6.835113342793640578e-01 2.304506220807415184e-01 3.727299857731285471e-01 5.450263991912108752e-01 6.951521210987908761e-01 6.474582745861203747e-01 6.316089475403589004e-01 5.672043967425510758e-02 9.034937506977609445e-01 2.332567550780038079e-01 1.096955741449157085e-02 8.870663813493575578e-01 4.384385452180562526e-01 7.100898998169548060e-01 3.245358176196319056e-01 +9.162009194452818139e-01 5.572224742426723498e-02 3.445910686865658601e-01 9.683564008127462097e-01 9.375063149031520604e-01 9.128188852869822956e-02 9.613605414326487075e-01 5.298598697556915482e-01 6.724799695520149445e-01 1.269103938571825019e-02 1.008406153387807480e-01 8.951105272379104028e-01 1.585460318853607609e-01 6.739986455059543413e-01 5.345419321702655768e-01 6.248843899572337213e-01 3.050288488994817859e-01 1.423645553465189284e-01 1.802121190541096096e-01 9.474646822694763326e-01 2.345716438587298613e-01 9.688281784764296578e-01 1.845165243240991515e-01 +2.548297646910531178e-01 2.580877375379494465e-01 1.355482532666937301e-01 6.478812986505504412e-01 9.971695982152032345e-01 2.606721082477282403e-01 5.483439686378906996e-01 4.409612606704470528e-01 4.396442074915688503e-01 7.414262832597111608e-01 7.308840725375539416e-01 8.072095530497225280e-02 6.829509968656330976e-01 5.700030854230387911e-01 3.801845336730320657e-01 2.481059916867158766e-01 3.977295094395927322e-03 5.749480512407895150e-01 4.112033136603401307e-01 8.676159710377848722e-01 9.062646588480167686e-01 3.326691167317923359e-01 8.498307982774666591e-01 +4.464338109330643345e-01 8.546516760817471914e-01 7.384800352329814466e-01 3.692485164984804502e-02 2.915662689505471583e-02 9.010049994217171898e-01 8.622900253010918892e-01 9.786230638032608065e-01 6.546824077297251909e-01 6.342297560006789903e-01 2.230339826582647955e-01 7.658846744185553446e-01 4.603043831539479491e-01 2.017100469861691225e-01 4.891590639893540482e-01 1.937140918314912419e-01 8.161582138652878626e-01 5.597293607114051106e-02 8.423261093326828153e-02 5.105392204475533990e-02 8.234193902673621057e-01 1.784268309975372002e-01 9.118997881986501408e-02 +8.588746913421980711e-01 1.479641118621310980e-02 1.375875301146138874e-01 7.533888774725254756e-01 5.782592791549248101e-01 9.128573037619659436e-01 1.831275762880391067e-01 3.471382864827737835e-01 4.859524740929310749e-02 8.955146541561730400e-01 4.787220791101074457e-01 4.222803577759057791e-01 8.469923964908064873e-01 6.300290047587608910e-02 1.020873237837905956e-01 3.585612487182909813e-02 6.320107119904569970e-01 5.891245970008752719e-01 1.104698053665007507e-01 4.233226558073774903e-01 4.432217054386708988e-01 2.864765416628194394e-01 2.489777211814803159e-02 +5.343810659756068615e-01 4.829076396403546578e-01 8.364480888953172988e-01 8.931374995414760321e-01 6.034161442354715188e-01 3.578336000768178593e-03 4.100579775972763574e-01 3.968667908067096128e-01 5.897163653686778861e-01 3.003241263928478899e-01 2.520935203143799264e-01 3.112129371563532310e-02 9.052865295974613646e-01 1.172285124002711010e-01 4.840001666149388315e-01 3.424620676348436588e-01 5.526057133826853818e-01 6.346139530261846184e-01 5.747945930485597321e-01 1.389915612177697879e-01 2.413801217666421417e-01 7.829900796662081497e-01 7.213528084845653998e-01 +9.384509283406079483e-01 6.303019601671526750e-01 1.787921522728125323e-01 1.556003868047917127e-02 5.662397078816850948e-01 3.437473614806091371e-01 8.615844972800188462e-01 7.624380237306396246e-01 1.096468347898514883e-01 1.276566836610887323e-01 8.479188493443535757e-01 3.634713454428405432e-01 7.478112314318967613e-01 9.856395696968375253e-01 6.250293654177319080e-02 1.919327272501809567e-01 1.415594476031050153e-01 7.224057351041784925e-01 8.452145259310355208e-01 5.434318833772002755e-01 5.177620959731277228e-02 3.358977598185840518e-01 2.542654881527960375e-01 +4.800909104006243489e-01 3.651345393613150137e-01 3.657093052788148446e-01 8.579662326651369408e-01 5.787694361240260932e-01 6.491966196891312268e-01 3.252508517294879775e-01 8.639694334693422961e-01 3.028097078756678551e-01 6.295814666338699350e-01 7.305627351548695803e-01 6.975931849120264872e-03 8.321205159004851915e-01 2.681809305821257761e-01 3.628869474597150591e-01 9.598981434716586936e-01 5.947913523332928332e-01 7.794864238003402779e-01 2.819511239444029149e-01 5.134200958476284882e-01 7.284684743064278045e-01 3.099571109539331903e-01 1.502222882866774967e-01 +2.463382654375219083e-01 4.465700737264240994e-01 7.180855317941433613e-01 5.056099420785193921e-01 6.182117344332578313e-01 2.370453793561340117e-01 9.831748018047525850e-01 6.397098184531551102e-01 8.260469782208745837e-02 7.474671691560941245e-01 9.963429983418570224e-02 5.450078811081275898e-01 5.370188678062637333e-02 2.774024442708808991e-01 2.082643088545442778e-01 2.704155352788065736e-01 7.225035580445194894e-01 4.866791976239246420e-01 1.357043111201584606e-01 7.911335827987711067e-01 7.278977102006007893e-01 6.880892094410231419e-01 1.029231496520791600e-01 +6.901796117735281566e-01 1.558248977395644275e-01 4.241818789360329855e-01 5.055658246392458199e-01 1.756288758075611467e-01 4.215083703818177652e-01 7.809231602323289945e-01 1.170053878686481141e-01 6.497026323614403243e-01 5.733120641440232479e-01 4.407703406152092551e-01 5.608677124532297498e-01 7.471045703286000039e-01 3.334604336022076732e-01 8.927208811415126011e-01 9.794565286182396191e-01 9.621542824973521313e-01 3.945825239405253981e-01 8.338963875792834157e-01 9.310552325082104286e-01 7.688283033784242271e-01 3.798823731047119567e-01 1.459993613028365278e-02 +7.848623555505630511e-01 2.681039365355797344e-03 7.833208051794043891e-01 8.184381915171493604e-01 4.682581645582317709e-01 2.391069309436419932e-01 1.765377537168698607e-01 9.863494676539893424e-01 4.378412300863872009e-01 7.494505491149090481e-01 1.942180356195394308e-01 9.981402467222395547e-01 7.992190944052800505e-01 1.350875702852057936e-01 4.950149186748543650e-01 7.243422481248201761e-01 3.544596746353472216e-01 8.320192561472177228e-01 9.776840296475269865e-01 7.733852731914863110e-01 2.305732998099923048e-01 9.746878189802981041e-01 7.747723331200035979e-01 +6.521099013127149568e-01 5.452399443648201505e-01 8.146707517183656710e-01 3.827256063695345656e-01 7.954832091744263867e-01 7.834427643148527132e-01 9.661317930643520402e-02 9.215673965718058636e-01 4.914305728788055383e-01 4.105628408027649501e-01 9.844647830893304974e-02 3.974831165301851987e-01 3.857608898053827007e-01 5.520210781401946321e-01 3.445787541654143915e-03 4.552922057017416702e-01 7.456544561760444223e-01 4.753985092154335845e-01 2.821385239833401615e-01 7.560136035104459973e-01 8.453142510471420845e-01 6.679627143276523071e-01 6.910882868284401459e-01 +8.526493480446283302e-01 1.183917973068240315e-01 6.163988861865119517e-01 5.751899460059114455e-01 1.638797964925038375e-01 8.214597298784013235e-01 5.424670654187370156e-01 1.806631819658732763e-01 9.268107278221827672e-01 4.127397378597359445e-01 7.529877485901653733e-01 1.714251090083847018e-01 2.601487784245806179e-01 2.028326156742237263e-01 5.299879450122358948e-01 7.587877062981395193e-01 4.070738595375062996e-01 3.546903049793261875e-01 8.695365138547607176e-01 1.447085661525142619e-01 3.193366245820845606e-01 8.797841086211429795e-01 2.666562188639977071e-01 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/iris.txt b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/iris.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d78390c2596beb41b1abff651a729e4e964c36e --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/iris.txt @@ -0,0 +1,150 @@ +5.099999999999999645e+00 3.500000000000000000e+00 1.399999999999999911e+00 2.000000000000000111e-01 +4.900000000000000355e+00 3.000000000000000000e+00 1.399999999999999911e+00 2.000000000000000111e-01 +4.700000000000000178e+00 3.200000000000000178e+00 1.300000000000000044e+00 2.000000000000000111e-01 +4.599999999999999645e+00 3.100000000000000089e+00 1.500000000000000000e+00 2.000000000000000111e-01 +5.000000000000000000e+00 3.600000000000000089e+00 1.399999999999999911e+00 2.000000000000000111e-01 +5.400000000000000355e+00 3.899999999999999911e+00 1.699999999999999956e+00 4.000000000000000222e-01 +4.599999999999999645e+00 3.399999999999999911e+00 1.399999999999999911e+00 2.999999999999999889e-01 +5.000000000000000000e+00 3.399999999999999911e+00 1.500000000000000000e+00 2.000000000000000111e-01 +4.400000000000000355e+00 2.899999999999999911e+00 1.399999999999999911e+00 2.000000000000000111e-01 +4.900000000000000355e+00 3.100000000000000089e+00 1.500000000000000000e+00 1.000000000000000056e-01 +5.400000000000000355e+00 3.700000000000000178e+00 1.500000000000000000e+00 2.000000000000000111e-01 +4.799999999999999822e+00 3.399999999999999911e+00 1.600000000000000089e+00 2.000000000000000111e-01 +4.799999999999999822e+00 3.000000000000000000e+00 1.399999999999999911e+00 1.000000000000000056e-01 +4.299999999999999822e+00 3.000000000000000000e+00 1.100000000000000089e+00 1.000000000000000056e-01 +5.799999999999999822e+00 4.000000000000000000e+00 1.199999999999999956e+00 2.000000000000000111e-01 +5.700000000000000178e+00 4.400000000000000355e+00 1.500000000000000000e+00 4.000000000000000222e-01 +5.400000000000000355e+00 3.899999999999999911e+00 1.300000000000000044e+00 4.000000000000000222e-01 +5.099999999999999645e+00 3.500000000000000000e+00 1.399999999999999911e+00 2.999999999999999889e-01 +5.700000000000000178e+00 3.799999999999999822e+00 1.699999999999999956e+00 2.999999999999999889e-01 +5.099999999999999645e+00 3.799999999999999822e+00 1.500000000000000000e+00 2.999999999999999889e-01 +5.400000000000000355e+00 3.399999999999999911e+00 1.699999999999999956e+00 2.000000000000000111e-01 +5.099999999999999645e+00 3.700000000000000178e+00 1.500000000000000000e+00 4.000000000000000222e-01 +4.599999999999999645e+00 3.600000000000000089e+00 1.000000000000000000e+00 2.000000000000000111e-01 +5.099999999999999645e+00 3.299999999999999822e+00 1.699999999999999956e+00 5.000000000000000000e-01 +4.799999999999999822e+00 3.399999999999999911e+00 1.899999999999999911e+00 2.000000000000000111e-01 +5.000000000000000000e+00 3.000000000000000000e+00 1.600000000000000089e+00 2.000000000000000111e-01 +5.000000000000000000e+00 3.399999999999999911e+00 1.600000000000000089e+00 4.000000000000000222e-01 +5.200000000000000178e+00 3.500000000000000000e+00 1.500000000000000000e+00 2.000000000000000111e-01 +5.200000000000000178e+00 3.399999999999999911e+00 1.399999999999999911e+00 2.000000000000000111e-01 +4.700000000000000178e+00 3.200000000000000178e+00 1.600000000000000089e+00 2.000000000000000111e-01 +4.799999999999999822e+00 3.100000000000000089e+00 1.600000000000000089e+00 2.000000000000000111e-01 +5.400000000000000355e+00 3.399999999999999911e+00 1.500000000000000000e+00 4.000000000000000222e-01 +5.200000000000000178e+00 4.099999999999999645e+00 1.500000000000000000e+00 1.000000000000000056e-01 +5.500000000000000000e+00 4.200000000000000178e+00 1.399999999999999911e+00 2.000000000000000111e-01 +4.900000000000000355e+00 3.100000000000000089e+00 1.500000000000000000e+00 1.000000000000000056e-01 +5.000000000000000000e+00 3.200000000000000178e+00 1.199999999999999956e+00 2.000000000000000111e-01 +5.500000000000000000e+00 3.500000000000000000e+00 1.300000000000000044e+00 2.000000000000000111e-01 +4.900000000000000355e+00 3.100000000000000089e+00 1.500000000000000000e+00 1.000000000000000056e-01 +4.400000000000000355e+00 3.000000000000000000e+00 1.300000000000000044e+00 2.000000000000000111e-01 +5.099999999999999645e+00 3.399999999999999911e+00 1.500000000000000000e+00 2.000000000000000111e-01 +5.000000000000000000e+00 3.500000000000000000e+00 1.300000000000000044e+00 2.999999999999999889e-01 +4.500000000000000000e+00 2.299999999999999822e+00 1.300000000000000044e+00 2.999999999999999889e-01 +4.400000000000000355e+00 3.200000000000000178e+00 1.300000000000000044e+00 2.000000000000000111e-01 +5.000000000000000000e+00 3.500000000000000000e+00 1.600000000000000089e+00 5.999999999999999778e-01 +5.099999999999999645e+00 3.799999999999999822e+00 1.899999999999999911e+00 4.000000000000000222e-01 +4.799999999999999822e+00 3.000000000000000000e+00 1.399999999999999911e+00 2.999999999999999889e-01 +5.099999999999999645e+00 3.799999999999999822e+00 1.600000000000000089e+00 2.000000000000000111e-01 +4.599999999999999645e+00 3.200000000000000178e+00 1.399999999999999911e+00 2.000000000000000111e-01 +5.299999999999999822e+00 3.700000000000000178e+00 1.500000000000000000e+00 2.000000000000000111e-01 +5.000000000000000000e+00 3.299999999999999822e+00 1.399999999999999911e+00 2.000000000000000111e-01 +7.000000000000000000e+00 3.200000000000000178e+00 4.700000000000000178e+00 1.399999999999999911e+00 +6.400000000000000355e+00 3.200000000000000178e+00 4.500000000000000000e+00 1.500000000000000000e+00 +6.900000000000000355e+00 3.100000000000000089e+00 4.900000000000000355e+00 1.500000000000000000e+00 +5.500000000000000000e+00 2.299999999999999822e+00 4.000000000000000000e+00 1.300000000000000044e+00 +6.500000000000000000e+00 2.799999999999999822e+00 4.599999999999999645e+00 1.500000000000000000e+00 +5.700000000000000178e+00 2.799999999999999822e+00 4.500000000000000000e+00 1.300000000000000044e+00 +6.299999999999999822e+00 3.299999999999999822e+00 4.700000000000000178e+00 1.600000000000000089e+00 +4.900000000000000355e+00 2.399999999999999911e+00 3.299999999999999822e+00 1.000000000000000000e+00 +6.599999999999999645e+00 2.899999999999999911e+00 4.599999999999999645e+00 1.300000000000000044e+00 +5.200000000000000178e+00 2.700000000000000178e+00 3.899999999999999911e+00 1.399999999999999911e+00 +5.000000000000000000e+00 2.000000000000000000e+00 3.500000000000000000e+00 1.000000000000000000e+00 +5.900000000000000355e+00 3.000000000000000000e+00 4.200000000000000178e+00 1.500000000000000000e+00 +6.000000000000000000e+00 2.200000000000000178e+00 4.000000000000000000e+00 1.000000000000000000e+00 +6.099999999999999645e+00 2.899999999999999911e+00 4.700000000000000178e+00 1.399999999999999911e+00 +5.599999999999999645e+00 2.899999999999999911e+00 3.600000000000000089e+00 1.300000000000000044e+00 +6.700000000000000178e+00 3.100000000000000089e+00 4.400000000000000355e+00 1.399999999999999911e+00 +5.599999999999999645e+00 3.000000000000000000e+00 4.500000000000000000e+00 1.500000000000000000e+00 +5.799999999999999822e+00 2.700000000000000178e+00 4.099999999999999645e+00 1.000000000000000000e+00 +6.200000000000000178e+00 2.200000000000000178e+00 4.500000000000000000e+00 1.500000000000000000e+00 +5.599999999999999645e+00 2.500000000000000000e+00 3.899999999999999911e+00 1.100000000000000089e+00 +5.900000000000000355e+00 3.200000000000000178e+00 4.799999999999999822e+00 1.800000000000000044e+00 +6.099999999999999645e+00 2.799999999999999822e+00 4.000000000000000000e+00 1.300000000000000044e+00 +6.299999999999999822e+00 2.500000000000000000e+00 4.900000000000000355e+00 1.500000000000000000e+00 +6.099999999999999645e+00 2.799999999999999822e+00 4.700000000000000178e+00 1.199999999999999956e+00 +6.400000000000000355e+00 2.899999999999999911e+00 4.299999999999999822e+00 1.300000000000000044e+00 +6.599999999999999645e+00 3.000000000000000000e+00 4.400000000000000355e+00 1.399999999999999911e+00 +6.799999999999999822e+00 2.799999999999999822e+00 4.799999999999999822e+00 1.399999999999999911e+00 +6.700000000000000178e+00 3.000000000000000000e+00 5.000000000000000000e+00 1.699999999999999956e+00 +6.000000000000000000e+00 2.899999999999999911e+00 4.500000000000000000e+00 1.500000000000000000e+00 +5.700000000000000178e+00 2.600000000000000089e+00 3.500000000000000000e+00 1.000000000000000000e+00 +5.500000000000000000e+00 2.399999999999999911e+00 3.799999999999999822e+00 1.100000000000000089e+00 +5.500000000000000000e+00 2.399999999999999911e+00 3.700000000000000178e+00 1.000000000000000000e+00 +5.799999999999999822e+00 2.700000000000000178e+00 3.899999999999999911e+00 1.199999999999999956e+00 +6.000000000000000000e+00 2.700000000000000178e+00 5.099999999999999645e+00 1.600000000000000089e+00 +5.400000000000000355e+00 3.000000000000000000e+00 4.500000000000000000e+00 1.500000000000000000e+00 +6.000000000000000000e+00 3.399999999999999911e+00 4.500000000000000000e+00 1.600000000000000089e+00 +6.700000000000000178e+00 3.100000000000000089e+00 4.700000000000000178e+00 1.500000000000000000e+00 +6.299999999999999822e+00 2.299999999999999822e+00 4.400000000000000355e+00 1.300000000000000044e+00 +5.599999999999999645e+00 3.000000000000000000e+00 4.099999999999999645e+00 1.300000000000000044e+00 +5.500000000000000000e+00 2.500000000000000000e+00 4.000000000000000000e+00 1.300000000000000044e+00 +5.500000000000000000e+00 2.600000000000000089e+00 4.400000000000000355e+00 1.199999999999999956e+00 +6.099999999999999645e+00 3.000000000000000000e+00 4.599999999999999645e+00 1.399999999999999911e+00 +5.799999999999999822e+00 2.600000000000000089e+00 4.000000000000000000e+00 1.199999999999999956e+00 +5.000000000000000000e+00 2.299999999999999822e+00 3.299999999999999822e+00 1.000000000000000000e+00 +5.599999999999999645e+00 2.700000000000000178e+00 4.200000000000000178e+00 1.300000000000000044e+00 +5.700000000000000178e+00 3.000000000000000000e+00 4.200000000000000178e+00 1.199999999999999956e+00 +5.700000000000000178e+00 2.899999999999999911e+00 4.200000000000000178e+00 1.300000000000000044e+00 +6.200000000000000178e+00 2.899999999999999911e+00 4.299999999999999822e+00 1.300000000000000044e+00 +5.099999999999999645e+00 2.500000000000000000e+00 3.000000000000000000e+00 1.100000000000000089e+00 +5.700000000000000178e+00 2.799999999999999822e+00 4.099999999999999645e+00 1.300000000000000044e+00 +6.299999999999999822e+00 3.299999999999999822e+00 6.000000000000000000e+00 2.500000000000000000e+00 +5.799999999999999822e+00 2.700000000000000178e+00 5.099999999999999645e+00 1.899999999999999911e+00 +7.099999999999999645e+00 3.000000000000000000e+00 5.900000000000000355e+00 2.100000000000000089e+00 +6.299999999999999822e+00 2.899999999999999911e+00 5.599999999999999645e+00 1.800000000000000044e+00 +6.500000000000000000e+00 3.000000000000000000e+00 5.799999999999999822e+00 2.200000000000000178e+00 +7.599999999999999645e+00 3.000000000000000000e+00 6.599999999999999645e+00 2.100000000000000089e+00 +4.900000000000000355e+00 2.500000000000000000e+00 4.500000000000000000e+00 1.699999999999999956e+00 +7.299999999999999822e+00 2.899999999999999911e+00 6.299999999999999822e+00 1.800000000000000044e+00 +6.700000000000000178e+00 2.500000000000000000e+00 5.799999999999999822e+00 1.800000000000000044e+00 +7.200000000000000178e+00 3.600000000000000089e+00 6.099999999999999645e+00 2.500000000000000000e+00 +6.500000000000000000e+00 3.200000000000000178e+00 5.099999999999999645e+00 2.000000000000000000e+00 +6.400000000000000355e+00 2.700000000000000178e+00 5.299999999999999822e+00 1.899999999999999911e+00 +6.799999999999999822e+00 3.000000000000000000e+00 5.500000000000000000e+00 2.100000000000000089e+00 +5.700000000000000178e+00 2.500000000000000000e+00 5.000000000000000000e+00 2.000000000000000000e+00 +5.799999999999999822e+00 2.799999999999999822e+00 5.099999999999999645e+00 2.399999999999999911e+00 +6.400000000000000355e+00 3.200000000000000178e+00 5.299999999999999822e+00 2.299999999999999822e+00 +6.500000000000000000e+00 3.000000000000000000e+00 5.500000000000000000e+00 1.800000000000000044e+00 +7.700000000000000178e+00 3.799999999999999822e+00 6.700000000000000178e+00 2.200000000000000178e+00 +7.700000000000000178e+00 2.600000000000000089e+00 6.900000000000000355e+00 2.299999999999999822e+00 +6.000000000000000000e+00 2.200000000000000178e+00 5.000000000000000000e+00 1.500000000000000000e+00 +6.900000000000000355e+00 3.200000000000000178e+00 5.700000000000000178e+00 2.299999999999999822e+00 +5.599999999999999645e+00 2.799999999999999822e+00 4.900000000000000355e+00 2.000000000000000000e+00 +7.700000000000000178e+00 2.799999999999999822e+00 6.700000000000000178e+00 2.000000000000000000e+00 +6.299999999999999822e+00 2.700000000000000178e+00 4.900000000000000355e+00 1.800000000000000044e+00 +6.700000000000000178e+00 3.299999999999999822e+00 5.700000000000000178e+00 2.100000000000000089e+00 +7.200000000000000178e+00 3.200000000000000178e+00 6.000000000000000000e+00 1.800000000000000044e+00 +6.200000000000000178e+00 2.799999999999999822e+00 4.799999999999999822e+00 1.800000000000000044e+00 +6.099999999999999645e+00 3.000000000000000000e+00 4.900000000000000355e+00 1.800000000000000044e+00 +6.400000000000000355e+00 2.799999999999999822e+00 5.599999999999999645e+00 2.100000000000000089e+00 +7.200000000000000178e+00 3.000000000000000000e+00 5.799999999999999822e+00 1.600000000000000089e+00 +7.400000000000000355e+00 2.799999999999999822e+00 6.099999999999999645e+00 1.899999999999999911e+00 +7.900000000000000355e+00 3.799999999999999822e+00 6.400000000000000355e+00 2.000000000000000000e+00 +6.400000000000000355e+00 2.799999999999999822e+00 5.599999999999999645e+00 2.200000000000000178e+00 +6.299999999999999822e+00 2.799999999999999822e+00 5.099999999999999645e+00 1.500000000000000000e+00 +6.099999999999999645e+00 2.600000000000000089e+00 5.599999999999999645e+00 1.399999999999999911e+00 +7.700000000000000178e+00 3.000000000000000000e+00 6.099999999999999645e+00 2.299999999999999822e+00 +6.299999999999999822e+00 3.399999999999999911e+00 5.599999999999999645e+00 2.399999999999999911e+00 +6.400000000000000355e+00 3.100000000000000089e+00 5.500000000000000000e+00 1.800000000000000044e+00 +6.000000000000000000e+00 3.000000000000000000e+00 4.799999999999999822e+00 1.800000000000000044e+00 +6.900000000000000355e+00 3.100000000000000089e+00 5.400000000000000355e+00 2.100000000000000089e+00 +6.700000000000000178e+00 3.100000000000000089e+00 5.599999999999999645e+00 2.399999999999999911e+00 +6.900000000000000355e+00 3.100000000000000089e+00 5.099999999999999645e+00 2.299999999999999822e+00 +5.799999999999999822e+00 2.700000000000000178e+00 5.099999999999999645e+00 1.899999999999999911e+00 +6.799999999999999822e+00 3.200000000000000178e+00 5.900000000000000355e+00 2.299999999999999822e+00 +6.700000000000000178e+00 3.299999999999999822e+00 5.700000000000000178e+00 2.500000000000000000e+00 +6.700000000000000178e+00 3.000000000000000000e+00 5.200000000000000178e+00 2.299999999999999822e+00 +6.299999999999999822e+00 2.500000000000000000e+00 5.000000000000000000e+00 1.899999999999999911e+00 +6.500000000000000000e+00 3.000000000000000000e+00 5.200000000000000178e+00 2.000000000000000000e+00 +6.200000000000000178e+00 3.399999999999999911e+00 5.400000000000000355e+00 2.299999999999999822e+00 +5.900000000000000355e+00 3.000000000000000000e+00 5.099999999999999645e+00 1.800000000000000044e+00 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-chebyshev-ml.txt b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-chebyshev-ml.txt new file mode 100644 index 0000000000000000000000000000000000000000..786486295935319c03a60a349f03328c127935b9 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-chebyshev-ml.txt @@ -0,0 +1 @@ + 8.9084734e-01 9.3573853e-01 9.3507398e-01 9.6040691e-01 9.2918157e-01 9.6617342e-01 9.0430930e-01 9.5753424e-01 8.7106898e-01 9.2169905e-01 9.7401159e-01 8.9013416e-01 9.3956689e-01 9.0041896e-01 9.2588355e-01 9.3849417e-01 8.9713468e-01 9.1481804e-01 9.7500539e-01 9.0012586e-01 9.0962559e-01 8.5860091e-01 8.6981095e-01 8.9995771e-01 8.8070172e-01 9.1456657e-01 8.6711474e-01 9.2593917e-01 8.7560376e-01 8.5193121e-01 9.0898542e-01 8.7765302e-01 8.6555584e-01 8.6093485e-01 9.0447028e-01 8.7614405e-01 9.4803522e-01 8.4998062e-01 7.8398996e-01 8.9538612e-01 8.3902291e-01 9.9039470e-01 9.5480519e-01 8.9152195e-01 9.1623329e-01 7.9094921e-01 9.1777100e-01 9.8972335e-01 9.0429093e-01 8.7646362e-01 9.2136649e-01 9.7178177e-01 8.9610979e-01 9.4710327e-01 9.3612450e-01 9.0241499e-01 7.7992538e-01 8.7262126e-01 9.3325183e-01 8.5796531e-01 9.4267977e-01 6.7224167e-01 7.9568368e-01 8.6411267e-01 9.3311642e-01 9.0160114e-01 9.0698887e-01 8.5833256e-01 9.6902830e-01 9.5072298e-01 8.6808495e-01 9.7879599e-01 8.8060729e-01 8.2818573e-01 8.4366706e-01 8.4506700e-01 9.4532981e-01 9.1792306e-01 7.8917825e-01 9.8337805e-01 8.1751613e-01 9.3037855e-01 9.1618832e-01 8.6568874e-01 8.9751397e-01 8.7923710e-01 8.6814329e-01 9.0330164e-01 8.2426213e-01 9.4644643e-01 8.8431293e-01 8.8497426e-01 9.0633818e-01 9.5537161e-01 8.2167575e-01 8.7771053e-01 9.0681167e-01 8.7626143e-01 8.7463464e-01 9.8033940e-01 9.2920881e-01 9.5108549e-01 9.1287466e-01 8.0052218e-01 9.2409517e-01 8.8252650e-01 8.7873923e-01 9.2989402e-01 9.1985043e-01 9.6172646e-01 8.8223856e-01 9.4477822e-01 8.8310948e-01 9.4461306e-01 9.1875210e-01 9.1233363e-01 9.2124013e-01 9.5460897e-01 8.4640982e-01 9.0882657e-01 9.8169468e-01 9.7828355e-01 8.4150533e-01 8.6888923e-01 9.7138825e-01 8.7988144e-01 9.6720910e-01 8.9450147e-01 9.5331584e-01 8.8871809e-01 8.9736685e-01 8.6258146e-01 9.1331565e-01 9.0968870e-01 9.4833654e-01 9.0536967e-01 9.5099871e-01 8.0251958e-01 9.2526150e-01 9.8971957e-01 9.0340947e-01 9.4955892e-01 9.6838162e-01 8.7534901e-01 9.1178797e-01 9.2649154e-01 9.5260993e-01 9.3178143e-01 9.4943000e-01 8.7816171e-01 9.6506542e-01 8.3422958e-01 9.3443585e-01 9.3220084e-01 8.5706573e-01 8.4666325e-01 9.0474744e-01 9.1080644e-01 9.2406899e-01 8.7901768e-01 9.3265263e-01 9.5992829e-01 9.5696271e-01 9.1932272e-01 8.0937044e-01 9.0904917e-01 8.9516756e-01 9.4797906e-01 8.4159421e-01 9.6773901e-01 9.7099825e-01 9.6941820e-01 9.8174088e-01 9.7569951e-01 9.3655362e-01 8.4130333e-01 9.5994549e-01 8.4235414e-01 9.1429418e-01 9.3418117e-01 8.4600977e-01 8.8166496e-01 8.7594776e-01 8.8571112e-01 9.6308174e-01 9.5315927e-01 8.6997519e-01 8.9383032e-01 9.4686804e-01 9.4399596e-01 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-double-inp.txt b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-double-inp.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a77021775ddb61d226aa8c4ba60f0af013e4a6c --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-double-inp.txt @@ -0,0 +1,20 @@ +8.278938049410748956e-01 9.035293984476246987e-01 1.862188994679486731e-01 8.921151312310462433e-01 2.061859119379583216e-02 3.440636727385729676e-01 1.533779912830328662e-01 5.701372300009802663e-01 5.510020730211558915e-01 1.792362258426003496e-01 8.086175120876580857e-01 6.115487184317183189e-01 1.233471787164852618e-02 1.441643531871039663e-03 4.044309209045688913e-01 3.561398959499905148e-01 1.281985712929750720e-01 8.663300833847481508e-01 8.696027786291581352e-01 3.611727370363766454e-01 5.283537658772616830e-01 1.440241088090119526e-01 3.112457227138950566e-01 6.031280796897889873e-01 9.230324792742518047e-01 2.332121881136874908e-01 3.192652267403439659e-02 3.466206294995559656e-01 2.988687728046366399e-01 5.116749542048093513e-02 2.584975830914494344e-01 4.302023478042227289e-01 8.003972751713522849e-01 9.364931911368097328e-01 9.737098649964673891e-01 4.718038453972229762e-01 4.526591686607864817e-01 1.056485678520797666e-01 5.883019714285405710e-01 3.846092237676981274e-01 6.461500053435473845e-01 1.013239729848824933e-01 1.216151561651189761e-01 5.159668929484659827e-01 8.452074473510227115e-01 9.885170962247968873e-01 7.623883073490128615e-01 2.291163243615434997e-02 5.775530980802381364e-01 7.820699896828091635e-01 8.239186345842965942e-01 3.391800105260227571e-01 9.546318451614538292e-01 3.789677917867695367e-01 4.526533399649290690e-02 8.366786473238587707e-01 3.082636811049858094e-01 1.173936820793450853e-01 7.631994969169442200e-02 2.997416650722183329e-01 5.795208655160232203e-01 3.942350892542011431e-01 1.175126383297261379e-01 4.928232513950027149e-01 9.421293996225950096e-01 8.365391053841342295e-02 6.868059693571844093e-01 3.589527962429440722e-01 7.592939427166059962e-01 5.623849466131448649e-01 2.110746828032050715e-01 9.824683704668600859e-01 2.661230142246236996e-01 6.162272315007123469e-01 5.023254536607497656e-01 5.202854476669782624e-02 5.835090668842095596e-01 7.864642118889143552e-01 2.504012386867506823e-01 6.728308641135989365e-01 4.610793534576096420e-01 4.820508770515909980e-01 9.720403251022265989e-01 3.100069285263498120e-01 7.681017126461753275e-01 7.956539306007082146e-02 2.593389637887737464e-01 1.137852590403054531e-01 3.885303073284454012e-01 8.599094660075957686e-01 5.215167875918280682e-02 1.620908248572288102e-01 1.859236090457663249e-01 6.247716512610480555e-01 3.415128495520775020e-01 7.034903368378029320e-01 6.037564640019568163e-01 2.338969434423310290e-01 1.002104885609900187e-02 7.866058403969036217e-01 +8.033694116033356369e-01 8.653264545544031572e-01 7.468340410754038539e-01 6.362430919910603278e-01 5.120006306625468628e-02 9.503348372633585450e-01 4.697732609626817935e-01 4.221305288459429317e-01 3.153452119838391354e-01 2.991014843442657556e-01 1.190667967280257811e-01 3.486567714509342109e-01 8.289493649885054660e-01 8.454811050800014049e-01 9.149673018211901265e-01 7.708707837193897738e-01 2.640157732122547785e-01 2.107897022189605396e-01 4.207633055054439408e-01 6.719500284654699174e-01 1.458031684893063007e-01 1.800412735886125493e-02 8.402733435220011149e-02 4.206760156883160295e-02 1.376933515041314227e-01 1.716717341022133692e-01 1.788220727652158892e-01 8.224310433402118869e-01 7.729093666867475898e-01 2.064223621025984556e-01 9.592092036227207741e-01 8.312490243754996344e-01 6.673289360369902834e-01 4.632847903690773261e-02 7.643954098358983762e-01 9.359341525615098023e-01 1.914966319163026176e-01 4.536590469402868031e-01 8.640836016538007147e-01 3.941529178175462444e-02 5.602101995205478469e-01 9.263806161941660067e-01 1.555995325944817820e-01 6.172208102950116348e-01 6.335576752812099866e-01 9.766975460368043649e-02 4.475795689539874278e-02 3.248842796104995934e-01 5.700377122149502540e-01 9.066962967256807504e-01 5.458460621505676347e-01 6.833401285581487405e-01 2.887244409544044155e-01 1.316338647016834784e-01 2.325673305245992140e-01 4.150121963188406760e-01 3.834845466366055833e-01 8.149365773968725302e-01 1.867003849450201702e-01 3.170322173543018707e-01 6.832093662682684476e-01 1.729728518929105618e-01 9.236557359702636250e-01 9.152941252150086360e-01 7.224879983096620384e-01 8.557920626598064517e-01 5.344883059251644974e-01 4.876873274449112783e-01 8.308277804506420949e-01 3.916624489322212410e-01 3.459695122273966916e-01 4.033512499027409604e-01 6.555726444913008155e-01 7.138452409380238173e-01 1.683937314599968094e-01 1.769382143486440961e-01 7.588683655178136700e-01 3.750589892880819010e-01 7.525176245126207197e-01 6.083961152538303052e-01 1.145972309907993258e-01 6.239614485809552580e-01 1.307655482065895880e-01 8.530458750670916190e-01 4.801602070124768584e-01 8.168122189863546989e-02 3.793139622744635675e-01 1.496986997776840189e-01 7.129023878302899186e-01 6.830979237438047358e-01 7.635375943876505644e-01 1.824004963251233402e-01 5.764695848992339444e-01 8.865113248731604223e-01 5.784337085544002388e-01 9.700026628755119562e-01 7.318207347905059112e-01 3.851401393936705331e-01 1.774291851193399161e-01 9.763423229242296220e-01 +9.287178470949695175e-01 1.748282433617460718e-01 9.238531711586964734e-01 8.291274445125006443e-01 9.513259272578692416e-01 7.486316801165745494e-01 6.257378457524477300e-01 2.062711693536473101e-01 3.970721244184766130e-01 2.738325225026445597e-01 8.735038948299954642e-01 5.415282140033768066e-01 5.176317904298315398e-01 5.347036264518250093e-01 7.482056965410627258e-01 4.140672582824351800e-01 8.709067272363142376e-01 9.499605569181273079e-01 5.380266748336398619e-01 4.369252161707162241e-01 8.235722216228258397e-03 4.308187193646527691e-01 6.030581482859224129e-01 7.316831195156517920e-01 5.540499846834291420e-01 2.044203040111662872e-01 8.645251782981867583e-01 1.816095717570278545e-01 9.639119168018674966e-01 3.572031072322333634e-01 5.580226816834680248e-01 5.586629875016585478e-01 7.213854320902782780e-01 8.513998260042524580e-01 6.308764347277173723e-02 4.299855362100638567e-01 8.789303907444128150e-01 9.178850359236285783e-01 2.275205845091231582e-01 1.899395443939643213e-01 7.103070862773533944e-01 9.450015289553428399e-01 1.691856364522159595e-01 7.368719616877857925e-01 9.600189536623833231e-01 5.128846522932454244e-01 6.209162727118655578e-02 7.992250598838029907e-01 9.141050280518014937e-01 1.471297785256820978e-01 7.466162372930541524e-01 4.656107650642931084e-01 6.399324135161845728e-01 2.023617619481610230e-01 1.019104648900100996e-01 4.390693688536728700e-02 9.822620353006089600e-01 2.881951852926285529e-01 6.191575015960482098e-02 8.989580763251467932e-01 4.635958631890454429e-01 1.781973138114967270e-02 7.906911683818984571e-02 6.525270776225711167e-02 3.620583622807886925e-01 2.651673718940715796e-01 5.829372395929610651e-01 2.118159824373908595e-01 5.900287159143694504e-01 9.405929925178391215e-01 9.262415619063500971e-01 5.639581506302312475e-01 4.529556154689695635e-02 2.873819210518682166e-01 5.718545934306838996e-01 9.877670791317306742e-01 4.120364488714320927e-01 9.896078045634184583e-01 3.796586997026456523e-01 1.178183652203194098e-01 6.641068305236120795e-01 4.045960610587706618e-03 2.262690437428437340e-01 7.839938005832693957e-01 7.695391333937223743e-01 3.713918392552509884e-01 4.245533341514018399e-01 1.475072494020331915e-01 6.011975181419888514e-01 5.158174017998343741e-01 1.788706151398071764e-01 8.880707130134481986e-01 6.463351030474082659e-01 6.499920635615744624e-01 8.570273676455353318e-01 6.055019270899113515e-01 2.123561211054603159e-02 2.027688787664126968e-01 1.930834215328548487e-01 5.131906052747271518e-01 +2.599990881903107010e-01 6.767857524909899336e-01 7.188217446352963558e-01 3.037178903357997672e-01 4.252381412838680541e-01 4.070924411439535984e-02 8.426710493038247485e-02 8.301517457289483426e-01 8.254603255702420705e-01 7.258533909453509514e-01 9.958706809470796451e-01 1.323408451651194584e-01 8.523995455245143571e-01 2.572405385832454705e-02 4.715363690065482727e-01 7.920130365690022378e-01 7.613745641534582775e-01 5.108305991695683002e-01 7.908714335912382376e-01 4.641131983754837043e-01 3.112627109831845873e-01 4.218013908715474436e-01 3.291577909008427394e-01 2.538715054071232213e-01 1.362470842487485401e-01 2.716429790290709745e-01 1.485325814161112534e-01 4.514539027544387517e-01 6.900835128673067365e-01 7.793407072946112457e-02 5.938024345270752624e-01 1.497853829906865553e-01 5.399567982652856424e-01 1.419209916759478496e-03 7.719776132867679497e-01 3.130795105576239523e-01 6.670071611167494030e-01 8.900596881158256979e-01 8.011158503301568645e-01 7.089295605187424520e-01 4.671116382997058114e-01 6.682965170673403899e-01 6.524835265739736823e-02 5.454288420771494783e-01 7.751910790556310049e-01 8.192595541387335256e-01 3.098855848167891835e-01 3.689971355659119601e-01 8.666507475054133769e-01 2.749042684253171220e-01 3.566565602478318775e-01 4.838173174723044978e-01 1.032975933616413489e-01 5.063065339610417492e-02 5.791168455729079900e-01 3.573337411289496668e-01 6.714098909652352898e-01 2.917057662433912846e-01 2.654964332620638467e-01 7.171804039048814694e-01 3.314488637898249657e-01 5.230399837442840649e-01 6.866534136026025692e-02 1.252966394621071178e-01 5.349397882659551184e-01 2.841423847455760709e-01 4.158473635710734362e-01 7.197062989831272128e-01 5.123869045047864113e-01 8.675622821594339840e-01 8.097441845042540054e-01 7.317178252133832439e-01 3.300847596465853462e-01 5.922311859141077273e-01 8.852619511417836318e-02 2.673412917259408994e-01 6.878259052441990651e-01 3.223000927116328462e-01 8.859387123976615319e-01 5.722722388300067742e-01 8.254877606669521750e-01 5.705299682290687624e-01 7.046478734972855262e-01 1.316324413616759559e-01 3.056358395675535800e-01 2.396516834600909140e-01 2.041201422493257311e-01 1.610755140653103989e-01 1.617012564641111538e-01 4.449920510036902144e-01 2.731012972755201274e-01 7.826874666257994662e-01 5.193612375350010746e-01 8.688804522977213729e-01 3.742157602758655610e-02 6.649628920608219307e-01 5.978149424619171315e-01 5.345645500553952711e-01 9.443202650415919441e-01 6.105837075491723498e-01 +6.387761328141735584e-01 4.210087412162694109e-01 3.777306694964789324e-01 3.576349403292201634e-01 7.272699618880260619e-01 9.173392803607671731e-02 1.212535698300880593e-01 3.871229381194544183e-01 7.735150198351389284e-01 4.687200483013695962e-01 5.161778571874678923e-01 9.839646447226980674e-01 8.626932748911960713e-01 9.618485576577924245e-01 2.997996427525421170e-01 3.955404657388794654e-01 8.480126027102616870e-01 8.194992325050480808e-01 2.800213436873294492e-01 7.188391466620779324e-01 2.289766105875049584e-01 3.838547514028287644e-01 1.363553401061209369e-01 2.131328253542326134e-01 2.666779468144075960e-02 3.252883844200405994e-01 4.207860197469600605e-01 2.991365385037647595e-01 9.180779845534067229e-01 8.787338732192649937e-01 5.404510999105649471e-01 1.735493827761729335e-01 7.405224640747264386e-01 3.927355563629583157e-01 3.957109873399460298e-01 1.313029813325972128e-01 6.434498219738993274e-01 7.162213694578050127e-01 6.454998257494671821e-01 3.808124530008022424e-01 2.027201015737234435e-01 6.667632842770417900e-01 1.609491052365198405e-01 1.192413785409307536e-02 4.546773323526854815e-01 7.733541911050207940e-01 3.902525737195561284e-01 4.006023779897505133e-01 5.156517815815246930e-01 6.135685498584592112e-01 7.062153114980724844e-01 5.505858882117883324e-01 3.541308807182554919e-01 5.237151122342533771e-01 5.230649229131387745e-01 1.973541027697351957e-01 7.940327858595511712e-01 9.998588700623055603e-01 3.878271015153827994e-01 4.455006584967207139e-01 8.376414508056347907e-01 3.310833863524501597e-01 8.020469097392601832e-01 1.890327633084128989e-01 3.830289472395409511e-01 8.605040171046141051e-02 9.978185524023941433e-01 8.333890591892906263e-01 4.509013468741837061e-01 6.355778557686052599e-01 1.422515991097305088e-01 9.549891485963732940e-01 7.535776302868563148e-01 9.306005301880662106e-01 2.444330347211679522e-01 5.828218427569508142e-01 1.261938242968304591e-01 2.829188731405173352e-01 8.100246952078660190e-01 2.032739130996042975e-01 3.997268448390065565e-01 3.882777703107541667e-01 1.102505652624736765e-01 5.826634725328041498e-01 6.508734477956333864e-01 1.777287661702166011e-01 4.857051012052149286e-02 6.850537712379254351e-01 5.012281307761055071e-01 3.329154880061502286e-01 5.006261767216675374e-01 4.542081454976160115e-01 6.777801995399822532e-01 4.271303586474960445e-01 7.820470659692947413e-01 5.143462618485082904e-01 4.071273891563575997e-02 8.503383643856671226e-01 6.877485768345151795e-01 6.498843855014626580e-01 +5.539512747016193117e-01 6.329206647391879548e-01 2.798533500321682688e-01 4.825977295850051307e-01 7.625297023172977751e-01 9.081309101427640362e-01 4.124792086535029600e-01 3.647019658319609059e-01 7.529595202332928228e-02 3.072404010876803593e-01 7.890673660964639957e-01 4.079781478915127657e-01 1.440519120695739064e-01 2.538968953804546791e-01 1.595028243568367143e-01 9.066545851872198636e-02 6.367601114674349416e-01 7.622263643880089479e-02 3.015728236404162654e-01 2.424070469873378375e-01 5.711440390241000475e-01 5.717001375511508998e-01 2.237853674032181939e-01 7.112101625753678436e-01 4.321054197012103026e-01 2.505322169010260058e-02 5.877307077139551916e-01 4.415771174397812304e-01 3.766022855145171322e-01 9.803490652619811785e-01 1.229258314111529860e-01 8.108351868714478439e-01 8.558595456964329662e-01 2.168217533833206589e-01 2.034022719386595623e-01 8.687457137579783772e-01 9.013327195854559104e-01 8.156766512673154779e-01 2.717576187546973943e-01 1.756417893371479133e-01 7.555856977566548505e-01 6.708809351312817748e-01 8.998789237886926085e-01 1.936367585946979775e-01 7.949724635465026390e-01 3.164799312763589834e-01 5.493048513173155456e-01 1.608917269168268493e-01 3.048667492191803330e-01 5.599401537727016764e-01 5.779501360842279611e-01 1.296714605309662316e-01 9.160752328055997706e-01 8.058674476110374574e-01 4.385508937505578908e-01 9.212419718012100356e-01 2.249887451242467140e-01 6.283927745352599903e-01 3.778992451536005159e-01 3.571958698867505611e-03 7.276526470528231760e-01 9.051678673805297892e-01 8.465837072484881931e-01 4.548317505393462135e-02 3.189318261926020748e-01 4.446388607398673587e-01 4.292356336344156365e-01 4.203980977718795309e-01 4.698059253071955599e-01 6.151991200848159203e-01 8.479986139404802614e-01 9.870993262459623052e-01 3.164206525899861955e-01 6.464672171639846976e-01 8.508781429592480183e-01 4.733667503354813677e-01 8.076014176740163863e-01 6.671443255679101458e-01 6.639213267047979761e-01 3.681688930741919830e-01 4.679870252651611162e-01 1.790041740686979521e-01 8.446070273663058847e-01 3.350737544979878191e-01 6.600272349677447359e-01 4.356083218487936115e-01 7.995134167346013010e-01 9.083660261041469619e-01 9.743975306734570241e-01 8.144839650654719376e-01 6.865011984586443239e-01 1.709747281999153268e-01 8.534933687161740945e-01 9.494753729726415070e-01 8.140124992294850426e-01 8.936241255316055287e-01 9.087976860818796077e-01 9.030687493451383663e-02 4.025785149840914734e-01 9.592005611533803711e-01 +5.714058727476275523e-01 7.913573761505965365e-02 9.301773447377043036e-01 4.302822433307075256e-01 4.618892554175407783e-01 1.882471300213742760e-01 6.231472878215863487e-01 2.350437450940777717e-01 8.483410480771292894e-01 8.580803842040533036e-01 4.246398783388435350e-01 5.667321565946502604e-01 7.247417018955526480e-02 5.373984417482219333e-01 8.794242091541510931e-01 9.699025554453030162e-01 8.254197752548814160e-01 7.739723972867470492e-01 6.365819416181199841e-01 3.451230687021222820e-02 1.829102490094791644e-02 9.179618383026147965e-01 4.481667270072077214e-01 4.771270250445739380e-01 1.588469404953456454e-01 3.766332499200618633e-01 5.057026248713025751e-02 9.125900914275182352e-01 8.438133644246305076e-01 3.282972411719701222e-01 6.042003956122835584e-01 7.423456085393266290e-01 1.389012737541106546e-02 3.674754266702850991e-02 2.126646727703802586e-01 3.085666164246750887e-01 4.303440338750976757e-01 1.749037978865556342e-01 2.177699993322510519e-01 6.675614739991906355e-01 1.926533336347433512e-01 8.032010572660308600e-01 4.611412981769049679e-01 9.907201268457492827e-01 8.973785930837320235e-01 6.286342392657409128e-01 8.111266245859546364e-01 1.154230969025437092e-01 8.382880466301794176e-01 1.053753927827069115e-01 9.921712862234919328e-01 9.041662667920956631e-01 3.626267376021269362e-01 2.262225368932846425e-02 8.669003741626111204e-01 7.597054897704164089e-01 4.700318514995387442e-01 4.338185014241978665e-01 1.205425463362067573e-01 2.413879270602589111e-01 5.483334840461459025e-01 2.042653841254596925e-01 5.452588940366013270e-01 3.164646091706100339e-01 1.878958248945691301e-01 2.188622304737641855e-01 2.970982599823450698e-01 5.952148400199362976e-01 9.614251220149501176e-01 5.446813400697393392e-01 5.900748097930779146e-01 2.653062526715309621e-01 5.459933097767216692e-01 3.174185404661935550e-01 1.412133354129242457e-01 1.487441669790685594e-01 3.953776242211952674e-01 5.274261039692862418e-01 1.756132307607755072e-01 4.481942852746899630e-01 6.390660088765629521e-01 2.860380430081067571e-01 5.866902519902850166e-03 3.026687645174785946e-02 1.952533570196290924e-01 2.154769096186736066e-01 8.920573593276575064e-01 5.644513191915436767e-01 5.551464696654353492e-01 4.378199413349500579e-01 8.685737643974280608e-01 7.493934764293597173e-02 9.556749726352036234e-01 6.386433482536227890e-01 8.714694524097754691e-02 1.722786161701279628e-01 6.526867532768643176e-01 8.950304705281527662e-01 6.158198776753203152e-01 9.587176904005377809e-01 +7.705718397401561948e-01 3.165816092999733655e-01 4.334200859975760878e-01 8.639807015515663657e-01 5.576514209532534849e-03 2.456745447057938625e-01 1.664686313299922338e-01 9.637084729617834133e-01 1.083448720752323569e-01 1.865218070380464388e-01 3.730358890475884426e-01 5.015351872138350542e-01 7.420710795841709562e-01 4.919420674769692248e-01 3.426558201886464872e-02 8.669984854934246199e-01 2.204243734202966376e-01 4.109792246853891662e-01 4.361732572946559472e-01 6.819306998053020763e-02 9.986304248057148447e-01 4.119289455392274313e-01 8.533050041845835487e-01 3.416914861912183632e-01 6.522191951039880697e-01 4.162803668786793088e-01 9.051674379917418189e-02 4.552378661306888397e-02 2.122677193466918633e-01 7.461518531655018105e-01 4.654688019259497489e-01 7.877564083548750373e-01 4.518328005682387127e-01 7.173857464237374248e-01 6.940056370290903498e-02 2.804574410412373764e-01 6.095681113112718652e-01 3.680596478602831123e-01 1.814569150719304025e-01 6.505055517979729807e-01 2.759585245701871026e-01 1.429501104786028431e-01 7.813891153083207808e-02 8.925314279991185540e-01 6.692056941902108091e-01 1.915141341107173822e-01 5.750233129581091562e-01 2.051961006251528108e-01 3.849013692629975614e-01 9.503788222043518807e-01 7.690419386411734282e-01 9.978147530014782607e-01 1.719584162437415298e-01 4.890758882401113894e-01 7.195660736040896399e-01 2.485818040997200828e-01 9.706486601870933928e-01 5.182604282071262558e-01 8.082072245463804983e-01 4.889961284821118248e-01 8.042893959057633158e-01 3.200685313413229593e-01 8.983245016887355661e-01 2.811495336955205371e-01 3.986095833814048417e-01 8.607229214132059436e-01 4.827620119717191960e-01 6.715610252037491623e-01 9.330824374137768329e-01 7.537710530085762750e-01 9.840804224010484269e-01 2.319352541177217564e-01 9.569114943157627229e-01 5.821928104654411351e-01 6.700479524814679788e-01 5.663434680086896211e-01 8.851091082101365526e-01 6.800562815862243315e-01 3.578475213752868589e-01 2.900164669281133367e-01 8.379170683569914235e-02 9.929972839740475177e-02 5.946248553621906741e-01 1.991332889320840405e-01 8.115065723822508792e-01 2.023388190440008616e-01 4.056545651129230823e-01 2.966825350250481552e-01 7.457176343507545546e-01 9.856015771246517954e-01 2.264338016147812160e-01 8.366528670045663141e-01 6.116829813603242849e-01 2.605933184296719274e-01 5.765962146558850643e-01 5.064075092266390188e-01 5.499615769589756287e-01 9.240234698632640020e-01 7.169900155229913530e-02 3.544181364560751168e-01 +8.154844535553099627e-01 4.797965609394789777e-01 7.476703385713100447e-01 9.086708404761600910e-01 3.191752505450355937e-01 7.611128630021511965e-01 6.246790343299296611e-01 1.942001426217137006e-01 2.789860414631386565e-01 3.236359785042408621e-02 3.178191288741717413e-01 8.372264298357038337e-01 8.872692914664047636e-01 9.589758852077276963e-01 3.123722260380168425e-01 8.980164015338999439e-01 7.260784140459818348e-01 6.567013512265649222e-01 1.028743505926521529e-01 6.821705410750319443e-01 6.889838995316139858e-01 5.587525493094736007e-02 6.921487028366646310e-01 3.616312929861494885e-01 1.673758008792780583e-01 6.626504595920326146e-01 9.125680913222075086e-01 1.424077784972291871e-01 6.508496429060767197e-01 6.615417385775157477e-01 9.654167310675311198e-01 5.536662974550183858e-01 7.092622144968085962e-03 6.694595400455760625e-01 1.828533619119211417e-01 3.421514408394116247e-01 1.242580151818144518e-01 9.888774797458224075e-01 9.777955172739735135e-01 4.271370765628749178e-01 1.211608384809655936e-01 1.580132417172936954e-01 3.242705395708289640e-01 3.268994391754735940e-01 5.213767653645562383e-03 4.475169480357120699e-01 9.593245219293577986e-01 6.994304536782350867e-01 7.063863152769014331e-01 8.381620829497931080e-01 2.760441799736219615e-01 3.755200946645842475e-01 3.627729621737311172e-01 9.518310606719182498e-01 3.577273025276901386e-01 3.991159901003488164e-01 4.187060513068554535e-01 7.422605403637314581e-01 6.697944269780702342e-01 6.020599837037767799e-01 1.571185850817550245e-01 7.519860911185742847e-01 6.635775704496444938e-01 9.487848173531471252e-01 7.900030232338028924e-01 4.143783957270819052e-01 5.618429740858444932e-01 3.737804619062014000e-01 6.179941187802344693e-01 6.553638605616040058e-01 1.009709416658691739e-01 4.935037098582963910e-01 5.485489972455533936e-01 1.024147956480448984e-01 1.195764707555347917e-01 4.910516327810896531e-01 3.551185778630389089e-01 3.857601645798814927e-01 2.074975219600547760e-01 2.084038664460790002e-01 5.268616653491025037e-01 6.948014877618717833e-01 6.179744044618615817e-01 7.063658085955483168e-01 7.925757227686872630e-01 6.199016959584816577e-01 1.163676037434490107e-01 7.425752264755586252e-01 5.403115665133301215e-01 2.546191951391015840e-01 6.961300925345208501e-01 4.003013072125547467e-01 5.906120962720950995e-02 5.879915846330325824e-01 1.213602408288709800e-01 3.801780679842765576e-01 1.731477742402802722e-01 4.624568816669496485e-01 3.304453744619206823e-01 8.810445876116090869e-02 +5.140190515373614932e-01 1.419225260054487459e-01 7.777845802285945354e-01 3.327562899409282071e-01 8.916875699762913943e-01 7.212852862736146564e-01 5.727327199433507321e-01 5.897820225918504189e-01 7.318614954542906892e-01 7.393985144455500480e-01 4.531340740296823100e-01 9.903061584426188224e-01 4.213350938331624773e-01 4.542342471963995987e-01 9.788786426453045530e-01 1.881707000343846303e-02 8.005433413647761176e-01 1.523502822273363755e-01 5.630164732287495921e-01 5.946603842470724599e-01 1.225547698678740582e-01 1.531136594724622491e-01 8.157973612638946825e-02 2.752046015644330490e-01 6.809045821946161370e-01 6.455289724528190387e-01 3.830356726830793646e-01 4.446144649678575034e-01 4.969038423960672191e-01 5.497873820641221432e-01 9.471879627821714331e-01 5.933046675329255448e-01 4.099233758501530378e-02 5.790409810134594659e-01 9.546095885251496549e-01 2.608616052375664074e-01 6.910160339170060562e-01 1.293709850476291168e-01 6.407264616302255078e-03 6.186037089828009261e-01 5.537861302543241049e-01 3.527421038298221845e-01 8.033232052121624944e-01 8.128114152830284711e-01 8.319982582278713235e-01 5.939566376046836460e-01 2.291090283499520597e-01 5.438101817725821130e-01 6.881146379117278888e-01 2.421968586304659166e-01 5.874047918543783275e-01 6.210102709484541794e-01 7.041387566450251212e-01 6.959223476278774134e-01 9.133877300988062498e-01 9.230647706207778525e-01 6.856884219815310155e-01 6.997988808693775820e-01 6.177944932528769417e-01 5.512902545683161515e-01 5.818280341729102911e-01 6.538267999985679646e-01 6.946673485935980219e-01 4.817938258357623571e-02 9.352008817207906333e-01 4.774162142215661042e-01 5.768063588692976529e-01 4.589648891483899540e-02 7.998946815651652997e-01 4.434260476954369201e-01 9.850053510925722566e-01 6.648626681529369309e-01 4.606293826856903140e-01 3.309042418210563774e-01 1.438901922508034614e-01 7.986559119276418484e-01 7.037818421334554042e-01 3.605119534240813772e-01 3.785959549258922641e-01 9.562491516841659100e-01 4.997955143590974147e-01 1.029540300938682762e-01 1.819017177001992502e-01 3.665425750262368831e-01 1.688063588370778412e-01 7.030735208313992901e-01 8.922375654244527610e-01 1.055706412056253152e-01 2.664739907746691561e-01 9.906029568647586325e-01 6.043845090140997911e-03 3.495786295043534775e-01 5.989441999519146131e-01 6.776147193866479679e-01 7.012991789852640601e-01 1.825838783477321536e-01 7.612293578749116385e-01 1.564769891240175292e-01 2.762157292905387251e-01 7.641900040015234818e-01 +4.746013333880729768e-01 7.609202966712714788e-01 2.537820854162747830e-01 1.709362234877408460e-01 1.886635378734374813e-01 2.439567014093724229e-02 7.640304718272151741e-01 3.483216170435471382e-01 7.744289278738043514e-01 4.190437573644867353e-01 5.319091476394965934e-02 8.580130976087452233e-01 6.259446446786639529e-01 8.793213970773006150e-01 2.441023074890465994e-01 7.753405549489799098e-01 8.760187573193888300e-01 5.946480724009295393e-02 2.873093046571124631e-01 8.710837851946537924e-01 9.103181731924696596e-01 6.534637257615111272e-01 4.128420398577182793e-01 4.905858108576378607e-01 6.178275806701372108e-02 6.368043900016381320e-01 2.865296941219959148e-01 6.371773028539067241e-01 4.924322796636745325e-01 1.709313290387282080e-01 1.856892551689268700e-01 9.592782603102242289e-01 5.402593764193130976e-02 7.287312244390512506e-01 5.679467572000697073e-01 6.255587794305905724e-02 3.069660218141317953e-01 1.089960430557104232e-01 5.550748245336984965e-01 2.555948886689661803e-01 4.140925514039996980e-01 1.180376445052062628e-01 8.832322629884041820e-01 7.784546946701487169e-02 3.177678935473182698e-01 6.681804863429485764e-02 7.047099396645268854e-01 4.133897376851528582e-01 5.600656990480865627e-01 3.883995683475501837e-01 4.459430113152932362e-01 4.214077227574740681e-01 4.763369230200156235e-01 2.701480661168440545e-01 4.296286564389811824e-01 9.601402258758658936e-01 6.326999441846863359e-01 2.442086919688498670e-01 8.407708423957936938e-01 3.626867985638081437e-01 3.641441713291436733e-01 7.932397565989488530e-01 8.902073520619256941e-01 1.929173010337000838e-01 7.309376779324568973e-01 7.305852858337777977e-01 6.510197444582447313e-01 9.512661608643838695e-01 8.461467164366111016e-01 9.245490147941206605e-01 2.658844813385705663e-01 9.538758859344749208e-01 8.215517204998477041e-01 8.217795540390903097e-01 7.569662091300560780e-01 6.262685322871274218e-01 5.597770510574888725e-01 8.155720175123675197e-01 8.545688745180864965e-01 8.986051518529034610e-01 2.477911506572628708e-01 8.462580108996445860e-01 6.065941220995090255e-01 6.500490804973033665e-01 1.120463882674053169e-01 9.299049132942927010e-02 1.388364074229719858e-02 5.901199124540731367e-01 2.795110110544174464e-02 1.644097083463245124e-01 5.341029647603202646e-01 5.276816677181681570e-01 5.439849107754858304e-01 5.371677986392331405e-02 4.515163125788429488e-01 5.036243367087100964e-01 5.721818679625961801e-01 5.271368612400184617e-03 7.720961020546839304e-01 9.015383457479009266e-01 +8.301526916287945701e-01 8.704609696144033348e-01 2.955689129581380303e-01 1.762209253489944727e-01 2.698172933050072553e-01 1.138095349991521399e-01 4.092588531860634760e-01 8.202978121681584467e-01 2.822241377079557356e-01 6.117376205659387223e-01 7.169923068016897938e-01 9.310256256264415331e-02 3.989664052931106708e-01 1.651874953308862803e-02 7.890202597932294282e-02 9.068686774810821305e-01 5.203866694486933842e-01 4.297748572844445336e-01 5.208786995443430712e-01 2.163224881365530816e-01 7.274307306357226111e-01 1.675784956180090823e-01 5.969822786565782691e-01 8.959750832846602453e-02 1.253794151891943764e-01 5.352628522116801291e-01 2.562706125890066300e-01 6.030433202137867044e-01 8.330717547440393833e-01 9.603613683422040914e-02 7.569714244468559450e-01 3.184801677796517128e-01 1.667069341164499896e-01 3.132470247801235619e-01 6.417752836394801097e-01 6.433909425912354152e-02 4.056860213146201710e-01 3.166772891331335327e-01 9.574059746098845247e-01 1.492907964460536974e-01 8.311513764927496162e-01 6.652928354977717396e-01 2.396804722185036374e-01 5.812361618600220270e-01 9.724228681350225445e-01 2.853983236378453414e-01 5.337719354896472979e-01 6.779446197712412081e-01 5.485102006140557540e-01 9.010109155962182648e-01 5.724439967467525037e-01 5.965540527411405947e-01 1.598667990086183321e-01 1.363934512727023041e-01 5.327536522697270405e-01 4.123866715061276222e-01 4.617251396918636841e-01 6.935944951381239898e-01 4.300337419593377453e-01 1.892407993760835128e-01 1.666936825594794724e-01 4.625634184864588772e-01 4.805197636774838355e-02 7.003542850133466224e-01 2.130226006716084974e-03 8.678863343041013367e-01 4.874478520451258623e-01 7.043560228741558848e-01 6.317719270475393722e-01 5.372392256296196766e-01 2.982649812986511995e-01 1.272558612133412037e-01 2.467337555730741983e-01 6.546893200021091097e-01 6.291921159383098150e-01 8.505920470407707379e-01 4.046520490181828578e-01 3.875732096593392795e-01 8.551517214319142024e-01 4.152602284179877090e-01 9.587779137989138611e-01 6.977437468944928112e-01 3.240620775541913634e-02 4.025873770391376061e-01 5.485549335619134270e-01 7.146066156157020455e-01 3.012702534568838519e-01 3.526414480395153594e-01 3.309707144485515284e-01 4.315687014460974913e-01 6.641934530697197747e-01 2.172886798352815507e-01 4.807480925564590057e-01 5.006795397998469177e-01 5.818100901154411586e-01 2.107716091585690732e-01 6.606606051140029301e-01 9.317629042790995797e-01 9.840326342340242061e-01 5.752000964817773898e-01 +9.843444595454536872e-01 1.339523968066913540e-02 6.082172659959028671e-03 7.828244785439336662e-01 5.069653703872761819e-01 2.804896494365415327e-01 2.112385836660957139e-02 6.016479440778699228e-02 7.457477935084961818e-01 3.445503949245375397e-01 4.063494277166557200e-01 8.630275274433116817e-01 5.948396018456146850e-01 1.400867933474212457e-01 6.997522422654076646e-01 5.766519767930851081e-01 5.419976500582250889e-01 7.474121304089603735e-01 2.951600193008566686e-01 7.980170422334191827e-01 1.829036799578199757e-01 6.317636496261300749e-01 2.812612231140887431e-02 5.464747656105657381e-01 3.909873503320924204e-01 4.940850205957293406e-01 8.157850130814222611e-01 5.111092739445756150e-01 9.336823640685747439e-01 7.157105167170837445e-01 7.778989455994214097e-01 1.398722535910470466e-01 5.642653936300449091e-01 3.218717164845980028e-01 9.717427501967056402e-01 3.665791984428700134e-01 3.874321311211759156e-02 9.437600858738082188e-02 5.679526822961932231e-01 5.141385991358327079e-01 7.497840799582222715e-02 5.736515309094968318e-01 1.928132849879083954e-01 6.924244068001785823e-01 1.748389677952593146e-01 4.469577663506929532e-01 1.738527450963387455e-01 7.195287763517190793e-01 8.861150811892871682e-01 1.058443750714600506e-01 1.941789362229970894e-01 9.188374820700584422e-02 7.706736301449305104e-01 6.718642548609364828e-01 5.981029087121966237e-01 4.832880127232569434e-01 3.073688779938709148e-01 5.156312334804930009e-01 1.777418420119527553e-01 8.885462205165685079e-01 4.486254681289014723e-02 1.345398129556140132e-01 7.467627984379916484e-01 4.384565546058830643e-01 7.217750080760946263e-01 3.949550352625393890e-01 4.307950907642028593e-01 6.087680934849041270e-01 3.294516167246774874e-01 1.316682090209408962e-01 1.824857738754404046e-01 5.332379826483617524e-01 3.567136182864261151e-02 1.976220743086236631e-01 5.849349042822560296e-01 1.133174406357483344e-01 7.711522754393199675e-01 8.557306786807005183e-01 3.038353471344266143e-01 4.422747047768413875e-01 2.537160404215925702e-01 2.372714099723788328e-01 5.906462765375103396e-01 4.849909323133470007e-01 2.692576210504484813e-01 4.540849506602829821e-01 9.664935719107857759e-01 2.044371576459835804e-01 4.505417469690352616e-01 7.110722322201217249e-01 3.051357995214963870e-01 8.978937034341526457e-01 6.090501112506481185e-01 6.595415779178889215e-01 6.565426836745864581e-01 6.565608489824376059e-01 2.679102664248229626e-01 3.819533138204529443e-01 6.609794961162380744e-01 2.289558446859882856e-01 +9.274935298374649140e-01 1.174096651033715855e-01 3.030761852629033637e-01 1.605508209527917174e-01 9.601854834873225775e-01 4.341959513718630648e-01 6.320768160802121560e-01 4.213429090614078110e-01 3.695553969042019160e-01 5.965457437116089556e-01 3.520335041155040479e-01 7.702703502247409961e-01 8.571112772962534709e-01 7.904077282532658844e-01 2.247339318352784554e-01 6.823720204503556097e-01 5.883435710582129996e-02 6.786037033312407596e-01 9.721137137641507886e-01 2.042576970668320557e-01 8.394085754806240862e-01 7.433082729552867862e-01 4.072614159870893147e-01 7.451483066617257123e-01 1.699472962789440045e-01 1.753052015584344314e-01 2.255269204788400428e-01 7.794755643807432799e-01 8.407732260470973662e-01 9.301182862857163558e-01 3.701995309382508648e-01 4.481909027604019657e-01 1.261889085033987001e-01 5.600591735875248833e-01 8.244692493969552061e-01 8.969188061645969601e-01 4.802217973423368313e-01 3.556164122713412201e-02 3.393317823164623270e-01 2.491242957582864292e-01 9.863253789366602797e-01 5.585415885291432625e-01 3.702350606362231344e-01 6.766101432620400535e-01 6.999259389475386284e-01 6.676108316872160220e-01 7.870681827507105544e-01 8.746765411259082024e-01 9.125268371282718727e-01 6.638849997061806452e-01 3.253268113800632522e-01 7.968625619248901337e-01 7.584122525443606211e-01 9.028886850768532701e-01 5.381622293189292083e-02 8.097562873320752752e-01 7.092942088208666895e-01 9.915538877968065323e-01 4.319294903327922652e-01 4.307127933969153721e-01 2.768507739641907772e-01 8.076253078288621046e-01 2.569233696442670967e-01 7.595893829724666979e-01 5.768081727897018673e-01 2.537536777625452045e-01 8.874419624636734616e-01 5.091705681832693342e-01 4.811826624992353585e-01 2.794462461940371290e-01 3.846927898276129021e-01 5.129562951959991679e-01 8.515004062224775794e-01 7.103144978683579858e-01 9.526388607201888847e-01 2.367905569592337889e-01 9.137336039323161740e-01 5.722969943101696710e-02 2.019723935481291255e-01 3.098764675203513619e-02 1.121146613918624357e-01 9.937693067724532314e-01 8.476717958861412772e-02 2.059652110343795917e-01 2.139791918759540446e-01 9.137860316709250919e-01 9.530862653366889425e-03 2.027843281683039400e-03 2.506229951837134484e-01 6.244523528392044165e-01 5.523937894075592325e-01 3.712168074031840792e-01 4.218847794299319665e-01 4.827576239387890711e-01 5.244634168840578425e-01 5.182241092381567604e-01 3.308639956263292881e-03 9.370528021570383448e-01 4.694554875029453012e-01 4.950447554541728135e-01 +1.525818111800841814e-01 4.708012184002630107e-02 3.899035965341954846e-01 3.928304521031263929e-01 5.602286661727436945e-01 9.738256658043862313e-01 9.404465779766183475e-01 5.750862754958349088e-01 9.547546956257608741e-01 2.750275291553152535e-01 1.682773435862793265e-01 5.865928471016079726e-04 8.543378154943809255e-01 3.547649971465383079e-01 5.058056647397523031e-01 9.116332486700751137e-02 7.534666421106954726e-01 3.082429494433007733e-01 4.527145111847344916e-01 5.456680635225539255e-01 2.504131242494785914e-01 2.509240770568589296e-01 3.949236999582302898e-01 8.782959620323271821e-03 2.474641132111736752e-01 8.229417958971670943e-01 3.444225768479134420e-01 4.000027489436257522e-01 4.247741954177396417e-01 2.497745404169693373e-02 4.325768602588443423e-01 7.336592463477830117e-01 7.667663267650381975e-02 4.179022553581047683e-01 8.745172741480690126e-01 9.417705509525042817e-02 2.807522782799587446e-01 8.212710101351362590e-01 2.211181944001613386e-01 4.319929503523877168e-01 1.858636923768219873e-02 6.737037795085246694e-01 7.997187114913413275e-01 2.976552505976116647e-01 3.272347030789168887e-01 5.550935453236346406e-01 9.224109746648162522e-01 3.192827922106745708e-01 3.500098324549234530e-01 7.821988386980260888e-01 4.478417135239194380e-01 1.580956175222456572e-01 5.300807813550156844e-01 5.806154798468634581e-01 9.456842911054151868e-01 7.688127895655872956e-01 8.456527833650537840e-01 1.784229089865225770e-01 8.114517450321339087e-01 8.062506298824222428e-01 2.113482500442499523e-01 2.629226789210241666e-01 6.478686221690072022e-01 6.006672861605766300e-02 7.013679843242253131e-01 8.784753961212666828e-01 3.487138165323044880e-02 4.928426758517070461e-01 5.976224683315064512e-01 7.629063997052759616e-01 2.761721278953045422e-01 7.240740503283805696e-01 6.131065729985127888e-01 1.630885615792579957e-01 8.473783868551159060e-01 8.347614542399306448e-02 8.137265626844719657e-01 8.512508664918938539e-01 2.777097816703766320e-01 1.729154355214796990e-01 2.203382750835449766e-01 6.134780912629795857e-01 3.524352564238901753e-01 5.370314860129862256e-01 8.013986113284543578e-02 2.555842138998117852e-01 6.553915758947851389e-01 9.679125599178584061e-01 2.549566319678178150e-01 4.008180804370896633e-01 9.145789951670967310e-01 2.787926039163850511e-01 8.599455912576436933e-02 9.637558000691170967e-02 8.274101203974880692e-01 1.803747268179315411e-01 2.175735407836230095e-01 7.825994939720237742e-01 7.928519890958951599e-02 8.707949373106749213e-01 +6.398420210047787160e-01 5.739624494012524059e-01 3.359672805578653998e-01 1.130399363175038641e-02 3.349439685346782269e-01 2.315484030880912147e-01 4.575228302577399875e-01 1.149494135594463229e-01 2.888244352925943836e-01 3.625470995156252485e-01 3.795973190611611203e-01 6.567047810450010736e-01 1.484039742710284715e-01 9.273251916560719676e-01 4.334256728976307871e-01 6.734771102219323513e-01 9.125080197222198430e-01 4.974393931097168542e-01 8.301481563280355136e-01 4.526450714147856047e-01 2.414236092573898151e-01 8.070129698367667359e-02 7.260400697427102923e-01 1.396509691839398215e-02 2.496450588391967429e-01 4.335741205447194435e-01 3.089314419194891803e-01 9.543503534526003307e-01 5.457977547458532364e-01 3.139663643587058406e-01 5.034762326753475792e-01 4.756788330475764104e-01 6.849334942793482428e-01 3.880666613022351052e-01 6.483446580176778218e-01 5.217503801099343530e-01 5.371145824070304720e-01 3.121260159429154468e-01 8.314121854062171968e-01 4.538695969561833410e-01 8.598896961203845724e-01 9.961993522734106099e-01 8.865717795946430613e-01 7.828987966783660379e-01 3.412415531643435695e-01 7.421170530151157685e-01 4.484104178639959359e-01 6.793217012099640462e-01 3.756179958191659951e-01 7.821287098222597933e-01 6.227726265188193722e-02 8.552983413221663112e-01 4.824668768009222619e-01 2.241531065858231031e-01 4.939536577599041856e-01 5.129566641128722182e-01 1.057984177672518511e-01 9.541452507300716146e-01 3.396646181755047511e-01 7.452588103611947901e-01 5.315559265659929311e-01 5.493475179850665358e-01 5.214824278139198466e-01 5.150075718147916204e-01 1.196075368500321146e-01 9.035665331176232495e-01 7.522653903639873185e-01 6.638708679914825384e-01 5.584174553800479446e-01 5.015819402508836511e-01 5.507698483308445248e-01 5.978677577011723976e-01 8.450418028759657529e-01 3.266677322748618995e-01 1.321610045897971819e-01 2.394354042746985600e-01 2.723972163557076831e-01 5.523301747352814539e-01 5.518043850608547185e-01 5.283968096837132755e-02 8.192733312104071297e-01 2.277106024970321219e-02 1.414998099027269252e-01 6.517281615256080851e-01 1.811694734825117781e-01 9.472370614713256920e-01 5.454497319021770485e-01 1.364119913158231556e-01 8.446142008509562871e-01 7.671725984742419069e-01 2.461161648406858804e-01 1.421724627107351369e-01 6.290652581179481118e-01 7.094144689448004248e-01 4.419656923472803367e-02 6.614741876652251440e-01 8.712193265403500586e-02 4.734931280852430202e-01 5.382037050480286133e-01 1.396459758005891283e-01 +9.709329844415439670e-01 8.998575745276288229e-01 9.151313462895852568e-01 6.920489275523904471e-01 2.892231405199537919e-01 6.750679746268205550e-01 5.515642485826798280e-01 1.065253097812824956e-01 2.957026803465776510e-01 8.937347659632134400e-01 9.800016515925590310e-01 7.745900896182087436e-01 1.570977683146633774e-01 1.482028765821026273e-01 2.111147779712029271e-01 9.683759902485811200e-01 6.550951580826911425e-01 8.728324682592377703e-01 5.044803166579884257e-01 8.285704754811143991e-01 1.693574499337324735e-02 6.032669995180495182e-02 1.687026879086964692e-01 7.701554026145973619e-01 1.429888016593102718e-01 5.881172815379975827e-02 9.704206919487038396e-01 4.450807650730836951e-01 1.597445784258376689e-01 9.849229394397314152e-01 4.220083573536804744e-01 9.357693600374825671e-01 2.313199262338369033e-01 4.556443403861323294e-01 2.590791012828855822e-01 8.438664994487065085e-01 5.519045677502344427e-01 4.702170125676508050e-01 6.814723205638187897e-01 7.418295483665861001e-01 3.684921032028853904e-01 1.501895844844561845e-01 4.214513377519605308e-01 8.600279963652578408e-01 6.625616611189292238e-01 5.200151456470966105e-01 7.881072743086801058e-01 2.771703241081423519e-01 9.034135930616548071e-01 5.848441705791300738e-01 8.341698181274771473e-01 1.966638677318299777e-01 7.059747894371543042e-01 7.013854316067694716e-01 1.828430942760242983e-01 4.745548949934464966e-01 6.306422394641082452e-01 7.760751707194470939e-01 9.813187212598396547e-01 2.293595795266353266e-01 7.749261876107090830e-01 2.384106107787011819e-01 9.721209688979495223e-01 2.715569353686980714e-01 2.915573577694993146e-01 3.579601509630966349e-01 3.085697512342830962e-01 4.070219981627976047e-01 1.989632411372218579e-01 7.330003339460906542e-01 5.397259604481572381e-01 6.931009942216573849e-01 1.385457419653816080e-01 1.140339999976658358e-01 3.980752590866034613e-01 9.471822621683767540e-01 5.476643721405823895e-01 6.824131903515884279e-02 5.844099130744569992e-01 2.346881692012994236e-01 9.436439228519653000e-01 4.855518260479008141e-02 8.157036123302675579e-01 1.169761256455048581e-01 5.532962903488753970e-01 1.100965596251435308e-01 9.789490602992410029e-01 8.433487462016989733e-01 1.272410782852178013e-01 2.885715258680641160e-01 7.990943955388217779e-01 1.565305358979097727e-01 9.160846960406943129e-02 8.521842244411678147e-01 4.474243106736998099e-01 3.843945818845087015e-01 4.710645906071458944e-01 2.398348154123419729e-01 6.435351435258193087e-01 7.656897921129046658e-01 +4.894328120406804539e-01 7.881019629214267574e-01 6.974585354155089512e-01 2.023858939857701156e-01 1.660984914264745926e-01 4.854517801734643534e-01 2.789848572630315715e-01 2.311636522410289718e-01 9.821076233980715608e-01 1.220641257408076052e-01 2.614036146663852866e-01 7.657560715165320220e-01 3.968360577545695378e-01 4.566023622802184434e-02 1.049701948619241598e-02 9.281162949127452766e-01 4.490137965769909201e-01 2.095846458383606725e-01 9.195504656719085679e-01 9.683515436855471004e-01 9.800174878114910060e-01 5.517610861380117804e-01 6.711570559348770670e-01 5.125258050287277989e-01 2.105581493613526423e-01 8.281813206544574868e-01 4.964783994807770995e-01 7.284974208756571645e-01 1.320629592816270348e-01 6.652194518096135045e-01 9.430156297917950958e-01 7.477263137894260003e-01 2.054087806450300979e-01 4.248209124837907247e-01 7.657518666018259257e-02 1.031614100713345028e-01 4.122242287567021712e-01 4.919658859336810686e-01 3.752650167259050651e-01 4.175771429986683270e-01 6.131376293448997927e-01 5.463797405837259591e-01 3.119918548921774004e-01 6.331762507678504459e-01 5.484632429281035559e-01 6.815448032785871302e-01 8.065695507425107991e-02 8.720129122297424207e-01 8.318188557125294480e-03 2.199323537180564170e-02 8.933872719887463454e-01 1.953120287872067706e-02 2.478721941404590234e-01 5.994061179859005994e-01 6.588362611693047155e-01 6.332808851020984564e-01 3.823849348043323326e-01 5.111091324899629251e-01 7.034808459110406531e-01 4.347681568463539481e-01 4.316973576672314961e-01 9.620411080123215664e-01 6.247837467655984467e-01 8.196961678222113301e-01 5.574601810887074294e-01 8.800635018469276094e-01 8.772255241161972528e-01 5.075275933138404527e-01 8.022583187266906224e-01 2.320670802521890286e-01 1.165626629103270195e-01 4.623759662685936744e-01 7.938327000737943617e-02 7.986374689793115378e-01 6.728842751465858862e-01 8.133909095059230765e-01 1.202639390769081329e-01 1.052937257108800262e-01 8.717600467040409473e-02 2.163819956545051104e-01 6.596483385763984852e-01 1.202843170392309258e-02 1.538789195854695091e-01 3.120247727263308901e-01 3.408168327248596308e-01 3.241861797851740556e-01 3.637074533655986208e-01 1.533669345890729119e-01 4.455921334699539660e-01 5.619140093874478437e-01 1.881731359879111887e-01 9.416670800570559052e-01 1.740018593664415247e-01 7.030242331869680505e-01 5.922055553954849172e-01 9.326211623391688077e-01 6.608322881013140027e-01 7.009721551241574478e-01 1.079126054675583202e-01 6.158176671761947940e-01 +5.185079639625639336e-01 9.613742991518259284e-01 5.555312825626229634e-01 2.647628827924735084e-01 6.003697207460141350e-01 5.392112376769145898e-01 6.781186965667050925e-01 9.908971748181496508e-01 4.124155872095397468e-01 9.814941401724619485e-02 2.684237785531295994e-02 1.774652505962848181e-01 1.707589529595294753e-01 4.640932098465534450e-01 2.882179883914587348e-01 7.276822905806898945e-01 6.145789546745269449e-01 1.100959863917608805e-01 6.798859723042820491e-01 9.096984032948918220e-01 3.971368455178179158e-01 2.959494950971321980e-01 3.742088799298171065e-02 1.960739526210202310e-01 7.536102695342027369e-01 6.680915510628401277e-01 4.136507204312135366e-01 3.613996339406737590e-01 3.605422038261204554e-01 7.098503555159476619e-01 8.093719147087541366e-01 6.344097009128880638e-01 3.990082448083617228e-01 2.805918009906902544e-01 7.078488167363675698e-01 9.969917259866583059e-01 9.442054998992396309e-01 1.329075240769165278e-01 6.810681350588387861e-02 8.503491437913293094e-01 8.347117439165431252e-01 2.381858201903953587e-01 7.884260706938626129e-01 7.109907917419661105e-01 6.390916681983604963e-02 6.174365227062991179e-01 5.085733343630816083e-01 1.716846139694149231e-01 9.065664924270055991e-02 5.625330757196970177e-01 3.539663480209681579e-01 8.937139525947165319e-01 3.981380511900556307e-02 7.403597927449541150e-01 3.803872284089604427e-02 6.729519695709765825e-01 5.306080908840085097e-01 2.091237680402112664e-01 5.902903662907804661e-01 2.094778612095482551e-01 7.323447855684165342e-01 3.644574495843493356e-01 2.006215478057034041e-01 3.737617545555030896e-01 5.253471759602216240e-01 4.287889547869583318e-01 7.086098806190446187e-01 4.510792335515292351e-01 6.383187180169215269e-01 8.779355722397681472e-01 4.221338898667141848e-01 6.375840144651815367e-01 8.683057298299173832e-01 6.093730356952498095e-01 9.297141161056151626e-01 7.770838342807246946e-01 6.549661287008456956e-02 2.835060738158660110e-01 4.474138867374952699e-01 8.530336387421445510e-01 3.160209657891883683e-01 8.301538680518486535e-01 6.646903097549101691e-01 7.187130118106234145e-01 1.651862041735395747e-01 9.578252676762609719e-01 6.490273812885494209e-02 9.777273484666341163e-01 8.930729829254262508e-01 9.851054752118463265e-01 4.094323402286751401e-01 1.139176240124337713e-01 7.612865863899589414e-01 2.266379302491570158e-01 6.998882496157835531e-01 9.945043379099228753e-01 7.111578056749194854e-01 7.806190603886183910e-01 3.410170920712443099e-01 9.446084168886822452e-01 +5.015172758330755931e-01 5.569527971282052237e-01 1.122406928736449094e-01 8.960352822124777461e-01 6.049568585854003810e-02 1.202196001338627918e-01 1.870314295763603196e-01 9.017590029396971296e-01 3.597904628087450485e-01 2.130941062746317671e-01 2.556281834629479111e-01 5.123669364829196438e-01 4.754061129282013409e-01 9.764470380372083369e-01 8.038663983900646848e-01 6.960491266420890666e-01 2.940135977911654264e-01 2.857282759910040326e-03 4.599343225832352999e-02 5.597554495210212977e-01 7.445266674304001908e-01 3.387528030535971180e-01 6.429542922125383031e-01 2.123331785532429627e-01 5.302332654117811739e-01 7.262555377662539557e-01 3.982425859900724507e-01 3.243388301740235402e-01 6.191064123738921898e-01 8.988047781373914580e-01 7.819700328765150088e-01 7.664269102804815992e-01 6.734095355422575757e-03 2.904762329148526945e-01 5.097537644843168625e-01 9.524734606001823423e-01 4.812869576591960463e-01 6.236868013640477493e-01 1.459170943214320726e-01 9.874505139403206844e-01 7.561708982837871407e-01 3.798591332432484924e-01 6.056633451375117438e-01 7.935708170258731764e-01 1.458141583518740569e-01 7.082511296391911237e-01 1.098798009731616343e-02 3.655618484905173160e-01 9.551862303858617009e-01 8.148959351152762487e-02 4.739306219219985294e-02 7.963357515359494876e-01 6.208332695202813944e-01 3.884182264923189409e-01 4.589167647950288531e-01 6.496652974138312775e-01 2.467528128074852889e-01 5.309593064844935206e-01 5.364606369543487574e-01 2.421352989851309756e-01 3.776834556696828660e-02 1.564861233558080267e-01 5.197231021782636740e-01 8.725375120634637494e-01 2.441225493455024820e-01 2.320363366041028330e-01 5.026358683423555185e-01 7.035766000474735771e-01 8.347805591467084563e-01 2.303229841813967393e-01 6.908373419683054850e-01 2.646662377366995056e-01 1.259467197942290007e-01 9.372770922994989595e-01 6.674216272867254940e-01 1.027944489143072238e-01 5.686267290346079806e-01 3.948222804451942958e-01 4.689706944496729868e-01 4.446117700449114807e-02 6.817992275557515081e-01 9.084821829413957106e-01 9.184021015315092518e-01 3.045815734169987632e-01 2.204958624923980537e-03 7.542672057172502553e-01 9.460844786545006269e-01 3.373139094575949848e-02 9.059565314915285494e-01 9.938525461318854504e-01 2.542072661725306437e-01 9.685734112479216229e-02 8.223629541824816203e-01 1.057429056898460118e-01 8.080679390260248063e-01 5.823014244609205914e-01 6.413551528031806725e-01 1.787341975438894170e-01 1.250471413912357388e-01 8.390281297596062782e-01 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-hamming-ml.txt b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-hamming-ml.txt new file mode 100644 index 0000000000000000000000000000000000000000..bc4e1ddcb6e1e8699570ecc410e2cb0cfdba2507 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-hamming-ml.txt @@ -0,0 +1 @@ + 4.6000000e-01 4.3000000e-01 4.3000000e-01 5.4000000e-01 4.1000000e-01 5.3000000e-01 4.3000000e-01 5.9000000e-01 4.8000000e-01 4.7000000e-01 4.6000000e-01 4.9000000e-01 4.5000000e-01 5.5000000e-01 5.3000000e-01 4.5000000e-01 4.8000000e-01 4.7000000e-01 4.8000000e-01 5.1000000e-01 4.9000000e-01 4.4000000e-01 4.9000000e-01 4.7000000e-01 4.9000000e-01 4.7000000e-01 5.2000000e-01 4.7000000e-01 4.2000000e-01 4.9000000e-01 4.7000000e-01 5.5000000e-01 3.9000000e-01 5.5000000e-01 4.6000000e-01 4.5000000e-01 4.0000000e-01 4.8000000e-01 4.5000000e-01 4.8000000e-01 4.8000000e-01 5.0000000e-01 4.8000000e-01 4.5000000e-01 6.4000000e-01 5.7000000e-01 4.6000000e-01 5.4000000e-01 5.6000000e-01 4.8000000e-01 4.8000000e-01 5.3000000e-01 5.4000000e-01 5.3000000e-01 4.5000000e-01 5.8000000e-01 4.2000000e-01 5.4000000e-01 6.0000000e-01 5.1000000e-01 4.6000000e-01 4.1000000e-01 4.4000000e-01 5.6000000e-01 5.4000000e-01 4.8000000e-01 4.8000000e-01 5.1000000e-01 5.2000000e-01 5.5000000e-01 4.5000000e-01 4.3000000e-01 4.7000000e-01 4.7000000e-01 5.6000000e-01 4.9000000e-01 4.8000000e-01 4.5000000e-01 4.9000000e-01 4.7000000e-01 4.5000000e-01 4.5000000e-01 5.6000000e-01 4.9000000e-01 5.8000000e-01 5.4000000e-01 4.6000000e-01 5.8000000e-01 5.3000000e-01 5.4000000e-01 5.5000000e-01 5.0000000e-01 5.2000000e-01 4.8000000e-01 5.0000000e-01 3.8000000e-01 5.3000000e-01 4.8000000e-01 5.1000000e-01 4.8000000e-01 5.2000000e-01 4.7000000e-01 5.0000000e-01 4.3000000e-01 4.8000000e-01 5.2000000e-01 5.0000000e-01 4.2000000e-01 4.2000000e-01 4.7000000e-01 5.4000000e-01 5.1000000e-01 5.4000000e-01 5.1000000e-01 4.8000000e-01 4.7000000e-01 5.2000000e-01 5.2000000e-01 5.4000000e-01 5.4000000e-01 5.0000000e-01 4.5000000e-01 4.4000000e-01 4.1000000e-01 5.7000000e-01 4.6000000e-01 5.1000000e-01 5.2000000e-01 5.0000000e-01 4.8000000e-01 5.0000000e-01 4.4000000e-01 5.3000000e-01 5.2000000e-01 4.9000000e-01 5.7000000e-01 5.8000000e-01 4.9000000e-01 5.1000000e-01 4.5000000e-01 5.3000000e-01 4.5000000e-01 4.4000000e-01 3.5000000e-01 4.2000000e-01 5.3000000e-01 5.2000000e-01 5.0000000e-01 3.8000000e-01 5.2000000e-01 5.6000000e-01 4.7000000e-01 4.4000000e-01 5.1000000e-01 5.7000000e-01 4.5000000e-01 5.7000000e-01 4.3000000e-01 5.1000000e-01 3.8000000e-01 5.3000000e-01 4.8000000e-01 4.4000000e-01 5.0000000e-01 4.8000000e-01 5.0000000e-01 4.7000000e-01 6.4000000e-01 4.9000000e-01 5.2000000e-01 4.8000000e-01 5.6000000e-01 4.3000000e-01 4.8000000e-01 4.7000000e-01 6.0000000e-01 5.4000000e-01 5.5000000e-01 4.0000000e-01 5.5000000e-01 5.6000000e-01 4.9000000e-01 5.0000000e-01 4.3000000e-01 5.7000000e-01 5.0000000e-01 5.7000000e-01 4.9000000e-01 4.2000000e-01 3.9000000e-01 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-jensenshannon-ml-iris.txt b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-jensenshannon-ml-iris.txt new file mode 100644 index 0000000000000000000000000000000000000000..da698cf511e6983e1db1bbed6e2974f3480a6903 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/data/pdist-jensenshannon-ml-iris.txt @@ -0,0 +1 @@ +0.0211635609063 0.00455072769716 0.0230610904531 0.0076674324982 0.037571216894 0.029561354778 0.0115281186735 0.0225809070507 0.0346801442638 0.00321049176948 0.0232839774828 0.0321124517082 0.0244179197971 0.0331466156799 0.0373949302575 0.0411984503375 0.0218945865519 0.0198268474453 0.02395616278 0.0254898420418 0.0394901943037 0.0396613298853 0.0621120626322 0.0458316817045 0.0334832834948 0.0445794256135 0.00775602650151 0.00770188279153 0.0277044809709 0.0292851269343 0.0408206135002 0.0384837880405 0.0229145225178 0.0346801442638 0.017155900548 0.0186865079856 0.0346801442638 0.011778601551 0.0109570078484 0.0244803928224 0.0609237090857 0.0125274661674 0.0713079215249 0.05072181848 0.0329702193648 0.018118079192 0.0129441131729 0.00461235131171 0.0077204189151 0.190781073622 0.196235539354 0.202381067744 0.215567934651 0.208951210729 0.208395222197 0.202977772245 0.185995150024 0.197488445796 0.207754344906 0.207231776416 0.200895721783 0.202370257926 0.208839694602 0.184151316466 0.18979501874 0.211864590783 0.187854399445 0.231134943838 0.195836809821 0.220082308081 0.191451897661 0.227329222402 0.204586412278 0.192443163994 0.193684928543 0.206841791869 0.21612354251 0.209268704489 0.175345617131 0.197941218478 0.190625337098 0.191065854858 0.2317867516 0.214902945463 0.200203629275 0.200116177839 0.216845735492 0.194043462589 0.208271280018 0.210985756639 0.203659220099 0.197020978632 0.188475437149 0.205376756706 0.190989482965 0.1980172695 0.194499957306 0.176357663314 0.198492265793 0.255232551639 0.244270007892 0.240469208946 0.23837051036 0.248414598574 0.246817944636 0.244919131711 0.239985639856 0.249741998486 0.238825041235 0.224881988223 0.240479022193 0.236919216915 0.253134884385 0.257281336145 0.239143010746 0.231252341207 0.230464321684 0.26688757037 0.243702807017 0.239741931201 0.243722504108 0.250777226737 0.230838149152 0.233779458319 0.227365052742 0.226996712309 0.224529623997 0.248876761269 0.223504306637 0.241222705588 0.218359591571 0.251783757155 0.22178058433 0.237922879169 0.243259140467 0.243757719218 0.229784431814 0.223834512884 0.231417699032 0.245855002542 0.23260880661 0.244270007892 0.24412721812 0.245364556958 0.239069534798 0.242476824971 0.231950344971 0.238821966724 0.23119389821 0.0191037071088 0.0192689125135 0.0279599225292 0.0400056720024 0.0369973592153 0.0168401225107 0.0156647141811 0.0317569365649 0.0209196472407 0.0261095572505 0.0295911292198 0.0387034711929 0.047943015361 0.0502801086558 0.0505076233319 0.0276985510653 0.0178344200842 0.0354647529806 0.0132311775272 0.0439058315568 0.0587698655317 0.0548749800113 0.0420211683289 0.0158475875817 0.0410205075223 0.0159821398213 0.0151652303532 0.0238719428054 0.0190485214781 0.0351129659726 0.0545720530766 0.0439213935019 0.0317569365649 0.0241815879409 0.0259117209292 0.0317569365649 0.0173211662353 0.0138864344566 0.0338981856531 0.0429537854169 0.027658485714 0.0691172321094 0.0500059403041 0.0236011037029 0.0316644090037 0.0201837094834 0.0228863104939 0.0134861623597 0.176517909274 0.183281450302 0.188265462996 0.20119658342 0.194697326208 0.195209942657 0.190674274015 0.172425697578 0.182894065786 0.195529592383 0.192026007893 0.188346396823 0.186051479032 0.19538200246 0.171605310091 0.175766658214 0.199864266754 0.173408334646 0.215875435175 0.181358449055 0.208526316653 0.177419239992 0.212710197663 0.190468934035 0.178153770213 0.179556589614 0.19192211318 0.202468559318 0.196181327665 0.160572766746 0.183327527516 0.175701303909 0.177066663037 0.218355819513 0.203310616701 0.188843124957 0.186323930321 0.201194011774 0.18173319992 0.194510318604 0.197386404553 0.190483994401 0.182718294182 0.174255518011 0.19205782762 0.178239088983 0.18513760318 0.180597338326 0.163063102971 0.185284857996 0.243887274146 0.231624187925 0.22689829944 0.225289762668 0.235796354542 0.23269059518 0.23306609907 0.225602345189 0.235244584021 0.226892475392 0.212543999067 0.226908361887 0.223757309255 0.240296170501 0.245569279789 0.227339668618 0.2180947426 0.217889650333 0.252259964829 0.228879839341 0.227150610245 0.231836962279 0.236076260811 0.217244760533 0.221438124324 0.213671912157 0.213819376581 0.212040793299 0.235827974309 0.209053467969 0.226615210725 0.205271111175 0.238869689413 0.207939309627 0.223898822147 0.229226063436 0.232630777303 0.217026651272 0.211532427176 0.218347693285 0.233460132247 0.219891922362 0.231624187925 0.231652249658 0.233525349836 0.226377769419 0.228579062405 0.219098380821 0.227789794229 0.219023772057 0.0212386043587 0.00993825296406 0.0336976816686 0.0262325070397 0.0107568964379 0.0200399473123 0.0369530877134 0.00706121377315 0.0225180018442 0.0345620434634 0.0288649599558 0.0352792061476 0.0352526366714 0.0385518621347 0.0178346265809 0.0156087458393 0.0212893461987 0.0244762891905 0.0356848493955 0.0412118431681 0.0577499958146 0.0449060313177 0.0316682156452 0.0402876009485 0.00791017688182 0.00777689270989 0.0262859463939 0.0275691765497 0.0363841779992 0.0426229028778 0.0258430808451 0.0369530877134 0.0171494695134 0.0197038085728 0.0369530877134 0.00856221073911 0.010146504576 0.0213442662268 0.0572142712511 0.0116574543859 0.0671597340405 0.047388301579 0.0285557929122 0.0192111185981 0.0112749020632 0.00781127840012 0.00637566729419 0.187210918471 0.192623672543 0.198833727144 0.212008654042 0.205367416257 0.204958566039 0.199402528264 0.182409867249 0.193987091024 0.204155295526 0.20374075502 0.197257634781 0.198980554413 0.205364330922 0.180426094029 0.186163209648 0.208359044162 0.184479344493 0.227592539467 0.192334488284 0.216529219774 0.187806950452 0.223855994439 0.201226354807 0.188864300604 0.190066520684 0.203335651256 0.212548662023 0.205705049083 0.171742464799 0.194417377469 0.187136601809 0.187472552212 0.228344421279 0.211429948522 0.196622478767 0.196530972542 0.21335054643 0.190497911091 0.204702393397 0.207598118603 0.200155023454 0.193467129006 0.184880183147 0.201857382862 0.187517863683 0.194486736804 0.1909406597 0.172553592377 0.194927231832 0.251743344729 0.240762357546 0.236947962879 0.234942968898 0.244909063494 0.243388177909 0.24143957892 0.236617606758 0.24633077256 0.235257992461 0.221275459366 0.23695229759 0.23334624948 0.249598367646 0.253700089415 0.235542233543 0.227782636512 0.227013807453 0.263469639225 0.240293112196 0.236160757703 0.240179126335 0.247395319298 0.227257451928 0.23025606552 0.223937789122 0.223399862869 0.220967129276 0.245363860699 0.220116506797 0.237779687596 0.214888164759 0.248252329967 0.218347705592 0.234681014401 0.239690494574 0.240217402996 0.22632629355 0.220260809644 0.227813787503 0.242262375922 0.228918020841 0.240762357546 0.240592325032 0.241778783496 0.235418026861 0.23890352284 0.228365058816 0.235271978928 0.227703766766 0.0251699105319 0.0326109277538 0.029278801732 0.0117605763436 0.00495713045566 0.032971458625 0.022273270218 0.00845866705418 0.0338325850006 0.0418060793613 0.0560571569439 0.0475989809058 0.0526432986362 0.0287512950603 0.0156470261333 0.0286833299321 0.0125318521449 0.039628171731 0.0605879245284 0.051592881751 0.0247889558 0.0185457610439 0.0360813596602 0.0162897964903 0.0233731835598 0.00555113851114 0.00851608036789 0.039350861172 0.0494281040416 0.0428445793936 0.032971458625 0.0361084743768 0.0382083791018 0.032971458625 0.0135564720949 0.0133460812565 0.0359059029584 0.0525998249753 0.020448013216 0.0653401649712 0.0366222774812 0.0276779812669 0.0199994530533 0.0115734561992 0.0214333278479 0.0186461579961 0.171344593808 0.177166238774 0.182950771401 0.19643196185 0.18986301898 0.188443393542 0.18388535541 0.166562175459 0.177783941311 0.188923487579 0.187724110496 0.182156573883 0.18272910062 0.189078094755 0.165787292858 0.170722554665 0.192539814536 0.167433166664 0.21234151671 0.17610118578 0.201339136255 0.172496446246 0.207869320447 0.184234771078 0.173084677744 0.174586008872 0.187343267555 0.197067369338 0.190065322687 0.155992913972 0.178370192697 0.170834599332 0.171756811444 0.21215391799 0.195543956872 0.181382419503 0.180878423055 0.197648250997 0.174630568935 0.189049602981 0.190809942592 0.184007315234 0.177572802284 0.169158986096 0.1858235382 0.171054750208 0.178472858167 0.175024341592 0.15843173338 0.179125844986 0.236753289776 0.225336913605 0.221457751336 0.21877528886 0.229546376269 0.227338714151 0.226000082599 0.220106179064 0.230241030133 0.220420771483 0.206398010187 0.221506177578 0.218254053746 0.234550382603 0.23950257292 0.221077875617 0.211789394262 0.210958467875 0.247726054061 0.224160699687 0.221293898641 0.225242596712 0.231164838975 0.212084684776 0.21483758197 0.207582855086 0.208324546741 0.205668770285 0.22999948579 0.203519866038 0.221791807814 0.198729512666 0.233119883684 0.201955632148 0.21744774227 0.224658382044 0.22554531899 0.210282327898 0.205086194314 0.212901784527 0.227685054369 0.214992030153 0.225336913605 0.225426421062 0.227294020009 0.22122180599 0.223891772502 0.213328572484 0.220627825622 0.212027719306 0.036692370716 0.0274221847228 0.014614486903 0.0257905543192 0.038185072385 0.00854173398239 0.0228846598272 0.0362880440778 0.0247966202157 0.0338575762449 0.0341810302892 0.0403839419732 0.023192138006 0.0228332335778 0.0207802721216 0.030246984472 0.0385095591836 0.036116458124 0.063670015249 0.0456331337141 0.0387265099713 0.0454622939774 0.0128829753328 0.0153687384621 0.0290580106985 0.032571197138 0.0439484983687 0.0345946330706 0.0178789804494 0.038185072385 0.0229066226947 0.0243703411158 0.038185072385 0.0145921399018 0.0157758022112 0.0246899780508 0.0667942861058 0.00859371091119 0.0713780561444 0.0493343894065 0.0370078971529 0.0136450272229 0.0138896306008 0.00617904291603 0.0146027872105 0.19304474601 0.19797909852 0.204564547012 0.217893788171 0.211240589753 0.210085035577 0.204424468033 0.187966151726 0.199846283114 0.209201380502 0.209876418912 0.202509063074 0.205495616388 0.210676243708 0.185852822947 0.192014733821 0.213129345848 0.190059083655 0.233894763867 0.198141527364 0.221234996275 0.193689434624 0.229701750232 0.206631480423 0.194727732257 0.195938025333 0.209362001884 0.218147207364 0.211029564766 0.177869147322 0.200327033561 0.193124484677 0.193233901741 0.233604044389 0.215980508747 0.201289146742 0.20219168761 0.219726845187 0.195458087393 0.2103246028 0.212820091698 0.205396680761 0.199290845186 0.190742394929 0.207189399227 0.192521418757 0.199651552643 0.196594932336 0.178468769167 0.200295584792 0.256297768876 0.245841451519 0.242438509968 0.240033073248 0.249980802879 0.248953385462 0.246144223104 0.242178797272 0.252036489845 0.240176361939 0.226412773569 0.242454491215 0.238768549206 0.254841315698 0.258625650226 0.240494667361 0.232976534038 0.231927166971 0.269283834821 0.246140263748 0.241374087503 0.245035624054 0.253144665918 0.232868387014 0.235226383286 0.229279397026 0.228861516729 0.226064745168 0.250630665997 0.225726655668 0.243583203961 0.220042394187 0.253511231548 0.2237596548 0.239872842457 0.245497904196 0.24478307529 0.231328796826 0.225307213023 0.233256809435 0.247442229489 0.234444929147 0.245841451519 0.245661961251 0.246724980668 0.240848644418 0.244659004118 0.233675626419 0.239814102397 0.232532592403 0.010524058973 0.0347497758249 0.0310026365608 0.0636683508445 0.039628310023 0.0339732471818 0.0632599742009 0.0607622080336 0.0610552648732 0.0274939888458 0.0321794296029 0.0203038890155 0.0225566538528 0.0181521200419 0.0426282898435 0.00971744861179 0.0573638408809 0.0304112802741 0.0463628234513 0.044509344874 0.0125060810098 0.0372012029006 0.0399195029174 0.0351572922113 0.0381714857339 0.0236066809352 0.0675880085279 0.0489234884353 0.0636683508445 0.0451372647506 0.0495218266562 0.0636683508445 0.028718795999 0.0363358460459 0.0247409309981 0.0536494939568 0.0291783067693 0.0362475310016 0.0206193569389 0.0238060051702 0.0365127412376 0.0303247636874 0.0382706081182 0.036679138207 0.165252454208 0.168745392776 0.176697416201 0.190075579294 0.183193768644 0.182210296577 0.174895117306 0.159516185843 0.172780618769 0.179424300801 0.183362052205 0.17275366841 0.180884475296 0.18272692365 0.155645508044 0.163596126812 0.183814684533 0.163944686708 0.206939183794 0.170981324059 0.191238016882 0.165171506195 0.202635613449 0.180157192696 0.16688029242 0.167662828735 0.182473473654 0.189606814944 0.182144568907 0.150494922446 0.17311022882 0.166528964674 0.165043072843 0.205731384902 0.18659197245 0.17105591364 0.173799739615 0.193545885155 0.166211621507 0.181959703917 0.185636136233 0.177047042786 0.171588556623 0.162758165885 0.178831153511 0.164197394115 0.170920859204 0.168528635782 0.148460744764 0.171573948268 0.226522867074 0.216861787556 0.214094925757 0.211961277469 0.220987866614 0.22168305754 0.216782699249 0.215609692292 0.225208444508 0.210312198283 0.19661028383 0.214074476024 0.209773195915 0.225810880474 0.228551359916 0.210341321513 0.204691741777 0.203336697222 0.242485382573 0.21963599647 0.211892193047 0.215282949931 0.226705435666 0.20418234927 0.205942096193 0.201754356567 0.199749686219 0.196656401255 0.221913123953 0.199165134473 0.216656797893 0.191759529503 0.224569670715 0.196349964299 0.213992874847 0.217230566883 0.214548114667 0.2028246956 0.195690210734 0.20400781198 0.217750478797 0.204445613083 0.216861787556 0.216379424168 0.21668692278 0.211041069144 0.216249554038 0.204373397921 0.209459569208 0.203359671893 0.0284940982012 0.0283835072119 0.0582564069663 0.0317209110303 0.0290062929149 0.0575581968066 0.0518675712793 0.0525497818792 0.0214648817816 0.0289820919427 0.0151981567712 0.0194868108708 0.00772151270155 0.0396885548083 0.0133170749495 0.0480498930165 0.0406834920646 0.0454173876936 0.04374952779 0.0223043370554 0.0304932603619 0.0333669476098 0.0323541790178 0.036462611527 0.0283937271683 0.0581656862224 0.0386849486073 0.0582564069663 0.0386824403573 0.0427767064205 0.0582564069663 0.0225670168648 0.0303996112373 0.0186317385432 0.0589532282724 0.020129496567 0.0456219811369 0.0271705146906 0.0266114081755 0.0281840659315 0.0237276312035 0.0300017258072 0.0304084467621 0.174689674341 0.17830904459 0.186122916272 0.199541551099 0.192698821274 0.191439303409 0.184399484383 0.168993025372 0.182083678728 0.188980455315 0.192641208359 0.182376656778 0.189869605957 0.192034662906 0.165406381187 0.173160863593 0.193190156667 0.172952807322 0.216356837462 0.18028635421 0.200707489616 0.174765006311 0.211946253402 0.189197004074 0.176338357547 0.17720731475 0.191796191467 0.199121585044 0.191634817003 0.159923924784 0.182459504488 0.175769947329 0.174536678956 0.215011640541 0.195898920801 0.180555890973 0.183304141459 0.202828623711 0.175640593326 0.191448592698 0.194762075203 0.186406863824 0.181008054094 0.172240816201 0.188223844291 0.17346231425 0.180325575914 0.177965844821 0.158332658267 0.181046361448 0.235882031296 0.226280867303 0.2235366831 0.221221709251 0.230404936567 0.23094802366 0.226136073658 0.224736021115 0.234431734165 0.219824894986 0.20619515856 0.223526372128 0.219308937798 0.235287233784 0.23807753807 0.219908336162 0.214027462834 0.212631745032 0.251732773763 0.228838671706 0.221440978506 0.224753704594 0.235866097652 0.213724598783 0.215380278296 0.210994508636 0.209321687173 0.206162323946 0.231346876087 0.208300913946 0.225928825898 0.201080283138 0.234037288778 0.20558958438 0.222856381147 0.226751911512 0.223989137906 0.212135251626 0.205214537461 0.213595258296 0.2273163389 0.21417146415 0.226280867303 0.225846388312 0.226229092302 0.220705781832 0.225781259333 0.213928177909 0.218913775078 0.212729498479 0.0122225873329 0.0303322491715 0.0105162282326 0.0130161869455 0.0294681125528 0.031169289575 0.0445674903412 0.0424428041907 0.0471592401326 0.0242919299586 0.0151911030837 0.0250458377851 0.0159588466859 0.0393890228389 0.0501712969027 0.0573610210315 0.0347635318983 0.0244162409612 0.040396894165 0.00489128055225 0.0130158047868 0.0162739668734 0.0181749821427 0.0399100872217 0.0416234365466 0.032217222914 0.0303322491715 0.0260818708259 0.0277812891175 0.0303322491715 0.0068887146948 0.00350057866506 0.0298024545939 0.056647752597 0.0131029257559 0.0689399130874 0.0438694576111 0.0295733202179 0.0148293487362 0.005187559828 0.00994170320116 0.00857601759458 0.181761248694 0.187453410696 0.193365623784 0.206702523841 0.20011934013 0.199105672278 0.194191029325 0.176999836949 0.188302627791 0.199111466491 0.198126208998 0.192301121465 0.193121251528 0.199652739338 0.175765384319 0.180985589904 0.202947496477 0.178262931918 0.22241465847 0.18663850283 0.211489015658 0.182706386145 0.218266373994 0.195039865626 0.18346554188 0.184856636434 0.197755074291 0.207324505424 0.200404409787 0.166355990223 0.188830148216 0.181378679541 0.182125858248 0.222660838403 0.205968099039 0.191576935017 0.19121911341 0.207887116649 0.185081160877 0.199380829938 0.201554837448 0.194543615891 0.187991163757 0.179524780118 0.19631300534 0.181726234556 0.188970696545 0.185468734774 0.168191837054 0.18953883133 0.246767069406 0.235545780286 0.231684267956 0.229269594484 0.239724337717 0.237749571543 0.236212316891 0.230682790909 0.240645700931 0.230405224861 0.216420479968 0.231715542723 0.228337424754 0.244594612682 0.249193956399 0.230909382279 0.222229420235 0.221427946934 0.257965809127 0.234573740528 0.231288571496 0.235259005866 0.241607850531 0.22220070955 0.225060949621 0.218149643482 0.218415896436 0.21585883122 0.24017332069 0.214149237729 0.232167950695 0.20925074028 0.243199182069 0.21253980165 0.228281003551 0.23468620257 0.235446691383 0.22074773222 0.215228869367 0.222922571895 0.237553097068 0.224606387466 0.235545780286 0.235534393045 0.23712429287 0.230940484692 0.233917063813 0.223401305554 0.230524046304 0.222353817706 0.0346270873711 0.0222428303765 0.0130402644662 0.0348466257078 0.042829178942 0.0549568638218 0.0464424131108 0.050271964255 0.0260503884653 0.0122224116333 0.0282180922789 0.011864843965 0.0376053109772 0.060340947297 0.0487164281844 0.0285013587126 0.0163929694658 0.0334500762426 0.015996373074 0.0216948948211 0.00981357617646 0.00897169179454 0.0351677992971 0.0524329239391 0.0436506734112 0.0346270873711 0.0336931600411 0.03612022228 0.0346270873711 0.0123155461924 0.0128144174355 0.033561339537 0.0480641708164 0.0212422700005 0.0628734973132 0.0366806543871 0.0231396795589 0.0230741684915 0.0123098386274 0.0219470818919 0.0170687771133 0.170097980345 0.176050748737 0.181762587161 0.195138740624 0.188553715994 0.187634353283 0.182931238354 0.165421712546 0.176600578903 0.187912731345 0.186408453678 0.181027641681 0.181322068987 0.188146844457 0.16448627936 0.169388121003 0.191757508168 0.166524283083 0.21084441712 0.17494262847 0.200480456222 0.171127562134 0.206682857942 0.183402734812 0.171811556423 0.173255302855 0.186064001811 0.195873759085 0.189008503285 0.154586199139 0.177136590503 0.16961214015 0.170511848112 0.211247624618 0.194873193684 0.18053463387 0.179673493778 0.196193226513 0.173758955601 0.187869379999 0.19001738823 0.183074417621 0.176342969787 0.167870586049 0.184832516957 0.170273119593 0.177542554096 0.173865162824 0.156824879402 0.178069468543 0.235952767235 0.224377142544 0.220334312937 0.217941307072 0.228582134377 0.226295522057 0.225204079848 0.219149120214 0.229153457047 0.219423743811 0.20530168342 0.220370010221 0.217084867796 0.233463162138 0.238411309618 0.220004052761 0.210872881275 0.210171938201 0.24654768975 0.223020715486 0.220176358035 0.224293060856 0.230089603615 0.210845620347 0.213914309618 0.206670271948 0.207126565579 0.204661896322 0.228953899388 0.202543633745 0.220637247329 0.197849243585 0.232039444595 0.201016080923 0.216786754649 0.223327252818 0.224678101179 0.209454984614 0.204079198003 0.211687536154 0.226538202286 0.213579234366 0.224377142544 0.224425108032 0.226231350025 0.219899730556 0.222577135927 0.212192019374 0.21976273048 0.211209202307 0.0316629149872 0.033505533415 0.00599304401229 0.03147088677 0.0569379974042 0.0709823822809 0.0748230813085 0.0531369685873 0.043263968218 0.0546416139704 0.0249055105449 0.0688386171987 0.0687094353901 0.0814189949505 0.0424145094995 0.0312264925083 0.0671690041442 0.0297074253675 0.0326159908833 0.0331671559449 0.0312681130066 0.0653641254742 0.040141041955 0.0490986378456 0.0 0.0426893927085 0.0408370899258 0.0 0.0368318621027 0.029200211213 0.0578784327643 0.069522041979 0.0411420259607 0.0955605905857 0.0684916485028 0.0536985636371 0.0361283833003 0.0349236407736 0.0326544016244 0.0320790517512 0.190734279342 0.198011093099 0.202165335147 0.215193803756 0.208989236133 0.207943677959 0.205067684162 0.186855165408 0.196324830352 0.210203923195 0.205342231011 0.203427359944 0.198526873832 0.208498767129 0.187766259392 0.1906844311 0.213518747065 0.185598548292 0.229746939288 0.194805777978 0.222821572969 0.192481412892 0.225723793976 0.202375384198 0.192457483521 0.194300696695 0.205376522136 0.216660688755 0.21030774241 0.175307222553 0.197000980201 0.189066511629 0.191539232379 0.231058906578 0.21669613367 0.203523564636 0.200666530244 0.214595210264 0.195827171333 0.208641958794 0.209561099462 0.203960485003 0.196717491572 0.188766309975 0.205678014795 0.191492051166 0.198961164508 0.194672996958 0.180152450124 0.19944864987 0.257583228235 0.245178524187 0.240481186058 0.23786630337 0.249341975217 0.245210894146 0.246468005973 0.237456371003 0.247563146561 0.241300303041 0.22729752986 0.240555698528 0.237993038095 0.254193700204 0.26033321069 0.24220764546 0.231128442611 0.230799434891 0.264694157473 0.241194058993 0.241588310222 0.245985985156 0.248083056968 0.231516408669 0.235215479132 0.226198260689 0.228320459149 0.22623565919 0.249391170367 0.221152204365 0.239284874476 0.218351963925 0.252686589685 0.220530747351 0.234477098822 0.243363395844 0.246969168578 0.229990071021 0.225895881072 0.232959826282 0.248113114083 0.23570223574 0.245178524187 0.245562733947 0.248226439579 0.241702239505 0.242775171886 0.233515238499 0.242256602421 0.232453320509 0.0221776604768 0.0292076409984 0.0225953199419 0.0344078822964 0.0402556862186 0.0443490345462 0.0247422055695 0.0212582761966 0.0262237412261 0.0239779350196 0.0420497572884 0.0414593565817 0.0638943455501 0.0444256278823 0.0324613404347 0.0464717210574 0.0064847144584 0.00810593532016 0.026613709743 0.0281809240896 0.0431707040779 0.0364933096346 0.0236549105265 0.0316629149872 0.0188389154871 0.0197111271958 0.0316629149872 0.0126413659503 0.00989228745945 0.0276229976961 0.0619054176788 0.0136582341261 0.0735855911944 0.0517713161376 0.0346942423053 0.0171636471405 0.0128780143687 0.00330427938484 0.00787705209324 0.191124461218 0.196709270957 0.202716444922 0.215924187936 0.209335641351 0.208652638742 0.203456445375 0.186385899088 0.197740844714 0.208277146555 0.207460837635 0.201437849638 0.202451222155 0.209128071387 0.184787202976 0.190226937612 0.212288039878 0.187958804478 0.231460569504 0.196093364986 0.220613648389 0.191903262961 0.227580912528 0.204703238288 0.192798319471 0.194099784682 0.207098250381 0.21653588119 0.209689452484 0.175692486622 0.198220686642 0.190843207535 0.19145100341 0.232063526554 0.215320322312 0.200757345911 0.200512766736 0.217080327154 0.194471953875 0.2086592247 0.211165769014 0.203992872465 0.197349559187 0.188849659131 0.205722712353 0.191296031353 0.198394567468 0.194851113014 0.177048060287 0.198893335106 0.255751598489 0.244696550693 0.240850735029 0.238657915403 0.248846650839 0.247061872969 0.245367004129 0.240135191988 0.249950401792 0.239377278919 0.225435027987 0.240867925442 0.23738991955 0.253602763959 0.257909304325 0.239755581779 0.231578379023 0.230802663983 0.267123052444 0.243889033036 0.240266927764 0.244253437447 0.250943098487 0.231284290644 0.234232349856 0.227603903464 0.227483301065 0.225010355342 0.249291083828 0.223656027478 0.241455021635 0.21867583866 0.252237135554 0.222012069338 0.237944922778 0.2436864771 0.244348021388 0.230119265967 0.224342678419 0.231928535106 0.246422479994 0.233277995952 0.244696550693 0.244602643666 0.245964063641 0.239692017082 0.242916090829 0.232449793011 0.23942553099 0.231611684555 0.0349328180738 0.0397488289561 0.055917859907 0.0466657680267 0.0537525131124 0.0312322620487 0.0204661899168 0.0273925121636 0.0188335643607 0.0409848131484 0.0582072876054 0.056071962696 0.0229170555223 0.0261517042618 0.0397135231185 0.0177392332242 0.0260266328079 0.00803879586998 0.0153514357558 0.0448019039458 0.0436873202641 0.0393784019825 0.033505533415 0.038918396453 0.040710313775 0.033505533415 0.0158373818638 0.0159973219202 0.0374102115892 0.0608056403948 0.0178404535904 0.0681680501025 0.0368277650919 0.0342153028534 0.0136005609896 0.0114725513671 0.0202654226306 0.0215111320227 0.175502940168 0.180846739943 0.186993890383 0.200622200655 0.194047326964 0.191871659177 0.187230724864 0.170446969951 0.181958118895 0.192296146604 0.192168805538 0.185737701873 0.187567790504 0.1926934047 0.169562979332 0.174903941965 0.195627236518 0.171332536122 0.216941277953 0.180224282629 0.204375817171 0.176708588212 0.212007481629 0.187940100384 0.177271729963 0.178782860675 0.191678059195 0.200974345147 0.193702992688 0.160469688943 0.182595805427 0.175146841545 0.17584337797 0.215706304999 0.198413689274 0.184386485081 0.184856323095 0.20232915317 0.177913816132 0.192982627694 0.194328346433 0.187558388958 0.181714052911 0.173349805963 0.189464804214 0.174374123803 0.181953747446 0.178997842818 0.162709307879 0.18281187754 0.239644460978 0.228724428122 0.225246554022 0.222159074766 0.232927392687 0.231188577573 0.229024014707 0.22394884582 0.234228075319 0.223668927512 0.209861178703 0.225307285274 0.221985913786 0.238104571085 0.242774657148 0.224369518131 0.215280858443 0.214174032708 0.25182022511 0.228290074367 0.224826938646 0.228427350702 0.235193041024 0.216001866886 0.218122104545 0.211215702011 0.212099059638 0.209083204086 0.233569980649 0.207419375116 0.225880589953 0.202184861075 0.236687676647 0.205660738951 0.22091809464 0.228766193737 0.228457617824 0.2135856342 0.208453819381 0.216659972673 0.231192094033 0.218856194539 0.228724428122 0.228811270004 0.230578167797 0.224983578773 0.22794936407 0.216954366886 0.223518672063 0.215168993038 0.0278789967436 0.0521696459286 0.0689795584164 0.0720246274977 0.0510464689946 0.0421745394626 0.053582971262 0.0252834975098 0.0677158937278 0.0652657188093 0.081318384881 0.0463957610979 0.0317140261778 0.0668416555065 0.0279143378417 0.0293650428541 0.0349789935753 0.032697569141 0.0634994948437 0.0398508689254 0.0464805914619 0.00599304401229 0.0382168939676 0.0359888514026 0.00599304401229 0.0356302780251 0.0278103138868 0.0553135387293 0.0681148770417 0.0401494228711 0.0950529579724 0.0698373790182 0.0522664102967 0.0364245082526 0.034367526255 0.0307005764611 0.0296340954647 0.193320448722 0.200616479264 0.204805927895 0.217738543992 0.211505662739 0.210916031972 0.207785188245 0.189492055249 0.199011305592 0.212849559963 0.207930120156 0.205973985295 0.201080290256 0.211364107387 0.190111543422 0.193149888648 0.216396870129 0.188564248953 0.232124397575 0.197509520178 0.225563508571 0.194908404083 0.228408782968 0.205404773753 0.195016829794 0.196780548006 0.207983584103 0.219251719916 0.21299620972 0.177753842585 0.19963357851 0.19173429638 0.194103048534 0.233951642516 0.219652294406 0.206249008964 0.203257559758 0.217055047586 0.19863330849 0.211258733702 0.212590735452 0.206794766477 0.199327419787 0.191301214309 0.208462930841 0.194441169877 0.201765843334 0.197324504463 0.182225897174 0.202147597456 0.260363424846 0.247928312505 0.243139798114 0.240805098305 0.252082485905 0.248010938223 0.249316339351 0.240374954583 0.250342905364 0.243926059402 0.229857687769 0.243199557315 0.240557269117 0.256813113392 0.262812568913 0.244726906585 0.233983032632 0.233739246967 0.267372287242 0.243942332388 0.244149194945 0.248660562989 0.250891907974 0.234045856002 0.23797517084 0.229108542601 0.230855414047 0.228916694642 0.252083674667 0.224056856634 0.242001030284 0.221239323161 0.255322806495 0.223424493307 0.237685672547 0.245823282675 0.249646734812 0.232903558602 0.228555612396 0.235462898266 0.250614129287 0.237926457957 0.247928312505 0.248245352669 0.250761236826 0.244030479155 0.2452417146 0.236085335476 0.244925556651 0.235313123462 0.0296919493074 0.0549103775111 0.0590022859872 0.0450958795243 0.0436647014416 0.0451669494409 0.0401697758406 0.0620690815544 0.0404063812339 0.0856236153109 0.0591687282212 0.0490473517415 0.0683619493373 0.0275851844543 0.0264002504943 0.0446945676427 0.0461763497794 0.0638640733472 0.0232220049486 0.0233353107646 0.03147088677 0.029201781247 0.02614478673 0.03147088677 0.0350355717384 0.030428654072 0.0452764113597 0.0802967236314 0.0328354116031 0.0941098407344 0.0724718577687 0.0562014612922 0.0313029110017 0.0340889421853 0.0234757814211 0.028925036543 0.210407140423 0.216202187825 0.221880775368 0.234954371989 0.228487320903 0.227668142527 0.22288748901 0.205844703323 0.216768667446 0.227711464843 0.226263348504 0.220970493431 0.220784685064 0.228189601628 0.204608420295 0.209669658318 0.231537789973 0.206793805641 0.250139733355 0.215157813925 0.239903556316 0.211352210266 0.246303099122 0.223431243026 0.212079033799 0.213468334673 0.225996065584 0.235701634712 0.228967589943 0.195079844754 0.217288259849 0.209835756349 0.210818201018 0.25087781482 0.234509225035 0.220284749566 0.219817558162 0.235704379779 0.213924917847 0.22786813663 0.229992970272 0.223192642095 0.216540668547 0.208207378637 0.224921456698 0.210576921517 0.217728441709 0.214138769918 0.196963968339 0.21825274764 0.274633184392 0.263620373951 0.259724001387 0.257434131046 0.267733388911 0.265627371082 0.264299751314 0.258574475793 0.268388644672 0.258492710734 0.244710620006 0.259750243754 0.256426981572 0.272472244255 0.27686351493 0.258914849135 0.250487159092 0.249742739323 0.285410306304 0.262307563551 0.259345413586 0.26329314732 0.269271128247 0.250334873532 0.253300294048 0.246383898221 0.246645581278 0.244211963707 0.268141898451 0.242252606427 0.260005586654 0.237699783889 0.271104326635 0.240825446457 0.256147548483 0.26253800174 0.263413026859 0.249061635853 0.243583443282 0.251070491042 0.265469741093 0.25252673858 0.263620373951 0.263585528386 0.265041995624 0.258842865142 0.261802853748 0.251583944502 0.25855292944 0.250657339544 0.0450102465608 0.0439198537957 0.0420878714528 0.0489743500788 0.0458798154287 0.0561577170329 0.0578224624858 0.0224176386409 0.0849412968827 0.0784422731853 0.0630254855321 0.0686145922194 0.0402128784166 0.0342259139129 0.0607491308771 0.0616063147121 0.059057582132 0.0469260494382 0.0248556117621 0.0569379974042 0.0246670677105 0.0228986845775 0.0569379974042 0.0437638412118 0.0433305015411 0.0375189879884 0.0808012493057 0.0411311531308 0.089015155435 0.0787045494767 0.0568803303557 0.0468151856286 0.0457099341419 0.0358166687321 0.0382966404493 0.218181156993 0.223322073096 0.229705702275 0.242392790988 0.23577882008 0.236467641295 0.230139466345 0.213525276303 0.225126391191 0.234563959305 0.23447409507 0.227593305984 0.22968375989 0.236629811874 0.210530415308 0.216784944512 0.23930372472 0.216469414915 0.257297585894 0.22354090813 0.246798396633 0.218282233397 0.254457430072 0.233053108231 0.219729861221 0.220661425884 0.234112165808 0.242944509178 0.236406584132 0.202708452811 0.225406734885 0.218378296681 0.218342011887 0.259279663879 0.242430236301 0.227193609953 0.227261836804 0.24357213448 0.22167035443 0.235341335514 0.239265397892 0.231386700827 0.224381906232 0.215751708357 0.232947742221 0.219218926008 0.225693206986 0.221969156729 0.202171293512 0.225894654473 0.281426284779 0.270908280604 0.267090828143 0.26583132699 0.274949583689 0.273930107263 0.271660675229 0.267616788632 0.276815360824 0.264923167376 0.251110814116 0.267050687079 0.263175899022 0.279239724537 0.282486166675 0.264820551157 0.258570659963 0.257930961428 0.293412787858 0.270816835713 0.265787467516 0.269896000193 0.277962792777 0.257183140616 0.260538043514 0.255036127386 0.253324298915 0.251219664681 0.275354649609 0.251349757403 0.268235077166 0.245978035775 0.277988121804 0.249516964722 0.266387651101 0.269264891567 0.269706692396 0.257221982881 0.25041421232 0.257531332229 0.271532129624 0.257608408712 0.270908280604 0.270491687514 0.271003440471 0.264326332823 0.268486578685 0.258229704408 0.264793848318 0.258283260113 0.0140423845301 0.0233496953457 0.0357817640834 0.0194594148752 0.0561842963236 0.0207161808072 0.0344686045315 0.0532665757163 0.0656105748711 0.0611304155975 0.0376479555399 0.0422417749013 0.0418045042693 0.0514369987371 0.0551924216967 0.0370785412495 0.0618826263881 0.03552992519 0.0709823822809 0.0406476055096 0.0445130802003 0.0709823822809 0.0367370595656 0.0436433053629 0.0176515558758 0.0714383400943 0.0313405988749 0.050902359898 0.0456873150128 0.0406736463161 0.0404939048403 0.0386201712918 0.0390017962403 0.0411563720519 0.190383467974 0.193150151335 0.201647820189 0.214732592431 0.207848794505 0.207203203998 0.199017728171 0.184517441065 0.198117570692 0.203251095462 0.208611510757 0.196706601418 0.206520396425 0.207659337014 0.179547279859 0.188402996857 0.207901541524 0.18984126915 0.231436366396 0.196328732424 0.214604717337 0.189868309751 0.227504737805 0.2057488472 0.191940214233 0.192467873609 0.207646738607 0.21397919575 0.206530920079 0.175883310987 0.198349858418 0.192082967323 0.190004696209 0.230325216181 0.21052421867 0.194679583814 0.198537353627 0.218586319181 0.19070413186 0.206580728089 0.210890308648 0.201869808993 0.196665077614 0.187817228574 0.203604184545 0.189253631177 0.195640755776 0.193545341254 0.172360847921 0.196255157011 0.249220192749 0.240515221189 0.238164043403 0.236348677214 0.244540315717 0.246252369823 0.240132469075 0.240621741867 0.249888930273 0.233326376677 0.219986372028 0.238112203328 0.233455949013 0.249164983161 0.25077275089 0.233019708703 0.229068571188 0.227556818352 0.266813634827 0.244493984417 0.235169888951 0.238293435462 0.251527743788 0.228140449415 0.22959779378 0.226587756924 0.223543468869 0.220396683922 0.245620426642 0.22441324809 0.241383050303 0.216373265669 0.24802824928 0.221314439433 0.239346728325 0.241091845042 0.237050180919 0.227111645095 0.219282181885 0.227591404442 0.240665010845 0.227138628142 0.240515221189 0.239782829874 0.239319392462 0.23385508337 0.24006053231 0.227964392353 0.231962468731 0.227162089828 0.0243523838323 0.0383436294161 0.0281380877138 0.0591143980174 0.023507742642 0.0377968944731 0.0517794728194 0.0731209286425 0.0626942772583 0.0386453724111 0.0461236944312 0.0433886463071 0.057222198299 0.0591551673076 0.0319956615202 0.0701506857494 0.0422353525103 0.0748230813085 0.0387325777977 0.0428806382505 0.0748230813085 0.0409657262208 0.047385502674 0.0175421295668 0.0657295550805 0.0386908764688 0.0489968109439 0.0522686619491 0.0387311691946 0.0491554938498 0.0442532671202 0.0441014308007 0.0436449779054 0.189947462454 0.193087358051 0.201270443197 0.213957860003 0.207091706444 0.207812220084 0.199357780455 0.184445706942 0.197758177741 0.203406879958 0.207785745488 0.196588475912 0.205402521621 0.207935882191 0.179126107639 0.187763747972 0.208638518258 0.190249811145 0.22994679767 0.196055336563 0.215105618909 0.189122223025 0.226906503622 0.206245399814 0.191418910324 0.191795589288 0.206939834769 0.213506326546 0.206505332082 0.175157210084 0.197857598212 0.191658112797 0.189588115864 0.230508642351 0.211553886467 0.195374420383 0.198156887541 0.217288762795 0.191332101287 0.206198782834 0.211486527696 0.202202827305 0.19621322526 0.187298385594 0.203764973183 0.190101114406 0.196046751208 0.193325009164 0.171164264517 0.19631141813 0.249660253112 0.240527632421 0.23771162293 0.236702658605 0.244516745342 0.245937622906 0.240621381598 0.240549902948 0.249413960629 0.233305723303 0.219774348622 0.237625603888 0.232924324305 0.248768260048 0.250380844287 0.232789805762 0.22924781342 0.228117670835 0.26599273559 0.24389404773 0.234785533178 0.238373023947 0.251062908639 0.227440481837 0.229791016779 0.226764157548 0.223002288166 0.220422035353 0.245352612988 0.224391473735 0.240777024278 0.216738025139 0.247654798968 0.221447853711 0.240056634641 0.240035093698 0.237386138185 0.227558813593 0.219321439562 0.226978987712 0.240167124817 0.22597544824 0.240527632421 0.2396924683 0.239078218682 0.232902821621 0.239060496039 0.227569835839 0.232339810664 0.227659905228 0.0141262101806 0.0139426926457 0.0350538423884 0.0193552608508 0.0431621025158 0.0440916637552 0.050896637678 0.0388189182549 0.0267566278127 0.024110231253 0.0232035130335 0.0336464874245 0.0349533341335 0.0219308383764 0.0572621322316 0.0349099564127 0.0531369685873 0.0252590392656 0.0298245046475 0.0531369685873 0.0175869571119 0.0244711956848 0.00818635303924 0.0514811245358 0.0194723442375 0.0509148981601 0.038941584783 0.0196470806889 0.0301479553419 0.021545096414 0.0245644202822 0.0217515624084 0.179463055971 0.184039565703 0.191058390156 0.204133065196 0.197353411589 0.197439799103 0.1907070948 0.174350095292 0.18671934138 0.195225461177 0.19666314681 0.188285080293 0.192912863971 0.197716084636 0.17106321505 0.177948538218 0.199885832792 0.178001143095 0.220003811923 0.185034059424 0.207478284578 0.179492397587 0.21650741229 0.194560853934 0.181055185718 0.181934852801 0.196069170377 0.204292703623 0.197354097622 0.164139489747 0.187014857055 0.180129327357 0.179477085752 0.220692949894 0.202951258529 0.187497059139 0.188415486818 0.206311756974 0.182082560815 0.196599300923 0.200515380101 0.192263706104 0.185773424375 0.176983033355 0.193914579783 0.179773695055 0.186365398569 0.183085748173 0.163066216827 0.18672092116 0.242702110891 0.232274391832 0.228788679171 0.227187602483 0.236388804345 0.235969651611 0.232778101812 0.229721372697 0.23914299349 0.226115326683 0.212181006801 0.228757804803 0.224715842477 0.240932159296 0.244193462731 0.226111600887 0.219855639469 0.218968698082 0.256162056313 0.233278273993 0.227208648321 0.231128251362 0.240452766308 0.218806638317 0.221623839858 0.216518120679 0.214708469871 0.212229381986 0.23697508889 0.213254322456 0.230501952576 0.207030794754 0.239662752997 0.210997516317 0.22840539478 0.231383198724 0.230834439598 0.218316993527 0.211375460714 0.218990694003 0.233092437868 0.219339375552 0.232274391832 0.231855607362 0.232408829329 0.226038981221 0.230519762371 0.219567778072 0.225824086027 0.219230412501 0.0195919993919 0.0221059172753 0.0271326704392 0.0528905379198 0.0431586822848 0.0385447030867 0.0255010287112 0.0262337606525 0.0170321830128 0.0195302099147 0.0206521036595 0.0209833716129 0.0252645250533 0.0550811186683 0.0394746523074 0.043263968218 0.0280088992059 0.0316978756724 0.043263968218 0.00932281475944 0.0154549635915 0.0220221996956 0.04619442006 0.0176558634743 0.0549187053198 0.0347531723104 0.0154010655624 0.0249961358537 0.0130624709681 0.021086975526 0.0157582275316 0.172494143831 0.17784850126 0.184169405165 0.197413015598 0.190717679771 0.190383314088 0.184688114733 0.167622718241 0.179385018024 0.189454833136 0.189254205537 0.182497405034 0.184783379021 0.190756581821 0.165553322788 0.171383967321 0.193742345805 0.169993474917 0.213189630746 0.17771520654 0.201947261672 0.173029090008 0.209395719039 0.186777092552 0.174149245532 0.175318744446 0.188797436083 0.197902799323 0.191019058185 0.156992121883 0.179797341784 0.172560525004 0.172720884671 0.213854773981 0.196871318612 0.181938976962 0.181811193063 0.198969640765 0.17576606357 0.19002752539 0.193102069722 0.185493626505 0.178790364297 0.170132074883 0.187191891105 0.172843021691 0.179777298661 0.176222723299 0.157641466463 0.180195116525 0.237394679003 0.226280310947 0.222447934209 0.220486140395 0.230450693082 0.229006895923 0.227001818993 0.222289337207 0.232008422548 0.22075701885 0.20666547313 0.222449960987 0.218801277251 0.235156691974 0.239347508119 0.221054539901 0.213261323555 0.212504634094 0.249237472588 0.225979854618 0.221636903122 0.225713392695 0.233125083698 0.212685206963 0.215719410439 0.209455227207 0.208792490533 0.206361366722 0.230902205737 0.20571314342 0.223403407555 0.200311655198 0.233802587518 0.20384593219 0.22048704521 0.225216247618 0.225812871456 0.211806271505 0.205652934938 0.213231447371 0.227786843309 0.214377387143 0.226280310947 0.226104307704 0.22732908173 0.220904746241 0.224415757753 0.21378854989 0.220845147853 0.213178189936 0.0385029210536 0.0191331286834 0.0412210559221 0.0479132957057 0.0465498029846 0.0439952648454 0.0294155998226 0.0263022819525 0.0288814535105 0.0321813612841 0.0366154046916 0.0324760763417 0.0515944791638 0.0310049526439 0.0546416139704 0.0338597056505 0.0376153728239 0.0546416139704 0.019563854062 0.0269885058553 0.0157505744896 0.0625368339241 0.0143844790412 0.0526157254036 0.0338348827289 0.0297234721938 0.0233631827985 0.0204547357021 0.0243203298193 0.026443786847 0.18161798851 0.18540629351 0.193056292253 0.206460086953 0.199649422865 0.198358533748 0.191516052491 0.176005251549 0.188914967022 0.196119500233 0.199392563722 0.189524653623 0.196344377596 0.19896940691 0.172616127427 0.180167039066 0.200264745011 0.179650753185 0.223145799502 0.187130729579 0.207841346365 0.181781654149 0.218763807382 0.195938920487 0.183272877978 0.184194077304 0.198599402775 0.206127033545 0.198682577368 0.166796826532 0.189310437328 0.182538488189 0.181515358431 0.221921151125 0.202966250725 0.187732725258 0.190303161279 0.209524247949 0.182729260129 0.198439370358 0.201593038612 0.193392410161 0.187922687561 0.179193994752 0.195211914017 0.180441261226 0.187358375156 0.184927566912 0.165517695891 0.188083050798 0.242939707589 0.233299699182 0.23049760933 0.228144979761 0.237419943564 0.237777043946 0.233178399857 0.23147925362 0.241203272786 0.226929567318 0.213318762235 0.230491320835 0.22634238089 0.242302330141 0.245163744774 0.227035678455 0.220985219764 0.21961554026 0.258484704802 0.235572463293 0.228516087928 0.231839786454 0.242586485114 0.220732170317 0.222439032331 0.217865646283 0.216378419732 0.213247698263 0.238336887242 0.215052759841 0.232719204269 0.208046041848 0.241045913549 0.212450914632 0.229495311164 0.23369764439 0.231101958719 0.219113353825 0.212320151032 0.220660185426 0.234399345217 0.221293359798 0.233299699182 0.23289508713 0.233336932661 0.227805970609 0.232744111879 0.220997609023 0.226042688917 0.219773770977 0.0487507947542 0.0650775470184 0.057802762893 0.0304021403296 0.00977564963899 0.0440829229362 0.0178656109143 0.0223657443481 0.0145412613726 0.00880579384939 0.0433599813023 0.0513293545711 0.0472054655688 0.0249055105449 0.0346274013058 0.0356855998153 0.0249055105449 0.0197624673252 0.0147740519949 0.0419438715965 0.0491548979957 0.0286642180871 0.0731736765881 0.0474587573553 0.0307413987988 0.0281173399333 0.0193986394444 0.0247050709585 0.0190246608336 0.173083486457 0.179910481276 0.184747521108 0.19795391577 0.191518465927 0.190900207622 0.18706914222 0.168885152715 0.179192090216 0.192134059909 0.188561922702 0.185169221179 0.182516578227 0.191341288573 0.168884190059 0.172666616329 0.195883736852 0.16891577804 0.213018522015 0.177613800133 0.204946600226 0.174427531668 0.209108112252 0.18591519295 0.174796071764 0.17642769541 0.188436163643 0.199161967465 0.19263437332 0.157387090389 0.179769711315 0.171986018804 0.173714059835 0.214316704261 0.199152104404 0.185242989169 0.182953007753 0.198045578814 0.177854485907 0.191103831493 0.192902410163 0.18652468984 0.179244739552 0.170950373519 0.188227253613 0.173938849831 0.181251003885 0.177034455953 0.160977434421 0.181661357468 0.240293898195 0.228034626835 0.223480275138 0.221169492968 0.232239647508 0.228872448909 0.229260803782 0.221433214134 0.231450162781 0.223643381382 0.209415344527 0.223528489107 0.220601809879 0.237045012584 0.242726403737 0.224366911478 0.21417342735 0.213777217999 0.24873924373 0.225124576112 0.224039797111 0.228474279829 0.232185938187 0.214138987375 0.217816173262 0.209554716543 0.210717178803 0.208592377005 0.232371287988 0.204873194809 0.222962175481 0.201230387345 0.23556954796 0.203837920113 0.218977104053 0.226289614933 0.229286064361 0.212971252918 0.208146125074 0.215343203422 0.230520772996 0.217614435398 0.228034626835 0.228243837858 0.230522424537 0.223850190554 0.225634530741 0.215935214332 0.224460188266 0.215143400901 0.0529022439667 0.0328752197893 0.0555054115849 0.0509289255649 0.0173017354019 0.040824127775 0.0418711547682 0.0429127577978 0.0455543968368 0.0222834470095 0.0700549868865 0.0480571802481 0.0688386171987 0.0442386440219 0.048830469586 0.0688386171987 0.0328274300443 0.0405886029945 0.0207406620607 0.0561033512724 0.0322786585229 0.0341942563123 0.0291137581017 0.0267606033099 0.0411628160219 0.0351933008888 0.0409598634365 0.0395653722748 0.170381496838 0.173513369262 0.181763628401 0.194913312213 0.188005094963 0.187597093173 0.17963993246 0.164619720579 0.178109956535 0.183964962678 0.188586214824 0.177259498897 0.186421186364 0.18797705999 0.159970598511 0.168451386355 0.188698976448 0.169860506279 0.211615177079 0.176329007245 0.195682012245 0.169937526308 0.20770892717 0.185927394166 0.171947667368 0.172526710735 0.187654893417 0.194331077481 0.186965121806 0.155693053512 0.178340485408 0.171986662107 0.170058435784 0.210831670623 0.191498228781 0.175622787806 0.178712133706 0.198589692402 0.171227708945 0.186830515333 0.191211132234 0.182219297429 0.176703062328 0.167807776518 0.183929379926 0.16962686739 0.17602523241 0.173642929989 0.152568053554 0.176555503125 0.230732284086 0.221405569072 0.21873883425 0.217008773205 0.22548132942 0.226701619289 0.221306518291 0.22096326048 0.23027897199 0.21448417882 0.200865540743 0.218691208961 0.214158679542 0.230111756153 0.232289297369 0.214283536851 0.209653108975 0.208315017711 0.24732302577 0.224778673487 0.216104443077 0.219500448511 0.231884032027 0.208661452368 0.210499772687 0.206990362059 0.20415679031 0.201176497026 0.226427503413 0.204626996142 0.221691815993 0.19686908479 0.228928610762 0.20163871009 0.219806895143 0.22162291443 0.218587303851 0.207804967644 0.200129766869 0.208295752101 0.221764477978 0.208127749312 0.221405569072 0.220756657587 0.220625985458 0.214861357139 0.220620889076 0.208723632287 0.213494112425 0.208081504586 0.0844496882978 0.080315980567 0.0726849367116 0.0675030089859 0.0473153931529 0.0443066374773 0.0646752376508 0.0680246382237 0.0621370759065 0.0478849585017 0.0214355468841 0.0687094353901 0.0384979333887 0.0389190825118 0.0687094353901 0.0480894783324 0.0504544201599 0.0366660205236 0.0908834253131 0.0410486802894 0.0843383154277 0.0744888741312 0.0620377042787 0.0466247584157 0.0490662247741 0.0410809318858 0.0466044522535 0.220241217585 0.22387872958 0.231567441105 0.244549509651 0.237801597966 0.237271702597 0.229957304486 0.214803305719 0.2275731622 0.234289966045 0.237659204867 0.227701989162 0.234344570768 0.237727323575 0.210730852647 0.218580436475 0.238765139748 0.218842181584 0.260585713889 0.225852980679 0.24576980875 0.220083510611 0.25691340974 0.234991633089 0.221816502308 0.222561867727 0.236939418607 0.244255467135 0.237073335368 0.205416544089 0.227870869354 0.221241762596 0.220094090971 0.260317043933 0.241447551617 0.226019474356 0.228752673734 0.247333244315 0.221529692284 0.236780633698 0.24056209021 0.232170242771 0.226474325634 0.217767662464 0.233885787279 0.219595234935 0.226160651319 0.223600212873 0.203287025744 0.22672247966 0.280139937217 0.271046573965 0.268296536378 0.26646171863 0.275059767728 0.275799812567 0.270911764394 0.269815722172 0.27914751079 0.264301608642 0.250949666543 0.26825814336 0.263926860434 0.279625941989 0.2816892989 0.264092216423 0.259287884013 0.257990452056 0.295945646215 0.273550874886 0.265902495939 0.269211685027 0.28055869892 0.258431707188 0.260338655828 0.256388822593 0.254096852299 0.25120005211 0.275959599762 0.25362897924 0.27069009133 0.246633770083 0.278453866291 0.251048870754 0.268244717954 0.271056483898 0.268212813274 0.257487053623 0.250194192056 0.258191856723 0.27145388403 0.257986657626 0.271046573965 0.2704506544 0.270310564507 0.264635332559 0.270115829289 0.258629632749 0.263204545545 0.257890609764 0.0600044713459 0.054351041098 0.0187381084361 0.0598557442646 0.0614469307754 0.0531453686192 0.0525559397425 0.0279315026787 0.094761292833 0.0773971393828 0.0814189949505 0.0655620569523 0.0700255275487 0.0814189949505 0.0520201647503 0.0579117136152 0.0488956649362 0.0419109171187 0.0567137285928 0.0208614229037 0.0306610868801 0.0327090237565 0.063272034339 0.0543984191577 0.0634480421324 0.0583685669148 0.139330779418 0.142986837848 0.150850053523 0.163810831232 0.156868051014 0.157669598862 0.149679456655 0.133871337528 0.147188409072 0.153968295801 0.157417010427 0.14689076955 0.155379198936 0.157713790383 0.129254210759 0.137253284329 0.159227064857 0.139693977567 0.180206197156 0.145463442392 0.166351059616 0.138682374547 0.176816693259 0.155870230339 0.140840856643 0.141328264666 0.156536274226 0.163539791349 0.156545980147 0.124345584519 0.147307265103 0.141019689746 0.139034333305 0.180682036747 0.1624502749 0.146314826392 0.147811298228 0.167221256708 0.141400345074 0.156004354912 0.161263479124 0.152006654871 0.145684418167 0.136675034398 0.153561404536 0.139874017863 0.145889334783 0.142806737922 0.121109447078 0.146069682444 0.201853472765 0.191504661606 0.188213817168 0.18712354961 0.1956147304 0.196211110416 0.19205227926 0.19059963575 0.199675257584 0.184878502918 0.170804202645 0.188147863911 0.18370042204 0.200019806255 0.20290821197 0.184684396544 0.179556147674 0.178663646288 0.216648538359 0.194032671841 0.185998827234 0.190015746161 0.201299319844 0.177907547001 0.180731098286 0.176760615884 0.17358596254 0.171156072474 0.196260390732 0.174185108777 0.190914661822 0.166819613367 0.198798087344 0.171315193398 0.190200635461 0.190703604042 0.189694426068 0.178004531696 0.170196606131 0.177783458136 0.19179082566 0.177644952 0.191504661606 0.190881968726 0.191068709685 0.184514101166 0.189756786146 0.178407147108 0.184631515769 0.178548671216 0.0324892056253 0.0484887435869 0.0393579503362 0.0472627188893 0.0192847169709 0.0232472318507 0.0583393494412 0.0585791036352 0.0611786752117 0.0424145094995 0.0604358052165 0.0620651155443 0.0424145094995 0.037432002363 0.036885144463 0.057932237922 0.0663347563067 0.0402054510934 0.0745837939532 0.0379481013657 0.0474009418109 0.0343119409392 0.0339409730754 0.0428079642087 0.0427340086613 0.16139435599 0.167055199688 0.172679145019 0.186588672878 0.18018023169 0.176484226078 0.173177363378 0.15635152751 0.167324305619 0.178559723881 0.177731707828 0.172313382954 0.172981201837 0.177656055027 0.157022451257 0.161367937512 0.181005231952 0.155624650937 0.203264920982 0.165557822324 0.190457367134 0.163325089652 0.197312648791 0.172046353812 0.163261037864 0.165144860989 0.177247951111 0.186978615056 0.179504291425 0.146841839918 0.168174392768 0.160512412099 0.161906209121 0.200546381048 0.18360708151 0.170619997941 0.170849280459 0.188248015891 0.163499893332 0.178863271671 0.178580924982 0.172741578769 0.16748001244 0.159472149167 0.174802823761 0.159227769495 0.167358477359 0.164720514491 0.151099028747 0.168548224145 0.225620984689 0.214383995774 0.210968404368 0.206923044041 0.218629792329 0.216162853062 0.214556733684 0.208383921822 0.219163632298 0.210075568948 0.196412311391 0.211088115641 0.208225582965 0.224211015109 0.229824216763 0.21125046128 0.200361910494 0.199102744226 0.237092122358 0.213257676298 0.21126821314 0.214648561609 0.219950980033 0.202248505777 0.203853239286 0.195906673599 0.198467217831 0.195092454721 0.219319110187 0.191906900165 0.211043854216 0.187166023617 0.222703227226 0.190377176965 0.20432458647 0.215124925944 0.214931156714 0.198560742198 0.194613360236 0.203185148475 0.217932610212 0.206623397839 0.214383995774 0.214797954846 0.217376030313 0.212326968407 0.214327312553 0.203281369316 0.21006441216 0.200554878155 0.0431984510448 0.0262542814372 0.0292765976694 0.0197548242369 0.011556553192 0.0420248516295 0.0609848282508 0.0559281666693 0.0312264925083 0.0398958448421 0.0413044920489 0.0312264925083 0.0264369738429 0.0230439739916 0.0462734733613 0.0414691510708 0.0363662025945 0.071550533242 0.0478849716213 0.0291862789915 0.0369373377392 0.0270714127462 0.0334218260536 0.0262390277171 0.165544038214 0.172891912863 0.17727312869 0.190354892388 0.183958646909 0.183879480011 0.180336742003 0.161635841791 0.171548979047 0.185413091762 0.180663093483 0.178298498301 0.174219977581 0.184181187608 0.161990271375 0.165191063063 0.189309277337 0.161423020955 0.205054842396 0.170020243784 0.198514733988 0.166940816324 0.201451973118 0.178556962169 0.167238778058 0.168913749918 0.180647428918 0.191850429195 0.185573186785 0.149626920851 0.172104358108 0.164216849771 0.166272040954 0.207167511585 0.192763981398 0.178906141081 0.175593499508 0.189955336349 0.171151190119 0.183759195847 0.185737635433 0.179470325207 0.171692649892 0.163416602952 0.18110112813 0.167100843514 0.174311352225 0.169649991905 0.153736698586 0.174561747041 0.233945473753 0.221154951746 0.216216020369 0.214168470951 0.225368727897 0.22143399553 0.222723035108 0.213934293134 0.223870685298 0.217024022208 0.202620827758 0.216260896263 0.213476245827 0.230060267172 0.236168299839 0.217779153489 0.207119858677 0.206977473585 0.241093686538 0.217416199365 0.217140656671 0.221883614074 0.224541883596 0.206836488872 0.211062122527 0.202310625247 0.203579796415 0.201763166599 0.225319290313 0.197343747333 0.215321920645 0.194216980733 0.228558415828 0.196533366942 0.211711953148 0.218804815465 0.22300715182 0.206091661798 0.201387938181 0.208241563837 0.223688965104 0.210605474313 0.221154951746 0.221416959944 0.223922313478 0.216868041009 0.218202757079 0.208934179216 0.218220121204 0.208484033719 0.0428988991781 0.0449811252912 0.038369173012 0.0392012066073 0.0184411809504 0.0770158611592 0.0591685049971 0.0671690041442 0.0499388239897 0.0544215157144 0.0671690041442 0.0346140690794 0.0413101969584 0.0321994698404 0.0440967531043 0.0383047062906 0.0298070621094 0.0211708137004 0.0201129458309 0.0453565917011 0.0368886973605 0.0457257341 0.0417220412167 0.154888546678 0.158813196403 0.166441835812 0.179672088583 0.172784191158 0.17260621351 0.165322327284 0.149403283235 0.162445964052 0.169819706795 0.172790429714 0.162919256631 0.17010598628 0.17291506116 0.145591206314 0.153172124666 0.174534430185 0.15398983773 0.196191934512 0.160693736573 0.182015889553 0.154710886954 0.192325902157 0.170351903358 0.156482289316 0.157229639215 0.171983255038 0.179460677481 0.172268582678 0.139849276668 0.162711676043 0.156097286205 0.154728919391 0.195984213978 0.177559464338 0.161865850494 0.163584898864 0.182768842876 0.156718657607 0.171791912863 0.175973376944 0.167287390852 0.161244255475 0.152349968293 0.168972557751 0.154707872737 0.161224201041 0.158337859598 0.137882544324 0.161653779265 0.217498297387 0.207268525118 0.204089016051 0.202387117484 0.211405750921 0.211656794766 0.207586435206 0.20562940887 0.215067539532 0.200874708618 0.186922162094 0.204055283767 0.199813802793 0.216054855672 0.219141272905 0.20087129472 0.194993585948 0.193928687775 0.232251037143 0.209370889696 0.202145172225 0.205917531589 0.216546254935 0.194031863501 0.196450064794 0.191939021682 0.189735506736 0.187016932169 0.212133736422 0.189132050882 0.20640648386 0.182100826622 0.214796842045 0.186466235268 0.20445164291 0.206912827034 0.205504201388 0.193322781156 0.186101115818 0.194015369065 0.208047565025 0.194373832921 0.207268525118 0.20679029341 0.207231461284 0.201073842632 0.205977385627 0.194517602543 0.200437951551 0.194023674059 0.00847168263945 0.0208969725179 0.0218078109788 0.0404437599311 0.0404194345362 0.0297098891496 0.0297074253675 0.0216763215811 0.0230614305273 0.0297074253675 0.00860159575866 0.00340785264521 0.0287758992091 0.0571909561512 0.0141236699669 0.0709946391549 0.0479055513747 0.0306293087394 0.0170492128659 0.00888265836541 0.00729150357409 0.00480661141663 0.185289458233 0.191085055337 0.196910034804 0.210145347321 0.203571296435 0.202882056675 0.197912476038 0.180625340213 0.191835734795 0.202787038295 0.201526915714 0.195916323017 0.19637797116 0.203353873438 0.179306027922 0.184472082084 0.206754233077 0.181958697279 0.225649748274 0.19019500565 0.21522963857 0.186167360837 0.221735787446 0.198765098442 0.186973637291 0.18833349586 0.201196357535 0.21085114286 0.204041973491 0.169797982896 0.192333522046 0.184882922721 0.185665053896 0.226331622925 0.209833971543 0.195365654989 0.194769866304 0.211153682947 0.188870228152 0.202929999244 0.20531727443 0.198262875463 0.19151438955 0.183035158868 0.19999216658 0.185562442931 0.19270971507 0.189054601453 0.171539131405 0.193197031986 0.250465285504 0.239173402368 0.23520366736 0.232978158323 0.24334022521 0.241285239552 0.239942028126 0.234265108703 0.244131225098 0.234017030201 0.220001112132 0.235226825533 0.231841004595 0.248118070359 0.252690451382 0.234464488853 0.225903274535 0.225189555796 0.261354129729 0.238022768459 0.234821661628 0.238891339667 0.245085625014 0.225666229038 0.228735290931 0.22181584168 0.221922833386 0.219499858076 0.243732621444 0.217749348385 0.235627115362 0.212972717548 0.246730042299 0.216195919385 0.232029501648 0.238052252824 0.239120375862 0.224484292562 0.218871170879 0.226408868705 0.241049615623 0.227943328268 0.239173402368 0.239135618661 0.240673256099 0.234329166611 0.237299131316 0.226939754937 0.234208016817 0.226098822397 0.028402190164 0.0277439229346 0.038946334352 0.0434213038922 0.0291676419928 0.0326159908833 0.0134911389606 0.0149220924049 0.0326159908833 0.0134816006656 0.0104081517294 0.0265932070523 0.0554420028459 0.0189257472352 0.0720390188254 0.0531694571518 0.0303305078503 0.0242591305562 0.0161087615284 0.011068875208 0.00482735633945 0.188743108882 0.194722342773 0.200407653053 0.213430694909 0.206860229667 0.206917746824 0.201757002065 0.18426566592 0.195342886433 0.206526515083 0.204777293747 0.199508643938 0.199425600774 0.207211939812 0.182702196026 0.187804249003 0.210815779569 0.185880854144 0.228533589019 0.193748347964 0.21913688541 0.189440254064 0.225129338462 0.202750726696 0.19038113591 0.191653574921 0.204515978518 0.214295374936 0.207718295645 0.173066791358 0.195766348142 0.188344856347 0.189125824359 0.230149564934 0.214039936134 0.199354571186 0.198257058594 0.214135463178 0.192868986061 0.206423136152 0.209356843732 0.20214026995 0.194966833942 0.186438002469 0.20377859508 0.189699928978 0.196612341236 0.192629244536 0.174503529883 0.196914127595 0.254336052592 0.242870282486 0.238667846113 0.23688365824 0.247015930548 0.244839615824 0.243870689439 0.237954961473 0.247598002179 0.237655720218 0.223546452001 0.238671394435 0.23524462783 0.251589019272 0.256100611398 0.237973407633 0.229710932979 0.229189966161 0.264623863258 0.241418386796 0.238285878732 0.24258651618 0.248557401899 0.228985563027 0.232518055884 0.22563381547 0.2253177885 0.223189662674 0.247286475663 0.221462369267 0.239019854286 0.21687609732 0.250217711381 0.219989961903 0.23614243802 0.241179806044 0.242913875985 0.228427928816 0.222559090866 0.229759973658 0.244438457877 0.230953863516 0.242870282486 0.24276526561 0.244179265793 0.237465205634 0.24045622955 0.230407994296 0.238017253402 0.230045288889 0.0082487413397 0.0434871339043 0.050183840328 0.0462946366306 0.0331671559449 0.0413860283481 0.0432754400997 0.0331671559449 0.018728526127 0.0181135727037 0.0407852007406 0.0552412031248 0.0241124765906 0.0672915663182 0.036051090003 0.0318240646756 0.0214815502931 0.0159437959725 0.0254901889174 0.0237539942631 0.169264556488 0.175091115152 0.180818741393 0.194404360875 0.187867354301 0.185963216048 0.181699481724 0.164435072081 0.175589139186 0.186821392192 0.185627969522 0.180155862933 0.18064011609 0.186712516664 0.164002900607 0.168780956251 0.190185205445 0.164923288811 0.210475012613 0.173888729012 0.199146471591 0.170598213889 0.205685349652 0.18167215365 0.171034804287 0.172628140609 0.185232711805 0.19500403819 0.187902476327 0.154050585176 0.176234935944 0.168650273349 0.169706859713 0.209769138775 0.193110247226 0.179199013537 0.178806517751 0.195681872133 0.172338947581 0.186953230165 0.188260629529 0.181677967227 0.175468629894 0.167135725316 0.183547713996 0.168593151982 0.176177479228 0.172880341359 0.156935998004 0.176943539051 0.234533528357 0.223126396017 0.219327759561 0.216348929156 0.227347260416 0.225044163552 0.22369577874 0.21767535265 0.227960059154 0.218355967949 0.204394726122 0.219392188471 0.216234012192 0.232478694207 0.237601395838 0.219130755018 0.209451421522 0.208536786671 0.245551542374 0.221905162641 0.219288471778 0.22312655653 0.228847315313 0.210092965448 0.212618708514 0.205172062073 0.20633435877 0.203530506813 0.227844403478 0.201098042061 0.219576265526 0.196351165226 0.231028528894 0.199557870197 0.214705957114 0.222733230705 0.223437599857 0.207886214359 0.202974139337 0.210949037245 0.225748458168 0.213347187038 0.223126396017 0.223291835541 0.22533034675 0.219465519892 0.221962474599 0.211307137623 0.21852957733 0.209700938041 0.0419192621384 0.0545713872547 0.0501857282151 0.0312681130066 0.0402402021553 0.0420008929211 0.0312681130066 0.0205720172252 0.0184919891428 0.0424913715031 0.0489285308529 0.0286939286865 0.0683097144827 0.0399734095489 0.0293256914093 0.0276858239832 0.0194684720042 0.0280123772406 0.0234918354003 0.166859723597 0.173331371512 0.178503598003 0.191909917223 0.185423378094 0.184241312774 0.180294315558 0.162400014304 0.173055799755 0.185412361634 0.18273757472 0.178554599617 0.177148633502 0.184812731656 0.162323255063 0.166439616176 0.188986532191 0.162572794611 0.207449709654 0.171422042516 0.198077549025 0.168235674576 0.203119984389 0.179508471538 0.16860424661 0.170241216483 0.182496759111 0.192879913246 0.186103766816 0.151333877107 0.173669495408 0.165942821486 0.167423536544 0.20787297933 0.192134128169 0.178241744028 0.176630454655 0.192503529595 0.170982423968 0.184797056786 0.186354645979 0.179907483835 0.173050611212 0.164731478826 0.181684378357 0.167089130276 0.174542724285 0.170681059367 0.154769008136 0.175102602238 0.233527638993 0.221524217045 0.217249882254 0.214634557666 0.225748190551 0.222754126699 0.222507410575 0.215313006261 0.225475910208 0.217028556203 0.202867049651 0.217307455338 0.214308520933 0.230711314018 0.236268919515 0.217806003587 0.207670960165 0.207072717123 0.242941912447 0.219250459655 0.217623775648 0.221838573376 0.226272530404 0.207953270239 0.211176611808 0.20315950921 0.204399143496 0.201993427277 0.226020220858 0.198708591851 0.217018221505 0.194631582156 0.229236199557 0.197470332905 0.212630809879 0.22033552246 0.22249437564 0.206324343096 0.201514537553 0.209042945777 0.224134795171 0.211456208554 0.221524217045 0.221736417649 0.223988271389 0.217633390228 0.219633609699 0.209536083539 0.217632171671 0.20838956304 0.0775399671204 0.0564468400456 0.0653641254742 0.0395849667657 0.0441943006701 0.0653641254742 0.03373111328 0.0393588608039 0.0260318537498 0.035188052496 0.0394930245033 0.0375459624937 0.0389958606199 0.0129896263362 0.048815943545 0.0379517413039 0.0436570210079 0.0373605585088 0.163502150984 0.168337389523 0.175155703097 0.187828039408 0.181028588214 0.182647655516 0.175454197959 0.158708602469 0.170954310136 0.179762949884 0.180496225683 0.172501221065 0.176828601599 0.182533215894 0.154901122018 0.161684635697 0.185118234991 0.163277975005 0.203127118484 0.169348081929 0.192443927773 0.163103682661 0.200509349698 0.179853786658 0.164993784705 0.165655282459 0.179962811264 0.188228038198 0.18173609183 0.147930089008 0.171080786567 0.164375611634 0.163483874158 0.205438086531 0.18853460221 0.172643498492 0.17244334411 0.189729944562 0.167174105351 0.180637454384 0.185804185303 0.177096116234 0.169809585441 0.16090449309 0.178551065139 0.165213876 0.171240797871 0.16732581913 0.146067131815 0.17119760345 0.227732558213 0.216775565291 0.212818856201 0.212113111804 0.220864451794 0.220275362854 0.217805693503 0.214378859193 0.223348154292 0.21056133718 0.196361313338 0.212749259626 0.208612136858 0.225027947273 0.228363013794 0.210350767964 0.204552905568 0.204069917831 0.240058060786 0.217392662406 0.211260278871 0.215701291359 0.224730833936 0.202526361799 0.206282759299 0.201292044309 0.198558189974 0.196671901675 0.221200953228 0.197951565037 0.214539723027 0.191917757906 0.223779418483 0.195733699704 0.214045901526 0.214810423689 0.215727246675 0.203293848315 0.195827563807 0.20277537878 0.217057373264 0.202583470922 0.216775565291 0.216238379997 0.216660429864 0.209514540125 0.213990052328 0.203581443905 0.210749726957 0.20422943306 0.0299803388219 0.040141041955 0.0494042382248 0.0475047073328 0.040141041955 0.0460070623429 0.0427394298505 0.0575631845334 0.0966299998606 0.0399614977189 0.101837465377 0.0750240618009 0.0697188576907 0.0332240827502 0.042809511279 0.0352142478257 0.0438103448171 0.215592317841 0.220449330264 0.226776190885 0.240218246846 0.233773884944 0.231038348034 0.2263684672 0.210465175429 0.221881119192 0.231308080404 0.231995396207 0.22508238649 0.227350588366 0.232024663305 0.209334152567 0.215020396004 0.234337406165 0.211132081266 0.256379513335 0.220156177626 0.242726282533 0.216799299323 0.251315628789 0.227298889535 0.217343977664 0.218830240402 0.231491464759 0.240364579333 0.233021642066 0.20102218505 0.222551706707 0.215242680103 0.215887525017 0.254517907032 0.236803444285 0.223122921939 0.224639449766 0.242029857582 0.217221787473 0.232566133597 0.233478733428 0.226945320336 0.221662735537 0.213507733767 0.228892766884 0.213808892567 0.221389781107 0.218899946715 0.202924474402 0.222409930936 0.277044937084 0.267058523642 0.264053120566 0.260683081125 0.271161003688 0.269928478944 0.266921380471 0.262757571495 0.272983826376 0.261803183737 0.248535164351 0.264118904646 0.260747941814 0.276361888176 0.280348586054 0.262426412526 0.254046125774 0.252677731845 0.290308221249 0.267228122274 0.26327783075 0.266417210913 0.273921992864 0.255063180539 0.256547903477 0.250174111671 0.251094888905 0.247813627121 0.2719924919 0.246585755223 0.264876118769 0.241182097008 0.275004799417 0.244784301617 0.259319666846 0.267703173929 0.266005774781 0.252196034878 0.247119094967 0.255516011217 0.269407340011 0.257485027934 0.267058523642 0.267089999571 0.268509786469 0.263477813268 0.266862086523 0.25569939087 0.261126117571 0.253527997613 0.0490986378456 0.0295882981502 0.0295703235206 0.0490986378456 0.0321772405698 0.0329624694956 0.0320791928311 0.0819581869701 0.0243777845713 0.0815880274595 0.0630031242432 0.0521832400773 0.0270041961787 0.0316626367995 0.0225970983534 0.0306135777155 0.209138400452 0.213486068388 0.2205444529 0.233810061497 0.227135531522 0.225853564299 0.21966357841 0.203852341272 0.216088869802 0.224306040557 0.226217923772 0.217746213648 0.22224285579 0.226486749722 0.201086071725 0.207931744175 0.228270944293 0.206490835508 0.249940445551 0.214362660634 0.235990805565 0.209564571075 0.245741550277 0.222850058752 0.210799114332 0.211874421056 0.225608964554 0.23378761328 0.226547651045 0.194192088088 0.216536885951 0.209533041634 0.209201443093 0.249253656365 0.230961033564 0.21611849601 0.218017866257 0.236081990375 0.21084689547 0.226091432124 0.228781524913 0.221104911259 0.215367366824 0.206800674523 0.222911320613 0.208230596605 0.21525709201 0.2125575014 0.193883560936 0.215963986139 0.270701972881 0.26093677072 0.257916700448 0.255512820108 0.265026258573 0.264740784654 0.260931231555 0.258198696768 0.267948592812 0.254868213876 0.241356571396 0.257920982238 0.254009770883 0.269860597717 0.272941762013 0.255029971614 0.248480879151 0.247232962401 0.285073136127 0.262202525959 0.256323297606 0.259707446043 0.269155247223 0.248322830219 0.250262594392 0.245086205405 0.244160084795 0.241180503071 0.265837705385 0.241871800867 0.259549733542 0.235634358004 0.268589453275 0.239657575868 0.255932774798 0.261005427367 0.2590787222 0.246704864465 0.240323606031 0.248446059436 0.262200385961 0.249216212931 0.26093677072 0.260623330072 0.261243034875 0.255650557301 0.260116706756 0.248814449864 0.254094104786 0.247584466928 0.0426893927085 0.0408370899258 0.0 0.0368318621027 0.029200211213 0.0578784327643 0.069522041979 0.0411420259607 0.0955605905857 0.0684916485028 0.0536985636371 0.0361283833003 0.0349236407736 0.0326544016244 0.0320790517512 0.190734279342 0.198011093099 0.202165335147 0.215193803756 0.208989236133 0.207943677959 0.205067684162 0.186855165408 0.196324830352 0.210203923195 0.205342231011 0.203427359944 0.198526873832 0.208498767129 0.187766259392 0.1906844311 0.213518747065 0.185598548292 0.229746939288 0.194805777978 0.222821572969 0.192481412892 0.225723793976 0.202375384198 0.192457483521 0.194300696695 0.205376522136 0.216660688755 0.21030774241 0.175307222553 0.197000980201 0.189066511629 0.191539232379 0.231058906578 0.21669613367 0.203523564636 0.200666530244 0.214595210264 0.195827171333 0.208641958794 0.209561099462 0.203960485003 0.196717491572 0.188766309975 0.205678014795 0.191492051166 0.198961164508 0.194672996958 0.180152450124 0.19944864987 0.257583228235 0.245178524187 0.240481186058 0.23786630337 0.249341975217 0.245210894146 0.246468005973 0.237456371003 0.247563146561 0.241300303041 0.22729752986 0.240555698528 0.237993038095 0.254193700204 0.26033321069 0.24220764546 0.231128442611 0.230799434891 0.264694157473 0.241194058993 0.241588310222 0.245985985156 0.248083056968 0.231516408669 0.235215479132 0.226198260689 0.228320459149 0.22623565919 0.249391170367 0.221152204365 0.239284874476 0.218351963925 0.252686589685 0.220530747351 0.234477098822 0.243363395844 0.246969168578 0.229990071021 0.225895881072 0.232959826282 0.248113114083 0.23570223574 0.245178524187 0.245562733947 0.248226439579 0.241702239505 0.242775171886 0.233515238499 0.242256602421 0.232453320509 0.00485178735022 0.0426893927085 0.0245520805084 0.0237321483943 0.0251494297711 0.057422445578 0.0278324002426 0.0740340192529 0.0616449531864 0.0345266403523 0.0352178925007 0.0280808122885 0.0215364121522 0.0177797202497 0.195939824645 0.201850357058 0.207610278022 0.22033675889 0.213750209485 0.214735564684 0.209038553623 0.191585516698 0.202707820911 0.213610139259 0.211883129405 0.206450810174 0.206559465565 0.214810867748 0.189369070647 0.194741318593 0.218356798216 0.193913039155 0.235021949805 0.201158850164 0.226304640977 0.196279111135 0.23226696484 0.210758787397 0.197506357802 0.198589557713 0.211650666116 0.221261000242 0.214919567324 0.180148757389 0.203013849771 0.195747568982 0.196259855385 0.237643665501 0.221708411792 0.206639148011 0.205357745264 0.220920753341 0.200419916968 0.213509698507 0.217293606067 0.209702604063 0.202159105945 0.193545439121 0.211227865592 0.19759997516 0.20414742205 0.199914048013 0.180698578828 0.204233409175 0.261355583351 0.249944219156 0.245612323364 0.244419283687 0.254044690674 0.252080319195 0.251117898529 0.245501499007 0.254799073865 0.244461494615 0.230320341653 0.245583982907 0.241986600586 0.258340089378 0.262471019952 0.244539880639 0.237114058797 0.236754636273 0.271527597514 0.248603215251 0.244975611097 0.24946627049 0.255826675762 0.23571262824 0.239663820665 0.233206292475 0.232061847402 0.230234276049 0.254229520181 0.229085095534 0.246138012907 0.224434087127 0.257016267918 0.227571722833 0.244299258458 0.247702557023 0.249766808341 0.235948695396 0.229554249737 0.236400217176 0.250952182681 0.236961759284 0.249944219156 0.249686907558 0.250743450736 0.243657848264 0.246990711027 0.237179418187 0.244877764453 0.237415960787 0.0408370899258 0.0273499265107 0.025297636378 0.0296282714156 0.0603715739764 0.030303105355 0.0787085816014 0.0655434565706 0.0387042108075 0.0365503327535 0.0303622028421 0.0226096284705 0.0196557817863 0.19925839426 0.205348957591 0.210921560248 0.223617852816 0.217069506306 0.218059817218 0.212575335145 0.194999027921 0.205926088655 0.217171129009 0.215009852018 0.21001079042 0.209452929226 0.218136520475 0.193003747556 0.198137341753 0.221866239829 0.197043564846 0.238162085485 0.204392911714 0.229893095054 0.199683809546 0.235440202166 0.213909407049 0.200827966888 0.201961005661 0.214826820158 0.224639586072 0.218358620882 0.183440825957 0.206248208954 0.198918059014 0.19962810203 0.240932955881 0.225232933618 0.210279009964 0.208738673233 0.223984027141 0.203935797463 0.216876482587 0.220527121607 0.21308659534 0.205456328664 0.196890655988 0.214607347176 0.201007750183 0.207588221909 0.203264328774 0.184313736826 0.207665740899 0.26489357675 0.253366014308 0.248941435732 0.247733075772 0.257464720601 0.255265649267 0.254603866324 0.248608337617 0.25792057227 0.248007634376 0.233863042555 0.24891723403 0.245404277418 0.261753780229 0.266030956859 0.248122124098 0.240457509587 0.240148573616 0.27462290691 0.251686093628 0.248460344614 0.252996524531 0.258897040073 0.239091218679 0.243138472416 0.236454818573 0.235503469176 0.233728803563 0.257606144443 0.232210377675 0.249277823428 0.227793973728 0.260419107201 0.230810446264 0.247340816609 0.251008018719 0.253372216044 0.239328849004 0.233077723245 0.239855175697 0.254460096493 0.240510971982 0.253366014308 0.253147704183 0.254305992093 0.247178452498 0.250316773817 0.240645130337 0.248503359474 0.240885126472 0.0368318621027 0.029200211213 0.0578784327643 0.069522041979 0.0411420259607 0.0955605905857 0.0684916485028 0.0536985636371 0.0361283833003 0.0349236407736 0.0326544016244 0.0320790517512 0.190734279342 0.198011093099 0.202165335147 0.215193803756 0.208989236133 0.207943677959 0.205067684162 0.186855165408 0.196324830352 0.210203923195 0.205342231011 0.203427359944 0.198526873832 0.208498767129 0.187766259392 0.1906844311 0.213518747065 0.185598548292 0.229746939288 0.194805777978 0.222821572969 0.192481412892 0.225723793976 0.202375384198 0.192457483521 0.194300696695 0.205376522136 0.216660688755 0.21030774241 0.175307222553 0.197000980201 0.189066511629 0.191539232379 0.231058906578 0.21669613367 0.203523564636 0.200666530244 0.214595210264 0.195827171333 0.208641958794 0.209561099462 0.203960485003 0.196717491572 0.188766309975 0.205678014795 0.191492051166 0.198961164508 0.194672996958 0.180152450124 0.19944864987 0.257583228235 0.245178524187 0.240481186058 0.23786630337 0.249341975217 0.245210894146 0.246468005973 0.237456371003 0.247563146561 0.241300303041 0.22729752986 0.240555698528 0.237993038095 0.254193700204 0.26033321069 0.24220764546 0.231128442611 0.230799434891 0.264694157473 0.241194058993 0.241588310222 0.245985985156 0.248083056968 0.231516408669 0.235215479132 0.226198260689 0.228320459149 0.22623565919 0.249391170367 0.221152204365 0.239284874476 0.218351963925 0.252686589685 0.220530747351 0.234477098822 0.243363395844 0.246969168578 0.229990071021 0.225895881072 0.232959826282 0.248113114083 0.23570223574 0.245178524187 0.245562733947 0.248226439579 0.241702239505 0.242775171886 0.233515238499 0.242256602421 0.232453320509 0.00792313459309 0.0236297776546 0.0536541076705 0.0108412074571 0.0628560371629 0.0400426717019 0.0242916508179 0.0172043768705 0.0050674059167 0.0120480352419 0.00909567999576 0.179733982337 0.185130641595 0.191359647755 0.204666509458 0.198016972069 0.197255576955 0.19185518721 0.174855164332 0.186481505005 0.196685913747 0.196375987191 0.189833405887 0.19172374648 0.197739787295 0.173088361774 0.178764981726 0.200732295255 0.17675687262 0.220477540317 0.184804954654 0.209049846749 0.180446207623 0.21646451476 0.193507677512 0.181414841832 0.18267689502 0.195938395299 0.20515640915 0.198204629073 0.164323672477 0.186951627799 0.179636188812 0.180004434454 0.220785829324 0.203765417111 0.189073881786 0.189075562692 0.206144383335 0.182845346787 0.197260508087 0.199879524686 0.192526504243 0.186000212457 0.177429453542 0.194272636643 0.179755047797 0.186852872988 0.183420017951 0.165408476828 0.187374971211 0.244379342624 0.233352874625 0.229596132577 0.227371143644 0.2375226222 0.23596534417 0.233967797192 0.229101525181 0.238946455726 0.22794079194 0.213947298319 0.229612039594 0.226054866131 0.242325328031 0.24658946328 0.228320886035 0.220245475488 0.219410638455 0.256223296024 0.232925784221 0.228888220536 0.232841814871 0.240004491346 0.219971839687 0.222802209556 0.216357175156 0.216093165101 0.213536808192 0.238014479196 0.212556057937 0.230415334062 0.207278762186 0.240959781593 0.210762423837 0.226970181506 0.23251461018 0.23290234378 0.218739342276 0.212847200349 0.220551656017 0.235068599786 0.221910374672 0.233352874625 0.233237981586 0.234568034713 0.228343429058 0.231717374059 0.221050886534 0.227947933137 0.220165226053 0.0299322879907 0.05484354778 0.015508827535 0.0698377856312 0.0461226756997 0.0288439268589 0.0179749812614 0.00820193608475 0.0104514607363 0.00601471721254 0.182232775094 0.188144985482 0.193867682363 0.20711590793 0.200550766912 0.199860135361 0.195016228938 0.177611309591 0.188739284934 0.199919451707 0.198412537874 0.193032154463 0.193186123937 0.200329430243 0.176446801251 0.181459762374 0.203862717343 0.178811701467 0.222599641879 0.187102653143 0.212418919228 0.183165138771 0.218668582765 0.195650642874 0.183922319896 0.18531418569 0.198099986213 0.207874048966 0.201087073424 0.166709980365 0.189246782627 0.18175655241 0.182635613511 0.223327464443 0.206968516624 0.19255440527 0.191763016548 0.208041469681 0.185943960269 0.199929552497 0.20225187193 0.195263692383 0.188456709089 0.17999008547 0.196992188976 0.182563902533 0.189736470349 0.186019084029 0.168664903455 0.190216873808 0.247702840069 0.236282170601 0.232244216334 0.230002139075 0.240457584895 0.238254033101 0.237105182737 0.231183514354 0.241076068443 0.231215767875 0.217161214732 0.232270602033 0.228935795245 0.245246208475 0.249963377759 0.231700745278 0.222930449716 0.222251471523 0.258324331925 0.234942120984 0.231973156153 0.236088516952 0.242009296663 0.222723564262 0.225860019318 0.218781249758 0.219012595092 0.216618271437 0.240821050718 0.214649402949 0.232567858569 0.209986385677 0.243846113816 0.213146433073 0.228924161819 0.235098611969 0.236391467186 0.22153411273 0.216011138881 0.223520339681 0.238239770023 0.225155295827 0.236282170601 0.236275025927 0.237908750738 0.231525069379 0.234355153325 0.224056763193 0.231485450395 0.223215654925 0.0572284762647 0.0227453897084 0.0528238726337 0.0444991634471 0.0266973769114 0.0336948312224 0.0272062333147 0.0274688740262 0.0263328670099 0.185716682176 0.189934662812 0.1972487923 0.210230033124 0.203429572326 0.203652674494 0.196491149306 0.180504651022 0.193104816748 0.200892549998 0.203064104164 0.193989394378 0.199601579765 0.203900303764 0.17670118478 0.184039790634 0.205684310622 0.1846555251 0.226113704537 0.191415178407 0.212986837934 0.185538541574 0.222740004307 0.201086740863 0.187280443615 0.188038576609 0.202414184433 0.210238732317 0.203278048935 0.170511460418 0.193354539773 0.186626010428 0.185640639557 0.226777734968 0.208697723096 0.193060716385 0.194490643156 0.212667601723 0.188009393157 0.2026405567 0.206872630148 0.198376957217 0.192017886181 0.18319542151 0.200010008529 0.185964041385 0.192410951726 0.189280207199 0.168712331151 0.192752182621 0.248024768226 0.23796255467 0.234657432476 0.23319215601 0.242043627351 0.242092609424 0.238343194449 0.236048752979 0.245336704526 0.231521041315 0.21770196943 0.234612870361 0.230403022013 0.246515101463 0.249324779094 0.231381623939 0.225835327084 0.224876205525 0.262239370649 0.239554652873 0.232726560614 0.236544998844 0.246724791051 0.22460575232 0.227286958934 0.222708966341 0.220419587826 0.21790450046 0.242699459353 0.219658545965 0.236700307439 0.213086341068 0.245286981729 0.217239309033 0.234909245332 0.237182720463 0.236050820735 0.224251890124 0.21698485009 0.224622112179 0.238475066919 0.224611061526 0.23796255467 0.237436165661 0.237675287343 0.231370754771 0.236291823604 0.225200181658 0.231029643716 0.224953381888 0.0635710838178 0.0592624543316 0.0596058092469 0.0328544820604 0.0698260168465 0.0573261822931 0.0634460229613 0.0541080958794 0.147303482686 0.155161448288 0.159136079545 0.171063643845 0.16463221595 0.168584971583 0.163624194761 0.144235266953 0.153827968777 0.168063795028 0.16179307599 0.160206770568 0.155398151635 0.167887347752 0.142965424509 0.146100735749 0.17375554158 0.146517107633 0.184102282814 0.152518687773 0.182006446517 0.147493939017 0.182975421708 0.163616999091 0.148716120222 0.149770660992 0.161958247648 0.173135111896 0.168022513735 0.130738265008 0.153920373487 0.146537313722 0.147925357539 0.190546691654 0.177990062346 0.162980599048 0.157241520972 0.169863812022 0.155438284157 0.165389918434 0.170707644688 0.163197102596 0.153422018191 0.144858486622 0.164315178145 0.152440808389 0.158128021971 0.151920639439 0.132537826475 0.157359920068 0.217237278816 0.203578319465 0.19760287759 0.197919551969 0.207681519923 0.203644803794 0.206313583309 0.1971606419 0.205828867849 0.199064489236 0.184178187026 0.197538822775 0.194404518771 0.211337912042 0.217143803133 0.199166671372 0.190313841849 0.191119829867 0.222105237734 0.199176357415 0.198307304 0.204224647581 0.206706114847 0.187412194782 0.19389709622 0.185837912363 0.184448440446 0.184112718669 0.207064291919 0.180760878547 0.196874753064 0.177987251583 0.20993106085 0.180009687643 0.197504007472 0.198552660709 0.205899002329 0.189953313758 0.183706289376 0.188863124638 0.204480568311 0.189510910124 0.203578319465 0.203449868624 0.205307715981 0.196398052452 0.198053210544 0.190139115088 0.201200460168 0.192215464603 0.0646648846724 0.0408252785942 0.0324283536149 0.0110096321254 0.0094227049932 0.0109851717283 0.0161323040352 0.185995265043 0.190666336997 0.197500031875 0.21094278484 0.204249573992 0.202798103731 0.196994111724 0.180742040387 0.192881751338 0.201790619427 0.203110749465 0.195155158145 0.19908437034 0.203454592708 0.178505994536 0.184932353957 0.205646229373 0.183039730424 0.227253690432 0.191142107437 0.213742423108 0.186621634482 0.222820224457 0.199555206103 0.187692696694 0.188884746385 0.202513734055 0.21103182776 0.203761324581 0.170936996707 0.193374027451 0.18623653615 0.18612791719 0.226436319176 0.208431967216 0.193707164322 0.195058461584 0.2131353075 0.187987297837 0.203202157658 0.20562939159 0.19810682266 0.192261721083 0.183685994256 0.199939039445 0.185109577754 0.192293556305 0.189460942551 0.171326016098 0.193018202765 0.248893705524 0.238594153414 0.235368547023 0.232809197602 0.242745772985 0.241996085058 0.238752248121 0.235253956997 0.245180784501 0.232837883436 0.219103668122 0.235388183401 0.231636120508 0.247694764978 0.251385734184 0.233178069062 0.225757404345 0.224585782777 0.262534762143 0.239353867417 0.234157942476 0.23769286224 0.246342255252 0.225805082888 0.227891511242 0.222153883963 0.221706146684 0.218742321816 0.243483255972 0.218775046696 0.236736505477 0.212768083313 0.246367071263 0.216653745648 0.232866084107 0.238586937872 0.237342638049 0.224021718097 0.217958679267 0.226108360277 0.24024082191 0.227355271774 0.238594153414 0.238405022146 0.239431466759 0.233734880392 0.237715653864 0.226470518154 0.232346541994 0.225139690924 0.0393671458325 0.0464638919444 0.0721210062546 0.0652058473437 0.0727980489441 0.069475743339 0.146377403101 0.147841270546 0.157364583964 0.169967544666 0.162961576643 0.163688135507 0.153695971045 0.140223917866 0.154815274304 0.157460458795 0.165356253736 0.150768971529 0.165320051187 0.163744020488 0.133177827162 0.143578449734 0.163063976092 0.148488500757 0.186880468442 0.153029883361 0.168852310214 0.144817839236 0.183493098196 0.16367577587 0.147767272784 0.147687188567 0.164075138054 0.168705452589 0.161392920433 0.132534765042 0.154766041608 0.149414499828 0.145576557217 0.186084208163 0.165901662347 0.149128920765 0.153742006284 0.175201329509 0.146090630422 0.161672202019 0.168031911823 0.15768748388 0.152605178523 0.14358929792 0.159222856143 0.14586039418 0.151244472229 0.149339889326 0.125727972716 0.151529925609 0.203450036676 0.194986217471 0.192843589773 0.192004248179 0.198946853705 0.202034655336 0.194735870429 0.197390062155 0.205967080811 0.187074482741 0.173679535364 0.192730113791 0.187498820793 0.203203985607 0.204052975425 0.186370806955 0.184428712838 0.182999565721 0.222535792393 0.200873648 0.188899014225 0.192203777807 0.208005111614 0.182384645276 0.184037705225 0.182740791406 0.177549631149 0.174643342307 0.20005255491 0.181451046073 0.197344936488 0.172060390986 0.202165775813 0.177622921879 0.197550181919 0.195373313831 0.190875275066 0.18253037262 0.173366080571 0.181382900577 0.194116360457 0.17989694523 0.194986217471 0.19390780748 0.192735187036 0.186889353803 0.194252690611 0.181881177771 0.185754237755 0.182012475847 0.0362542669235 0.0433946419111 0.0395938166279 0.0499100911085 0.0488639489089 0.15152574063 0.154669324023 0.162831337388 0.176665753207 0.169806006203 0.167122046596 0.160378494232 0.145396880889 0.158882941041 0.165181343889 0.170013544811 0.158841213295 0.168049497633 0.168033584387 0.142281111905 0.150191536414 0.168801703096 0.149137142763 0.194382185315 0.156984666205 0.176687245859 0.151908690894 0.188914920448 0.165149458367 0.153257832095 0.154263633953 0.168972071115 0.175883337588 0.16793063653 0.137286345822 0.159386690667 0.152763325671 0.151315442368 0.191108253021 0.171290308569 0.156330262179 0.159994057675 0.180780718652 0.151317533557 0.168120728135 0.170508979962 0.162341150711 0.157837504705 0.149168793021 0.164327797527 0.148926027403 0.156192253462 0.154516403486 0.136169004568 0.157264478091 0.212122717934 0.202635989676 0.200296956824 0.197168424809 0.206818081536 0.207593997357 0.202129844535 0.201194661862 0.211287068634 0.196367072918 0.182828097116 0.200324517328 0.196191886272 0.212106652571 0.215234691898 0.196754062665 0.19012565359 0.188386972878 0.22899541165 0.20586463613 0.198258080181 0.201204812212 0.21275581912 0.190751178898 0.191559171703 0.187109767024 0.186199376174 0.182482910799 0.207969239625 0.184720534783 0.20291190811 0.176982703379 0.210810911009 0.181756418439 0.198660937707 0.204191111601 0.200384835859 0.187985877445 0.181554202617 0.190580156178 0.204342491841 0.191957847677 0.202635989676 0.20235052312 0.203098119616 0.198224746232 0.20315819974 0.190687755597 0.195275148422 0.188602851388 0.0403137962831 0.0283224608052 0.0352856337592 0.0279159520938 0.163417587591 0.169024399252 0.175174902733 0.188097949492 0.181377843525 0.182339093803 0.176249889638 0.158817342465 0.170474967966 0.180845899763 0.180001842158 0.173609983768 0.175496658011 0.182387588854 0.15630806646 0.162058645803 0.185720747493 0.161911269413 0.203372519665 0.168871283524 0.193698104443 0.163602773962 0.200347540332 0.178768462478 0.164992621499 0.165987648079 0.179610648238 0.188805808549 0.182291142535 0.147635227715 0.17074779736 0.163623388142 0.163626611748 0.205461058801 0.189137857415 0.173807850394 0.172761367844 0.189359771077 0.167597338583 0.181003226921 0.18512092453 0.177130623568 0.169726367846 0.160952549622 0.178668367454 0.164953979441 0.171439694143 0.167334380068 0.147661742228 0.171525073941 0.229212799489 0.217687295638 0.213458314816 0.212251579761 0.221837277994 0.220248120289 0.218836902814 0.21380890761 0.22315649106 0.21209021333 0.197773787684 0.213427195478 0.209691510886 0.226221325989 0.230427027381 0.212200169545 0.204828617154 0.204406576244 0.240127225685 0.217034360511 0.212651201199 0.2171572787 0.224326708779 0.203421180278 0.207247009432 0.201076078606 0.199634752126 0.197699005741 0.222082174457 0.197241869749 0.214400679015 0.192020712279 0.224886788727 0.195424460906 0.212817607264 0.215705919361 0.217495203752 0.20360497892 0.196993475509 0.204019508797 0.218720046506 0.204677118987 0.217687295638 0.217405004007 0.218489214457 0.2114130463 0.214943199157 0.204771656731 0.212548494217 0.204988233676 0.0127227257693 0.0140144534262 0.021205315625 0.187358882688 0.192125766059 0.198772205734 0.212374766999 0.205757524042 0.203466313878 0.198269360384 0.182075536022 0.193992765379 0.203217006858 0.204331991647 0.196763084721 0.20014614007 0.2043270841 0.180514052654 0.186571402116 0.206603961592 0.183553088594 0.228871000555 0.192232411854 0.214999691886 0.188338267885 0.22392031363 0.199986970587 0.189109313009 0.19048409561 0.20374267521 0.212452607057 0.205039311477 0.17250608603 0.194596025922 0.187333202847 0.187569910566 0.227244357321 0.209244321292 0.19501056691 0.196469921009 0.214533216851 0.189073691369 0.204563213894 0.206128514596 0.199072106837 0.193573357583 0.185159768848 0.200995780718 0.185854759801 0.193344854477 0.190733738309 0.173803583444 0.194263918799 0.250041858008 0.239752334137 0.236636793113 0.233547933384 0.243918830588 0.242901503454 0.239756635176 0.235879076786 0.246072511959 0.234292093183 0.220682667006 0.236686092505 0.233134556981 0.249085035733 0.25310029637 0.234845006722 0.226672837999 0.225370202913 0.263585873652 0.240269679341 0.235701118126 0.239046163418 0.247138992266 0.227341018945 0.229062028698 0.222899030248 0.223273394137 0.220071420598 0.244719204688 0.219434280561 0.237757183282 0.213627898609 0.247720909818 0.217420020786 0.232895251914 0.240194731922 0.23871780496 0.22484832656 0.219342733041 0.227741527766 0.241902288188 0.229541957119 0.239752334137 0.239709455557 0.241056941465 0.235717844261 0.239326740269 0.227988125157 0.23374803561 0.226117504317 0.0112944223112 0.0116717916683 0.180315550232 0.185624806253 0.191893759643 0.205310880338 0.198677901623 0.19742928403 0.192207723586 0.175346071899 0.186991091141 0.197103740487 0.197010559796 0.19035952375 0.192415460068 0.198031907427 0.173783350974 0.179445795208 0.200920544981 0.176980189616 0.221316628359 0.185291341794 0.209338461592 0.181164395028 0.216995536327 0.193672787851 0.182024521085 0.183353435463 0.196543976916 0.205721030365 0.198641096205 0.165041173069 0.187513275437 0.180170480519 0.180593580969 0.221064954649 0.203852710847 0.189347093282 0.189636739108 0.206917162302 0.183105626465 0.197802407018 0.200023916345 0.192826802002 0.18656768388 0.178054301819 0.194629767109 0.179904508376 0.187157083025 0.183925453374 0.166391248738 0.187799823192 0.244629672801 0.233720966397 0.230103074977 0.227589285523 0.237898238239 0.236372028201 0.234191133105 0.229406521443 0.239389014631 0.228371263352 0.214461216321 0.2301324354 0.226622699222 0.242825477857 0.247136546528 0.228837795311 0.220544275821 0.219592617307 0.256764787312 0.233410014962 0.229423132068 0.233225877209 0.240428033421 0.220595144087 0.223137647662 0.216631996383 0.216685610877 0.213950565713 0.238461242313 0.212870718316 0.230920602382 0.207535413146 0.241449474994 0.211057657029 0.226977435726 0.233226691247 0.233226664174 0.218957536154 0.213267398065 0.221167604245 0.235644719391 0.222751284499 0.233720966397 0.233655964463 0.235074683268 0.229091941731 0.232416283617 0.221592631388 0.228271321964 0.220395124759 0.00983853465038 0.190743576675 0.196127634014 0.202306832097 0.215593264431 0.208988714901 0.208014681254 0.202752493595 0.185879141861 0.197391448173 0.207587390017 0.207246286244 0.200818808941 0.202436920976 0.208560198594 0.184214467342 0.189842910423 0.211501430369 0.187510793393 0.23133568248 0.195719341744 0.219810583637 0.19153274103 0.227260064133 0.204197085901 0.192430421495 0.193730481177 0.206830708645 0.216081015857 0.209114627207 0.175418662775 0.197889059256 0.19054759391 0.191035254803 0.231501988345 0.214455580052 0.199914298524 0.200065006525 0.216984298716 0.193736240961 0.208206242937 0.210580217034 0.203385972256 0.196973524191 0.188474338185 0.205152454389 0.190585220715 0.197746685322 0.194398694242 0.176663593729 0.198324488838 0.254937931723 0.244068220209 0.240384243666 0.238035456407 0.248220627185 0.246646653405 0.244595211757 0.23972473422 0.249602645305 0.238680104062 0.224803483418 0.240405804306 0.236887764418 0.253050369586 0.257246240499 0.239074958955 0.230985078994 0.230097737916 0.266839316033 0.24359819756 0.239685124735 0.243538571704 0.250621888643 0.230851769093 0.233547789712 0.227075078845 0.226983547477 0.224362834405 0.248743336169 0.223247552937 0.241135379781 0.21805286342 0.251689329115 0.22150661959 0.237406179341 0.243353541542 0.243526559835 0.229448331316 0.22367406721 0.231427017129 0.245837459247 0.232818717405 0.244068220209 0.243969807123 0.245289155672 0.239200193755 0.242559723366 0.231895813526 0.238589343178 0.230870674099 0.185074856292 0.190966638679 0.196734232482 0.209858428647 0.203274864625 0.203041999473 0.197926990268 0.180506604609 0.191676192961 0.202741147438 0.201237653931 0.195771907735 0.19601488206 0.203403274574 0.179018659177 0.184172394714 0.206916175569 0.182062920029 0.225161143908 0.190059350101 0.215302655172 0.18583273214 0.22154130677 0.198918819466 0.186732254392 0.188033674574 0.200939126942 0.210648020541 0.203966819203 0.169461460201 0.19212652304 0.184698761261 0.185447917615 0.226382707341 0.210090272929 0.195464472523 0.194576182922 0.210724866996 0.188972223666 0.202749851185 0.205495551665 0.198311837225 0.191308228071 0.1827836447 0.199987032182 0.185758986102 0.192762378199 0.188912535548 0.170985151469 0.193136210612 0.250560354729 0.239150130043 0.235042618318 0.233086785485 0.243308731763 0.241203268173 0.2400598963 0.234273096316 0.244008938502 0.233948328457 0.219858019146 0.235053540533 0.231632132621 0.247968147104 0.252509320758 0.234318201257 0.225941055535 0.225341346975 0.261132458323 0.237859251815 0.23464919225 0.238863315744 0.244976238699 0.225403382703 0.228750181247 0.221868483944 0.221696049716 0.219446034661 0.243630311449 0.217754591911 0.235449762585 0.213055210612 0.246589228494 0.216230229788 0.232311179578 0.237695560547 0.239155145074 0.22460119101 0.218814227997 0.226159982187 0.240839111604 0.227492031494 0.239150130043 0.239069291581 0.240533107829 0.233959974366 0.236956995194 0.226761267667 0.234246750663 0.226212511867 0.0174000566092 0.0121264323023 0.0260599074483 0.0195345881682 0.024716388707 0.0269505563041 0.00951579401108 0.00952789620934 0.0312682326376 0.021380704298 0.0249359987416 0.0321633377276 0.0221499569656 0.0250465767598 0.00649156808823 0.035247677191 0.0178478945632 0.0452164673822 0.00796037757126 0.0457327534983 0.00806434689496 0.0382898633561 0.0225581753549 0.00212523780258 0.00635071079008 0.0188691425113 0.0279463141708 0.0242753243704 0.0170805625395 0.00904309726647 0.0106287996797 0.00382969063087 0.0441552932431 0.0411601284763 0.0356126616505 0.0116846154421 0.0333280322148 0.0231237862627 0.0194233884188 0.0266405905658 0.0193741592831 0.00647009737397 0.00313195595137 0.0192809490983 0.0198825976901 0.0184223649953 0.00589033700008 0.0274923472821 0.0151107135826 0.0780209291989 0.0597893086125 0.0526478480904 0.0519801851129 0.0640907142958 0.0582643284706 0.0646025548306 0.0523589334483 0.0615531482061 0.0598796956679 0.0449628875056 0.0527423072945 0.051324556652 0.0686409960047 0.0805660524071 0.0623345264313 0.0443847427682 0.0466830846559 0.0795542446923 0.0559362746494 0.057180756356 0.0643259703348 0.063211257271 0.0437185245372 0.0513548290636 0.0393714351609 0.0417127661704 0.0422926634719 0.0628873923745 0.0360959787877 0.0527047257983 0.0325588237609 0.0667976247829 0.0335624409497 0.0547114607912 0.0561052885987 0.069799650238 0.0449738444715 0.0432370258673 0.0470356297838 0.065014861143 0.055075271212 0.0597893086125 0.0609892947965 0.0676680770887 0.0593149961508 0.0554983088895 0.0479751568133 0.0662596895747 0.0497728280187 0.01649842894 0.0270940163297 0.0209738779605 0.0204474607028 0.0105739124512 0.0137437284214 0.0226997190651 0.0150100972387 0.0323011347894 0.008153166122 0.0475382748664 0.0187641297777 0.0164404692163 0.0144508320868 0.0210394349685 0.0305556358824 0.0486242638836 0.0214513889049 0.030175982789 0.0140964441541 0.0400575333251 0.0288561044949 0.0169609734513 0.0143755139823 0.0284025548343 0.0224203703593 0.013792641556 0.0304826758057 0.0219984307058 0.027438003111 0.0138498079587 0.0391014082904 0.0268826742795 0.0187520448645 0.0110691342578 0.0424853466078 0.0102534616754 0.0160596134656 0.0266767705353 0.0127998581546 0.0173245890268 0.0173211449901 0.0131739480428 0.0158792885006 0.00835247262611 0.012879803971 0.0267489620569 0.00596710410688 0.0651865992814 0.0501303259948 0.0467631748034 0.0452702235248 0.0544981935174 0.0559804285713 0.0526556031199 0.0527154428326 0.0609295585885 0.0469864956417 0.0318305696362 0.0467870360621 0.0428839020559 0.0596952123639 0.0682904935263 0.0490145651142 0.0373470202066 0.0372051258398 0.0783094346225 0.0573164765228 0.0464381891239 0.0518006344835 0.0637213875725 0.037328539174 0.0398159521041 0.0364375689163 0.0327311081772 0.0300536558581 0.0548465086523 0.039011355091 0.052932116237 0.0253006236285 0.0581288867478 0.0321437027692 0.0554616174667 0.0518535767409 0.0554912699649 0.0361633495108 0.0300500951908 0.0376868875379 0.053920841562 0.0443787428504 0.0501303259948 0.0504194914628 0.0549441033997 0.0487769599128 0.0505784847321 0.0377653392745 0.0513716565226 0.0379306609676 0.0148794236891 0.00906915259144 0.016817766784 0.022666965263 0.0181414830259 0.00943690730089 0.0254552863313 0.0160108606893 0.0217205062473 0.032481574081 0.0125336713193 0.0301048876528 0.0144114156753 0.0283145714635 0.0236499286545 0.0356994826174 0.00980454037789 0.0380382046842 0.0138535372004 0.0270349861178 0.0179310476065 0.0105970849446 0.0106446588122 0.0120133548138 0.0166102052911 0.0152998463146 0.0290596443937 0.00834773888805 0.0173051001714 0.012006228531 0.0322817993752 0.0342847484172 0.0334965405575 0.00578703582339 0.0267498608594 0.0233284219943 0.00836772170811 0.018341518653 0.0127097608473 0.00603575216863 0.0145919151911 0.0109073587446 0.0221533308504 0.0160929276419 0.00858765501382 0.0355326614102 0.0120318414802 0.067539995406 0.0482957753815 0.0406895564252 0.0402187129364 0.0525505335689 0.046337446086 0.0538706159523 0.0410532995186 0.049945680623 0.0498002070389 0.0356573113194 0.0408170339224 0.03998131445 0.0570983300794 0.0703114871106 0.0527515586 0.0326360388464 0.0357703215059 0.068007735392 0.0448145788863 0.0463929144792 0.0539072356938 0.0519007533329 0.0322898342044 0.0406511091494 0.027662402635 0.0308780067072 0.0322628111538 0.0510866561777 0.025649802677 0.0412344607513 0.0218269690426 0.0551778627391 0.0220763570226 0.0446670171533 0.0446820028935 0.060359178521 0.0338187065026 0.0337343667693 0.0362783001067 0.0544202026485 0.046225200341 0.0482957753815 0.049817335882 0.0576169439653 0.0494636606912 0.0440581542748 0.0371733017103 0.0573289834367 0.0394045266195 0.00716257307188 0.0232372547893 0.0296783885761 0.0325204811189 0.0212497773179 0.0292620976856 0.016439517739 0.0284506794787 0.0345599570635 0.018188478274 0.0407426749726 0.0271225123173 0.0318719827355 0.0364884226289 0.021914442716 0.0227786086069 0.0376078412936 0.0256329664506 0.0146652900265 0.0257412263731 0.0241649286356 0.0229439407062 0.0136364254073 0.0100256292567 0.0184950349026 0.0419365600148 0.0198805169881 0.0290470401931 0.0259349477967 0.0239003990661 0.037192775807 0.0407761513521 0.0178664543062 0.0194803036782 0.0346542430628 0.0113663417752 0.0228609184952 0.0221099611853 0.0198250112005 0.0281996969801 0.0190943935979 0.0354944682629 0.027502181016 0.0232700909093 0.0459502516894 0.0236639254111 0.0598327139142 0.0386582188727 0.0283018389706 0.0317793366202 0.0424462733589 0.0338166029949 0.0467225177273 0.0306128740578 0.0370882424143 0.0426365040044 0.0306518342203 0.028353389025 0.0289452185335 0.045605512774 0.0609187137277 0.0456096520871 0.0247062397964 0.0307047013286 0.0541889879817 0.0321986089116 0.0369056583991 0.0461635888574 0.0394191509814 0.0202672234717 0.0339543216254 0.0203331670034 0.0216962768111 0.0275014797571 0.0396207876643 0.019383687668 0.028134515225 0.0203542968498 0.0436399460151 0.0166979961449 0.0388212853573 0.0305366541923 0.0544557347145 0.0282093601187 0.0297076171269 0.0264388860156 0.0445799816631 0.0375526043804 0.0386582188727 0.0404030430489 0.0493940445937 0.0396161416365 0.0302144214958 0.0282464575757 0.0525063270042 0.0345543356833 0.0213910735206 0.0254312508458 0.0257342205184 0.0165221855109 0.0259539784544 0.0158835031429 0.0233831935041 0.0339323139257 0.0162106434865 0.03399674531 0.0200386549433 0.0299171917849 0.0320747556874 0.028046974659 0.0176794481153 0.0368388291758 0.0185008751091 0.0214981879903 0.0241691252685 0.0175651807134 0.0158907875652 0.0122369735355 0.0109776452166 0.015527622801 0.0354957768624 0.014819189185 0.0241388300803 0.019062303667 0.0287269405771 0.0357916006232 0.0364633531028 0.011219279972 0.0225748368915 0.0292557862917 0.00702356235419 0.0224830610185 0.0181511844266 0.0136555064616 0.0214280218685 0.0152776051594 0.0301845131917 0.0223495731118 0.0167956602264 0.0389756608983 0.0178914144765 0.0630604023291 0.0430080788574 0.0341031378994 0.0365519407683 0.0470199755056 0.0406269394348 0.0499352678431 0.0370580183618 0.0441324571638 0.0450148609009 0.0313792244576 0.0341228348643 0.0333031398497 0.0505703458181 0.0642434790604 0.0476293944045 0.0288843329998 0.0334940865056 0.0612862785232 0.0392274534904 0.040346557275 0.0490663667656 0.0464449738085 0.0249899515531 0.0366261362911 0.0249803957153 0.0245501998624 0.0286632518672 0.0448796558538 0.024023128213 0.0352285170514 0.0213779538928 0.0486983458625 0.0204530056806 0.043719713027 0.0366461987398 0.0563678077617 0.0312461294161 0.0303695774583 0.0296679302141 0.0480887950749 0.0392929320337 0.0430080788574 0.0443169978812 0.0521158245642 0.0424602914349 0.0361040078467 0.0312787145737 0.0538034695951 0.0366723303295 0.0189709615579 0.0257111384326 0.0217335533305 0.0223113040739 0.027967478664 0.0234852927562 0.0436322798908 0.00521461133699 0.0364760096592 0.0272343470762 0.018116669378 0.0277674536165 0.043201573857 0.0213285903305 0.0304538340544 0.0273512817022 0.0293661558134 0.0151289313008 0.0243064809516 0.0249356313294 0.024322988602 0.0196946095768 0.0139855052687 0.0405716245072 0.0223909261135 0.0287463352875 0.0239629151594 0.02510185342 0.0224608355139 0.0285849443101 0.0178309380123 0.0374526670612 0.0202528662781 0.0157931802813 0.00761690092841 0.00770880409418 0.0208073209186 0.0270724449013 0.00859765119326 0.0186885305588 0.0137210864523 0.0193875507074 0.0459800217652 0.015147695259 0.0594631949225 0.0420406539118 0.0381002519704 0.0316288068581 0.0464453358384 0.0430647918893 0.0450678160713 0.0372936406921 0.0478953066773 0.0442824164473 0.0325191406563 0.038458833697 0.0379877190085 0.0530105166244 0.0661049064219 0.0485790002934 0.0252530768184 0.0252616659632 0.0666194586026 0.0445179442461 0.0429682351205 0.0475934680999 0.0500384221554 0.032835448251 0.0334890161181 0.0210425012042 0.0304165560005 0.0269992218058 0.0464858418191 0.0236376116141 0.0409389296383 0.0119097107153 0.0510342004912 0.0165862044563 0.0364051705359 0.0459765436849 0.0532158463913 0.0240697227805 0.0285944307415 0.0355069812685 0.0512442268801 0.0481793211849 0.0420406539118 0.0442662890139 0.05312949223 0.0494910921385 0.04499219501 0.0346009573898 0.0502278687979 0.0300299308142 0.0232712606741 0.0301762988443 0.00654363258115 0.0383536762695 0.0073760670349 0.0549696818035 0.0187358469678 0.0230006125585 0.0248620926321 0.0112006081505 0.0374302122761 0.0507791829944 0.0291552453818 0.0203658790931 0.0243768337268 0.0405838171216 0.0318523232399 0.0264398107213 0.0241549255092 0.0342108755533 0.021966370396 0.0115756992435 0.0406700397742 0.029726336077 0.0360763557135 0.0237708872008 0.0347952708329 0.0167090325333 0.0113314779127 0.0183460384347 0.0474972085699 0.0106130759732 0.0188803713912 0.0264255690706 0.0134988723184 0.025504997589 0.027405254367 0.0141844800377 0.0190580694839 0.0107985492476 0.0215712414492 0.0353201768965 0.0121413245456 0.0563908913649 0.043304104353 0.0426898087169 0.0394771848203 0.0476531796683 0.0529352282385 0.044079995111 0.0508430194559 0.0587105683651 0.0390993709526 0.0249779507582 0.042747127932 0.0380731496814 0.053623295371 0.0609715530482 0.0414798961609 0.0320700568798 0.0299307088952 0.0756552859532 0.0564187262812 0.0401315540974 0.0437426630802 0.0619393205223 0.0343990170803 0.0321000148697 0.0335451545545 0.0286757976711 0.0228629245983 0.0490706959168 0.039335399235 0.0517108395757 0.0207931533413 0.0521719534959 0.0306385429933 0.0529657791748 0.0494073245643 0.0465244230409 0.0294935309982 0.022387741137 0.033135656028 0.0474637239198 0.0404408008061 0.043304104353 0.0434045699957 0.0474449484031 0.0438694244062 0.0478136370998 0.032205230585 0.0422362620829 0.0295056722481 0.0179188644218 0.028584000913 0.0304649482776 0.0216593575612 0.0411663680687 0.024518088592 0.0182283990095 0.00873472135302 0.0327024039756 0.0205661418233 0.0530659348987 0.0158790060301 0.0434559310925 0.0108179884547 0.0451149656888 0.0268976993783 0.0108384529361 0.0115766662184 0.0275567255166 0.0316971869351 0.0249562865626 0.0177154908046 0.0178955252093 0.0182906823384 0.00749636648732 0.0477714342488 0.038203551745 0.0295629098692 0.0151014852938 0.0424264199032 0.016651504005 0.0233361298106 0.0296064297332 0.0193940010564 0.0143559792056 0.00822362335298 0.0204472045407 0.0141062231631 0.0150969431722 0.00961358010122 0.0230069897016 0.0136344497691 0.0781873312389 0.061922358127 0.0567947708263 0.0549074799203 0.0663268940474 0.0637272258979 0.0651276548535 0.0583171690756 0.0677292446726 0.060191106251 0.0450946120505 0.0568830058441 0.0543288790766 0.0714589426004 0.081454303397 0.0624243269582 0.0472314410611 0.0479124253475 0.0858307227677 0.0627914211132 0.0589518720089 0.0648453559762 0.0697319385158 0.0476614784634 0.0522209851297 0.0435983831723 0.0443141443319 0.0427834724857 0.0660408790701 0.0423473900154 0.0592034189384 0.034371623803 0.0697342027164 0.0381294864628 0.0595840773258 0.0612390496755 0.0689539919437 0.0466415215244 0.0431628346368 0.0495434525796 0.0666477691564 0.0566659496435 0.061922358127 0.062735676927 0.0682003243846 0.0612178236271 0.0603270178223 0.0499653486319 0.0649071391116 0.0500436367076 0.0339571590708 0.013301666182 0.0294613107742 0.0254168369791 0.0187830848109 0.0337456927151 0.0151760493477 0.0360233288761 0.0161149469416 0.039042024755 0.00229536147376 0.0468249873675 0.0157583667263 0.0310761119725 0.0155497505138 0.0090301180649 0.0126416375855 0.0110159110748 0.0254616235228 0.0242814349621 0.0236690592627 0.00236651040215 0.00842631126501 0.0122777482071 0.0387720000407 0.041740632348 0.0402615455592 0.013434165421 0.0259957062278 0.0279382933191 0.0176919201528 0.0210030594474 0.0190515742522 0.00605687883869 0.0125440326934 0.0183812478057 0.0234838433483 0.0213287295776 0.0102611349583 0.0367608397052 0.018305882063 0.0762459812633 0.0565232340764 0.0480647092458 0.0470080033305 0.0607210126742 0.0515562009886 0.0623134210102 0.0445281266442 0.0542656274802 0.0589172543408 0.0450145677229 0.0482495838943 0.0484339717661 0.0652417317109 0.0793204584838 0.0620264746962 0.0400173133601 0.0433798691859 0.0725797896424 0.0481813389954 0.055284998644 0.0628522019597 0.0554630904838 0.0404443368976 0.0493602212467 0.0332419109523 0.0398075629785 0.0413677301877 0.0589057964055 0.0278286603835 0.0454781228443 0.029186418008 0.0632373035893 0.027173160002 0.0466041993476 0.0516248397682 0.0694549695494 0.0414073101396 0.0429302830553 0.0451579600752 0.0633364240737 0.0552241510377 0.0565232340764 0.0584051969715 0.0667610558404 0.0584355181495 0.0512925402879 0.046128531904 0.0664605075068 0.0478081299752 0.0403784266437 0.00849189377523 0.0578244327435 0.0213782060591 0.0260735654855 0.0286868543518 0.0107633182991 0.0429130184438 0.049112317265 0.0332704837119 0.015321761445 0.0277153223765 0.0397499694056 0.0358925712782 0.0304111104426 0.027505083584 0.0362377250231 0.020296537393 0.0117771620381 0.0454349567531 0.033203078919 0.0404550655622 0.028050968973 0.0329121522515 0.0157822820327 0.0128529655968 0.0212960295437 0.0481179953609 0.0170390332384 0.0197624290813 0.029414246654 0.0176689832365 0.0290804953813 0.0317312604538 0.0173770193477 0.0255273516204 0.0167054579942 0.0259572392414 0.0381174742782 0.0168503391466 0.0510138836142 0.0385473731826 0.0388016187658 0.0368178557203 0.04276997017 0.0503778656709 0.0392641023806 0.0497809957465 0.0564266766196 0.0332095636962 0.0188695135337 0.0387787813789 0.0331502464834 0.0483979502244 0.0548055143427 0.035228159998 0.0294466884888 0.0272290139414 0.0724784935896 0.0548011964122 0.0344489111929 0.0380672364964 0.060026483594 0.0303284751736 0.0272545178904 0.0327801045139 0.0239410636139 0.0177611799925 0.0444007657806 0.0400513163792 0.0497786040376 0.0204852863736 0.0470867569115 0.0308376722615 0.0532970554221 0.0452748414675 0.0407821021663 0.0268849282918 0.0168086928609 0.0279020988959 0.0414910752652 0.0343097482389 0.0385473731826 0.0381367753126 0.0413101510581 0.0377465227401 0.0435171810289 0.0268955341281 0.0365057689544 0.0254190380854 0.0372013435687 0.018490846954 0.023945912793 0.0440067159299 0.0253810829497 0.0423576975539 0.0266775910883 0.0276067076791 0.0155964706338 0.0512008225103 0.0249293141549 0.0224091031216 0.0208182862063 0.0201027144741 0.0218258381078 0.00416795120123 0.0250523436876 0.0291897433081 0.0336104473732 0.0126474260294 0.0179897427224 0.0236687976248 0.0357767564718 0.0479080621505 0.0493839683144 0.0213027411218 0.0127962715487 0.0389577422313 0.0207982711984 0.0248766740175 0.0272195414617 0.0164396805465 0.0239043019084 0.0253433507519 0.0357454256498 0.0316012688091 0.0218891275488 0.046006369731 0.0279699723289 0.0753592714385 0.0538300830176 0.0427234551893 0.0442120918411 0.0575724115573 0.0438794711912 0.0617203772126 0.0368175055007 0.0452394206226 0.0586608041232 0.0462735571883 0.0428932945735 0.0449361857949 0.0608531076963 0.0771129194067 0.0618336393757 0.0380658785095 0.0436894906412 0.0627047512052 0.0382553066725 0.0531191059331 0.0620656917263 0.0459127944105 0.0362899248016 0.0492167826913 0.0301637294202 0.0380460619309 0.0426550687382 0.0544072702012 0.0218332584664 0.0362322493618 0.031226533309 0.0587827461321 0.024852970378 0.0417700516252 0.0442659791406 0.0702283886386 0.0412678777108 0.0447863140602 0.0428258891548 0.0608093994885 0.0534400200237 0.0538300830176 0.0561072465285 0.0656581075266 0.0558523559264 0.0444818994194 0.044500683531 0.0680831562837 0.0488303143353 0.0536500814259 0.0217777943298 0.0182308184172 0.0213271967089 0.017640751735 0.0384313065823 0.0491331799987 0.0285490579199 0.0236508921207 0.0203518801552 0.0410544418794 0.0345949638149 0.024163451076 0.0208164937907 0.0331698057229 0.0214370208347 0.0129093004816 0.0378630437774 0.0285080143021 0.0349561724295 0.0213643314537 0.0378207671811 0.023156173716 0.0144257922467 0.0164328495191 0.0458898771597 0.0137078716838 0.0180512548912 0.0303444429836 0.0166949845236 0.0239375979831 0.0248141920509 0.0164642247089 0.0221495310709 0.0136759772653 0.0203492925476 0.0297801495496 0.012327304817 0.0589145352863 0.0454329273476 0.0436909431618 0.0429329583507 0.0496681092117 0.0546717056664 0.0472190839202 0.0531335572006 0.0601545762173 0.0404993410456 0.025351107679 0.0436326686228 0.0384109589864 0.0546942484238 0.0614849395874 0.0420121432296 0.0350678306139 0.034181943564 0.076607832627 0.0574938051354 0.0407284888751 0.0455392391661 0.0634559846389 0.0340449392101 0.0346886110004 0.0365094436091 0.0283668149646 0.0247441178114 0.0505723350307 0.0414268809452 0.0526572068528 0.0247442404176 0.0533283030157 0.0333480302269 0.0569330422551 0.0488445949836 0.0485166318417 0.0334202567015 0.0241088713298 0.0327605074387 0.0477602460683 0.037977730422 0.0454329273476 0.0450703466816 0.0481686005759 0.0425586735361 0.0473020173719 0.0326189496433 0.0442268465034 0.033265894657 0.0406164099076 0.0563656658624 0.0369501005762 0.0596090654503 0.0304778147307 0.0384204581231 0.0270915625525 0.0693260105947 0.0372136698768 0.037023479673 0.0317062741776 0.0317712182279 0.034804368211 0.0223060266021 0.0435100417127 0.047004084469 0.0362329082931 0.0256397756303 0.0233719185841 0.035627421568 0.0521858971919 0.0650552610656 0.0654396577163 0.0372909018916 0.0208395075735 0.0532980934411 0.0387978654626 0.0393043110859 0.0433091405548 0.0302415541747 0.0340064333643 0.0421117035102 0.0478257550421 0.0466283489206 0.035247716101 0.054937797688 0.0434605477125 0.0930034854114 0.0710598303107 0.0590352084097 0.0604460830393 0.0745555173324 0.0571183440581 0.0793617023165 0.0484952151811 0.0566053521726 0.0768259736839 0.0646780127707 0.0592344115045 0.0623544010763 0.0773513341111 0.0945306112415 0.0799933849368 0.0552426925494 0.0610368934489 0.0726391747026 0.0486086004123 0.0708765879781 0.0800292786375 0.0558578611463 0.0537011940437 0.0672777153371 0.0461852109805 0.0561622363977 0.0610513630375 0.0708198771797 0.0350965268909 0.0484124521051 0.0486964562823 0.075256407672 0.04111323881 0.0515712020719 0.0592740141065 0.0884497661149 0.0586350703024 0.0632023881374 0.0606979638812 0.078320587954 0.0707259147977 0.0710598303107 0.0736338137855 0.0835800603427 0.0732101393079 0.0599496433571 0.0625510181289 0.0864139348042 0.0667637091982 0.0350728110176 0.0244007367668 0.0194418255357 0.0278046770383 0.0387260088041 0.0187288873365 0.0305873095592 0.0241694671072 0.0257451620145 0.0153815378858 0.021353060898 0.0215496497445 0.0200984831417 0.0151985245998 0.0110228413253 0.0387908454863 0.0190085239065 0.026530841158 0.0213969993651 0.0238500818663 0.0245406806879 0.0294364678495 0.0140120927131 0.0331219747793 0.0211978333105 0.0108265764026 0.00897607251111 0.00677241917437 0.0173770286262 0.0245814114194 0.00563326172159 0.0204952689406 0.0138047466575 0.0168569359655 0.0438273371107 0.0133334601758 0.0592456323654 0.0408071286879 0.0355509485261 0.031081872872 0.0452073820968 0.0410054777303 0.04495248578 0.0356498623047 0.0456510923283 0.0431383529174 0.0306345228853 0.0358468154266 0.0353265194622 0.0511623196634 0.0646276367384 0.0471382277373 0.0240229200194 0.0254553208119 0.0642596486532 0.0418982548195 0.0409421092482 0.0466753277343 0.0478887558589 0.0293373407147 0.0326161841904 0.0195735225778 0.0272641873276 0.0254981680323 0.0447040552558 0.0215611560931 0.0381253844376 0.0111199895155 0.0491745036263 0.0145691145727 0.0368088577033 0.0425091408325 0.0528722213324 0.0238501784613 0.0272505069941 0.0325614342759 0.0492627638247 0.0449950723976 0.0408071286879 0.042880253884 0.0517160325388 0.0466599102029 0.0415966376912 0.0321745745193 0.0499813496989 0.030016506883 0.0195067443548 0.034042657808 0.0387050626348 0.0607833234632 0.0322025711177 0.0402462250605 0.019510297358 0.0548235012014 0.0429595771314 0.0252279466187 0.0223273919125 0.0407231129516 0.0368354815518 0.0293953937656 0.0290463736365 0.0328461116776 0.035143771121 0.0215923461726 0.0549385181088 0.0388660143681 0.023623079783 0.0246479181765 0.0539903453268 0.0196033552608 0.0309786977565 0.0425462811063 0.0289476729073 0.0283702078661 0.0228452205014 0.0295421719187 0.0252210967901 0.0230772437036 0.0240590413191 0.0135013916275 0.0218148808282 0.0760114892469 0.0634029220478 0.0607879784498 0.0607163777171 0.0675569624906 0.0711706523576 0.0650651993965 0.0685468614076 0.0760185520266 0.0575507664821 0.0425210776149 0.0606855343213 0.0555229333703 0.0720565340813 0.0774071503121 0.0581416488422 0.0527363337967 0.0521637490438 0.0926239124524 0.0722776295267 0.0578783465588 0.0627903946651 0.0789206671037 0.0503951188312 0.0527646858335 0.0526096570134 0.04527903594 0.0427992547678 0.0682205850017 0.0547141937568 0.067807383273 0.0414244261778 0.0707718291801 0.0483573376556 0.0717861683592 0.0644987372167 0.0647006499463 0.0513517515126 0.0419564687506 0.0495341433982 0.064364362053 0.0518587226049 0.0634029220478 0.0627776825589 0.0645131806296 0.0578143875392 0.0631581345522 0.0499505499876 0.0600491597535 0.0513712453732 0.034450057717 0.0238219725869 0.0465207238958 0.0139550218762 0.0437148052069 0.00245848843359 0.0406705408218 0.027996084796 0.00617634742576 0.0042140797155 0.0226171443798 0.0276920922451 0.023535776763 0.0178395839466 0.0140356715542 0.0166022608919 0.00395476408572 0.0457707350729 0.0404789526881 0.0327219703849 0.0114454148489 0.0362680793242 0.0217463268752 0.0196365744138 0.030477863462 0.0206103562172 0.0102714385383 0.00460445870022 0.0203218526125 0.0212018784388 0.0183586998171 0.00829854248447 0.0218572079865 0.0144443422105 0.0767877803745 0.0594466868469 0.0529468255635 0.0532520366965 0.0637146030163 0.0600960587419 0.0639510014921 0.0553587219012 0.0637257572972 0.0581054381403 0.0427611569751 0.0529559974475 0.050488809742 0.0679805704334 0.0784814462329 0.0599903091584 0.0453120703317 0.0472956572051 0.081207762841 0.0585100350392 0.0557351056658 0.0628545029747 0.0657746195642 0.0431635890019 0.0505766161122 0.0416755044323 0.0404647152471 0.0409948112567 0.0627553433594 0.0398795409571 0.0548556307871 0.0337749376373 0.0662751646447 0.0362650420971 0.058753352182 0.0559719307576 0.0677796526261 0.0456851568449 0.0415580901724 0.0455930571155 0.0632638085815 0.0519953365762 0.0594466868469 0.0601182129397 0.0655928087235 0.0568949371894 0.0552051980285 0.0466604473635 0.0640269526764 0.0493532376221 0.0428114080859 0.0513187599237 0.0353300715155 0.0136841990953 0.0339659047401 0.0392151216672 0.0331598693815 0.0346578423583 0.0330024342773 0.038276947888 0.0230224626975 0.0145213724276 0.0501490398304 0.0358866478447 0.0428680377048 0.0326728263648 0.0285835471973 0.0062386073831 0.0158573158736 0.0257280664001 0.0503050577684 0.0188536314021 0.0230369129683 0.0250256284162 0.0171680840121 0.0324912467925 0.0363869094834 0.0177612063031 0.0250245943265 0.0179627656197 0.0294197347692 0.0465091219099 0.0203095862842 0.0478795030574 0.0357982238192 0.0376241763265 0.0315623347361 0.0401002610089 0.0474897864671 0.0350582543035 0.0460362585126 0.0538318698513 0.0325365856585 0.0213227378076 0.0377990397126 0.0335693240845 0.0471274543161 0.054885621667 0.0360897261843 0.0253548565171 0.0209548211511 0.0704293001167 0.0527939534511 0.0346935966786 0.0364130601336 0.0572327947958 0.0318390512051 0.0244040617426 0.0286768378156 0.0261566126157 0.0173773142125 0.0423900706098 0.037171182215 0.0481110975007 0.0159454061909 0.0456669379702 0.0274665151098 0.0469607798152 0.0462908705234 0.039158298024 0.0212460794033 0.0171141394847 0.0299475002492 0.0420369583583 0.0395076063195 0.0357982238192 0.0363456286371 0.0414629362584 0.0409765644808 0.0445581081298 0.027819794971 0.035217696192 0.0204081888775 0.0536229831874 0.0147412411033 0.0555896434003 0.0257773616784 0.0440411962897 0.0177255540439 0.0193829905731 0.0239978426404 0.0256940264499 0.0393410530504 0.0352411367288 0.0221731284836 0.0182277280594 0.0123391761488 0.020654487001 0.0493059373458 0.0475681206642 0.0451873269558 0.025880190654 0.0390288508413 0.0309214453223 0.0313196281309 0.0264802426158 0.0266703711652 0.0198006836859 0.019520741807 0.0278514155812 0.0221034761072 0.026690732588 0.0196134707881 0.0412756734602 0.0265178671443 0.0867397306197 0.0682110021203 0.0611304199736 0.0570363597121 0.0725160802101 0.0632079822129 0.072465417084 0.0544582268199 0.0658094706322 0.0704919404627 0.0569226677141 0.0614294730407 0.0618668588016 0.0780403185985 0.0918397178914 0.0740264863472 0.0508436174709 0.0525989509117 0.0847012354501 0.0597190404435 0.0681089165817 0.0742388101657 0.0664380426823 0.0545769816583 0.0603124830305 0.0434291774759 0.0533790593767 0.0526730162139 0.07136820435 0.037381607524 0.0577096830472 0.0386410478706 0.0759869972218 0.0373998132392 0.052181569916 0.0660810261842 0.0798969360666 0.0510826914494 0.0540234946407 0.0588039304452 0.0763291167757 0.0694007087698 0.0682110021203 0.0704124305268 0.0789271737863 0.0724038719993 0.0657040131714 0.0590995697111 0.0765722724394 0.0574793382897 0.0411299322154 0.0539204969946 0.0448971315706 0.0161163886115 0.0424587591617 0.0433134103038 0.0424269061508 0.0282464178608 0.029088245744 0.0394507490162 0.0585960215177 0.0377541724349 0.0451043153526 0.0458829699472 0.0321616028135 0.0557124503126 0.0614547711288 0.0392057260171 0.0177559051904 0.0564954090624 0.0332181102648 0.0405454141429 0.0436595776199 0.0393478274847 0.0472383546112 0.0407140770645 0.0569892712332 0.049354627832 0.0439022116296 0.0637553975882 0.0455099918129 0.0646100490562 0.0426539647843 0.028431151743 0.0378227253234 0.0447657124399 0.0278050988657 0.0537117960824 0.0274550112394 0.027109787916 0.0505464931112 0.0435971104256 0.0283504838988 0.0329648689469 0.0443909167392 0.0625701957077 0.0530229796398 0.0343871989004 0.0426240079793 0.0396498771349 0.0213457948378 0.0419360502864 0.0525916757896 0.0283713421426 0.0258314536376 0.0439902997437 0.0303827915475 0.0319578433558 0.0415135400801 0.039276736137 0.0267873256159 0.0192565547583 0.03784586336 0.0425882783228 0.0300770949457 0.0411024644378 0.0235206108502 0.0627241786637 0.0400442811976 0.0439864971889 0.0336917004451 0.0474026229045 0.0427127785344 0.0426539647843 0.0447428873557 0.0545498150062 0.042821329639 0.024917555179 0.03656154349 0.0624685036836 0.0466134489709 0.0464211898017 0.0148261628982 0.0328892390973 0.0156382282878 0.00785541359441 0.0118772597644 0.013233811524 0.0262299374219 0.024092235964 0.0223052815816 0.00384479195153 0.00794260597132 0.0107672255318 0.0397240757159 0.0410257993957 0.038935751255 0.0130110302225 0.0282819504394 0.0262547979726 0.0181226466418 0.0211289335782 0.0182692961983 0.00567846812613 0.0110316054291 0.0179271098732 0.0214946645806 0.0199268176637 0.00875561948865 0.0354271933282 0.0171443662393 0.0766374045646 0.0572939392451 0.0492966948245 0.0478544225205 0.061544844174 0.0531089657897 0.0627070970919 0.0461191081801 0.0559975469422 0.0592703107177 0.045198111152 0.0494826907565 0.0493817865713 0.0662451441692 0.0799247658409 0.0623534855885 0.040791427856 0.0437433118438 0.0743922416686 0.0500539193261 0.0559768175635 0.0632764008918 0.057263588801 0.0415573436738 0.0497522962913 0.0342757495228 0.040553840562 0.0415809282216 0.0599519771943 0.0293556022635 0.0472716928172 0.029440558171 0.0642586667075 0.0282006776448 0.047766217977 0.0531327322745 0.069584244341 0.041867047458 0.0430265316703 0.045945636772 0.0640480645613 0.0558532996544 0.0572939392451 0.0591020248311 0.0672221887664 0.0591786574244 0.0527229760336 0.0467939943149 0.066452337221 0.048010057481 0.0426662887331 0.0439949614302 0.0453715955589 0.0447907897725 0.0421151539334 0.0471958607455 0.0276193449237 0.02275846799 0.0605343055308 0.046215555164 0.0540603153716 0.0428037480825 0.0313795261955 0.0125172459763 0.0208290281635 0.0350621941996 0.0569552641601 0.0299458792012 0.0309610087229 0.0366049420462 0.0292005950568 0.0426658539169 0.046550389751 0.0288402250941 0.037809939324 0.0299619998536 0.0401350153602 0.0525912191682 0.0310232912468 0.0370292782789 0.029362531619 0.0351405044644 0.0315813022426 0.0330583005053 0.0477931213785 0.0264557013575 0.0497838425895 0.0546676669155 0.0214340498054 0.0132666230167 0.0351245595911 0.0284887636401 0.0394555006358 0.0430107543153 0.024001610215 0.0265746605534 0.0212961513987 0.0686985381679 0.055308045978 0.0261438582005 0.0256904480148 0.0588231449389 0.030013743601 0.0177237336601 0.0340579413817 0.023239083403 0.0129096702298 0.0366510262704 0.0448114548715 0.0501265325324 0.0238298010541 0.0385248366721 0.0348349791307 0.0531258509004 0.0432244806886 0.0265717263897 0.0222990802833 0.0105547424712 0.0247433535936 0.0319856839059 0.0308056274733 0.029362531619 0.0282110860995 0.0297242114734 0.0317000113527 0.0411003820059 0.0219347750579 0.022168792283 0.0155469808263 0.0395024062804 0.0287670845389 0.00713435108734 0.00340555052012 0.0220748170877 0.0261325674803 0.0225086838709 0.0197301659136 0.0143163055062 0.0179547860371 0.00552830002832 0.044759241534 0.0400746442935 0.0324889323762 0.0105741309237 0.0352991764051 0.0223168808509 0.0183878728912 0.0306904112299 0.0206021475442 0.0104104939928 0.00670538845305 0.0199820984844 0.0226302237702 0.0187233601872 0.00907856174289 0.0219615297998 0.0143637742128 0.0753337380263 0.0579434032885 0.0513377916805 0.0521965044751 0.0621695045027 0.0588078748161 0.0626419440897 0.0544695113035 0.0624620281749 0.0565017669041 0.0411290585403 0.0513195597187 0.0487054775012 0.0662358469866 0.0766400648909 0.0582604755345 0.0441853596565 0.0463597810918 0.0797172873623 0.0573362873289 0.0539615186802 0.0612879452506 0.064613107429 0.0413483548752 0.0492060616579 0.0408536005927 0.0386658835072 0.0395835329499 0.0611338973657 0.0393645393569 0.053572584104 0.0330686361703 0.0645571164168 0.0355933171917 0.0584689678276 0.0540828717859 0.066313152361 0.0447064134304 0.0401381607957 0.0437269026711 0.0614075519202 0.0498319714223 0.0579434032885 0.0585128670581 0.0638402708268 0.0548281142484 0.0533071471302 0.0449195692472 0.0626153729552 0.0482343239424 0.0295703841711 0.0366619395227 0.036500768924 0.0216100790796 0.0202086133386 0.0291081498137 0.0538445159183 0.0304641791012 0.0384602037434 0.0388505887816 0.0184939827468 0.0431509831641 0.0514497599393 0.0311190761962 0.0193480902941 0.0458820296239 0.0240997287473 0.0258856149523 0.0318135695083 0.0318488388141 0.0408906139312 0.0292269134036 0.0455476970026 0.0384416242221 0.0355459730354 0.0604270677753 0.0357756191383 0.0579552498509 0.0353240752803 0.0225716123758 0.0257247517645 0.0383804782921 0.0215488097029 0.044948702922 0.0168022708851 0.0235401078242 0.0440963094143 0.0364932029322 0.0228797040221 0.0280879909327 0.0410984380147 0.0600604954457 0.0480533666552 0.0218775898923 0.029791578282 0.04158778439 0.0178882014032 0.0370867411707 0.0460836281119 0.0252155578724 0.0208740877353 0.0346851323104 0.0150019768994 0.0257835645737 0.0322493089661 0.0342156649906 0.0116183721538 0.0144931943243 0.0240188836176 0.0389068297986 0.0144502424291 0.0276541092905 0.0250061652639 0.0558771163058 0.0271661677907 0.0351271284352 0.0289684152137 0.0442230773604 0.0424650907505 0.0353240752803 0.0384614305736 0.0501641618566 0.0418657156844 0.0254781137454 0.0303709238998 0.055175185082 0.0355533707256 0.0226236551837 0.0257243733887 0.0188708962976 0.0280458447088 0.0259495603965 0.0358126700891 0.0173972925493 0.0203550537734 0.0241984077655 0.0326658712857 0.0374251933899 0.0415458358848 0.0220997016335 0.0314739029711 0.0297599572502 0.0224642058136 0.0101652715813 0.0184552115135 0.0190795074292 0.0255522841638 0.0188568594492 0.0233889500734 0.0229626929389 0.0201746110209 0.0490887418108 0.0229752614583 0.0721999639917 0.0528489482775 0.0457665690796 0.0402580156331 0.0570067559398 0.0466251225309 0.0575932728503 0.0376765605581 0.0495760090258 0.0573857799099 0.0457294834507 0.0461836275015 0.0478961825174 0.0628300939828 0.0782978077723 0.0617431168606 0.0349090738753 0.0372003973766 0.0687801137491 0.0442160827041 0.0545072221083 0.0604041835816 0.0503536267186 0.0412302683921 0.0462335606677 0.0265634879228 0.0409252862943 0.0403112510912 0.055713517152 0.0210444978159 0.042126696634 0.0241765196843 0.060672423249 0.020816933598 0.0350778856202 0.052005350197 0.067095717986 0.0355534496705 0.0422916670752 0.0461036815572 0.0628103650139 0.0591144676161 0.0528489482775 0.055715116829 0.0658580956838 0.0605104799512 0.0516473901424 0.0459913703308 0.0644951804376 0.0433395766961 0.00476396676703 0.0174479971383 0.0262117873028 0.0230803954362 0.0185627883328 0.00806620839217 0.0113814933623 0.00390319674762 0.0428177321428 0.0406615445683 0.0354624532828 0.0102263088249 0.0317971018832 0.0234656694869 0.0177926588758 0.0262276626577 0.0188776295133 0.00499606040594 0.00424685036617 0.0184558479833 0.02090066744 0.0184013332257 0.00554993882929 0.0277916235291 0.0146255258466 0.0766584518429 0.0582536091399 0.0509151171955 0.0506702158494 0.0625265075224 0.0566765790267 0.063296553303 0.0510181236531 0.0599592936046 0.0584198089247 0.0435074153396 0.0509940717271 0.0495661398107 0.0669266401341 0.0789376730305 0.0608280979649 0.0430167703447 0.0455496571569 0.0778333721022 0.054368608181 0.0555188693765 0.0628704456678 0.0616793859568 0.0418722056261 0.0499801315934 0.0381178178645 0.0399704213931 0.0409195428074 0.0612188756554 0.0349680364553 0.0510653686933 0.0315069690942 0.065089713199 0.0323745393338 0.0538917621299 0.0541667478451 0.0685063182149 0.0437802642661 0.0419063387522 0.0452698567868 0.063322234724 0.0532615551019 0.0582536091399 0.0594215850469 0.0661130238288 0.0575059193505 0.0535703049783 0.046311202915 0.0650382074097 0.0485914933238 0.018842155525 0.0240370427448 0.0209528738511 0.0208089534878 0.0111203801188 0.015917150662 0.00453488271341 0.0421322819802 0.0391458330884 0.0331083714381 0.00824403626512 0.0325132311882 0.0224847085725 0.015999516676 0.0277730715816 0.0186363801442 0.00708833477439 0.00642676690132 0.0178233234532 0.0220593051536 0.0178006297585 0.00678573265748 0.0253531080362 0.0132629896024 0.0741187095157 0.0561450172019 0.0491350925935 0.0497514221438 0.060382347286 0.0560370322524 0.0611402268324 0.051356658754 0.059586423134 0.0554899035837 0.04031824984 0.0491508161478 0.0470289167752 0.0645318276086 0.0757421493114 0.057544117742 0.0418388253062 0.0443100927117 0.0770487593095 0.0543425693403 0.0526585588221 0.0601234502371 0.0616298008584 0.039475560154 0.0476974484773 0.0379820027229 0.0371681460661 0.0383173623723 0.0591794106435 0.0360653613059 0.0506856040282 0.0307481929538 0.0627839311984 0.0325910275487 0.055237903563 0.0520576344331 0.0655568890209 0.042566036842 0.0391094232808 0.0423459347432 0.0602723002528 0.0493981873195 0.0561450172019 0.0569656980973 0.0629553672806 0.0540102942581 0.0513493159685 0.0435185067862 0.0620208318713 0.0466647868495 0.0214518595921 0.0250574648372 0.0327035392179 0.0101396500324 0.0173409936704 0.0207342919251 0.0332931627481 0.0438902497615 0.0452760516117 0.0173962021214 0.0155474932413 0.035097178823 0.0167075078514 0.0219356609327 0.0232161362242 0.0132718217012 0.0215035441799 0.0212520758854 0.0323731403418 0.0276797560909 0.0185474301296 0.0436301136913 0.0240030156204 0.0722858985812 0.0511085237891 0.0406650357841 0.0417405611462 0.0549851372416 0.0429543701734 0.0585838550979 0.0363293620395 0.0449605458738 0.0553495140321 0.0426335340052 0.0408304262584 0.0422933209142 0.0585800490211 0.0742997883793 0.058525226517 0.035206446299 0.0404435198283 0.0627381786217 0.038449263747 0.0501920171677 0.058888502058 0.0460241869732 0.0337463818569 0.0458812434451 0.0278046680998 0.0349131438742 0.0389869853015 0.0521722266345 0.0209043609517 0.0359181281747 0.0276204608178 0.056526179998 0.0222941226577 0.041152359144 0.0428797366789 0.0667722352073 0.0380782189511 0.0410414680557 0.0398735425148 0.0580112990116 0.0505852294836 0.0511085237891 0.0532571789631 0.0625496034306 0.0531021220011 0.042888351609 0.0413906647006 0.0644729984699 0.0453457651066 0.0107504885543 0.0445819304752 0.0242663168605 0.0336280858548 0.0265479358211 0.0206001571473 0.0282052030653 0.0325495864256 0.0168933846577 0.0294835383052 0.0291726320507 0.0086711740192 0.0221542204142 0.0179304565478 0.022368606911 0.0297212987281 0.0149126627655 0.0323363820236 0.0229519593576 0.0235384954017 0.0445361789109 0.0198724736323 0.0521573964273 0.0325036248406 0.025391360945 0.0274230805005 0.0366454630316 0.0344942582431 0.0390192491133 0.0331598239249 0.0393605141281 0.0342759827638 0.021260474927 0.0254073365966 0.0234580906639 0.0407813070352 0.054069225305 0.037293221414 0.0193616327469 0.0235535155332 0.0561408138747 0.0363119747552 0.0299684113953 0.0382284111131 0.0425406057591 0.0161350959097 0.0256650379468 0.0189027762255 0.0143744986934 0.0179840539103 0.0351859317755 0.0236434364907 0.0314452790447 0.0139598707264 0.0389431870684 0.016485382228 0.040355084472 0.0299825744277 0.0456481354326 0.0213868007101 0.0199817088813 0.0197684998025 0.0379808456048 0.0313521152981 0.0325036248406 0.0336827412775 0.0416843797906 0.0334784161986 0.0289224474022 0.0207816321212 0.0432746333929 0.0259935830061 0.0406061367838 0.0235468720047 0.0318323205013 0.0219961670771 0.0255808203638 0.0205571609912 0.0226267329863 0.013012280745 0.0369778751619 0.0187789289183 0.00874641080149 0.0197571101094 0.00977736627462 0.0202312308031 0.0257037864622 0.00771970434192 0.0231557657352 0.0133099999073 0.0187324692629 0.0392432332024 0.0115155558073 0.0539655210169 0.0370438046358 0.0333786958054 0.0316409822149 0.0414621149977 0.0426991016561 0.0406517401284 0.0403678402162 0.0480644681966 0.0361424947464 0.0218113942904 0.0334641673727 0.0302365918541 0.0468956611505 0.0577004528593 0.0391757625612 0.0236163775334 0.0242576354959 0.0653230280715 0.0452817250312 0.0345762397733 0.0404850805623 0.0511603238121 0.024886725311 0.0274569982987 0.0236904007661 0.0206352481789 0.0182697542476 0.041544522984 0.0289437440201 0.0406091261712 0.0126309522282 0.0451825068417 0.020490830418 0.0442568377481 0.0395946754396 0.0458654268305 0.0228937718071 0.0192636370802 0.0258703030154 0.0425840964507 0.0360074541747 0.0370438046358 0.0378930233244 0.0444471390747 0.0387351356827 0.0382610925586 0.0256300024801 0.0425154846229 0.0258478343265 0.0236633695184 0.0174990292841 0.0186530571476 0.0611247172591 0.0557656094279 0.0466088592976 0.0282320418304 0.0445464770565 0.0341524834412 0.0362512556468 0.0421364706395 0.0353354631726 0.0231187500774 0.0150626098763 0.0358189243099 0.0298015850374 0.0324439491366 0.0223980477721 0.0244141427415 0.0302418773665 0.0942328467355 0.076594072426 0.0693014297828 0.0689934026371 0.0808708035071 0.0745545238332 0.0811424703773 0.0680895982601 0.0772898278285 0.0757784502044 0.0604959493699 0.069358828284 0.0677179794145 0.0851546659241 0.0961465377952 0.0777040409423 0.0614342341624 0.0635105029946 0.0949910709403 0.0710640834394 0.0733442971825 0.0804630717269 0.0785569975811 0.0599979412685 0.0679000809486 0.0561320615274 0.0579270123928 0.0585056430456 0.0796324819571 0.0514612322157 0.068283919556 0.0494122791964 0.083373938553 0.0501855689957 0.06973964597 0.0718399524308 0.0852557355766 0.0618964253913 0.0591154896454 0.0630863991576 0.0809010142666 0.069276699042 0.076594072426 0.0775503896407 0.0833112406891 0.0743754315619 0.0713482849075 0.0642332894623 0.0813844341388 0.0662752735857 0.00943572253542 0.0115403490897 0.0385513754293 0.041736873589 0.0399182937318 0.0122452242861 0.025058464158 0.0280391379811 0.0165428935252 0.0220426648475 0.0191966494038 0.00476868066193 0.0119136882752 0.0182274158907 0.024378991238 0.0214350053307 0.00987681971674 0.0356319079498 0.0179231346829 0.0755450486118 0.0557883449648 0.0471764532756 0.0467952668235 0.0599617395979 0.0511011457957 0.0617632501287 0.0445144536833 0.0538325742859 0.0579584947727 0.0439095831377 0.0473266714409 0.047305404175 0.0642647315012 0.0781620294368 0.0608980895696 0.0396397814207 0.0431908772896 0.0719383279912 0.0477762451878 0.0541744509705 0.0619776318533 0.0551486398926 0.0392023772277 0.0486708484659 0.0332061436773 0.0385651962783 0.0405238673503 0.0580675371179 0.0281169946403 0.0449308552075 0.0290979509914 0.0622896414172 0.0272386507233 0.0473564201885 0.0503681043652 0.0686223309305 0.0411717919112 0.0420509879955 0.0438795889294 0.0621492769732 0.0535535915584 0.0557883449648 0.0575324110618 0.0656578520536 0.0569417414102 0.0500298962763 0.0450037013757 0.0656341911967 0.0473715492692 0.0144468889514 0.0470085725986 0.0485138320241 0.0451352115307 0.0199583230651 0.0301372822106 0.0318368515531 0.0256571286264 0.0278614622113 0.0258405224944 0.0121251490044 0.0124421067497 0.0257425497103 0.0259848999587 0.0265128839547 0.0147071440313 0.0356921243154 0.0239599037157 0.0844599977072 0.0649098127865 0.0563152793656 0.0552526867175 0.0691043038035 0.0592804769565 0.0705635348052 0.0516880380121 0.0615515055365 0.0669939251635 0.0527850317966 0.0564919445965 0.0566781222463 0.0735436571696 0.0873943668972 0.0699246931044 0.0483722118201 0.0515510652048 0.079719756552 0.0550353446586 0.0634817595673 0.0710367571435 0.0623974106591 0.0485852577555 0.0576004808055 0.0413084590066 0.0479219849808 0.0493882028299 0.0672450344951 0.0348287223508 0.0527462211198 0.037276801135 0.071549415301 0.0351894067592 0.0529630080357 0.0594111357934 0.0773657257414 0.0496448829109 0.0507905960575 0.0532464315334 0.0714643731585 0.0625646663061 0.0649098127865 0.0667401518449 0.0748051995393 0.0661923309263 0.0591726016457 0.0543093310288 0.074196762744 0.0559244370052 0.043526425057 0.0386335179446 0.0321768451862 0.00974615024856 0.0352347635745 0.0201959159339 0.0180776754132 0.0269400303578 0.0177526168011 0.0076128290146 0.00378003726334 0.0176983136317 0.0184021944475 0.015957334363 0.00467338620778 0.0252008449064 0.0123712673884 0.075769333126 0.0581585948517 0.051685391535 0.0510927438975 0.0624824329901 0.058237684953 0.0625760535583 0.0529652916573 0.0618856654756 0.0574514448673 0.0423317456956 0.0517508019275 0.0497149058646 0.0670867680773 0.0782168275888 0.0597219475669 0.0433096265158 0.0451925797267 0.0797528207091 0.0566461202307 0.0551309318768 0.0620350591031 0.0638200944876 0.0424059873805 0.0493045876457 0.0391604065434 0.0398454975082 0.039967724192 0.0615633801452 0.0371201263022 0.0531118088353 0.0313373664803 0.0653089870936 0.0335830893361 0.0556009421638 0.055317918307 0.0671296861942 0.0435900410315 0.0407047621897 0.0451219309698 0.062876852421 0.0526327547566 0.0581585948517 0.0591116735406 0.0652217833265 0.0570769673892 0.0545681643866 0.0459758175248 0.0634453480612 0.0477419996054 0.0302678050916 0.043643410877 0.0344015081898 0.0370220423378 0.041870658286 0.0264826423168 0.0239447061259 0.0294219290852 0.0381639993795 0.046622906961 0.0276160450358 0.0433656713185 0.0358877725536 0.0394027280044 0.0640596529703 0.0352660320463 0.0428200645336 0.0215929365207 0.0157178006612 0.00848045847957 0.0252717757761 0.0193974269851 0.0287059984697 0.018482386953 0.0255997786212 0.0310533244304 0.0267766622161 0.0163561213819 0.0202134806234 0.03120585866 0.04895305148 0.036414957876 0.00526673250197 0.012342309222 0.0430330254803 0.0254800237585 0.0266262246906 0.032239986602 0.0289090953163 0.0180202124552 0.0199181330431 0.00657470209208 0.019948180111 0.0209059906389 0.0237059104211 0.0188029876251 0.0211536929916 0.0148162018356 0.0289733910382 0.0123381921604 0.0242362829274 0.0258181102869 0.0413288399769 0.0100525363381 0.0238857865216 0.0223322555234 0.0341921837249 0.0380927560603 0.0215929365207 0.0255329039423 0.0383043472154 0.0356000313746 0.0249953874006 0.0211460552822 0.0407734262534 0.0193178864072 0.0180029141367 0.0318989553395 0.055546862882 0.0233316606029 0.0289827460324 0.0288090712213 0.0227692137274 0.0384790428766 0.0423072029606 0.0235668927239 0.02923399269 0.0234314824709 0.0353361736228 0.0517306479462 0.0262806947106 0.0449202216804 0.0352666691701 0.0392752718621 0.0317034826189 0.0393329164331 0.0490036652351 0.0325915526781 0.0480657848217 0.0556144972514 0.0314856186317 0.0229568301755 0.0394863344353 0.0351981784458 0.0468732286627 0.0534614036052 0.0352961935447 0.0267660200432 0.0206247795688 0.0716485928619 0.0553558677376 0.0350658679333 0.0348297205048 0.0591015265332 0.0349050611526 0.0239450314084 0.0312292238968 0.0291976883203 0.0191245738332 0.0424715605102 0.0407160306554 0.0507428891398 0.0195019979736 0.0455419926359 0.03094049167 0.0480027022309 0.0486981223658 0.0364405293098 0.0216724201002 0.0185311393183 0.0322264249099 0.0418711782883 0.0415641388928 0.0352666691701 0.0357541599092 0.040359691621 0.0422344237589 0.0468672273606 0.0294846892119 0.0324923805449 0.019122055634 0.028756827183 0.0587035417373 0.0144079071022 0.0301136242831 0.0361947314748 0.0239369814187 0.0355016994114 0.0354103580658 0.0251937444079 0.0240593800606 0.0195703439645 0.0308533101673 0.0370972551577 0.0220259748313 0.0576270076145 0.0487199724465 0.0508215730415 0.0469075647824 0.0527931212734 0.0620087731623 0.046977227863 0.0607033064427 0.0681905343994 0.0417090362137 0.0294084394516 0.0508408090071 0.0450400867986 0.0589677823649 0.0627613332544 0.0433082894199 0.0402642195426 0.0363046359117 0.084449006206 0.0666097386489 0.0449981598795 0.0463289168121 0.0716590868723 0.0429897098207 0.0369198960753 0.0432687121664 0.0364322199569 0.0287560795721 0.0553568599314 0.0500592177291 0.0617693212505 0.0302484927047 0.057817398636 0.0410079662048 0.0620054930234 0.0578523465697 0.0464641684152 0.0365865801942 0.0272374209649 0.0399576916682 0.0513227964118 0.0446516670985 0.0487199724465 0.0480806958126 0.0495453699635 0.0482845333883 0.0560302897599 0.0385013240981 0.0415333749992 0.0339802993211 0.0315416974034 0.0191639032899 0.00845591360235 0.0214341775937 0.0115218935447 0.00798589270321 0.013178045677 0.0100207917484 0.0199483776485 0.012776598265 0.00707708079363 0.0310463483447 0.00779811122226 0.0665120827305 0.0484731432285 0.0421114253952 0.0418216626354 0.0527853739739 0.0493966141428 0.0532439640509 0.0450980317427 0.0534929652418 0.0482563107324 0.0334028573772 0.0421738253769 0.0400650763191 0.0574010770653 0.0690115183659 0.0507807835016 0.033887277172 0.0360916940356 0.0712074753299 0.0488889567075 0.0456112729946 0.052712754383 0.0558191941722 0.0328832629679 0.0398769847778 0.0305626616722 0.03027845702 0.0307126203306 0.051853149134 0.0304315085387 0.0449208583046 0.0225669791691 0.0556104755493 0.0254498384131 0.0488668504899 0.0462046921677 0.0583599392562 0.0343786096194 0.0317076229625 0.0356254179774 0.0534761774586 0.0442401179536 0.0484731432285 0.0494784680372 0.0560638562362 0.0481022882275 0.0453399462887 0.0364100969213 0.0549637988767 0.0386182470618 0.0499647144567 0.0286991839096 0.0337581082348 0.0374427591873 0.0282526339036 0.0355267981937 0.0350362485208 0.0478325170984 0.0425063748067 0.0336958297594 0.0552821351039 0.0385952701325 0.0756759815417 0.0533281139759 0.0401812481707 0.0447968354881 0.0564105044414 0.0391525826608 0.0629279047421 0.0332827070938 0.0388499692511 0.059944716429 0.0494848197832 0.0402828974015 0.0438747097228 0.0580998054312 0.0757410641548 0.0628856838636 0.039846534973 0.047063496141 0.0543336344875 0.0312551117596 0.052807685198 0.0627916823231 0.0390112066642 0.0353058185357 0.0513654740192 0.0323261929775 0.0392211101217 0.0463708307148 0.0519963158873 0.0234224809867 0.0301000678074 0.0372829629985 0.056085164213 0.0288004220361 0.0417416575715 0.0389119081863 0.0720657500645 0.0444323135884 0.0487706488559 0.0429521653792 0.0596874437499 0.052997284605 0.0533281139759 0.055723410021 0.0657462146584 0.0546473494099 0.0397492903292 0.0452841873006 0.0707319601823 0.0522103619845 0.0233361726138 0.027152976334 0.0145101369991 0.0239084572879 0.0231796409398 0.016731423623 0.010101659051 0.00781108168527 0.0186261838058 0.0315054768721 0.011535154356 0.0661583467836 0.0529737197777 0.0515726376237 0.0472342284059 0.0573866872941 0.0602508051558 0.0535888133028 0.0563499220883 0.0655740867856 0.0494619373879 0.0355566546043 0.0516986354459 0.0478914383755 0.0636133913461 0.0714227137745 0.0519652065027 0.040040966538 0.0379888138015 0.0833863881089 0.0624232672218 0.0504889561622 0.0539483262888 0.0682863371673 0.0434492287718 0.0419221148541 0.0393420375143 0.0383919560321 0.0330409365388 0.0586345177546 0.0426935993753 0.0582011555148 0.0271038932712 0.0620596992058 0.0353264927481 0.0565229896235 0.0582751327845 0.0564224406826 0.0375222198637 0.032781658946 0.0431629716317 0.0579468986629 0.0506284295544 0.0529737197777 0.0534601982596 0.057907553496 0.0543043024926 0.0568716124285 0.0423649825393 0.0520169525452 0.0387128090446 0.0188041113809 0.0118952838684 0.0140246174547 0.0212962400935 0.00885470160798 0.025142494638 0.0164130694781 0.0148836892075 0.0382106616301 0.012548123562 0.0595024177002 0.0405144987261 0.0336829979036 0.0339109092342 0.0447786612887 0.0412528753098 0.0460796810618 0.0378073897699 0.0456289595962 0.0415318567716 0.0274434917532 0.0337586538145 0.0320559171837 0.0493065770936 0.0620181140557 0.044443934423 0.0259210454652 0.0290199212383 0.0631931226882 0.0415500215307 0.0381432011516 0.0457001329437 0.0482419653282 0.0247624956339 0.0326912275918 0.0231122641396 0.0226732232029 0.0241582823353 0.0435758556102 0.024717419625 0.037259551901 0.0161507334564 0.0474557435456 0.0186437176733 0.0428749718429 0.0381563349544 0.0522572394535 0.0270598018866 0.0256825354187 0.028101885414 0.0461630275758 0.0383904744963 0.0405144987261 0.041758354552 0.0492909187408 0.0413606774947 0.0372601462253 0.0289047461458 0.049343864353 0.0319066136889 0.0140884736377 0.0220949123029 0.0294588701998 0.0141915109934 0.0236402055251 0.0202701530019 0.0222961106051 0.0508328337578 0.0209048958304 0.0623063919994 0.0435137888223 0.0379179155361 0.0310595169631 0.0477275300573 0.0401401417297 0.0476510837343 0.0326069267264 0.0442002914704 0.048031576146 0.0373260310255 0.0383688546713 0.0396828967833 0.0540671732006 0.0691161945626 0.052697233895 0.0256698443155 0.0271866198766 0.063343063446 0.0401717345523 0.0456835007162 0.0508390224232 0.0457136947621 0.0340506342939 0.0366331778515 0.0185292352758 0.0332180639746 0.031402826069 0.0470047522912 0.0178796862446 0.0373117201537 0.0146755143259 0.0519441438124 0.0134308953908 0.0305516636587 0.0455547559496 0.0574314455133 0.025677142017 0.0334820400224 0.038185075996 0.053981408775 0.0519396327628 0.0435137888223 0.0464090712113 0.0566586435278 0.0526391071675 0.0449104072539 0.0375180555166 0.0549667946861 0.0334290223836 0.0161979126548 0.0212360948185 0.00313302296585 0.0148689509772 0.00704616989296 0.0135603719097 0.0384674432624 0.00748847746612 0.0612097252086 0.0442840466006 0.040253243803 0.0360606427738 0.0487530949526 0.0470778914902 0.0473392259982 0.0422963534873 0.051949068182 0.0444929580638 0.0309270188278 0.040473799777 0.0386243677637 0.0547837147046 0.066413370926 0.0479885282668 0.0287291459439 0.0288822795748 0.0702829288423 0.0483322868723 0.0432975336344 0.0484818516534 0.0543652596123 0.0329738645592 0.0348525988976 0.0258401695463 0.029652318833 0.0266204166929 0.0488217477328 0.0282325775461 0.0443695558327 0.0152728890442 0.0529346931915 0.0211960241124 0.0432358961175 0.0470042201915 0.0535566647123 0.027654993588 0.0276975587847 0.0349524016669 0.051450438001 0.0457471816551 0.0442840466006 0.0457673454519 0.0531390087147 0.0482613634351 0.0459084957324 0.0344895940573 0.0501063288839 0.0320407745768 0.00918896575074 0.0152928703979 0.0213827580606 0.0175694634428 0.00562421953864 0.0321021314886 0.0136468651148 0.0732793088732 0.0542749513511 0.0465599433205 0.0461499050441 0.0585282720374 0.0518386762248 0.0596896497046 0.0460357977086 0.0551020502351 0.055347044484 0.0408456006547 0.0466734650365 0.0457685420886 0.0629790052567 0.0758414718388 0.0580725583953 0.0386295601195 0.0415657641172 0.0731303485703 0.0495368191967 0.0520897347287 0.0595893389246 0.0567976430466 0.0379647331116 0.046441942626 0.0333063038869 0.036500108881 0.0377849182347 0.0570420811998 0.030012797008 0.0462545479693 0.027461418846 0.0610786707463 0.0275181743774 0.0490679602747 0.0500650525841 0.0657243645716 0.0396831631678 0.0390548397455 0.0418579268325 0.0600259300869 0.0509169565607 0.0542749513511 0.0557017357111 0.0631094362549 0.0546387182457 0.049518990394 0.0428637051261 0.0625010516077 0.0450876329039 0.0212339329669 0.0205215987455 0.0193863377449 0.00780222226267 0.0244911121423 0.0160871853235 0.0794295187203 0.0616053956912 0.0547043851777 0.0543405312398 0.065903416382 0.0608093966938 0.066232208671 0.0551996275352 0.0641635601661 0.0610614868167 0.0459223913813 0.0547672174246 0.0529488520021 0.0703578675954 0.081658744309 0.063265127216 0.046621662518 0.0487339053303 0.0819943915105 0.0586031447424 0.058550915637 0.0656481691263 0.065908662167 0.0454276311919 0.0529363521846 0.0420305180364 0.0431442291715 0.0436296366503 0.0648039964872 0.0390809908814 0.0552779379516 0.0347675148936 0.0685677387884 0.0363124697872 0.0577513229251 0.0579440183076 0.0708163109663 0.0470812852612 0.0443909494169 0.0483964045026 0.0662606542355 0.055662814312 0.0616053956912 0.0625961650479 0.0687432253304 0.0602433308395 0.0572909174214 0.0493875071422 0.0671416550914 0.051430941132 0.0176886906331 0.00927892520724 0.0135248076976 0.0386449359276 0.00800171044068 0.0600153322548 0.0424684845259 0.0378068586886 0.0345103555887 0.0469162632965 0.044830307306 0.0461600332306 0.040366688607 0.0496446456688 0.0429723253644 0.0292480429291 0.0380026884561 0.0362133523808 0.0526530252718 0.0646507321938 0.046401756397 0.0269368048123 0.0278152961494 0.0678508831968 0.0459682894465 0.0412431425852 0.0469952418705 0.0521326959742 0.0302237793371 0.0333696851677 0.0240543524356 0.0271406206916 0.0250182744726 0.0466959485275 0.0265020349694 0.0419000008026 0.0139599809083 0.0507909794153 0.0193885955935 0.0423195771966 0.0441873849773 0.0525088605043 0.0263754966172 0.0262639235251 0.0325133748369 0.0494223408625 0.0434143928247 0.0424684845259 0.0439360734198 0.0514706200386 0.0459476778298 0.0431249041549 0.0322850478478 0.0492075262735 0.0310084454102 0.0100495790147 0.0163467110294 0.0344303173981 0.0138069903297 0.0725561849391 0.0577945355153 0.0549392051976 0.0494983401957 0.0622636335656 0.0613281176125 0.0591564733558 0.055493289997 0.0660163516694 0.0564238014063 0.0428263617367 0.0551692935595 0.0528843814502 0.0687090576791 0.0786138592889 0.0595588928727 0.0427108568825 0.041401774003 0.084635719422 0.0620469795992 0.0566375025559 0.0605458826833 0.0680826326606 0.0475957607451 0.0475121922821 0.0395532575369 0.0436869591597 0.0392259763169 0.0629613789201 0.0403138291944 0.0584854937624 0.0290090498587 0.0669476358426 0.0346983343078 0.0538333844175 0.0617823249939 0.0639146571596 0.0406990904981 0.0395983575205 0.0488304003282 0.0645174644364 0.0580105192199 0.0577945355153 0.0590354282227 0.0651460923012 0.0612394393036 0.0606595939265 0.0481508588685 0.059835663962 0.0439114917806 0.0129734005291 0.03341718436 0.00522839839082 0.0644629130549 0.0490781090479 0.0460393397147 0.042060104717 0.0535473784021 0.0537667869164 0.051142158888 0.0493088817338 0.0587609555936 0.047452874403 0.0333145084294 0.0462018313471 0.0433659094036 0.0595851004773 0.0694769331379 0.0504124171924 0.034671039268 0.0338977868767 0.0768809318577 0.0551957563061 0.0471765984426 0.0517762151648 0.0612752535588 0.0380650031758 0.0388085089346 0.0327052071067 0.0338902731124 0.0299610722521 0.054082097785 0.0351427472718 0.0511360689963 0.0213341577068 0.0578680354373 0.0282202368919 0.0499673113862 0.0525352197686 0.0557073834724 0.0329893658613 0.0303826410135 0.0390336792828 0.055051644837 0.0480885204528 0.0490781090479 0.0500556489468 0.0560168186449 0.0513666437929 0.0513150683738 0.038531174408 0.0517653307967 0.0358776775011 0.0293027668161 0.00949293329263 0.0725916210374 0.0547194436592 0.0483620571735 0.0469816749901 0.0590799074957 0.0545464429945 0.0590974756513 0.0490311503881 0.0583273766433 0.0546626805606 0.0398779297173 0.0484777367952 0.0468025950712 0.0639880374892 0.0757199402524 0.0573199482995 0.0393352512936 0.0411552751652 0.0764775433632 0.0532445829203 0.052349146311 0.0590579753428 0.0602574471507 0.039602885829 0.0459481099263 0.0349175810863 0.037150941259 0.03692168916 0.0582151479375 0.0331340211018 0.0497159727507 0.0271028830882 0.0621502493207 0.0292960379946 0.0512053897158 0.0526539904442 0.0643939446385 0.0395489605444 0.0378591872997 0.0425337328718 0.0602822057384 0.0511975943751 0.0547194436592 0.0559279154126 0.0626694858218 0.0551041885347 0.0518894661445 0.0431638732605 0.0608387750183 0.0441361499894 0.0310183369902 0.0871660444405 0.0733823196322 0.0689340787316 0.0705597518969 0.0774366556509 0.07861295118 0.076322371958 0.075585060054 0.0826717778434 0.0681219966699 0.0528565530205 0.0687776982924 0.0640973745197 0.0810751053908 0.0869333145725 0.0682988428223 0.0623918658396 0.0630905228705 0.0989058396189 0.0780506774957 0.0672923155095 0.0734447484338 0.0852286487915 0.0580045491026 0.0635565107431 0.0611199283903 0.0538632888957 0.0534691027903 0.0772908944799 0.0610905623861 0.073927736475 0.0514266311669 0.0797776498939 0.0563654161709 0.0797411714169 0.0709112220147 0.075991910188 0.061929266059 0.0528794637266 0.0580761513446 0.0735774043698 0.0591743277341 0.0733823196322 0.0727567267189 0.0745131906994 0.065799040936 0.0698581828462 0.0592195179672 0.0715468662712 0.0627789092372 0.0648080233561 0.0484745494015 0.0442686485072 0.0419227062634 0.0529072690322 0.05220865754 0.0515658331236 0.0480206916873 0.056947174509 0.0470499747083 0.0323100366631 0.0443750009709 0.0415450201967 0.0583606796016 0.0686531977281 0.0497286282257 0.0341488382347 0.0345013563823 0.0748626932863 0.0530450676854 0.0459172064129 0.0515384326883 0.0594738121093 0.0355453778886 0.038659716641 0.0319368788596 0.0317165879728 0.0293867131129 0.0529269518286 0.0336846365247 0.0489212862277 0.021422361173 0.0566364033836 0.0272163294484 0.0501093106191 0.0498104699827 0.056059086848 0.0332712049783 0.0299486671345 0.0369730112559 0.0537844555125 0.0455932312627 0.0484745494015 0.0493458255088 0.0553162838774 0.0492693292197 0.0486803630647 0.0369835817896 0.0522579331221 0.0364683447074 0.022730708667 0.036743783492 0.0359076620395 0.0204805867516 0.0463990865864 0.0147686048767 0.0549817963395 0.0525592871928 0.0196223614467 0.0349129145261 0.0366443799136 0.0327544499352 0.0228629827418 0.017026958725 0.0209966358066 0.0388626349727 0.035298115438 0.0567980979705 0.0582058906835 0.0248891314561 0.0145753449597 0.0570907866116 0.0413833987479 0.0269969183741 0.0492024913366 0.039440986454 0.0359176091053 0.0265520996515 0.0615044403055 0.0540461620767 0.0485288037826 0.0239710979892 0.0540724881457 0.0603640513646 0.0428978851084 0.0134027017241 0.0368991356344 0.0354211161184 0.0355257446359 0.0210753154172 0.0377446845484 0.022730708667 0.0201673641607 0.0164841811953 0.0313109932769 0.041027522952 0.0326196770584 0.0184701671719 0.0295244972329 0.0144112105736 0.0158826147242 0.00448936000965 0.0256749628733 0.0117360219999 0.0336843126425 0.0324372887253 0.0146677582401 0.0224513170291 0.0144322007261 0.0126899376427 0.0122323800999 0.0277700509569 0.0197834641463 0.0180901487399 0.0184584697146 0.0418096588931 0.0367452397055 0.0108947935426 0.0131136689852 0.0371795334759 0.0204476192567 0.0118634455346 0.0280193213775 0.0206906520081 0.0208413846069 0.00805992628737 0.0397245591306 0.032104964073 0.0301985227624 0.0107001908888 0.0330988349255 0.0415195660786 0.0230606485279 0.0236410923325 0.0187109927435 0.0222023475244 0.0175662614613 0.0151089228932 0.0279798538754 0.0 0.00504787707871 0.0188703006517 0.0219245561497 0.0212095981034 0.0149942492526 0.0252687339312 0.016225955903 0.014982874816 0.0165713440517 0.0153992347708 0.0258128425881 0.0233194300829 0.0216404069707 0.0254243329386 0.0254506863873 0.000788850240312 0.00972288497974 0.0186083951584 0.0381473811219 0.0294157845108 0.0147578085456 0.0211609731312 0.0342335739858 0.0241609615185 0.0173732430671 0.0259324451415 0.0264222187829 0.0114741578552 0.0193298399608 0.0206022299244 0.0166568971164 0.0229188388297 0.0118905814083 0.0295550989457 0.0189848877848 0.0268759492012 0.0164070102936 0.0252554335694 0.0350869023834 0.0115237799469 0.0366507700029 0.0195520445986 0.0253794363549 0.0152320827299 0.0231766024476 0.028592244401 0.0144112105736 0.0173074040909 0.0296651493536 0.0243710502336 0.0103105434217 0.0155263675354 0.0374184940592 0.0231136537684 0.0190194510466 0.0188364876977 0.0221985054079 0.0214172478398 0.0257446692843 0.0273354856863 0.0275203255805 0.0156705691302 0.0195445751869 0.0258686776409 0.0435616108671 0.0331590462603 0.00822908256455 0.0111578554098 0.0408350390305 0.0282065307084 0.0237933699021 0.0272090918986 0.029489839001 0.0211107451245 0.0171676820259 0.0146997004559 0.0226821224304 0.022183640748 0.0185118691459 0.0264356522916 0.0241910879615 0.0208215741795 0.0237366538469 0.0205889692169 0.0263075431581 0.0263185412116 0.036342074994 0.0103523538982 0.0247947799759 0.0232770954007 0.0303205821473 0.0382875192266 0.0158826147242 0.0205185838207 0.033936913628 0.0342591359421 0.0252819239657 0.0211775313941 0.0366164619483 0.0168683582115 0.0265527528137 0.0122930639405 0.0356399190513 0.0329283261088 0.0161748547301 0.0260907486468 0.0165355786656 0.0155013603686 0.00871939649167 0.0250375042482 0.0205784254424 0.0221880849182 0.0226228234946 0.0402265547715 0.0380348851944 0.0126404460089 0.0132955588731 0.037673161045 0.023685130291 0.0159536871846 0.0317445670434 0.0244407629294 0.0250650773838 0.00665080674841 0.0431354773139 0.0336884150562 0.0346100496205 0.00755310734613 0.037025240188 0.043753914844 0.0237910793513 0.023601769275 0.0229675330633 0.0262374537149 0.0207694947791 0.0142462256068 0.0294419901123 0.00448936000965 0.00600179744469 0.0181950411243 0.0225874743282 0.0220819669003 0.0185630904749 0.0261141108628 0.0202600233978 0.0359510556228 0.0122181959079 0.00715556619596 0.0393252590156 0.0398773260728 0.015944130912 0.025117898688 0.0279596047113 0.0495072405762 0.0438343802676 0.0223301740975 0.0291958566192 0.0238057945893 0.0121408308676 0.0320439133203 0.0386704894061 0.0116681269474 0.0246115626131 0.0320654848123 0.02212022544 0.0306066666749 0.0360151785428 0.0212925403107 0.0265520435189 0.0096564754612 0.0336990775918 0.0258669646258 0.0271355620099 0.0255831511409 0.0192288539502 0.0492946436623 0.0275425360484 0.0388140188539 0.0303333899385 0.0366778253264 0.0435686919256 0.0256749628733 0.0298872725089 0.043124071299 0.0391147976424 0.020010154166 0.0305524053833 0.0506217525372 0.0340993001213 0.0427032875962 0.0428000340065 0.0127191401929 0.0244302586973 0.0258873530415 0.0224665589838 0.0196473040025 0.0259734946343 0.0180766430627 0.0245847379502 0.0205530555257 0.0515531804399 0.047284161103 0.0168752313645 0.0093274994833 0.0472940785154 0.0299859804517 0.013568861355 0.0349261393238 0.0278394065303 0.0233159306106 0.018729490125 0.0474594716238 0.0428441003445 0.0340535271365 0.0192623858156 0.0396592952666 0.0474504224755 0.0346317498496 0.0155232846365 0.0221965807346 0.023519540409 0.0251795457747 0.0186807942123 0.0328466600284 0.0117360219999 0.0111390258469 0.0171063233969 0.027248122956 0.0326787410905 0.0216187767654 0.0174724607588 0.0153838329478 0.013043913361 0.0463610530412 0.0437380208469 0.0239671160531 0.0322222363953 0.0387763357271 0.0597848214293 0.0513617728094 0.0233595904168 0.0301944308891 0.0320013483014 0.010613155035 0.0399275999718 0.0464560281456 0.0134988892532 0.0290788786594 0.0367911394695 0.0175534572334 0.0343523037935 0.0386269072909 0.0314326304477 0.0171757591643 0.0105270444576 0.0306360966619 0.0365489530298 0.0211256372062 0.0155275027866 0.0282406353658 0.0565789653078 0.0282583663219 0.0416523037157 0.0359394330813 0.0459783038684 0.0506220203623 0.0336843126425 0.0381881672141 0.0517807652888 0.0474506190651 0.0289707556044 0.0360504295798 0.0570578425896 0.0371145976129 0.0460576692202 0.0464080290911 0.0220712004031 0.0312574889523 0.0332251791601 0.054841891687 0.0503036463483 0.028999517533 0.0360798173432 0.0193249539307 0.00826557354917 0.0383933246971 0.0453200679143 0.00495350657916 0.0300295268949 0.0391229254289 0.0271682846269 0.036533385441 0.0426841669003 0.0272844902637 0.0287077471132 0.00927183865749 0.0394174170837 0.0313084618098 0.0314758507766 0.0267668687054 0.022518491913 0.05600898249 0.0343402195218 0.045509854552 0.0363226467926 0.0425348706975 0.0488168350062 0.0324372887253 0.0364696219213 0.0493441352532 0.0445105561012 0.0238995483109 0.0369592605884 0.0574925847452 0.0411978099793 0.0155890245102 0.0251635522565 0.0177733966699 0.020295344741 0.0224627747939 0.00622985531203 0.0261058920982 0.0236064364637 0.0550881017687 0.0493624804622 0.00949195374034 0.00547982782741 0.0508785947136 0.0251610996387 0.0119430843146 0.0365988456521 0.0213251727582 0.0179868212343 0.0207990673061 0.0485594746656 0.044214664825 0.0334335462828 0.0201922684831 0.0401754502972 0.0535325351547 0.0317074448278 0.0123261383208 0.0244676333996 0.0171629247157 0.0177983318365 0.0115555789303 0.021116940732 0.0146677582401 0.0105358367361 0.00915254671069 0.0167177891777 0.0294167459865 0.0147897849251 0.0123457776793 0.0164773765795 0.0252103273152 0.0171988861049 0.0305685435552 0.0365843208441 0.0175183238357 0.0218496748957 0.0205878945245 0.0593121863314 0.0470793832619 0.0159816216856 0.020744505382 0.0508661739809 0.018760262956 0.0128710885205 0.0300992820345 0.0117566522917 0.00679248320348 0.0280357325772 0.040552704264 0.0415859654681 0.0232256372519 0.0295874345059 0.0314690999231 0.0507965688951 0.0314591977122 0.0256867489287 0.0204093797109 0.00501434925534 0.0122277574576 0.0226603353781 0.0187158051887 0.0224513170291 0.020695499337 0.0231928239165 0.0198154038419 0.0292814261752 0.0103125125409 0.0227651885673 0.0153489133169 0.00921470910295 0.0183064776985 0.0377227049932 0.0290274791546 0.0152667053733 0.0215933253328 0.0343731361054 0.0245700487863 0.0169471424987 0.0257260078813 0.0268671065769 0.0111334970897 0.0193681705086 0.0211865368155 0.0163734248392 0.0228777878657 0.0117963493804 0.030068869871 0.0193762721713 0.027229886316 0.0161367068103 0.025740037188 0.0358416431275 0.0109673463454 0.0364509080414 0.0199965660095 0.0252914478428 0.014773865842 0.0226846344514 0.0279231861054 0.0144322007261 0.0171240275822 0.0292730788831 0.0236979129564 0.00966001570023 0.0151974508598 0.0372251985508 0.023311727536 0.0176051360114 0.032564167101 0.0208965631668 0.0168197313548 0.0207764664023 0.0423976172234 0.033280884693 0.00922898632458 0.0197200489933 0.0360385863631 0.00893473638299 0.014132357355 0.0250685285695 0.0103719683171 0.0167517993882 0.0135716425012 0.0350532380107 0.027879799306 0.0265658749664 0.015993421345 0.0286771298489 0.0428550440411 0.0151980773887 0.0298292088786 0.0196261415194 0.0184891599574 0.00655621912452 0.016322105066 0.0195353045374 0.0126899376427 0.0127701290018 0.0223217848231 0.0157154460048 0.0129568955631 0.00690277207156 0.0299954575682 0.0193817024498 0.0216771607077 0.0225658582257 0.0286138040807 0.0305011727447 0.0368382394098 0.0388439321615 0.0148355509953 0.0174423915339 0.0380129595202 0.0258082909988 0.0229842208153 0.0373470514825 0.0277476701907 0.0308564780706 0.00775830938446 0.0476084837369 0.0347450599297 0.0411478727988 0.00231111578025 0.0424821924039 0.0488975441679 0.0213563138315 0.0271270574956 0.0304501786922 0.0318897260431 0.0231190177025 0.0126379860304 0.0283589858381 0.0122323800999 0.0114136835682 0.0186791916183 0.0206672138954 0.0199079464104 0.0222007080182 0.0303110417953 0.0280827256688 0.0197052367937 0.0449418080277 0.0438182248518 0.0562345701188 0.0602081091792 0.0243728506009 0.019370550695 0.0596171194363 0.0411146925258 0.0330451422406 0.0550957050319 0.039836592047 0.0399228025142 0.0284759591968 0.0663825437409 0.0558252777705 0.0548034680532 0.0236830779253 0.0595333433641 0.0686676458986 0.0397842689041 0.0197958382609 0.0446061287137 0.0392287195509 0.0347118369491 0.0164026290561 0.0305214779366 0.0277700509569 0.0235155079857 0.0135888018548 0.0240589544851 0.0380160024194 0.0333951182547 0.0242014401746 0.0377621051763 0.0314790157987 0.029423203836 0.0582327389784 0.0535477924002 0.0121411421616 0.00926081168701 0.0551978876015 0.028043323191 0.0176337854836 0.041735927557 0.023998617859 0.0217227463265 0.0246395690903 0.0532994548372 0.0483234850835 0.0380931811444 0.0229416680066 0.0449797181572 0.059280772983 0.0338814552602 0.0120128740103 0.0301325195322 0.0203844916972 0.0200232099081 0.0110576598728 0.0182627991321 0.0197834641463 0.0150522414851 0.006556855609 0.0144048387324 0.0315801243453 0.0179150623078 0.0119668288225 0.022167313587 0.00841936493113 0.0453128226707 0.029523676844 0.0223084484022 0.0276272783534 0.0327625901905 0.0158536182647 0.0147737513682 0.0106741877313 0.0162753926733 0.0159900539559 0.0215767754577 0.023165172617 0.0247814128516 0.0137331692872 0.0264980088885 0.0153005942866 0.0291348842471 0.0254286093168 0.0364522562324 0.00599431682769 0.0189173783639 0.0184393411187 0.0301213577678 0.0340540018144 0.0180901487399 0.0215314467503 0.0337154513722 0.0315978293722 0.024169684578 0.0166816873996 0.0356875981931 0.0143747226708 0.0514837021891 0.0372994668391 0.0230531280792 0.0250236859971 0.0398047566678 0.021934356719 0.0118568359564 0.016712157857 0.0198275637831 0.0143476538676 0.0244078681037 0.0292462046489 0.0328036706334 0.014376575397 0.0286902071785 0.0204063466033 0.0328369650405 0.0322219054124 0.0320787692664 0.00266433410273 0.0165423262191 0.0215926529236 0.03047036768 0.0358252300263 0.0184584697146 0.0213100670766 0.032046547675 0.0333108429689 0.0306570115485 0.018573514939 0.030845411182 0.00824349209933 0.0254197321026 0.047324533609 0.0530498030585 0.0193482837408 0.0436475526218 0.0516271254066 0.0457976820229 0.0502335440929 0.0569904536613 0.0344485460271 0.0478896186827 0.0272539439051 0.0572698534085 0.0358919394369 0.050424643016 0.0434868500285 0.0309009333379 0.0632686594285 0.0501928388473 0.0594250482186 0.0482632028717 0.0482478127894 0.0570074387196 0.0418096588931 0.0448198518438 0.0553205633247 0.0514202209421 0.0325934353049 0.0491031454545 0.0660730408504 0.0548573842682 0.041501827818 0.0494594306994 0.00785967991178 0.0297803384368 0.0414124759052 0.0250716250932 0.0363814861914 0.0430927179021 0.0324719682551 0.0232534554573 0.00557282966442 0.0375065288364 0.0368003051347 0.0279998159154 0.0252506405295 0.0244125277036 0.0601405054627 0.0351799654171 0.0460355764213 0.0372310571496 0.0466115069454 0.0500875517655 0.0367452397055 0.0406580228721 0.0534802351733 0.0469702184582 0.0259083864948 0.0383091416944 0.0609916135179 0.0430915691708 0.0113258711014 0.0432937210726 0.0175511998728 0.0124157338497 0.03212454916 0.0158561794962 0.0176896387613 0.0143093876737 0.043181186929 0.0362893217044 0.0315177649353 0.0141883840024 0.0359022033885 0.0492746305333 0.0223018781484 0.0211045193185 0.0229404408443 0.0181288833884 0.0110415500412 0.00837364985146 0.0172572597582 0.0108947935426 0.00733209366776 0.0131427681285 0.0115359384743 0.0199994152339 0.0092535804352 0.0217603350968 0.0184515208125 0.0501255669434 0.0279388443296 0.0142651752221 0.0382528821443 0.0251771073359 0.0222405400165 0.018907996804 0.050407441851 0.0445874581383 0.03644061978 0.0176520694999 0.0424414179548 0.05338489352 0.0323923780445 0.0108812710336 0.0260958051981 0.0217902651406 0.0213471493056 0.0109405604951 0.0249968133743 0.0131136689852 0.00893536297262 0.00802513881767 0.0192978256402 0.0302167707341 0.018365059649 0.0130926236805 0.0184689475202 0.0343186729394 0.0436186327673 0.0296745080858 0.0408423177906 0.0469164023153 0.0321355072481 0.0293927572919 0.0115197032526 0.0423686398976 0.036129628995 0.0335484782832 0.0259949361948 0.0269704035297 0.0607621523939 0.0380404513983 0.049798653181 0.0409231346593 0.0474755392541 0.0535032395417 0.0371795334759 0.0413092694389 0.054257484038 0.0493594143559 0.0284802931744 0.0415978746211 0.0622229836323 0.0453773607077 0.0190484002067 0.0207749590973 0.00729318592678 0.0172314224853 0.0208211767145 0.0287420582694 0.0242194382748 0.0225231335589 0.0240241380028 0.0229680498334 0.0402905798782 0.0150429891355 0.0374592753112 0.0199314954492 0.0195088829088 0.00836639606005 0.0247669201274 0.0222507048525 0.0204476192567 0.021382719754 0.0305241597933 0.0212891172726 0.0134776031268 0.0107610654817 0.0367912026131 0.0227467739819 0.0252412358335 0.0153803531506 0.0101093922522 0.0191555088988 0.0375470144869 0.0363071087647 0.0222024666973 0.021689493319 0.0288400411309 0.0426974513075 0.0286740878663 0.0220177373135 0.0125507954062 0.0111145943691 0.0144555489867 0.0195277194831 0.0258649050915 0.0118634455346 0.0120062047089 0.0205457113931 0.0226933950128 0.0265812358015 0.010518368073 0.0210005232169 0.00605049088221 0.0227443548737 0.0240543202468 0.0298445720969 0.0127076622649 0.0212764877573 0.0130936058244 0.0350960563748 0.00616350137707 0.0220053566886 0.029182207808 0.0470447673611 0.0143549158293 0.0270200109504 0.0261798064669 0.0399469917707 0.0417589617676 0.0280193213775 0.0317524179632 0.0441157592928 0.0402109444472 0.0286518076954 0.0255338040061 0.0460635600013 0.0240522825965 0.0109052701432 0.0235077295524 0.0318665250498 0.0308330797717 0.0201941994246 0.026209699134 0.024272445381 0.0437154276666 0.0215651164399 0.0334110813047 0.0182660118628 0.0127706309369 0.00547043300996 0.0236736081076 0.0193715054877 0.0206906520081 0.0206326054685 0.0278093020752 0.0196854448654 0.0196839348285 0.00687772387853 0.0318861086105 0.0187829482374 0.026787715043 0.0349795665731 0.0377560197701 0.0169430774361 0.0294826475268 0.02554791035 0.044564230482 0.0307872765104 0.0282156909894 0.0140157312571 0.00304825224888 0.013131876158 0.0254598849649 0.0242359050423 0.0208413846069 0.0206178921833 0.0265921223132 0.024263714379 0.0287692622655 0.0105411880456 0.0255889461915 0.0107722057824 0.040112260752 0.0281920691871 0.0346799127455 0.00547299312148 0.0351698801996 0.0413927767406 0.0178561632488 0.0295704831304 0.0240442618733 0.028433519767 0.0198981190422 0.0164655446546 0.0293872770207 0.00805992628737 0.0103936152637 0.0224915115769 0.0226675446032 0.0164308167554 0.0187959028502 0.0318543120786 0.0236941138036 0.0214366809685 0.02158460699 0.0453234846239 0.0100348820296 0.0205571695519 0.0349449705288 0.0594353543617 0.0269232145172 0.0378563722335 0.0357646095878 0.0508614713562 0.0504482642845 0.0397245591306 0.0434432071263 0.055747628056 0.0497669350857 0.035138779746 0.0361053959246 0.0583555321158 0.0365217793435 0.0331608473129 0.0326376645366 0.0244797200401 0.0260351758605 0.0198255233977 0.055257342683 0.030607412773 0.0406724640229 0.0316826493706 0.0417015250124 0.044714144963 0.032104964073 0.0358439539437 0.0485265392209 0.0417397589401 0.0211023329973 0.0328093106913 0.055937896155 0.0382216948189 0.039178382683 0.0116462448464 0.0328449320294 0.0356452166019 0.0431040247851 0.0127555033307 0.0192172916283 0.0248029464024 0.0398126039657 0.0387676066679 0.0301985227624 0.0325088097853 0.0421224110282 0.0389248491362 0.0344395222458 0.0235149407659 0.0407276725105 0.0194230824244 0.0402634745964 0.0467109968717 0.0198309327046 0.0277799641062 0.0285372209353 0.0306922978338 0.0217975015009 0.0133715864954 0.0282760151712 0.0107001908888 0.0106288988311 0.0196086000788 0.0208164998238 0.0183683750797 0.0208677693371 0.0306825719595 0.0267116346537 0.0244223076771 0.0326854873354 0.0507787766717 0.0180800830921 0.0283453513368 0.0285692206738 0.0439964220236 0.0435330479692 0.0330988349255 0.0364104267178 0.047999984958 0.0430530022579 0.0322085934623 0.0282999450485 0.049271623839 0.0273166054796 0.0425206430225 0.0621747559979 0.0316259435967 0.0474306721161 0.0460270405303 0.055722110458 0.0616108763567 0.0415195660786 0.0463779068274 0.0600405303926 0.058487923658 0.0428330727641 0.0451145046508 0.0622811849491 0.040848815271 0.0429266615699 0.0304603009077 0.0328680598837 0.0193092495687 0.0257358921646 0.0276472175985 0.0230606485279 0.0242829569209 0.0335562675322 0.0240112096757 0.0023427193432 0.0215758461563 0.0440130376785 0.0333268924093 0.0336594835101 0.0267459293135 0.0300685155468 0.0190916149163 0.0302108808069 0.0236410923325 0.0197983357166 0.0116623663768 0.025981149508 0.0407083432987 0.0270506742353 0.00533601555818 0.0243947872794 0.0165388277645 0.0203993556991 0.0306449706863 0.0351570407904 0.0187109927435 0.0216936002715 0.0328125704384 0.0328483395342 0.0289711567156 0.0177290185247 0.0324417191457 0.00990245486371 0.0144435719572 0.0253854499058 0.0235470613048 0.0222023475244 0.0213735089495 0.0257216217897 0.0239916366178 0.0307770932602 0.0118559544137 0.0237036848381 0.0117425310805 0.0184555467145 0.0157796168272 0.0175662614613 0.0166625646078 0.0231975140243 0.0146299258577 0.0171445810537 0.00397360286698 0.029169512627 0.0192796522734 0.0184254094366 0.0151089228932 0.0104342480435 0.00810789324855 0.0108902089943 0.0236280097789 0.017384613998 0.0213096795304 0.0252727380791 0.0279798538754 0.0244605767611 0.021500625639 0.00796997392989 0.0256665045822 0.0176058458155 0.0292950203769 0.0310442784549 0.00504787707871 0.0188703006517 0.0219245561497 0.0212095981034 0.0149942492526 0.0252687339312 0.016225955903 0.0138989004364 0.0180767927652 0.0222193332382 0.0141002986592 0.0215877207325 0.0172352062998 0.0156454524782 0.031383424491 0.0212505739868 0.0142951058056 0.0253431933269 0.0219030212003 0.0156518397703 0.0263438271284 0.0285058962486 0.0193626993311 0.0417261059969 0.0313743437253 0.0260801912789 0.0155074292908 0.0227467684355 diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test__plotutils.py b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test__plotutils.py new file mode 100644 index 0000000000000000000000000000000000000000..0e2553bf7ad56e97b567e5b334ccf17921f7f7f3 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test__plotutils.py @@ -0,0 +1,91 @@ +import pytest +import numpy as np +from numpy.testing import assert_, assert_array_equal, assert_allclose + +try: + import matplotlib + matplotlib.rcParams['backend'] = 'Agg' + import matplotlib.pyplot as plt + has_matplotlib = True +except Exception: + has_matplotlib = False + +from scipy.spatial import \ + delaunay_plot_2d, voronoi_plot_2d, convex_hull_plot_2d, \ + Delaunay, Voronoi, ConvexHull + + +@pytest.mark.skipif(not has_matplotlib, reason="Matplotlib not available") +class TestPlotting: + points = [(0,0), (0,1), (1,0), (1,1)] + + def test_delaunay(self): + # Smoke test + fig = plt.figure() + obj = Delaunay(self.points) + s_before = obj.simplices.copy() + r = delaunay_plot_2d(obj, ax=fig.gca()) + assert_array_equal(obj.simplices, s_before) # shouldn't modify + assert_(r is fig) + delaunay_plot_2d(obj, ax=fig.gca()) + + def test_voronoi(self): + # Smoke test + fig = plt.figure() + obj = Voronoi(self.points) + r = voronoi_plot_2d(obj, ax=fig.gca()) + assert_(r is fig) + voronoi_plot_2d(obj) + voronoi_plot_2d(obj, show_vertices=False) + + def test_convex_hull(self): + # Smoke test + fig = plt.figure() + tri = ConvexHull(self.points) + r = convex_hull_plot_2d(tri, ax=fig.gca()) + assert_(r is fig) + convex_hull_plot_2d(tri) + + def test_gh_19653(self): + # aspect ratio sensitivity of voronoi_plot_2d + # infinite Voronoi edges + points = np.array([[245.059986986012, 10.971011721360075], + [320.49044143557785, 10.970258360366753], + [239.79023081978914, 13.108487516946218], + [263.38325791238833, 12.93241352743668], + [219.53334398353175, 13.346107628161008]]) + vor = Voronoi(points) + fig = voronoi_plot_2d(vor) + ax = fig.gca() + infinite_segments = ax.collections[1].get_segments() + expected_segments = np.array([[[282.77256, -254.76904], + [282.729714, -4544.744698]], + [[282.77256014, -254.76904029], + [430.08561382, 4032.67658742]], + [[229.26733285, -20.39957514], + [-168.17167404, -4291.92545966]], + [[289.93433364, 5151.40412217], + [330.40553385, 9441.18887532]]]) + assert_allclose(infinite_segments, expected_segments) + + def test_gh_19653_smaller_aspect(self): + # reasonable behavior for less extreme aspect + # ratio + points = np.array([[24.059986986012, 10.971011721360075], + [32.49044143557785, 10.970258360366753], + [23.79023081978914, 13.108487516946218], + [26.38325791238833, 12.93241352743668], + [21.53334398353175, 13.346107628161008]]) + vor = Voronoi(points) + fig = voronoi_plot_2d(vor) + ax = fig.gca() + infinite_segments = ax.collections[1].get_segments() + expected_segments = np.array([[[28.274979, 8.335027], + [28.270463, -42.19763338]], + [[28.27497869, 8.33502697], + [43.73223829, 56.44555501]], + [[22.51805823, 11.8621754], + [-12.09266506, -24.95694485]], + [[29.53092448, 78.46952378], + [33.82572726, 128.81934455]]]) + assert_allclose(infinite_segments, expected_segments) diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test__procrustes.py b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test__procrustes.py new file mode 100644 index 0000000000000000000000000000000000000000..42a3c4d35bd55e2ffecefb691c805f517c56d6ca --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test__procrustes.py @@ -0,0 +1,116 @@ +import numpy as np +from numpy.testing import assert_allclose, assert_equal, assert_almost_equal +from pytest import raises as assert_raises + +from scipy.spatial import procrustes + + +class TestProcrustes: + def setup_method(self): + """creates inputs""" + # an L + self.data1 = np.array([[1, 3], [1, 2], [1, 1], [2, 1]], 'd') + + # a larger, shifted, mirrored L + self.data2 = np.array([[4, -2], [4, -4], [4, -6], [2, -6]], 'd') + + # an L shifted up 1, right 1, and with point 4 shifted an extra .5 + # to the right + # pointwise distance disparity with data1: 3*(2) + (1 + 1.5^2) + self.data3 = np.array([[2, 4], [2, 3], [2, 2], [3, 2.5]], 'd') + + # data4, data5 are standardized (trace(A*A') = 1). + # procrustes should return an identical copy if they are used + # as the first matrix argument. + shiftangle = np.pi / 8 + self.data4 = np.array([[1, 0], [0, 1], [-1, 0], + [0, -1]], 'd') / np.sqrt(4) + self.data5 = np.array([[np.cos(shiftangle), np.sin(shiftangle)], + [np.cos(np.pi / 2 - shiftangle), + np.sin(np.pi / 2 - shiftangle)], + [-np.cos(shiftangle), + -np.sin(shiftangle)], + [-np.cos(np.pi / 2 - shiftangle), + -np.sin(np.pi / 2 - shiftangle)]], + 'd') / np.sqrt(4) + + def test_procrustes(self): + # tests procrustes' ability to match two matrices. + # + # the second matrix is a rotated, shifted, scaled, and mirrored version + # of the first, in two dimensions only + # + # can shift, mirror, and scale an 'L'? + a, b, disparity = procrustes(self.data1, self.data2) + assert_allclose(b, a) + assert_almost_equal(disparity, 0.) + + # if first mtx is standardized, leaves first mtx unchanged? + m4, m5, disp45 = procrustes(self.data4, self.data5) + assert_equal(m4, self.data4) + + # at worst, data3 is an 'L' with one point off by .5 + m1, m3, disp13 = procrustes(self.data1, self.data3) + #assert_(disp13 < 0.5 ** 2) + + def test_procrustes2(self): + # procrustes disparity should not depend on order of matrices + m1, m3, disp13 = procrustes(self.data1, self.data3) + m3_2, m1_2, disp31 = procrustes(self.data3, self.data1) + assert_almost_equal(disp13, disp31) + + # try with 3d, 8 pts per + rand1 = np.array([[2.61955202, 0.30522265, 0.55515826], + [0.41124708, -0.03966978, -0.31854548], + [0.91910318, 1.39451809, -0.15295084], + [2.00452023, 0.50150048, 0.29485268], + [0.09453595, 0.67528885, 0.03283872], + [0.07015232, 2.18892599, -1.67266852], + [0.65029688, 1.60551637, 0.80013549], + [-0.6607528, 0.53644208, 0.17033891]]) + + rand3 = np.array([[0.0809969, 0.09731461, -0.173442], + [-1.84888465, -0.92589646, -1.29335743], + [0.67031855, -1.35957463, 0.41938621], + [0.73967209, -0.20230757, 0.52418027], + [0.17752796, 0.09065607, 0.29827466], + [0.47999368, -0.88455717, -0.57547934], + [-0.11486344, -0.12608506, -0.3395779], + [-0.86106154, -0.28687488, 0.9644429]]) + res1, res3, disp13 = procrustes(rand1, rand3) + res3_2, res1_2, disp31 = procrustes(rand3, rand1) + assert_almost_equal(disp13, disp31) + + def test_procrustes_shape_mismatch(self): + assert_raises(ValueError, procrustes, + np.array([[1, 2], [3, 4]]), + np.array([[5, 6, 7], [8, 9, 10]])) + + def test_procrustes_empty_rows_or_cols(self): + empty = np.array([[]]) + assert_raises(ValueError, procrustes, empty, empty) + + def test_procrustes_no_variation(self): + assert_raises(ValueError, procrustes, + np.array([[42, 42], [42, 42]]), + np.array([[45, 45], [45, 45]])) + + def test_procrustes_bad_number_of_dimensions(self): + # fewer dimensions in one dataset + assert_raises(ValueError, procrustes, + np.array([1, 1, 2, 3, 5, 8]), + np.array([[1, 2], [3, 4]])) + + # fewer dimensions in both datasets + assert_raises(ValueError, procrustes, + np.array([1, 1, 2, 3, 5, 8]), + np.array([1, 1, 2, 3, 5, 8])) + + # zero dimensions + assert_raises(ValueError, procrustes, np.array(7), np.array(11)) + + # extra dimensions + assert_raises(ValueError, procrustes, + np.array([[[11], [7]]]), + np.array([[[5, 13]]])) + diff --git a/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test_spherical_voronoi.py b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test_spherical_voronoi.py new file mode 100644 index 0000000000000000000000000000000000000000..621a961ebb6e495701763af4ded10b431a63a992 --- /dev/null +++ b/openflamingo/lib/python3.10/site-packages/scipy/spatial/tests/test_spherical_voronoi.py @@ -0,0 +1,358 @@ +import numpy as np +import itertools +from numpy.testing import (assert_equal, + assert_almost_equal, + assert_array_equal, + assert_array_almost_equal) +import pytest +from pytest import raises as assert_raises +from scipy.spatial import SphericalVoronoi, distance +from scipy.optimize import linear_sum_assignment +from scipy.constants import golden as phi +from scipy.special import gamma + + +TOL = 1E-10 + + +def _generate_tetrahedron(): + return np.array([[1, 1, 1], [1, -1, -1], [-1, 1, -1], [-1, -1, 1]]) + + +def _generate_cube(): + return np.array(list(itertools.product([-1, 1.], repeat=3))) + + +def _generate_octahedron(): + return np.array([[-1, 0, 0], [+1, 0, 0], [0, -1, 0], + [0, +1, 0], [0, 0, -1], [0, 0, +1]]) + + +def _generate_dodecahedron(): + + x1 = _generate_cube() + x2 = np.array([[0, -phi, -1 / phi], + [0, -phi, +1 / phi], + [0, +phi, -1 / phi], + [0, +phi, +1 / phi]]) + x3 = np.array([[-1 / phi, 0, -phi], + [+1 / phi, 0, -phi], + [-1 / phi, 0, +phi], + [+1 / phi, 0, +phi]]) + x4 = np.array([[-phi, -1 / phi, 0], + [-phi, +1 / phi, 0], + [+phi, -1 / phi, 0], + [+phi, +1 / phi, 0]]) + return np.concatenate((x1, x2, x3, x4)) + + +def _generate_icosahedron(): + x = np.array([[0, -1, -phi], + [0, -1, +phi], + [0, +1, -phi], + [0, +1, +phi]]) + return np.concatenate([np.roll(x, i, axis=1) for i in range(3)]) + + +def _generate_polytope(name): + polygons = ["triangle", "square", "pentagon", "hexagon", "heptagon", + "octagon", "nonagon", "decagon", "undecagon", "dodecagon"] + polyhedra = ["tetrahedron", "cube", "octahedron", "dodecahedron", + "icosahedron"] + if name not in polygons and name not in polyhedra: + raise ValueError("unrecognized polytope") + + if name in polygons: + n = polygons.index(name) + 3 + thetas = np.linspace(0, 2 * np.pi, n, endpoint=False) + p = np.vstack([np.cos(thetas), np.sin(thetas)]).T + elif name == "tetrahedron": + p = _generate_tetrahedron() + elif name == "cube": + p = _generate_cube() + elif name == "octahedron": + p = _generate_octahedron() + elif name == "dodecahedron": + p = _generate_dodecahedron() + elif name == "icosahedron": + p = _generate_icosahedron() + + return p / np.linalg.norm(p, axis=1, keepdims=True) + + +def _hypersphere_area(dim, radius): + # https://en.wikipedia.org/wiki/N-sphere#Closed_forms + return 2 * np.pi**(dim / 2) / gamma(dim / 2) * radius**(dim - 1) + + +def _sample_sphere(n, dim, seed=None): + # Sample points uniformly at random from the hypersphere + rng = np.random.RandomState(seed=seed) + points = rng.randn(n, dim) + points /= np.linalg.norm(points, axis=1, keepdims=True) + return points + + +class TestSphericalVoronoi: + + def setup_method(self): + self.points = np.array([ + [-0.78928481, -0.16341094, 0.59188373], + [-0.66839141, 0.73309634, 0.12578818], + [0.32535778, -0.92476944, -0.19734181], + [-0.90177102, -0.03785291, -0.43055335], + [0.71781344, 0.68428936, 0.12842096], + [-0.96064876, 0.23492353, -0.14820556], + [0.73181537, -0.22025898, -0.6449281], + [0.79979205, 0.54555747, 0.25039913]] + ) + + def test_constructor(self): + center = np.array([1, 2, 3]) + radius = 2 + s1 = SphericalVoronoi(self.points) + # user input checks in SphericalVoronoi now require + # the radius / center to match the generators so adjust + # accordingly here + s2 = SphericalVoronoi(self.points * radius, radius) + s3 = SphericalVoronoi(self.points + center, center=center) + s4 = SphericalVoronoi(self.points * radius + center, radius, center) + assert_array_equal(s1.center, np.array([0, 0, 0])) + assert_equal(s1.radius, 1) + assert_array_equal(s2.center, np.array([0, 0, 0])) + assert_equal(s2.radius, 2) + assert_array_equal(s3.center, center) + assert_equal(s3.radius, 1) + assert_array_equal(s4.center, center) + assert_equal(s4.radius, radius) + + # Test a non-sequence/-ndarray based array-like + s5 = SphericalVoronoi(memoryview(self.points)) # type: ignore[arg-type] + assert_array_equal(s5.center, np.array([0, 0, 0])) + assert_equal(s5.radius, 1) + + def test_vertices_regions_translation_invariance(self): + sv_origin = SphericalVoronoi(self.points) + center = np.array([1, 1, 1]) + sv_translated = SphericalVoronoi(self.points + center, center=center) + assert_equal(sv_origin.regions, sv_translated.regions) + assert_array_almost_equal(sv_origin.vertices + center, + sv_translated.vertices) + + def test_vertices_regions_scaling_invariance(self): + sv_unit = SphericalVoronoi(self.points) + sv_scaled = SphericalVoronoi(self.points * 2, 2) + assert_equal(sv_unit.regions, sv_scaled.regions) + assert_array_almost_equal(sv_unit.vertices * 2, + sv_scaled.vertices) + + def test_old_radius_api_error(self): + with pytest.raises(ValueError, match='`radius` is `None`. *'): + SphericalVoronoi(self.points, radius=None) + + def test_sort_vertices_of_regions(self): + sv = SphericalVoronoi(self.points) + unsorted_regions = sv.regions + sv.sort_vertices_of_regions() + assert_equal(sorted(sv.regions), sorted(unsorted_regions)) + + def test_sort_vertices_of_regions_flattened(self): + expected = sorted([[0, 6, 5, 2, 3], [2, 3, 10, 11, 8, 7], [0, 6, 4, 1], + [4, 8, 7, 5, 6], [9, 11, 10], [2, 7, 5], + [1, 4, 8, 11, 9], [0, 3, 10, 9, 1]]) + expected = list(itertools.chain(*sorted(expected))) # type: ignore + sv = SphericalVoronoi(self.points) + sv.sort_vertices_of_regions() + actual = list(itertools.chain(*sorted(sv.regions))) + assert_array_equal(actual, expected) + + def test_sort_vertices_of_regions_dimensionality(self): + points = np.array([[1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + [0.5, 0.5, 0.5, 0.5]]) + with pytest.raises(TypeError, match="three-dimensional"): + sv = SphericalVoronoi(points) + sv.sort_vertices_of_regions() + + def test_num_vertices(self): + # for any n >= 3, a spherical Voronoi diagram has 2n - 4 + # vertices; this is a direct consequence of Euler's formula + # as explained by Dinis and Mamede (2010) Proceedings of the + # 2010 International Symposium on Voronoi Diagrams in Science + # and Engineering + sv = SphericalVoronoi(self.points) + expected = self.points.shape[0] * 2 - 4 + actual = sv.vertices.shape[0] + assert_equal(actual, expected) + + def test_voronoi_circles(self): + sv = SphericalVoronoi(self.points) + for vertex in sv.vertices: + distances = distance.cdist(sv.points, np.array([vertex])) + closest = np.array(sorted(distances)[0:3]) + assert_almost_equal(closest[0], closest[1], 7, str(vertex)) + assert_almost_equal(closest[0], closest[2], 7, str(vertex)) + + def test_duplicate_point_handling(self): + # an exception should be raised for degenerate generators + # related to Issue# 7046 + self.degenerate = np.concatenate((self.points, self.points)) + with assert_raises(ValueError): + SphericalVoronoi(self.degenerate) + + def test_incorrect_radius_handling(self): + # an exception should be raised if the radius provided + # cannot possibly match the input generators + with assert_raises(ValueError): + SphericalVoronoi(self.points, radius=0.98) + + def test_incorrect_center_handling(self): + # an exception should be raised if the center provided + # cannot possibly match the input generators + with assert_raises(ValueError): + SphericalVoronoi(self.points, center=[0.1, 0, 0]) + + @pytest.mark.parametrize("dim", range(2, 6)) + @pytest.mark.parametrize("shift", [False, True]) + def test_single_hemisphere_handling(self, dim, shift): + n = 10 + points = _sample_sphere(n, dim, seed=0) + points[:, 0] = np.abs(points[:, 0]) + center = (np.arange(dim) + 1) * shift + sv = SphericalVoronoi(points + center, center=center) + dots = np.einsum('ij,ij->i', sv.vertices - center, + sv.points[sv._simplices[:, 0]] - center) + circumradii = np.arccos(np.clip(dots, -1, 1)) + assert np.max(circumradii) > np.pi / 2 + + @pytest.mark.parametrize("n", [1, 2, 10]) + @pytest.mark.parametrize("dim", range(2, 6)) + @pytest.mark.parametrize("shift", [False, True]) + def test_rank_deficient(self, n, dim, shift): + center = (np.arange(dim) + 1) * shift + points = _sample_sphere(n, dim - 1, seed=0) + points = np.hstack([points, np.zeros((n, 1))]) + with pytest.raises(ValueError, match="Rank of input points"): + SphericalVoronoi(points + center, center=center) + + @pytest.mark.parametrize("dim", range(2, 6)) + def test_higher_dimensions(self, dim): + n = 100 + points = _sample_sphere(n, dim, seed=0) + sv = SphericalVoronoi(points) + assert sv.vertices.shape[1] == dim + assert len(sv.regions) == n + + # verify Euler characteristic + cell_counts = [] + simplices = np.sort(sv._simplices) + for i in range(1, dim + 1): + cells = [] + for indices in itertools.combinations(range(dim), i): + cells.append(simplices[:, list(indices)]) + cells = np.unique(np.concatenate(cells), axis=0) + cell_counts.append(len(cells)) + expected_euler = 1 + (-1)**(dim-1) + actual_euler = sum([(-1)**i * e for i, e in enumerate(cell_counts)]) + assert expected_euler == actual_euler + + @pytest.mark.parametrize("dim", range(2, 6)) + def test_cross_polytope_regions(self, dim): + # The hypercube is the dual of the cross-polytope, so the voronoi + # vertices of the cross-polytope lie on the points of the hypercube. + + # generate points of the cross-polytope + points = np.concatenate((-np.eye(dim), np.eye(dim))) + sv = SphericalVoronoi(points) + assert all([len(e) == 2**(dim - 1) for e in sv.regions]) + + # generate points of the hypercube + expected = np.vstack(list(itertools.product([-1, 1], repeat=dim))) + expected = expected.astype(np.float64) / np.sqrt(dim) + + # test that Voronoi vertices are correctly placed + dist = distance.cdist(sv.vertices, expected) + res = linear_sum_assignment(dist) + assert dist[res].sum() < TOL + + @pytest.mark.parametrize("dim", range(2, 6)) + def test_hypercube_regions(self, dim): + # The cross-polytope is the dual of the hypercube, so the voronoi + # vertices of the hypercube lie on the points of the cross-polytope. + + # generate points of the hypercube + points = np.vstack(list(itertools.product([-1, 1], repeat=dim))) + points = points.astype(np.float64) / np.sqrt(dim) + sv = SphericalVoronoi(points) + + # generate points of the cross-polytope + expected = np.concatenate((-np.eye(dim), np.eye(dim))) + + # test that Voronoi vertices are correctly placed + dist = distance.cdist(sv.vertices, expected) + res = linear_sum_assignment(dist) + assert dist[res].sum() < TOL + + @pytest.mark.parametrize("n", [10, 500]) + @pytest.mark.parametrize("dim", [2, 3]) + @pytest.mark.parametrize("radius", [0.5, 1, 2]) + @pytest.mark.parametrize("shift", [False, True]) + @pytest.mark.parametrize("single_hemisphere", [False, True]) + def test_area_reconstitution(self, n, dim, radius, shift, + single_hemisphere): + points = _sample_sphere(n, dim, seed=0) + + # move all points to one side of the sphere for single-hemisphere test + if single_hemisphere: + points[:, 0] = np.abs(points[:, 0]) + + center = (np.arange(dim) + 1) * shift + points = radius * points + center + + sv = SphericalVoronoi(points, radius=radius, center=center) + areas = sv.calculate_areas() + assert_almost_equal(areas.sum(), _hypersphere_area(dim, radius)) + + @pytest.mark.parametrize("poly", ["triangle", "dodecagon", + "tetrahedron", "cube", "octahedron", + "dodecahedron", "icosahedron"]) + def test_equal_area_reconstitution(self, poly): + points = _generate_polytope(poly) + n, dim = points.shape + sv = SphericalVoronoi(points) + areas = sv.calculate_areas() + assert_almost_equal(areas, _hypersphere_area(dim, 1) / n) + + def test_area_unsupported_dimension(self): + dim = 4 + points = np.concatenate((-np.eye(dim), np.eye(dim))) + sv = SphericalVoronoi(points) + with pytest.raises(TypeError, match="Only supported"): + sv.calculate_areas() + + @pytest.mark.parametrize("radius", [1, 1.]) + @pytest.mark.parametrize("center", [None, (1, 2, 3), (1., 2., 3.)]) + def test_attribute_types(self, radius, center): + points = radius * self.points + if center is not None: + points += center + + sv = SphericalVoronoi(points, radius=radius, center=center) + assert sv.points.dtype is np.dtype(np.float64) + assert sv.center.dtype is np.dtype(np.float64) + assert isinstance(sv.radius, float) + + def test_region_types(self): + # Tests that region integer type does not change + # See Issue #13412 + sv = SphericalVoronoi(self.points) + dtype = type(sv.regions[0][0]) + # also enforce nested list type per gh-19177 + for region in sv.regions: + assert isinstance(region, list) + sv.sort_vertices_of_regions() + assert type(sv.regions[0][0]) == dtype + sv.sort_vertices_of_regions() + assert type(sv.regions[0][0]) == dtype diff --git a/phi4/lib/python3.10/site-packages/numpy/_core/__pycache__/fromnumeric.cpython-310.pyc b/phi4/lib/python3.10/site-packages/numpy/_core/__pycache__/fromnumeric.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bdbdd1207482c67cc1ca0d034dc2c479783a2711 --- /dev/null +++ b/phi4/lib/python3.10/site-packages/numpy/_core/__pycache__/fromnumeric.cpython-310.pyc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf135b5f582a3ff2dc30b3e7699e7af572e82048a193bf6c1b2589997c2d67b4 +size 139645 diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_array_api.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_array_api.py new file mode 100644 index 0000000000000000000000000000000000000000..4af24d1a35ac294e59c0c0c21225c55cbba5b2f1 --- /dev/null +++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_array_api.py @@ -0,0 +1,595 @@ +"""Utility functions to use Python Array API compatible libraries. + +For the context about the Array API see: +https://data-apis.org/array-api/latest/purpose_and_scope.html + +The SciPy use case of the Array API is described on the following page: +https://data-apis.org/array-api/latest/use_cases.html#use-case-scipy +""" +import os + +from types import ModuleType +from typing import Any, Literal, TypeAlias + +import numpy as np +import numpy.typing as npt + +from scipy._lib import array_api_compat +from scipy._lib.array_api_compat import ( + is_array_api_obj, + size as xp_size, + numpy as np_compat, + device as xp_device, + is_numpy_namespace as is_numpy, + is_cupy_namespace as is_cupy, + is_torch_namespace as is_torch, + is_jax_namespace as is_jax, + is_array_api_strict_namespace as is_array_api_strict +) + +__all__ = [ + '_asarray', 'array_namespace', 'assert_almost_equal', 'assert_array_almost_equal', + 'get_xp_devices', + 'is_array_api_strict', 'is_complex', 'is_cupy', 'is_jax', 'is_numpy', 'is_torch', + 'SCIPY_ARRAY_API', 'SCIPY_DEVICE', 'scipy_namespace_for', + 'xp_assert_close', 'xp_assert_equal', 'xp_assert_less', + 'xp_copy', 'xp_copysign', 'xp_device', + 'xp_moveaxis_to_end', 'xp_ravel', 'xp_real', 'xp_sign', 'xp_size', + 'xp_take_along_axis', 'xp_unsupported_param_msg', 'xp_vector_norm', +] + + +# To enable array API and strict array-like input validation +SCIPY_ARRAY_API: str | bool = os.environ.get("SCIPY_ARRAY_API", False) +# To control the default device - for use in the test suite only +SCIPY_DEVICE = os.environ.get("SCIPY_DEVICE", "cpu") + +_GLOBAL_CONFIG = { + "SCIPY_ARRAY_API": SCIPY_ARRAY_API, + "SCIPY_DEVICE": SCIPY_DEVICE, +} + + +Array: TypeAlias = Any # To be changed to a Protocol later (see array-api#589) +ArrayLike: TypeAlias = Array | npt.ArrayLike + + +def _compliance_scipy(arrays): + """Raise exceptions on known-bad subclasses. + + The following subclasses are not supported and raise and error: + - `numpy.ma.MaskedArray` + - `numpy.matrix` + - NumPy arrays which do not have a boolean or numerical dtype + - Any array-like which is neither array API compatible nor coercible by NumPy + - Any array-like which is coerced by NumPy to an unsupported dtype + """ + for i in range(len(arrays)): + array = arrays[i] + + from scipy.sparse import issparse + # this comes from `_util._asarray_validated` + if issparse(array): + msg = ('Sparse arrays/matrices are not supported by this function. ' + 'Perhaps one of the `scipy.sparse.linalg` functions ' + 'would work instead.') + raise ValueError(msg) + + if isinstance(array, np.ma.MaskedArray): + raise TypeError("Inputs of type `numpy.ma.MaskedArray` are not supported.") + elif isinstance(array, np.matrix): + raise TypeError("Inputs of type `numpy.matrix` are not supported.") + if isinstance(array, np.ndarray | np.generic): + dtype = array.dtype + if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)): + raise TypeError(f"An argument has dtype `{dtype!r}`; " + f"only boolean and numerical dtypes are supported.") + elif not is_array_api_obj(array): + try: + array = np.asanyarray(array) + except TypeError: + raise TypeError("An argument is neither array API compatible nor " + "coercible by NumPy.") + dtype = array.dtype + if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)): + message = ( + f"An argument was coerced to an unsupported dtype `{dtype!r}`; " + f"only boolean and numerical dtypes are supported." + ) + raise TypeError(message) + arrays[i] = array + return arrays + + +def _check_finite(array: Array, xp: ModuleType) -> None: + """Check for NaNs or Infs.""" + msg = "array must not contain infs or NaNs" + try: + if not xp.all(xp.isfinite(array)): + raise ValueError(msg) + except TypeError: + raise ValueError(msg) + + +def array_namespace(*arrays: Array) -> ModuleType: + """Get the array API compatible namespace for the arrays xs. + + Parameters + ---------- + *arrays : sequence of array_like + Arrays used to infer the common namespace. + + Returns + ------- + namespace : module + Common namespace. + + Notes + ----- + Thin wrapper around `array_api_compat.array_namespace`. + + 1. Check for the global switch: SCIPY_ARRAY_API. This can also be accessed + dynamically through ``_GLOBAL_CONFIG['SCIPY_ARRAY_API']``. + 2. `_compliance_scipy` raise exceptions on known-bad subclasses. See + its definition for more details. + + When the global switch is False, it defaults to the `numpy` namespace. + In that case, there is no compliance check. This is a convenience to + ease the adoption. Otherwise, arrays must comply with the new rules. + """ + if not _GLOBAL_CONFIG["SCIPY_ARRAY_API"]: + # here we could wrap the namespace if needed + return np_compat + + _arrays = [array for array in arrays if array is not None] + + _arrays = _compliance_scipy(_arrays) + + return array_api_compat.array_namespace(*_arrays) + + +def _asarray( + array: ArrayLike, + dtype: Any = None, + order: Literal['K', 'A', 'C', 'F'] | None = None, + copy: bool | None = None, + *, + xp: ModuleType | None = None, + check_finite: bool = False, + subok: bool = False, + ) -> Array: + """SciPy-specific replacement for `np.asarray` with `order`, `check_finite`, and + `subok`. + + Memory layout parameter `order` is not exposed in the Array API standard. + `order` is only enforced if the input array implementation + is NumPy based, otherwise `order` is just silently ignored. + + `check_finite` is also not a keyword in the array API standard; included + here for convenience rather than that having to be a separate function + call inside SciPy functions. + + `subok` is included to allow this function to preserve the behaviour of + `np.asanyarray` for NumPy based inputs. + """ + if xp is None: + xp = array_namespace(array) + if is_numpy(xp): + # Use NumPy API to support order + if copy is True: + array = np.array(array, order=order, dtype=dtype, subok=subok) + elif subok: + array = np.asanyarray(array, order=order, dtype=dtype) + else: + array = np.asarray(array, order=order, dtype=dtype) + else: + try: + array = xp.asarray(array, dtype=dtype, copy=copy) + except TypeError: + coerced_xp = array_namespace(xp.asarray(3)) + array = coerced_xp.asarray(array, dtype=dtype, copy=copy) + + if check_finite: + _check_finite(array, xp) + + return array + + +def xp_copy(x: Array, *, xp: ModuleType | None = None) -> Array: + """ + Copies an array. + + Parameters + ---------- + x : array + + xp : array_namespace + + Returns + ------- + copy : array + Copied array + + Notes + ----- + This copy function does not offer all the semantics of `np.copy`, i.e. the + `subok` and `order` keywords are not used. + """ + # Note: for older NumPy versions, `np.asarray` did not support the `copy` kwarg, + # so this uses our other helper `_asarray`. + if xp is None: + xp = array_namespace(x) + + return _asarray(x, copy=True, xp=xp) + + +def _strict_check(actual, desired, xp, *, + check_namespace=True, check_dtype=True, check_shape=True, + check_0d=True): + __tracebackhide__ = True # Hide traceback for py.test + if check_namespace: + _assert_matching_namespace(actual, desired) + + # only NumPy distinguishes between scalars and arrays; we do if check_0d=True. + # do this first so we can then cast to array (and thus use the array API) below. + if is_numpy(xp) and check_0d: + _msg = ("Array-ness does not match:\n Actual: " + f"{type(actual)}\n Desired: {type(desired)}") + assert ((xp.isscalar(actual) and xp.isscalar(desired)) + or (not xp.isscalar(actual) and not xp.isscalar(desired))), _msg + + actual = xp.asarray(actual) + desired = xp.asarray(desired) + + if check_dtype: + _msg = f"dtypes do not match.\nActual: {actual.dtype}\nDesired: {desired.dtype}" + assert actual.dtype == desired.dtype, _msg + + if check_shape: + _msg = f"Shapes do not match.\nActual: {actual.shape}\nDesired: {desired.shape}" + assert actual.shape == desired.shape, _msg + + desired = xp.broadcast_to(desired, actual.shape) + return actual, desired + + +def _assert_matching_namespace(actual, desired): + __tracebackhide__ = True # Hide traceback for py.test + actual = actual if isinstance(actual, tuple) else (actual,) + desired_space = array_namespace(desired) + for arr in actual: + arr_space = array_namespace(arr) + _msg = (f"Namespaces do not match.\n" + f"Actual: {arr_space.__name__}\n" + f"Desired: {desired_space.__name__}") + assert arr_space == desired_space, _msg + + +def xp_assert_equal(actual, desired, *, check_namespace=True, check_dtype=True, + check_shape=True, check_0d=True, err_msg='', xp=None): + __tracebackhide__ = True # Hide traceback for py.test + if xp is None: + xp = array_namespace(actual) + + actual, desired = _strict_check( + actual, desired, xp, check_namespace=check_namespace, + check_dtype=check_dtype, check_shape=check_shape, + check_0d=check_0d + ) + + if is_cupy(xp): + return xp.testing.assert_array_equal(actual, desired, err_msg=err_msg) + elif is_torch(xp): + # PyTorch recommends using `rtol=0, atol=0` like this + # to test for exact equality + err_msg = None if err_msg == '' else err_msg + return xp.testing.assert_close(actual, desired, rtol=0, atol=0, equal_nan=True, + check_dtype=False, msg=err_msg) + # JAX uses `np.testing` + return np.testing.assert_array_equal(actual, desired, err_msg=err_msg) + + +def xp_assert_close(actual, desired, *, rtol=None, atol=0, check_namespace=True, + check_dtype=True, check_shape=True, check_0d=True, + err_msg='', xp=None): + __tracebackhide__ = True # Hide traceback for py.test + if xp is None: + xp = array_namespace(actual) + + actual, desired = _strict_check( + actual, desired, xp, + check_namespace=check_namespace, check_dtype=check_dtype, + check_shape=check_shape, check_0d=check_0d + ) + + floating = xp.isdtype(actual.dtype, ('real floating', 'complex floating')) + if rtol is None and floating: + # multiplier of 4 is used as for `np.float64` this puts the default `rtol` + # roughly half way between sqrt(eps) and the default for + # `numpy.testing.assert_allclose`, 1e-7 + rtol = xp.finfo(actual.dtype).eps**0.5 * 4 + elif rtol is None: + rtol = 1e-7 + + if is_cupy(xp): + return xp.testing.assert_allclose(actual, desired, rtol=rtol, + atol=atol, err_msg=err_msg) + elif is_torch(xp): + err_msg = None if err_msg == '' else err_msg + return xp.testing.assert_close(actual, desired, rtol=rtol, atol=atol, + equal_nan=True, check_dtype=False, msg=err_msg) + # JAX uses `np.testing` + return np.testing.assert_allclose(actual, desired, rtol=rtol, + atol=atol, err_msg=err_msg) + + +def xp_assert_less(actual, desired, *, check_namespace=True, check_dtype=True, + check_shape=True, check_0d=True, err_msg='', verbose=True, xp=None): + __tracebackhide__ = True # Hide traceback for py.test + if xp is None: + xp = array_namespace(actual) + + actual, desired = _strict_check( + actual, desired, xp, check_namespace=check_namespace, + check_dtype=check_dtype, check_shape=check_shape, + check_0d=check_0d + ) + + if is_cupy(xp): + return xp.testing.assert_array_less(actual, desired, + err_msg=err_msg, verbose=verbose) + elif is_torch(xp): + if actual.device.type != 'cpu': + actual = actual.cpu() + if desired.device.type != 'cpu': + desired = desired.cpu() + # JAX uses `np.testing` + return np.testing.assert_array_less(actual, desired, + err_msg=err_msg, verbose=verbose) + + +def assert_array_almost_equal(actual, desired, decimal=6, *args, **kwds): + """Backwards compatible replacement. In new code, use xp_assert_close instead. + """ + rtol, atol = 0, 1.5*10**(-decimal) + return xp_assert_close(actual, desired, + atol=atol, rtol=rtol, check_dtype=False, check_shape=False, + *args, **kwds) + + +def assert_almost_equal(actual, desired, decimal=7, *args, **kwds): + """Backwards compatible replacement. In new code, use xp_assert_close instead. + """ + rtol, atol = 0, 1.5*10**(-decimal) + return xp_assert_close(actual, desired, + atol=atol, rtol=rtol, check_dtype=False, check_shape=False, + *args, **kwds) + + +def xp_unsupported_param_msg(param: Any) -> str: + return f'Providing {param!r} is only supported for numpy arrays.' + + +def is_complex(x: Array, xp: ModuleType) -> bool: + return xp.isdtype(x.dtype, 'complex floating') + + +def get_xp_devices(xp: ModuleType) -> list[str] | list[None]: + """Returns a list of available devices for the given namespace.""" + devices: list[str] = [] + if is_torch(xp): + devices += ['cpu'] + import torch # type: ignore[import] + num_cuda = torch.cuda.device_count() + for i in range(0, num_cuda): + devices += [f'cuda:{i}'] + if torch.backends.mps.is_available(): + devices += ['mps'] + return devices + elif is_cupy(xp): + import cupy # type: ignore[import] + num_cuda = cupy.cuda.runtime.getDeviceCount() + for i in range(0, num_cuda): + devices += [f'cuda:{i}'] + return devices + elif is_jax(xp): + import jax # type: ignore[import] + num_cpu = jax.device_count(backend='cpu') + for i in range(0, num_cpu): + devices += [f'cpu:{i}'] + num_gpu = jax.device_count(backend='gpu') + for i in range(0, num_gpu): + devices += [f'gpu:{i}'] + num_tpu = jax.device_count(backend='tpu') + for i in range(0, num_tpu): + devices += [f'tpu:{i}'] + return devices + + # given namespace is not known to have a list of available devices; + # return `[None]` so that one can use this in tests for `device=None`. + return [None] + + +def scipy_namespace_for(xp: ModuleType) -> ModuleType | None: + """Return the `scipy`-like namespace of a non-NumPy backend + + That is, return the namespace corresponding with backend `xp` that contains + `scipy` sub-namespaces like `linalg` and `special`. If no such namespace + exists, return ``None``. Useful for dispatching. + """ + + if is_cupy(xp): + import cupyx # type: ignore[import-not-found,import-untyped] + return cupyx.scipy + + if is_jax(xp): + import jax # type: ignore[import-not-found] + return jax.scipy + + if is_torch(xp): + return xp + + return None + + +# temporary substitute for xp.moveaxis, which is not yet in all backends +# or covered by array_api_compat. +def xp_moveaxis_to_end( + x: Array, + source: int, + /, *, + xp: ModuleType | None = None) -> Array: + xp = array_namespace(xp) if xp is None else xp + axes = list(range(x.ndim)) + temp = axes.pop(source) + axes = axes + [temp] + return xp.permute_dims(x, axes) + + +# temporary substitute for xp.copysign, which is not yet in all backends +# or covered by array_api_compat. +def xp_copysign(x1: Array, x2: Array, /, *, xp: ModuleType | None = None) -> Array: + # no attempt to account for special cases + xp = array_namespace(x1, x2) if xp is None else xp + abs_x1 = xp.abs(x1) + return xp.where(x2 >= 0, abs_x1, -abs_x1) + + +# partial substitute for xp.sign, which does not cover the NaN special case +# that I need. (https://github.com/data-apis/array-api-compat/issues/136) +def xp_sign(x: Array, /, *, xp: ModuleType | None = None) -> Array: + xp = array_namespace(x) if xp is None else xp + if is_numpy(xp): # only NumPy implements the special cases correctly + return xp.sign(x) + sign = xp.zeros_like(x) + one = xp.asarray(1, dtype=x.dtype) + sign = xp.where(x > 0, one, sign) + sign = xp.where(x < 0, -one, sign) + sign = xp.where(xp.isnan(x), xp.nan*one, sign) + return sign + +# maybe use `scipy.linalg` if/when array API support is added +def xp_vector_norm(x: Array, /, *, + axis: int | tuple[int] | None = None, + keepdims: bool = False, + ord: int | float = 2, + xp: ModuleType | None = None) -> Array: + xp = array_namespace(x) if xp is None else xp + + if SCIPY_ARRAY_API: + # check for optional `linalg` extension + if hasattr(xp, 'linalg'): + return xp.linalg.vector_norm(x, axis=axis, keepdims=keepdims, ord=ord) + else: + if ord != 2: + raise ValueError( + "only the Euclidean norm (`ord=2`) is currently supported in " + "`xp_vector_norm` for backends not implementing the `linalg` " + "extension." + ) + # return (x @ x)**0.5 + # or to get the right behavior with nd, complex arrays + return xp.sum(xp.conj(x) * x, axis=axis, keepdims=keepdims)**0.5 + else: + # to maintain backwards compatibility + return np.linalg.norm(x, ord=ord, axis=axis, keepdims=keepdims) + + +def xp_ravel(x: Array, /, *, xp: ModuleType | None = None) -> Array: + # Equivalent of np.ravel written in terms of array API + # Even though it's one line, it comes up so often that it's worth having + # this function for readability + xp = array_namespace(x) if xp is None else xp + return xp.reshape(x, (-1,)) + + +def xp_real(x: Array, /, *, xp: ModuleType | None = None) -> Array: + # Convenience wrapper of xp.real that allows non-complex input; + # see data-apis/array-api#824 + xp = array_namespace(x) if xp is None else xp + return xp.real(x) if xp.isdtype(x.dtype, 'complex floating') else x + + +def xp_take_along_axis(arr: Array, + indices: Array, /, *, + axis: int = -1, + xp: ModuleType | None = None) -> Array: + # Dispatcher for np.take_along_axis for backends that support it; + # see data-apis/array-api/pull#816 + xp = array_namespace(arr) if xp is None else xp + if is_torch(xp): + return xp.take_along_dim(arr, indices, dim=axis) + elif is_array_api_strict(xp): + raise NotImplementedError("Array API standard does not define take_along_axis") + else: + return xp.take_along_axis(arr, indices, axis) + + +# utility to broadcast arrays and promote to common dtype +def xp_broadcast_promote(*args, ensure_writeable=False, force_floating=False, xp=None): + xp = array_namespace(*args) if xp is None else xp + + args = [(_asarray(arg, subok=True) if arg is not None else arg) for arg in args] + args_not_none = [arg for arg in args if arg is not None] + + # determine minimum dtype + default_float = xp.asarray(1.).dtype + dtypes = [arg.dtype for arg in args_not_none] + try: # follow library's prefered mixed promotion rules + dtype = xp.result_type(*dtypes) + if force_floating and xp.isdtype(dtype, 'integral'): + # If we were to add `default_float` before checking whether the result + # type is otherwise integral, we risk promotion from lower float. + dtype = xp.result_type(dtype, default_float) + except TypeError: # mixed type promotion isn't defined + float_dtypes = [dtype for dtype in dtypes + if not xp.isdtype(dtype, 'integral')] + if float_dtypes: + dtype = xp.result_type(*float_dtypes, default_float) + elif force_floating: + dtype = default_float + else: + dtype = xp.result_type(*dtypes) + + # determine result shape + shapes = {arg.shape for arg in args_not_none} + try: + shape = (np.broadcast_shapes(*shapes) if len(shapes) != 1 + else args_not_none[0].shape) + except ValueError as e: + message = "Array shapes are incompatible for broadcasting." + raise ValueError(message) from e + + out = [] + for arg in args: + if arg is None: + out.append(arg) + continue + + # broadcast only if needed + # Even if two arguments need broadcasting, this is faster than + # `broadcast_arrays`, especially since we've already determined `shape` + if arg.shape != shape: + kwargs = {'subok': True} if is_numpy(xp) else {} + arg = xp.broadcast_to(arg, shape, **kwargs) + + # convert dtype/copy only if needed + if (arg.dtype != dtype) or ensure_writeable: + arg = xp.astype(arg, dtype, copy=True) + out.append(arg) + + return out + + +def xp_float_to_complex(arr: Array, xp: ModuleType | None = None) -> Array: + xp = array_namespace(arr) if xp is None else xp + arr_dtype = arr.dtype + # The standard float dtypes are float32 and float64. + # Convert float32 to complex64, + # and float64 (and non-standard real dtypes) to complex128 + if xp.isdtype(arr_dtype, xp.float32): + arr = xp.astype(arr, xp.complex64) + elif xp.isdtype(arr_dtype, 'real floating'): + arr = xp.astype(arr, xp.complex128) + + return arr diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_bunch.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_bunch.py new file mode 100644 index 0000000000000000000000000000000000000000..bb562e4348f46dc1137afe3d3ce50f1149c85376 --- /dev/null +++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_bunch.py @@ -0,0 +1,225 @@ +import sys as _sys +from keyword import iskeyword as _iskeyword + + +def _validate_names(typename, field_names, extra_field_names): + """ + Ensure that all the given names are valid Python identifiers that + do not start with '_'. Also check that there are no duplicates + among field_names + extra_field_names. + """ + for name in [typename] + field_names + extra_field_names: + if not isinstance(name, str): + raise TypeError('typename and all field names must be strings') + if not name.isidentifier(): + raise ValueError('typename and all field names must be valid ' + f'identifiers: {name!r}') + if _iskeyword(name): + raise ValueError('typename and all field names cannot be a ' + f'keyword: {name!r}') + + seen = set() + for name in field_names + extra_field_names: + if name.startswith('_'): + raise ValueError('Field names cannot start with an underscore: ' + f'{name!r}') + if name in seen: + raise ValueError(f'Duplicate field name: {name!r}') + seen.add(name) + + +# Note: This code is adapted from CPython:Lib/collections/__init__.py +def _make_tuple_bunch(typename, field_names, extra_field_names=None, + module=None): + """ + Create a namedtuple-like class with additional attributes. + + This function creates a subclass of tuple that acts like a namedtuple + and that has additional attributes. + + The additional attributes are listed in `extra_field_names`. The + values assigned to these attributes are not part of the tuple. + + The reason this function exists is to allow functions in SciPy + that currently return a tuple or a namedtuple to returned objects + that have additional attributes, while maintaining backwards + compatibility. + + This should only be used to enhance *existing* functions in SciPy. + New functions are free to create objects as return values without + having to maintain backwards compatibility with an old tuple or + namedtuple return value. + + Parameters + ---------- + typename : str + The name of the type. + field_names : list of str + List of names of the values to be stored in the tuple. These names + will also be attributes of instances, so the values in the tuple + can be accessed by indexing or as attributes. At least one name + is required. See the Notes for additional restrictions. + extra_field_names : list of str, optional + List of names of values that will be stored as attributes of the + object. See the notes for additional restrictions. + + Returns + ------- + cls : type + The new class. + + Notes + ----- + There are restrictions on the names that may be used in `field_names` + and `extra_field_names`: + + * The names must be unique--no duplicates allowed. + * The names must be valid Python identifiers, and must not begin with + an underscore. + * The names must not be Python keywords (e.g. 'def', 'and', etc., are + not allowed). + + Examples + -------- + >>> from scipy._lib._bunch import _make_tuple_bunch + + Create a class that acts like a namedtuple with length 2 (with field + names `x` and `y`) that will also have the attributes `w` and `beta`: + + >>> Result = _make_tuple_bunch('Result', ['x', 'y'], ['w', 'beta']) + + `Result` is the new class. We call it with keyword arguments to create + a new instance with given values. + + >>> result1 = Result(x=1, y=2, w=99, beta=0.5) + >>> result1 + Result(x=1, y=2, w=99, beta=0.5) + + `result1` acts like a tuple of length 2: + + >>> len(result1) + 2 + >>> result1[:] + (1, 2) + + The values assigned when the instance was created are available as + attributes: + + >>> result1.y + 2 + >>> result1.beta + 0.5 + """ + if len(field_names) == 0: + raise ValueError('field_names must contain at least one name') + + if extra_field_names is None: + extra_field_names = [] + _validate_names(typename, field_names, extra_field_names) + + typename = _sys.intern(str(typename)) + field_names = tuple(map(_sys.intern, field_names)) + extra_field_names = tuple(map(_sys.intern, extra_field_names)) + + all_names = field_names + extra_field_names + arg_list = ', '.join(field_names) + full_list = ', '.join(all_names) + repr_fmt = ''.join(('(', + ', '.join(f'{name}=%({name})r' for name in all_names), + ')')) + tuple_new = tuple.__new__ + _dict, _tuple, _zip = dict, tuple, zip + + # Create all the named tuple methods to be added to the class namespace + + s = f"""\ +def __new__(_cls, {arg_list}, **extra_fields): + return _tuple_new(_cls, ({arg_list},)) + +def __init__(self, {arg_list}, **extra_fields): + for key in self._extra_fields: + if key not in extra_fields: + raise TypeError("missing keyword argument '%s'" % (key,)) + for key, val in extra_fields.items(): + if key not in self._extra_fields: + raise TypeError("unexpected keyword argument '%s'" % (key,)) + self.__dict__[key] = val + +def __setattr__(self, key, val): + if key in {repr(field_names)}: + raise AttributeError("can't set attribute %r of class %r" + % (key, self.__class__.__name__)) + else: + self.__dict__[key] = val +""" + del arg_list + namespace = {'_tuple_new': tuple_new, + '__builtins__': dict(TypeError=TypeError, + AttributeError=AttributeError), + '__name__': f'namedtuple_{typename}'} + exec(s, namespace) + __new__ = namespace['__new__'] + __new__.__doc__ = f'Create new instance of {typename}({full_list})' + __init__ = namespace['__init__'] + __init__.__doc__ = f'Instantiate instance of {typename}({full_list})' + __setattr__ = namespace['__setattr__'] + + def __repr__(self): + 'Return a nicely formatted representation string' + return self.__class__.__name__ + repr_fmt % self._asdict() + + def _asdict(self): + 'Return a new dict which maps field names to their values.' + out = _dict(_zip(self._fields, self)) + out.update(self.__dict__) + return out + + def __getnewargs_ex__(self): + 'Return self as a plain tuple. Used by copy and pickle.' + return _tuple(self), self.__dict__ + + # Modify function metadata to help with introspection and debugging + for method in (__new__, __repr__, _asdict, __getnewargs_ex__): + method.__qualname__ = f'{typename}.{method.__name__}' + + # Build-up the class namespace dictionary + # and use type() to build the result class + class_namespace = { + '__doc__': f'{typename}({full_list})', + '_fields': field_names, + '__new__': __new__, + '__init__': __init__, + '__repr__': __repr__, + '__setattr__': __setattr__, + '_asdict': _asdict, + '_extra_fields': extra_field_names, + '__getnewargs_ex__': __getnewargs_ex__, + } + for index, name in enumerate(field_names): + + def _get(self, index=index): + return self[index] + class_namespace[name] = property(_get) + for name in extra_field_names: + + def _get(self, name=name): + return self.__dict__[name] + class_namespace[name] = property(_get) + + result = type(typename, (tuple,), class_namespace) + + # For pickling to work, the __module__ variable needs to be set to the + # frame where the named tuple is created. Bypass this step in environments + # where sys._getframe is not defined (Jython for example) or sys._getframe + # is not defined for arguments greater than 0 (IronPython), or where the + # user has specified a particular module. + if module is None: + try: + module = _sys._getframe(1).f_globals.get('__name__', '__main__') + except (AttributeError, ValueError): + pass + if module is not None: + result.__module__ = module + __new__.__module__ = module + + return result diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_ccallback.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_ccallback.py new file mode 100644 index 0000000000000000000000000000000000000000..1980d06f5489e6633fb611c35bfb56903bd63e7f --- /dev/null +++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_ccallback.py @@ -0,0 +1,251 @@ +from . import _ccallback_c + +import ctypes + +PyCFuncPtr = ctypes.CFUNCTYPE(ctypes.c_void_p).__bases__[0] + +ffi = None + +class CData: + pass + +def _import_cffi(): + global ffi, CData + + if ffi is not None: + return + + try: + import cffi + ffi = cffi.FFI() + CData = ffi.CData + except ImportError: + ffi = False + + +class LowLevelCallable(tuple): + """ + Low-level callback function. + + Some functions in SciPy take as arguments callback functions, which + can either be python callables or low-level compiled functions. Using + compiled callback functions can improve performance somewhat by + avoiding wrapping data in Python objects. + + Such low-level functions in SciPy are wrapped in `LowLevelCallable` + objects, which can be constructed from function pointers obtained from + ctypes, cffi, Cython, or contained in Python `PyCapsule` objects. + + .. seealso:: + + Functions accepting low-level callables: + + `scipy.integrate.quad`, `scipy.ndimage.generic_filter`, + `scipy.ndimage.generic_filter1d`, `scipy.ndimage.geometric_transform` + + Usage examples: + + :ref:`ndimage-ccallbacks`, :ref:`quad-callbacks` + + Parameters + ---------- + function : {PyCapsule, ctypes function pointer, cffi function pointer} + Low-level callback function. + user_data : {PyCapsule, ctypes void pointer, cffi void pointer} + User data to pass on to the callback function. + signature : str, optional + Signature of the function. If omitted, determined from *function*, + if possible. + + Attributes + ---------- + function + Callback function given. + user_data + User data given. + signature + Signature of the function. + + Methods + ------- + from_cython + Class method for constructing callables from Cython C-exported + functions. + + Notes + ----- + The argument ``function`` can be one of: + + - PyCapsule, whose name contains the C function signature + - ctypes function pointer + - cffi function pointer + + The signature of the low-level callback must match one of those expected + by the routine it is passed to. + + If constructing low-level functions from a PyCapsule, the name of the + capsule must be the corresponding signature, in the format:: + + return_type (arg1_type, arg2_type, ...) + + For example:: + + "void (double)" + "double (double, int *, void *)" + + The context of a PyCapsule passed in as ``function`` is used as ``user_data``, + if an explicit value for ``user_data`` was not given. + + """ + + # Make the class immutable + __slots__ = () + + def __new__(cls, function, user_data=None, signature=None): + # We need to hold a reference to the function & user data, + # to prevent them going out of scope + item = cls._parse_callback(function, user_data, signature) + return tuple.__new__(cls, (item, function, user_data)) + + def __repr__(self): + return f"LowLevelCallable({self.function!r}, {self.user_data!r})" + + @property + def function(self): + return tuple.__getitem__(self, 1) + + @property + def user_data(self): + return tuple.__getitem__(self, 2) + + @property + def signature(self): + return _ccallback_c.get_capsule_signature(tuple.__getitem__(self, 0)) + + def __getitem__(self, idx): + raise ValueError() + + @classmethod + def from_cython(cls, module, name, user_data=None, signature=None): + """ + Create a low-level callback function from an exported Cython function. + + Parameters + ---------- + module : module + Cython module where the exported function resides + name : str + Name of the exported function + user_data : {PyCapsule, ctypes void pointer, cffi void pointer}, optional + User data to pass on to the callback function. + signature : str, optional + Signature of the function. If omitted, determined from *function*. + + """ + try: + function = module.__pyx_capi__[name] + except AttributeError as e: + message = "Given module is not a Cython module with __pyx_capi__ attribute" + raise ValueError(message) from e + except KeyError as e: + message = f"No function {name!r} found in __pyx_capi__ of the module" + raise ValueError(message) from e + return cls(function, user_data, signature) + + @classmethod + def _parse_callback(cls, obj, user_data=None, signature=None): + _import_cffi() + + if isinstance(obj, LowLevelCallable): + func = tuple.__getitem__(obj, 0) + elif isinstance(obj, PyCFuncPtr): + func, signature = _get_ctypes_func(obj, signature) + elif isinstance(obj, CData): + func, signature = _get_cffi_func(obj, signature) + elif _ccallback_c.check_capsule(obj): + func = obj + else: + raise ValueError("Given input is not a callable or a " + "low-level callable (pycapsule/ctypes/cffi)") + + if isinstance(user_data, ctypes.c_void_p): + context = _get_ctypes_data(user_data) + elif isinstance(user_data, CData): + context = _get_cffi_data(user_data) + elif user_data is None: + context = 0 + elif _ccallback_c.check_capsule(user_data): + context = user_data + else: + raise ValueError("Given user data is not a valid " + "low-level void* pointer (pycapsule/ctypes/cffi)") + + return _ccallback_c.get_raw_capsule(func, signature, context) + + +# +# ctypes helpers +# + +def _get_ctypes_func(func, signature=None): + # Get function pointer + func_ptr = ctypes.cast(func, ctypes.c_void_p).value + + # Construct function signature + if signature is None: + signature = _typename_from_ctypes(func.restype) + " (" + for j, arg in enumerate(func.argtypes): + if j == 0: + signature += _typename_from_ctypes(arg) + else: + signature += ", " + _typename_from_ctypes(arg) + signature += ")" + + return func_ptr, signature + + +def _typename_from_ctypes(item): + if item is None: + return "void" + elif item is ctypes.c_void_p: + return "void *" + + name = item.__name__ + + pointer_level = 0 + while name.startswith("LP_"): + pointer_level += 1 + name = name[3:] + + if name.startswith('c_'): + name = name[2:] + + if pointer_level > 0: + name += " " + "*"*pointer_level + + return name + + +def _get_ctypes_data(data): + # Get voidp pointer + return ctypes.cast(data, ctypes.c_void_p).value + + +# +# CFFI helpers +# + +def _get_cffi_func(func, signature=None): + # Get function pointer + func_ptr = ffi.cast('uintptr_t', func) + + # Get signature + if signature is None: + signature = ffi.getctype(ffi.typeof(func)).replace('(*)', ' ') + + return func_ptr, signature + + +def _get_cffi_data(data): + # Get pointer + return ffi.cast('uintptr_t', data) diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_disjoint_set.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_disjoint_set.py new file mode 100644 index 0000000000000000000000000000000000000000..683c5c8e518705e710212dafc01363f92a2f947d --- /dev/null +++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_disjoint_set.py @@ -0,0 +1,254 @@ +""" +Disjoint set data structure +""" + + +class DisjointSet: + """ Disjoint set data structure for incremental connectivity queries. + + .. versionadded:: 1.6.0 + + Attributes + ---------- + n_subsets : int + The number of subsets. + + Methods + ------- + add + merge + connected + subset + subset_size + subsets + __getitem__ + + Notes + ----- + This class implements the disjoint set [1]_, also known as the *union-find* + or *merge-find* data structure. The *find* operation (implemented in + `__getitem__`) implements the *path halving* variant. The *merge* method + implements the *merge by size* variant. + + References + ---------- + .. [1] https://en.wikipedia.org/wiki/Disjoint-set_data_structure + + Examples + -------- + >>> from scipy.cluster.hierarchy import DisjointSet + + Initialize a disjoint set: + + >>> disjoint_set = DisjointSet([1, 2, 3, 'a', 'b']) + + Merge some subsets: + + >>> disjoint_set.merge(1, 2) + True + >>> disjoint_set.merge(3, 'a') + True + >>> disjoint_set.merge('a', 'b') + True + >>> disjoint_set.merge('b', 'b') + False + + Find root elements: + + >>> disjoint_set[2] + 1 + >>> disjoint_set['b'] + 3 + + Test connectivity: + + >>> disjoint_set.connected(1, 2) + True + >>> disjoint_set.connected(1, 'b') + False + + List elements in disjoint set: + + >>> list(disjoint_set) + [1, 2, 3, 'a', 'b'] + + Get the subset containing 'a': + + >>> disjoint_set.subset('a') + {'a', 3, 'b'} + + Get the size of the subset containing 'a' (without actually instantiating + the subset): + + >>> disjoint_set.subset_size('a') + 3 + + Get all subsets in the disjoint set: + + >>> disjoint_set.subsets() + [{1, 2}, {'a', 3, 'b'}] + """ + def __init__(self, elements=None): + self.n_subsets = 0 + self._sizes = {} + self._parents = {} + # _nbrs is a circular linked list which links connected elements. + self._nbrs = {} + # _indices tracks the element insertion order in `__iter__`. + self._indices = {} + if elements is not None: + for x in elements: + self.add(x) + + def __iter__(self): + """Returns an iterator of the elements in the disjoint set. + + Elements are ordered by insertion order. + """ + return iter(self._indices) + + def __len__(self): + return len(self._indices) + + def __contains__(self, x): + return x in self._indices + + def __getitem__(self, x): + """Find the root element of `x`. + + Parameters + ---------- + x : hashable object + Input element. + + Returns + ------- + root : hashable object + Root element of `x`. + """ + if x not in self._indices: + raise KeyError(x) + + # find by "path halving" + parents = self._parents + while self._indices[x] != self._indices[parents[x]]: + parents[x] = parents[parents[x]] + x = parents[x] + return x + + def add(self, x): + """Add element `x` to disjoint set + """ + if x in self._indices: + return + + self._sizes[x] = 1 + self._parents[x] = x + self._nbrs[x] = x + self._indices[x] = len(self._indices) + self.n_subsets += 1 + + def merge(self, x, y): + """Merge the subsets of `x` and `y`. + + The smaller subset (the child) is merged into the larger subset (the + parent). If the subsets are of equal size, the root element which was + first inserted into the disjoint set is selected as the parent. + + Parameters + ---------- + x, y : hashable object + Elements to merge. + + Returns + ------- + merged : bool + True if `x` and `y` were in disjoint sets, False otherwise. + """ + xr = self[x] + yr = self[y] + if self._indices[xr] == self._indices[yr]: + return False + + sizes = self._sizes + if (sizes[xr], self._indices[yr]) < (sizes[yr], self._indices[xr]): + xr, yr = yr, xr + self._parents[yr] = xr + self._sizes[xr] += self._sizes[yr] + self._nbrs[xr], self._nbrs[yr] = self._nbrs[yr], self._nbrs[xr] + self.n_subsets -= 1 + return True + + def connected(self, x, y): + """Test whether `x` and `y` are in the same subset. + + Parameters + ---------- + x, y : hashable object + Elements to test. + + Returns + ------- + result : bool + True if `x` and `y` are in the same set, False otherwise. + """ + return self._indices[self[x]] == self._indices[self[y]] + + def subset(self, x): + """Get the subset containing `x`. + + Parameters + ---------- + x : hashable object + Input element. + + Returns + ------- + result : set + Subset containing `x`. + """ + if x not in self._indices: + raise KeyError(x) + + result = [x] + nxt = self._nbrs[x] + while self._indices[nxt] != self._indices[x]: + result.append(nxt) + nxt = self._nbrs[nxt] + return set(result) + + def subset_size(self, x): + """Get the size of the subset containing `x`. + + Note that this method is faster than ``len(self.subset(x))`` because + the size is directly read off an internal field, without the need to + instantiate the full subset. + + Parameters + ---------- + x : hashable object + Input element. + + Returns + ------- + result : int + Size of the subset containing `x`. + """ + return self._sizes[self[x]] + + def subsets(self): + """Get all the subsets in the disjoint set. + + Returns + ------- + result : list + Subsets in the disjoint set. + """ + result = [] + visited = set() + for x in self: + if x not in visited: + xset = self.subset(x) + visited.update(xset) + result.append(xset) + return result diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_gcutils.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_gcutils.py new file mode 100644 index 0000000000000000000000000000000000000000..854ae36228614f3eb8849e9f95abf0dd387b5d35 --- /dev/null +++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_gcutils.py @@ -0,0 +1,105 @@ +""" +Module for testing automatic garbage collection of objects + +.. autosummary:: + :toctree: generated/ + + set_gc_state - enable or disable garbage collection + gc_state - context manager for given state of garbage collector + assert_deallocated - context manager to check for circular references on object + +""" +import weakref +import gc + +from contextlib import contextmanager +from platform import python_implementation + +__all__ = ['set_gc_state', 'gc_state', 'assert_deallocated'] + + +IS_PYPY = python_implementation() == 'PyPy' + + +class ReferenceError(AssertionError): + pass + + +def set_gc_state(state): + """ Set status of garbage collector """ + if gc.isenabled() == state: + return + if state: + gc.enable() + else: + gc.disable() + + +@contextmanager +def gc_state(state): + """ Context manager to set state of garbage collector to `state` + + Parameters + ---------- + state : bool + True for gc enabled, False for disabled + + Examples + -------- + >>> with gc_state(False): + ... assert not gc.isenabled() + >>> with gc_state(True): + ... assert gc.isenabled() + """ + orig_state = gc.isenabled() + set_gc_state(state) + yield + set_gc_state(orig_state) + + +@contextmanager +def assert_deallocated(func, *args, **kwargs): + """Context manager to check that object is deallocated + + This is useful for checking that an object can be freed directly by + reference counting, without requiring gc to break reference cycles. + GC is disabled inside the context manager. + + This check is not available on PyPy. + + Parameters + ---------- + func : callable + Callable to create object to check + \\*args : sequence + positional arguments to `func` in order to create object to check + \\*\\*kwargs : dict + keyword arguments to `func` in order to create object to check + + Examples + -------- + >>> class C: pass + >>> with assert_deallocated(C) as c: + ... # do something + ... del c + + >>> class C: + ... def __init__(self): + ... self._circular = self # Make circular reference + >>> with assert_deallocated(C) as c: #doctest: +IGNORE_EXCEPTION_DETAIL + ... # do something + ... del c + Traceback (most recent call last): + ... + ReferenceError: Remaining reference(s) to object + """ + if IS_PYPY: + raise RuntimeError("assert_deallocated is unavailable on PyPy") + + with gc_state(False): + obj = func(*args, **kwargs) + ref = weakref.ref(obj) + yield obj + del obj + if ref() is not None: + raise ReferenceError("Remaining reference(s) to object") diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_pep440.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_pep440.py new file mode 100644 index 0000000000000000000000000000000000000000..d546e32a0349461a0aab76bfb4636ebf25227ca0 --- /dev/null +++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_pep440.py @@ -0,0 +1,487 @@ +"""Utility to compare pep440 compatible version strings. + +The LooseVersion and StrictVersion classes that distutils provides don't +work; they don't recognize anything like alpha/beta/rc/dev versions. +""" + +# Copyright (c) Donald Stufft and individual contributors. +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +import collections +import itertools +import re + + +__all__ = [ + "parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN", +] + + +# BEGIN packaging/_structures.py + + +class Infinity: + def __repr__(self): + return "Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return False + + def __le__(self, other): + return False + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return True + + def __ge__(self, other): + return True + + def __neg__(self): + return NegativeInfinity + + +Infinity = Infinity() + + +class NegativeInfinity: + def __repr__(self): + return "-Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return True + + def __le__(self, other): + return True + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return False + + def __ge__(self, other): + return False + + def __neg__(self): + return Infinity + + +# BEGIN packaging/version.py + + +NegativeInfinity = NegativeInfinity() + +_Version = collections.namedtuple( + "_Version", + ["epoch", "release", "dev", "pre", "post", "local"], +) + + +def parse(version): + """ + Parse the given version string and return either a :class:`Version` object + or a :class:`LegacyVersion` object depending on if the given version is + a valid PEP 440 version or a legacy version. + """ + try: + return Version(version) + except InvalidVersion: + return LegacyVersion(version) + + +class InvalidVersion(ValueError): + """ + An invalid version was found, users should refer to PEP 440. + """ + + +class _BaseVersion: + + def __hash__(self): + return hash(self._key) + + def __lt__(self, other): + return self._compare(other, lambda s, o: s < o) + + def __le__(self, other): + return self._compare(other, lambda s, o: s <= o) + + def __eq__(self, other): + return self._compare(other, lambda s, o: s == o) + + def __ge__(self, other): + return self._compare(other, lambda s, o: s >= o) + + def __gt__(self, other): + return self._compare(other, lambda s, o: s > o) + + def __ne__(self, other): + return self._compare(other, lambda s, o: s != o) + + def _compare(self, other, method): + if not isinstance(other, _BaseVersion): + return NotImplemented + + return method(self._key, other._key) + + +class LegacyVersion(_BaseVersion): + + def __init__(self, version): + self._version = str(version) + self._key = _legacy_cmpkey(self._version) + + def __str__(self): + return self._version + + def __repr__(self): + return f"" + + @property + def public(self): + return self._version + + @property + def base_version(self): + return self._version + + @property + def local(self): + return None + + @property + def is_prerelease(self): + return False + + @property + def is_postrelease(self): + return False + + +_legacy_version_component_re = re.compile( + r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE, +) + +_legacy_version_replacement_map = { + "pre": "c", "preview": "c", "-": "final-", "rc": "c", "dev": "@", +} + + +def _parse_version_parts(s): + for part in _legacy_version_component_re.split(s): + part = _legacy_version_replacement_map.get(part, part) + + if not part or part == ".": + continue + + if part[:1] in "0123456789": + # pad for numeric comparison + yield part.zfill(8) + else: + yield "*" + part + + # ensure that alpha/beta/candidate are before final + yield "*final" + + +def _legacy_cmpkey(version): + # We hardcode an epoch of -1 here. A PEP 440 version can only have an epoch + # greater than or equal to 0. This will effectively put the LegacyVersion, + # which uses the defacto standard originally implemented by setuptools, + # as before all PEP 440 versions. + epoch = -1 + + # This scheme is taken from pkg_resources.parse_version setuptools prior to + # its adoption of the packaging library. + parts = [] + for part in _parse_version_parts(version.lower()): + if part.startswith("*"): + # remove "-" before a prerelease tag + if part < "*final": + while parts and parts[-1] == "*final-": + parts.pop() + + # remove trailing zeros from each series of numeric parts + while parts and parts[-1] == "00000000": + parts.pop() + + parts.append(part) + parts = tuple(parts) + + return epoch, parts + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?P(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+
+    _regex = re.compile(
+        r"^\s*" + VERSION_PATTERN + r"\s*$",
+        re.VERBOSE | re.IGNORECASE,
+    )
+
+    def __init__(self, version):
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(
+                match.group("pre_l"),
+                match.group("pre_n"),
+            ),
+            post=_parse_letter_version(
+                match.group("post_l"),
+                match.group("post_n1") or match.group("post_n2"),
+            ),
+            dev=_parse_letter_version(
+                match.group("dev_l"),
+                match.group("dev_n"),
+            ),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self):
+        return f""
+
+    def __str__(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append(f"{self._version.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        # Pre-release
+        if self._version.pre is not None:
+            parts.append("".join(str(x) for x in self._version.pre))
+
+        # Post-release
+        if self._version.post is not None:
+            parts.append(f".post{self._version.post[1]}")
+
+        # Development release
+        if self._version.dev is not None:
+            parts.append(f".dev{self._version.dev[1]}")
+
+        # Local version segment
+        if self._version.local is not None:
+            parts.append(
+                "+{}".format(".".join(str(x) for x in self._version.local))
+            )
+
+        return "".join(parts)
+
+    @property
+    def public(self):
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append(f"{self._version.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        return "".join(parts)
+
+    @property
+    def local(self):
+        version_string = str(self)
+        if "+" in version_string:
+            return version_string.split("+", 1)[1]
+
+    @property
+    def is_prerelease(self):
+        return bool(self._version.dev or self._version.pre)
+
+    @property
+    def is_postrelease(self):
+        return bool(self._version.post)
+
+
+def _parse_letter_version(letter, number):
+    if letter:
+        # We assume there is an implicit 0 in a pre-release if there is
+        # no numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower-case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume that if we are given a number but not given a letter,
+        # then this is using the implicit post release syntax (e.g., 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+
+_local_version_seperators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local):
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_seperators.split(local)
+        )
+
+
+def _cmpkey(epoch, release, pre, post, dev, local):
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non-zero, then take the rest,
+    # re-reverse it back into the correct order, and make it a tuple and use
+    # that for our sorting key.
+    release = tuple(
+        reversed(list(
+            itertools.dropwhile(
+                lambda x: x == 0,
+                reversed(release),
+            )
+        ))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre-segment, but we _only_ want to do this
+    # if there is no pre- or a post-segment. If we have one of those, then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        pre = -Infinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        pre = Infinity
+
+    # Versions without a post-segment should sort before those with one.
+    if post is None:
+        post = -Infinity
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        dev = Infinity
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        local = -Infinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alphanumeric segments sort before numeric segments
+        # - Alphanumeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        local = tuple(
+            (i, "") if isinstance(i, int) else (-Infinity, i)
+            for i in local
+        )
+
+    return epoch, release, pre, post, dev, local
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_test_deprecation_call.cpython-310-x86_64-linux-gnu.so b/phi4/lib/python3.10/site-packages/scipy/_lib/_test_deprecation_call.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..34c0e024dcd5924c7287e6ce8105975c4d9ab7f5
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/_test_deprecation_call.cpython-310-x86_64-linux-gnu.so differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_test_deprecation_def.cpython-310-x86_64-linux-gnu.so b/phi4/lib/python3.10/site-packages/scipy/_lib/_test_deprecation_def.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..60dc2423372bf2fb24dece8ab51b1f9b7f26d7eb
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/_test_deprecation_def.cpython-310-x86_64-linux-gnu.so differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_testutils.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_testutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8da7e403dec5de5cb7d9a98d8c69a2c49e377c6a
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_testutils.py
@@ -0,0 +1,369 @@
+"""
+Generic test utilities.
+
+"""
+
+import inspect
+import os
+import re
+import shutil
+import subprocess
+import sys
+import sysconfig
+import threading
+from importlib.util import module_from_spec, spec_from_file_location
+
+import numpy as np
+import scipy
+
+try:
+    # Need type: ignore[import-untyped] for mypy >= 1.6
+    import cython  # type: ignore[import-untyped]
+    from Cython.Compiler.Version import (  # type: ignore[import-untyped]
+        version as cython_version,
+    )
+except ImportError:
+    cython = None
+else:
+    from scipy._lib import _pep440
+    required_version = '3.0.8'
+    if _pep440.parse(cython_version) < _pep440.Version(required_version):
+        # too old or wrong cython, skip Cython API tests
+        cython = None
+
+
+__all__ = ['PytestTester', 'check_free_memory', '_TestPythranFunc', 'IS_MUSL']
+
+
+IS_MUSL = False
+# alternate way is
+# from packaging.tags import sys_tags
+#     _tags = list(sys_tags())
+#     if 'musllinux' in _tags[0].platform:
+_v = sysconfig.get_config_var('HOST_GNU_TYPE') or ''
+if 'musl' in _v:
+    IS_MUSL = True
+
+
+IS_EDITABLE = 'editable' in scipy.__path__[0]
+
+
+class FPUModeChangeWarning(RuntimeWarning):
+    """Warning about FPU mode change"""
+    pass
+
+
+class PytestTester:
+    """
+    Run tests for this namespace
+
+    ``scipy.test()`` runs tests for all of SciPy, with the default settings.
+    When used from a submodule (e.g., ``scipy.cluster.test()``, only the tests
+    for that namespace are run.
+
+    Parameters
+    ----------
+    label : {'fast', 'full'}, optional
+        Whether to run only the fast tests, or also those marked as slow.
+        Default is 'fast'.
+    verbose : int, optional
+        Test output verbosity. Default is 1.
+    extra_argv : list, optional
+        Arguments to pass through to Pytest.
+    doctests : bool, optional
+        Whether to run doctests or not. Default is False.
+    coverage : bool, optional
+        Whether to run tests with code coverage measurements enabled.
+        Default is False.
+    tests : list of str, optional
+        List of module names to run tests for. By default, uses the module
+        from which the ``test`` function is called.
+    parallel : int, optional
+        Run tests in parallel with pytest-xdist, if number given is larger than
+        1. Default is 1.
+
+    """
+    def __init__(self, module_name):
+        self.module_name = module_name
+
+    def __call__(self, label="fast", verbose=1, extra_argv=None, doctests=False,
+                 coverage=False, tests=None, parallel=None):
+        import pytest
+
+        module = sys.modules[self.module_name]
+        module_path = os.path.abspath(module.__path__[0])
+
+        pytest_args = ['--showlocals', '--tb=short']
+
+        if extra_argv:
+            pytest_args += list(extra_argv)
+
+        if verbose and int(verbose) > 1:
+            pytest_args += ["-" + "v"*(int(verbose)-1)]
+
+        if coverage:
+            pytest_args += ["--cov=" + module_path]
+
+        if label == "fast":
+            pytest_args += ["-m", "not slow"]
+        elif label != "full":
+            pytest_args += ["-m", label]
+
+        if tests is None:
+            tests = [self.module_name]
+
+        if parallel is not None and parallel > 1:
+            if _pytest_has_xdist():
+                pytest_args += ['-n', str(parallel)]
+            else:
+                import warnings
+                warnings.warn('Could not run tests in parallel because '
+                              'pytest-xdist plugin is not available.',
+                              stacklevel=2)
+
+        pytest_args += ['--pyargs'] + list(tests)
+
+        try:
+            code = pytest.main(pytest_args)
+        except SystemExit as exc:
+            code = exc.code
+
+        return (code == 0)
+
+
+class _TestPythranFunc:
+    '''
+    These are situations that can be tested in our pythran tests:
+    - A function with multiple array arguments and then
+      other positional and keyword arguments.
+    - A function with array-like keywords (e.g. `def somefunc(x0, x1=None)`.
+    Note: list/tuple input is not yet tested!
+
+    `self.arguments`: A dictionary which key is the index of the argument,
+                      value is tuple(array value, all supported dtypes)
+    `self.partialfunc`: A function used to freeze some non-array argument
+                        that of no interests in the original function
+    '''
+    ALL_INTEGER = [np.int8, np.int16, np.int32, np.int64, np.intc, np.intp]
+    ALL_FLOAT = [np.float32, np.float64]
+    ALL_COMPLEX = [np.complex64, np.complex128]
+
+    def setup_method(self):
+        self.arguments = {}
+        self.partialfunc = None
+        self.expected = None
+
+    def get_optional_args(self, func):
+        # get optional arguments with its default value,
+        # used for testing keywords
+        signature = inspect.signature(func)
+        optional_args = {}
+        for k, v in signature.parameters.items():
+            if v.default is not inspect.Parameter.empty:
+                optional_args[k] = v.default
+        return optional_args
+
+    def get_max_dtype_list_length(self):
+        # get the max supported dtypes list length in all arguments
+        max_len = 0
+        for arg_idx in self.arguments:
+            cur_len = len(self.arguments[arg_idx][1])
+            if cur_len > max_len:
+                max_len = cur_len
+        return max_len
+
+    def get_dtype(self, dtype_list, dtype_idx):
+        # get the dtype from dtype_list via index
+        # if the index is out of range, then return the last dtype
+        if dtype_idx > len(dtype_list)-1:
+            return dtype_list[-1]
+        else:
+            return dtype_list[dtype_idx]
+
+    def test_all_dtypes(self):
+        for type_idx in range(self.get_max_dtype_list_length()):
+            args_array = []
+            for arg_idx in self.arguments:
+                new_dtype = self.get_dtype(self.arguments[arg_idx][1],
+                                           type_idx)
+                args_array.append(self.arguments[arg_idx][0].astype(new_dtype))
+            self.pythranfunc(*args_array)
+
+    def test_views(self):
+        args_array = []
+        for arg_idx in self.arguments:
+            args_array.append(self.arguments[arg_idx][0][::-1][::-1])
+        self.pythranfunc(*args_array)
+
+    def test_strided(self):
+        args_array = []
+        for arg_idx in self.arguments:
+            args_array.append(np.repeat(self.arguments[arg_idx][0],
+                                        2, axis=0)[::2])
+        self.pythranfunc(*args_array)
+
+
+def _pytest_has_xdist():
+    """
+    Check if the pytest-xdist plugin is installed, providing parallel tests
+    """
+    # Check xdist exists without importing, otherwise pytests emits warnings
+    from importlib.util import find_spec
+    return find_spec('xdist') is not None
+
+
+def check_free_memory(free_mb):
+    """
+    Check *free_mb* of memory is available, otherwise do pytest.skip
+    """
+    import pytest
+
+    try:
+        mem_free = _parse_size(os.environ['SCIPY_AVAILABLE_MEM'])
+        msg = '{} MB memory required, but environment SCIPY_AVAILABLE_MEM={}'.format(
+            free_mb, os.environ['SCIPY_AVAILABLE_MEM'])
+    except KeyError:
+        mem_free = _get_mem_available()
+        if mem_free is None:
+            pytest.skip("Could not determine available memory; set SCIPY_AVAILABLE_MEM "
+                        "variable to free memory in MB to run the test.")
+        msg = f'{free_mb} MB memory required, but {mem_free/1e6} MB available'
+
+    if mem_free < free_mb * 1e6:
+        pytest.skip(msg)
+
+
+def _parse_size(size_str):
+    suffixes = {'': 1e6,
+                'b': 1.0,
+                'k': 1e3, 'M': 1e6, 'G': 1e9, 'T': 1e12,
+                'kb': 1e3, 'Mb': 1e6, 'Gb': 1e9, 'Tb': 1e12,
+                'kib': 1024.0, 'Mib': 1024.0**2, 'Gib': 1024.0**3, 'Tib': 1024.0**4}
+    m = re.match(r'^\s*(\d+)\s*({})\s*$'.format('|'.join(suffixes.keys())),
+                 size_str,
+                 re.I)
+    if not m or m.group(2) not in suffixes:
+        raise ValueError("Invalid size string")
+
+    return float(m.group(1)) * suffixes[m.group(2)]
+
+
+def _get_mem_available():
+    """
+    Get information about memory available, not counting swap.
+    """
+    try:
+        import psutil
+        return psutil.virtual_memory().available
+    except (ImportError, AttributeError):
+        pass
+
+    if sys.platform.startswith('linux'):
+        info = {}
+        with open('/proc/meminfo') as f:
+            for line in f:
+                p = line.split()
+                info[p[0].strip(':').lower()] = float(p[1]) * 1e3
+
+        if 'memavailable' in info:
+            # Linux >= 3.14
+            return info['memavailable']
+        else:
+            return info['memfree'] + info['cached']
+
+    return None
+
+def _test_cython_extension(tmp_path, srcdir):
+    """
+    Helper function to test building and importing Cython modules that
+    make use of the Cython APIs for BLAS, LAPACK, optimize, and special.
+    """
+    import pytest
+    try:
+        subprocess.check_call(["meson", "--version"])
+    except FileNotFoundError:
+        pytest.skip("No usable 'meson' found")
+
+    # Make safe for being called by multiple threads within one test
+    tmp_path = tmp_path / str(threading.get_ident())
+
+    # build the examples in a temporary directory
+    mod_name = os.path.split(srcdir)[1]
+    shutil.copytree(srcdir, tmp_path / mod_name)
+    build_dir = tmp_path / mod_name / 'tests' / '_cython_examples'
+    target_dir = build_dir / 'build'
+    os.makedirs(target_dir, exist_ok=True)
+
+    # Ensure we use the correct Python interpreter even when `meson` is
+    # installed in a different Python environment (see numpy#24956)
+    native_file = str(build_dir / 'interpreter-native-file.ini')
+    with open(native_file, 'w') as f:
+        f.write("[binaries]\n")
+        f.write(f"python = '{sys.executable}'")
+
+    if sys.platform == "win32":
+        subprocess.check_call(["meson", "setup",
+                               "--buildtype=release",
+                               "--native-file", native_file,
+                               "--vsenv", str(build_dir)],
+                              cwd=target_dir,
+                              )
+    else:
+        subprocess.check_call(["meson", "setup",
+                               "--native-file", native_file, str(build_dir)],
+                              cwd=target_dir
+                              )
+    subprocess.check_call(["meson", "compile", "-vv"], cwd=target_dir)
+
+    # import without adding the directory to sys.path
+    suffix = sysconfig.get_config_var('EXT_SUFFIX')
+
+    def load(modname):
+        so = (target_dir / modname).with_suffix(suffix)
+        spec = spec_from_file_location(modname, so)
+        mod = module_from_spec(spec)
+        spec.loader.exec_module(mod)
+        return mod
+
+    # test that the module can be imported
+    return load("extending"), load("extending_cpp")
+
+
+def _run_concurrent_barrier(n_workers, fn, *args, **kwargs):
+    """
+    Run a given function concurrently across a given number of threads.
+
+    This is equivalent to using a ThreadPoolExecutor, but using the threading
+    primitives instead. This function ensures that the closure passed by
+    parameter gets called concurrently by setting up a barrier before it gets
+    called before any of the threads.
+
+    Arguments
+    ---------
+    n_workers: int
+        Number of concurrent threads to spawn.
+    fn: callable
+        Function closure to execute concurrently. Its first argument will
+        be the thread id.
+    *args: tuple
+        Variable number of positional arguments to pass to the function.
+    **kwargs: dict
+        Keyword arguments to pass to the function.
+    """
+    barrier = threading.Barrier(n_workers)
+
+    def closure(i, *args, **kwargs):
+        barrier.wait()
+        fn(i, *args, **kwargs)
+
+    workers = []
+    for i in range(0, n_workers):
+        workers.append(threading.Thread(
+            target=closure,
+            args=(i,) + args, kwargs=kwargs))
+
+    for worker in workers:
+        worker.start()
+
+    for worker in workers:
+        worker.join()
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_tmpdirs.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_tmpdirs.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f9fd546a9d2ae3e9a20c0684f79eb0b3d61ee92
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_tmpdirs.py
@@ -0,0 +1,86 @@
+''' Contexts for *with* statement providing temporary directories
+'''
+import os
+from contextlib import contextmanager
+from shutil import rmtree
+from tempfile import mkdtemp
+
+
+@contextmanager
+def tempdir():
+    """Create and return a temporary directory. This has the same
+    behavior as mkdtemp but can be used as a context manager.
+
+    Upon exiting the context, the directory and everything contained
+    in it are removed.
+
+    Examples
+    --------
+    >>> import os
+    >>> with tempdir() as tmpdir:
+    ...     fname = os.path.join(tmpdir, 'example_file.txt')
+    ...     with open(fname, 'wt') as fobj:
+    ...         _ = fobj.write('a string\\n')
+    >>> os.path.exists(tmpdir)
+    False
+    """
+    d = mkdtemp()
+    yield d
+    rmtree(d)
+
+
+@contextmanager
+def in_tempdir():
+    ''' Create, return, and change directory to a temporary directory
+
+    Examples
+    --------
+    >>> import os
+    >>> my_cwd = os.getcwd()
+    >>> with in_tempdir() as tmpdir:
+    ...     _ = open('test.txt', 'wt').write('some text')
+    ...     assert os.path.isfile('test.txt')
+    ...     assert os.path.isfile(os.path.join(tmpdir, 'test.txt'))
+    >>> os.path.exists(tmpdir)
+    False
+    >>> os.getcwd() == my_cwd
+    True
+    '''
+    pwd = os.getcwd()
+    d = mkdtemp()
+    os.chdir(d)
+    yield d
+    os.chdir(pwd)
+    rmtree(d)
+
+
+@contextmanager
+def in_dir(dir=None):
+    """ Change directory to given directory for duration of ``with`` block
+
+    Useful when you want to use `in_tempdir` for the final test, but
+    you are still debugging. For example, you may want to do this in the end:
+
+    >>> with in_tempdir() as tmpdir:
+    ...     # do something complicated which might break
+    ...     pass
+
+    But, indeed, the complicated thing does break, and meanwhile, the
+    ``in_tempdir`` context manager wiped out the directory with the
+    temporary files that you wanted for debugging. So, while debugging, you
+    replace with something like:
+
+    >>> with in_dir() as tmpdir: # Use working directory by default
+    ...     # do something complicated which might break
+    ...     pass
+
+    You can then look at the temporary file outputs to debug what is happening,
+    fix, and finally replace ``in_dir`` with ``in_tempdir`` again.
+    """
+    cwd = os.getcwd()
+    if dir is None:
+        yield cwd
+        return
+    os.chdir(dir)
+    yield dir
+    os.chdir(cwd)
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/_util.py b/phi4/lib/python3.10/site-packages/scipy/_lib/_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d16072007395221fdf7a1f5352fd2838fd7a6ec
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/_util.py
@@ -0,0 +1,1179 @@
+import re
+from contextlib import contextmanager
+import functools
+import operator
+import warnings
+import numbers
+from collections import namedtuple
+import inspect
+import math
+from typing import TypeAlias, TypeVar
+
+import numpy as np
+from scipy._lib._array_api import array_namespace, is_numpy, xp_size
+from scipy._lib._docscrape import FunctionDoc, Parameter
+
+
+AxisError: type[Exception]
+ComplexWarning: type[Warning]
+VisibleDeprecationWarning: type[Warning]
+
+if np.lib.NumpyVersion(np.__version__) >= '1.25.0':
+    from numpy.exceptions import (
+        AxisError, ComplexWarning, VisibleDeprecationWarning,
+        DTypePromotionError
+    )
+else:
+    from numpy import (  # type: ignore[attr-defined, no-redef]
+        AxisError, ComplexWarning, VisibleDeprecationWarning  # noqa: F401
+    )
+    DTypePromotionError = TypeError  # type: ignore
+
+np_long: type
+np_ulong: type
+
+if np.lib.NumpyVersion(np.__version__) >= "2.0.0.dev0":
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                r".*In the future `np\.long` will be defined as.*",
+                FutureWarning,
+            )
+            np_long = np.long  # type: ignore[attr-defined]
+            np_ulong = np.ulong  # type: ignore[attr-defined]
+    except AttributeError:
+            np_long = np.int_
+            np_ulong = np.uint
+else:
+    np_long = np.int_
+    np_ulong = np.uint
+
+IntNumber = int | np.integer
+DecimalNumber = float | np.floating | np.integer
+
+copy_if_needed: bool | None
+
+if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
+    copy_if_needed = None
+elif np.lib.NumpyVersion(np.__version__) < "1.28.0":
+    copy_if_needed = False
+else:
+    # 2.0.0 dev versions, handle cases where copy may or may not exist
+    try:
+        np.array([1]).__array__(copy=None)  # type: ignore[call-overload]
+        copy_if_needed = None
+    except TypeError:
+        copy_if_needed = False
+
+
+_RNG: TypeAlias = np.random.Generator | np.random.RandomState
+SeedType: TypeAlias = IntNumber | _RNG | None
+
+GeneratorType = TypeVar("GeneratorType", bound=_RNG)
+
+# Since Generator was introduced in numpy 1.17, the following condition is needed for
+# backward compatibility
+try:
+    from numpy.random import Generator as Generator
+except ImportError:
+    class Generator:  # type: ignore[no-redef]
+        pass
+
+
+def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
+    """Return elements chosen from two possibilities depending on a condition
+
+    Equivalent to ``f(*arrays) if cond else fillvalue`` performed elementwise.
+
+    Parameters
+    ----------
+    cond : array
+        The condition (expressed as a boolean array).
+    arrays : tuple of array
+        Arguments to `f` (and `f2`). Must be broadcastable with `cond`.
+    f : callable
+        Where `cond` is True, output will be ``f(arr1[cond], arr2[cond], ...)``
+    fillvalue : object
+        If provided, value with which to fill output array where `cond` is
+        not True.
+    f2 : callable
+        If provided, output will be ``f2(arr1[cond], arr2[cond], ...)`` where
+        `cond` is not True.
+
+    Returns
+    -------
+    out : array
+        An array with elements from the output of `f` where `cond` is True
+        and `fillvalue` (or elements from the output of `f2`) elsewhere. The
+        returned array has data type determined by Type Promotion Rules
+        with the output of `f` and `fillvalue` (or the output of `f2`).
+
+    Notes
+    -----
+    ``xp.where(cond, x, fillvalue)`` requires explicitly forming `x` even where
+    `cond` is False. This function evaluates ``f(arr1[cond], arr2[cond], ...)``
+    onle where `cond` ``is True.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
+    >>> def f(a, b):
+    ...     return a*b
+    >>> _lazywhere(a > 2, (a, b), f, np.nan)
+    array([ nan,  nan,  21.,  32.])
+
+    """
+    xp = array_namespace(cond, *arrays)
+
+    if (f2 is fillvalue is None) or (f2 is not None and fillvalue is not None):
+        raise ValueError("Exactly one of `fillvalue` or `f2` must be given.")
+
+    args = xp.broadcast_arrays(cond, *arrays)
+    bool_dtype = xp.asarray([True]).dtype  # numpy 1.xx doesn't have `bool`
+    cond, arrays = xp.astype(args[0], bool_dtype, copy=False), args[1:]
+
+    temp1 = xp.asarray(f(*(arr[cond] for arr in arrays)))
+
+    if f2 is None:
+        # If `fillvalue` is a Python scalar and we convert to `xp.asarray`, it gets the
+        # default `int` or `float` type of `xp`, so `result_type` could be wrong.
+        # `result_type` should/will handle mixed array/Python scalars;
+        # remove this special logic when it does.
+        if type(fillvalue) in {bool, int, float, complex}:
+            with np.errstate(invalid='ignore'):
+                dtype = (temp1 * fillvalue).dtype
+        else:
+           dtype = xp.result_type(temp1.dtype, fillvalue)
+        out = xp.full(cond.shape, dtype=dtype,
+                      fill_value=xp.asarray(fillvalue, dtype=dtype))
+    else:
+        ncond = ~cond
+        temp2 = xp.asarray(f2(*(arr[ncond] for arr in arrays)))
+        dtype = xp.result_type(temp1, temp2)
+        out = xp.empty(cond.shape, dtype=dtype)
+        out[ncond] = temp2
+
+    out[cond] = temp1
+
+    return out
+
+
+def _lazyselect(condlist, choicelist, arrays, default=0):
+    """
+    Mimic `np.select(condlist, choicelist)`.
+
+    Notice, it assumes that all `arrays` are of the same shape or can be
+    broadcasted together.
+
+    All functions in `choicelist` must accept array arguments in the order
+    given in `arrays` and must return an array of the same shape as broadcasted
+    `arrays`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(6)
+    >>> np.select([x <3, x > 3], [x**2, x**3], default=0)
+    array([  0,   1,   4,   0,  64, 125])
+
+    >>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,))
+    array([   0.,    1.,    4.,   0.,   64.,  125.])
+
+    >>> a = -np.ones_like(x)
+    >>> _lazyselect([x < 3, x > 3],
+    ...             [lambda x, a: x**2, lambda x, a: a * x**3],
+    ...             (x, a), default=np.nan)
+    array([   0.,    1.,    4.,   nan,  -64., -125.])
+
+    """
+    arrays = np.broadcast_arrays(*arrays)
+    tcode = np.mintypecode([a.dtype.char for a in arrays])
+    out = np.full(np.shape(arrays[0]), fill_value=default, dtype=tcode)
+    for func, cond in zip(choicelist, condlist):
+        if np.all(cond is False):
+            continue
+        cond, _ = np.broadcast_arrays(cond, arrays[0])
+        temp = tuple(np.extract(cond, arr) for arr in arrays)
+        np.place(out, cond, func(*temp))
+    return out
+
+
+def _aligned_zeros(shape, dtype=float, order="C", align=None):
+    """Allocate a new ndarray with aligned memory.
+
+    Primary use case for this currently is working around a f2py issue
+    in NumPy 1.9.1, where dtype.alignment is such that np.zeros() does
+    not necessarily create arrays aligned up to it.
+
+    """
+    dtype = np.dtype(dtype)
+    if align is None:
+        align = dtype.alignment
+    if not hasattr(shape, '__len__'):
+        shape = (shape,)
+    size = functools.reduce(operator.mul, shape) * dtype.itemsize
+    buf = np.empty(size + align + 1, np.uint8)
+    offset = buf.__array_interface__['data'][0] % align
+    if offset != 0:
+        offset = align - offset
+    # Note: slices producing 0-size arrays do not necessarily change
+    # data pointer --- so we use and allocate size+1
+    buf = buf[offset:offset+size+1][:-1]
+    data = np.ndarray(shape, dtype, buf, order=order)
+    data.fill(0)
+    return data
+
+
+def _prune_array(array):
+    """Return an array equivalent to the input array. If the input
+    array is a view of a much larger array, copy its contents to a
+    newly allocated array. Otherwise, return the input unchanged.
+    """
+    if array.base is not None and array.size < array.base.size // 2:
+        return array.copy()
+    return array
+
+
+def float_factorial(n: int) -> float:
+    """Compute the factorial and return as a float
+
+    Returns infinity when result is too large for a double
+    """
+    return float(math.factorial(n)) if n < 171 else np.inf
+
+
+_rng_desc = (
+    r"""If `rng` is passed by keyword, types other than `numpy.random.Generator` are
+    passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+    If `rng` is already a ``Generator`` instance, then the provided instance is
+    used. Specify `rng` for repeatable function behavior.
+
+    If this argument is passed by position or `{old_name}` is passed by keyword,
+    legacy behavior for the argument `{old_name}` applies:
+
+    - If `{old_name}` is None (or `numpy.random`), the `numpy.random.RandomState`
+      singleton is used.
+    - If `{old_name}` is an int, a new ``RandomState`` instance is used,
+      seeded with `{old_name}`.
+    - If `{old_name}` is already a ``Generator`` or ``RandomState`` instance then
+      that instance is used.
+
+    .. versionchanged:: 1.15.0
+        As part of the `SPEC-007 `_
+        transition from use of `numpy.random.RandomState` to
+        `numpy.random.Generator`, this keyword was changed from `{old_name}` to `rng`.
+        For an interim period, both keywords will continue to work, although only one
+        may be specified at a time. After the interim period, function calls using the
+        `{old_name}` keyword will emit warnings. The behavior of both `{old_name}` and
+        `rng` are outlined above, but only the `rng` keyword should be used in new code.
+        """
+)
+
+
+# SPEC 7
+def _transition_to_rng(old_name, *, position_num=None, end_version=None,
+                       replace_doc=True):
+    """Example decorator to transition from old PRNG usage to new `rng` behavior
+
+    Suppose the decorator is applied to a function that used to accept parameter
+    `old_name='random_state'` either by keyword or as a positional argument at
+    `position_num=1`. At the time of application, the name of the argument in the
+    function signature is manually changed to the new name, `rng`. If positional
+    use was allowed before, this is not changed.*
+
+    - If the function is called with both `random_state` and `rng`, the decorator
+      raises an error.
+    - If `random_state` is provided as a keyword argument, the decorator passes
+      `random_state` to the function's `rng` argument as a keyword. If `end_version`
+      is specified, the decorator will emit a `DeprecationWarning` about the
+      deprecation of keyword `random_state`.
+    - If `random_state` is provided as a positional argument, the decorator passes
+      `random_state` to the function's `rng` argument by position. If `end_version`
+      is specified, the decorator will emit a `FutureWarning` about the changing
+      interpretation of the argument.
+    - If `rng` is provided as a keyword argument, the decorator validates `rng` using
+      `numpy.random.default_rng` before passing it to the function.
+    - If `end_version` is specified and neither `random_state` nor `rng` is provided
+      by the user, the decorator checks whether `np.random.seed` has been used to set
+      the global seed. If so, it emits a `FutureWarning`, noting that usage of
+      `numpy.random.seed` will eventually have no effect. Either way, the decorator
+      calls the function without explicitly passing the `rng` argument.
+
+    If `end_version` is specified, a user must pass `rng` as a keyword to avoid
+    warnings.
+
+    After the deprecation period, the decorator can be removed, and the function
+    can simply validate the `rng` argument by calling `np.random.default_rng(rng)`.
+
+    * A `FutureWarning` is emitted when the PRNG argument is used by
+      position. It indicates that the "Hinsen principle" (same
+      code yielding different results in two versions of the software)
+      will be violated, unless positional use is deprecated. Specifically:
+
+      - If `None` is passed by position and `np.random.seed` has been used,
+        the function will change from being seeded to being unseeded.
+      - If an integer is passed by position, the random stream will change.
+      - If `np.random` or an instance of `RandomState` is passed by position,
+        an error will be raised.
+
+      We suggest that projects consider deprecating positional use of
+      `random_state`/`rng` (i.e., change their function signatures to
+      ``def my_func(..., *, rng=None)``); that might not make sense
+      for all projects, so this SPEC does not make that
+      recommendation, neither does this decorator enforce it.
+
+    Parameters
+    ----------
+    old_name : str
+        The old name of the PRNG argument (e.g. `seed` or `random_state`).
+    position_num : int, optional
+        The (0-indexed) position of the old PRNG argument (if accepted by position).
+        Maintainers are welcome to eliminate this argument and use, for example,
+        `inspect`, if preferred.
+    end_version : str, optional
+        The full version number of the library when the behavior described in
+        `DeprecationWarning`s and `FutureWarning`s will take effect. If left
+        unspecified, no warnings will be emitted by the decorator.
+    replace_doc : bool, default: True
+        Whether the decorator should replace the documentation for parameter `rng` with
+        `_rng_desc` (defined above), which documents both new `rng` keyword behavior
+        and typical legacy `random_state`/`seed` behavior. If True, manually replace
+        the first paragraph of the function's old `random_state`/`seed` documentation
+        with the desired *final* `rng` documentation; this way, no changes to
+        documentation are needed when the decorator is removed. Documentation of `rng`
+        after the first blank line is preserved. Use False if the function's old
+        `random_state`/`seed` behavior does not match that described by `_rng_desc`.
+
+    """
+    NEW_NAME = "rng"
+
+    cmn_msg = (
+        "To silence this warning and ensure consistent behavior in SciPy "
+        f"{end_version}, control the RNG using argument `{NEW_NAME}`. Arguments passed "
+        f"to keyword `{NEW_NAME}` will be validated by `np.random.default_rng`, so the "
+        "behavior corresponding with a given value may change compared to use of "
+        f"`{old_name}`. For example, "
+        "1) `None` will result in unpredictable random numbers, "
+        "2) an integer will result in a different stream of random numbers, (with the "
+        "same distribution), and "
+        "3) `np.random` or `RandomState` instances will result in an error. "
+        "See the documentation of `default_rng` for more information."
+    )
+
+    def decorator(fun):
+        @functools.wraps(fun)
+        def wrapper(*args, **kwargs):
+            # Determine how PRNG was passed
+            as_old_kwarg = old_name in kwargs
+            as_new_kwarg = NEW_NAME in kwargs
+            as_pos_arg = position_num is not None and len(args) >= position_num + 1
+            emit_warning = end_version is not None
+
+            # Can only specify PRNG one of the three ways
+            if int(as_old_kwarg) + int(as_new_kwarg) + int(as_pos_arg) > 1:
+                message = (
+                    f"{fun.__name__}() got multiple values for "
+                    f"argument now known as `{NEW_NAME}`. Specify one of "
+                    f"`{NEW_NAME}` or `{old_name}`."
+                )
+                raise TypeError(message)
+
+            # Check whether global random state has been set
+            global_seed_set = np.random.mtrand._rand._bit_generator._seed_seq is None
+
+            if as_old_kwarg:  # warn about deprecated use of old kwarg
+                kwargs[NEW_NAME] = kwargs.pop(old_name)
+                if emit_warning:
+                    message = (
+                        f"Use of keyword argument `{old_name}` is "
+                        f"deprecated and replaced by `{NEW_NAME}`.  "
+                        f"Support for `{old_name}` will be removed "
+                        f"in SciPy {end_version}. "
+                    ) + cmn_msg
+                    warnings.warn(message, DeprecationWarning, stacklevel=2)
+
+            elif as_pos_arg:
+                # Warn about changing meaning of positional arg
+
+                # Note that this decorator does not deprecate positional use of the
+                # argument; it only warns that the behavior will change in the future.
+                # Simultaneously transitioning to keyword-only use is another option.
+
+                arg = args[position_num]
+                # If the argument is None and the global seed wasn't set, or if the
+                # argument is one of a few new classes, the user will not notice change
+                # in behavior.
+                ok_classes = (
+                    np.random.Generator,
+                    np.random.SeedSequence,
+                    np.random.BitGenerator,
+                )
+                if (arg is None and not global_seed_set) or isinstance(arg, ok_classes):
+                    pass
+                elif emit_warning:
+                    message = (
+                        f"Positional use of `{NEW_NAME}` (formerly known as "
+                        f"`{old_name}`) is still allowed, but the behavior is "
+                        "changing: the argument will be normalized using "
+                        f"`np.random.default_rng` beginning in SciPy {end_version}, "
+                        "and the resulting `Generator` will be used to generate "
+                        "random numbers."
+                    ) + cmn_msg
+                    warnings.warn(message, FutureWarning, stacklevel=2)
+
+            elif as_new_kwarg:  # no warnings; this is the preferred use
+                # After the removal of the decorator, normalization with
+                # np.random.default_rng will be done inside the decorated function
+                kwargs[NEW_NAME] = np.random.default_rng(kwargs[NEW_NAME])
+
+            elif global_seed_set and emit_warning:
+                # Emit FutureWarning if `np.random.seed` was used and no PRNG was passed
+                message = (
+                    "The NumPy global RNG was seeded by calling "
+                    f"`np.random.seed`. Beginning in {end_version}, this "
+                    "function will no longer use the global RNG."
+                ) + cmn_msg
+                warnings.warn(message, FutureWarning, stacklevel=2)
+
+            return fun(*args, **kwargs)
+
+        if replace_doc:
+            doc = FunctionDoc(wrapper)
+            parameter_names = [param.name for param in doc['Parameters']]
+            if 'rng' in parameter_names:
+                _type = "{None, int, `numpy.random.Generator`}, optional"
+                _desc = _rng_desc.replace("{old_name}", old_name)
+                old_doc = doc['Parameters'][parameter_names.index('rng')].desc
+                old_doc_keep = old_doc[old_doc.index("") + 1:] if "" in old_doc else []
+                new_doc = [_desc] + old_doc_keep
+                _rng_parameter_doc = Parameter('rng', _type, new_doc)
+                doc['Parameters'][parameter_names.index('rng')] = _rng_parameter_doc
+                doc = str(doc).split("\n", 1)[1]  # remove signature
+                wrapper.__doc__ = str(doc)
+        return wrapper
+
+    return decorator
+
+
+# copy-pasted from scikit-learn utils/validation.py
+def check_random_state(seed):
+    """Turn `seed` into a `np.random.RandomState` instance.
+
+    Parameters
+    ----------
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+    Returns
+    -------
+    seed : {`numpy.random.Generator`, `numpy.random.RandomState`}
+        Random number generator.
+
+    """
+    if seed is None or seed is np.random:
+        return np.random.mtrand._rand
+    if isinstance(seed, numbers.Integral | np.integer):
+        return np.random.RandomState(seed)
+    if isinstance(seed, np.random.RandomState | np.random.Generator):
+        return seed
+
+    raise ValueError(f"'{seed}' cannot be used to seed a numpy.random.RandomState"
+                     " instance")
+
+
+def _asarray_validated(a, check_finite=True,
+                       sparse_ok=False, objects_ok=False, mask_ok=False,
+                       as_inexact=False):
+    """
+    Helper function for SciPy argument validation.
+
+    Many SciPy linear algebra functions do support arbitrary array-like
+    input arguments. Examples of commonly unsupported inputs include
+    matrices containing inf/nan, sparse matrix representations, and
+    matrices with complicated elements.
+
+    Parameters
+    ----------
+    a : array_like
+        The array-like input.
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+        Default: True
+    sparse_ok : bool, optional
+        True if scipy sparse matrices are allowed.
+    objects_ok : bool, optional
+        True if arrays with dype('O') are allowed.
+    mask_ok : bool, optional
+        True if masked arrays are allowed.
+    as_inexact : bool, optional
+        True to convert the input array to a np.inexact dtype.
+
+    Returns
+    -------
+    ret : ndarray
+        The converted validated array.
+
+    """
+    if not sparse_ok:
+        import scipy.sparse
+        if scipy.sparse.issparse(a):
+            msg = ('Sparse arrays/matrices are not supported by this function. '
+                   'Perhaps one of the `scipy.sparse.linalg` functions '
+                   'would work instead.')
+            raise ValueError(msg)
+    if not mask_ok:
+        if np.ma.isMaskedArray(a):
+            raise ValueError('masked arrays are not supported')
+    toarray = np.asarray_chkfinite if check_finite else np.asarray
+    a = toarray(a)
+    if not objects_ok:
+        if a.dtype is np.dtype('O'):
+            raise ValueError('object arrays are not supported')
+    if as_inexact:
+        if not np.issubdtype(a.dtype, np.inexact):
+            a = toarray(a, dtype=np.float64)
+    return a
+
+
+def _validate_int(k, name, minimum=None):
+    """
+    Validate a scalar integer.
+
+    This function can be used to validate an argument to a function
+    that expects the value to be an integer.  It uses `operator.index`
+    to validate the value (so, for example, k=2.0 results in a
+    TypeError).
+
+    Parameters
+    ----------
+    k : int
+        The value to be validated.
+    name : str
+        The name of the parameter.
+    minimum : int, optional
+        An optional lower bound.
+    """
+    try:
+        k = operator.index(k)
+    except TypeError:
+        raise TypeError(f'{name} must be an integer.') from None
+    if minimum is not None and k < minimum:
+        raise ValueError(f'{name} must be an integer not less '
+                         f'than {minimum}') from None
+    return k
+
+
+# Add a replacement for inspect.getfullargspec()/
+# The version below is borrowed from Django,
+# https://github.com/django/django/pull/4846.
+
+# Note an inconsistency between inspect.getfullargspec(func) and
+# inspect.signature(func). If `func` is a bound method, the latter does *not*
+# list `self` as a first argument, while the former *does*.
+# Hence, cook up a common ground replacement: `getfullargspec_no_self` which
+# mimics `inspect.getfullargspec` but does not list `self`.
+#
+# This way, the caller code does not need to know whether it uses a legacy
+# .getfullargspec or a bright and shiny .signature.
+
+FullArgSpec = namedtuple('FullArgSpec',
+                         ['args', 'varargs', 'varkw', 'defaults',
+                          'kwonlyargs', 'kwonlydefaults', 'annotations'])
+
+
+def getfullargspec_no_self(func):
+    """inspect.getfullargspec replacement using inspect.signature.
+
+    If func is a bound method, do not list the 'self' parameter.
+
+    Parameters
+    ----------
+    func : callable
+        A callable to inspect
+
+    Returns
+    -------
+    fullargspec : FullArgSpec(args, varargs, varkw, defaults, kwonlyargs,
+                              kwonlydefaults, annotations)
+
+        NOTE: if the first argument of `func` is self, it is *not*, I repeat
+        *not*, included in fullargspec.args.
+        This is done for consistency between inspect.getargspec() under
+        Python 2.x, and inspect.signature() under Python 3.x.
+
+    """
+    sig = inspect.signature(func)
+    args = [
+        p.name for p in sig.parameters.values()
+        if p.kind in [inspect.Parameter.POSITIONAL_OR_KEYWORD,
+                      inspect.Parameter.POSITIONAL_ONLY]
+    ]
+    varargs = [
+        p.name for p in sig.parameters.values()
+        if p.kind == inspect.Parameter.VAR_POSITIONAL
+    ]
+    varargs = varargs[0] if varargs else None
+    varkw = [
+        p.name for p in sig.parameters.values()
+        if p.kind == inspect.Parameter.VAR_KEYWORD
+    ]
+    varkw = varkw[0] if varkw else None
+    defaults = tuple(
+        p.default for p in sig.parameters.values()
+        if (p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD and
+            p.default is not p.empty)
+    ) or None
+    kwonlyargs = [
+        p.name for p in sig.parameters.values()
+        if p.kind == inspect.Parameter.KEYWORD_ONLY
+    ]
+    kwdefaults = {p.name: p.default for p in sig.parameters.values()
+                  if p.kind == inspect.Parameter.KEYWORD_ONLY and
+                  p.default is not p.empty}
+    annotations = {p.name: p.annotation for p in sig.parameters.values()
+                   if p.annotation is not p.empty}
+    return FullArgSpec(args, varargs, varkw, defaults, kwonlyargs,
+                       kwdefaults or None, annotations)
+
+
+class _FunctionWrapper:
+    """
+    Object to wrap user's function, allowing picklability
+    """
+    def __init__(self, f, args):
+        self.f = f
+        self.args = [] if args is None else args
+
+    def __call__(self, x):
+        return self.f(x, *self.args)
+
+
+class MapWrapper:
+    """
+    Parallelisation wrapper for working with map-like callables, such as
+    `multiprocessing.Pool.map`.
+
+    Parameters
+    ----------
+    pool : int or map-like callable
+        If `pool` is an integer, then it specifies the number of threads to
+        use for parallelization. If ``int(pool) == 1``, then no parallel
+        processing is used and the map builtin is used.
+        If ``pool == -1``, then the pool will utilize all available CPUs.
+        If `pool` is a map-like callable that follows the same
+        calling sequence as the built-in map function, then this callable is
+        used for parallelization.
+    """
+    def __init__(self, pool=1):
+        self.pool = None
+        self._mapfunc = map
+        self._own_pool = False
+
+        if callable(pool):
+            self.pool = pool
+            self._mapfunc = self.pool
+        else:
+            from multiprocessing import Pool
+            # user supplies a number
+            if int(pool) == -1:
+                # use as many processors as possible
+                self.pool = Pool()
+                self._mapfunc = self.pool.map
+                self._own_pool = True
+            elif int(pool) == 1:
+                pass
+            elif int(pool) > 1:
+                # use the number of processors requested
+                self.pool = Pool(processes=int(pool))
+                self._mapfunc = self.pool.map
+                self._own_pool = True
+            else:
+                raise RuntimeError("Number of workers specified must be -1,"
+                                   " an int >= 1, or an object with a 'map' "
+                                   "method")
+
+    def __enter__(self):
+        return self
+
+    def terminate(self):
+        if self._own_pool:
+            self.pool.terminate()
+
+    def join(self):
+        if self._own_pool:
+            self.pool.join()
+
+    def close(self):
+        if self._own_pool:
+            self.pool.close()
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        if self._own_pool:
+            self.pool.close()
+            self.pool.terminate()
+
+    def __call__(self, func, iterable):
+        # only accept one iterable because that's all Pool.map accepts
+        try:
+            return self._mapfunc(func, iterable)
+        except TypeError as e:
+            # wrong number of arguments
+            raise TypeError("The map-like callable must be of the"
+                            " form f(func, iterable)") from e
+
+
+def rng_integers(gen, low, high=None, size=None, dtype='int64',
+                 endpoint=False):
+    """
+    Return random integers from low (inclusive) to high (exclusive), or if
+    endpoint=True, low (inclusive) to high (inclusive). Replaces
+    `RandomState.randint` (with endpoint=False) and
+    `RandomState.random_integers` (with endpoint=True).
+
+    Return random integers from the "discrete uniform" distribution of the
+    specified dtype. If high is None (the default), then results are from
+    0 to low.
+
+    Parameters
+    ----------
+    gen : {None, np.random.RandomState, np.random.Generator}
+        Random number generator. If None, then the np.random.RandomState
+        singleton is used.
+    low : int or array-like of ints
+        Lowest (signed) integers to be drawn from the distribution (unless
+        high=None, in which case this parameter is 0 and this value is used
+        for high).
+    high : int or array-like of ints
+        If provided, one above the largest (signed) integer to be drawn from
+        the distribution (see above for behavior if high=None). If array-like,
+        must contain integer values.
+    size : array-like of ints, optional
+        Output shape. If the given shape is, e.g., (m, n, k), then m * n * k
+        samples are drawn. Default is None, in which case a single value is
+        returned.
+    dtype : {str, dtype}, optional
+        Desired dtype of the result. All dtypes are determined by their name,
+        i.e., 'int64', 'int', etc, so byteorder is not available and a specific
+        precision may have different C types depending on the platform.
+        The default value is 'int64'.
+    endpoint : bool, optional
+        If True, sample from the interval [low, high] instead of the default
+        [low, high) Defaults to False.
+
+    Returns
+    -------
+    out: int or ndarray of ints
+        size-shaped array of random integers from the appropriate distribution,
+        or a single such random int if size not provided.
+    """
+    if isinstance(gen, Generator):
+        return gen.integers(low, high=high, size=size, dtype=dtype,
+                            endpoint=endpoint)
+    else:
+        if gen is None:
+            # default is RandomState singleton used by np.random.
+            gen = np.random.mtrand._rand
+        if endpoint:
+            # inclusive of endpoint
+            # remember that low and high can be arrays, so don't modify in
+            # place
+            if high is None:
+                return gen.randint(low + 1, size=size, dtype=dtype)
+            if high is not None:
+                return gen.randint(low, high=high + 1, size=size, dtype=dtype)
+
+        # exclusive
+        return gen.randint(low, high=high, size=size, dtype=dtype)
+
+
+@contextmanager
+def _fixed_default_rng(seed=1638083107694713882823079058616272161):
+    """Context with a fixed np.random.default_rng seed."""
+    orig_fun = np.random.default_rng
+    np.random.default_rng = lambda seed=seed: orig_fun(seed)
+    try:
+        yield
+    finally:
+        np.random.default_rng = orig_fun
+
+
+def _rng_html_rewrite(func):
+    """Rewrite the HTML rendering of ``np.random.default_rng``.
+
+    This is intended to decorate
+    ``numpydoc.docscrape_sphinx.SphinxDocString._str_examples``.
+
+    Examples are only run by Sphinx when there are plot involved. Even so,
+    it does not change the result values getting printed.
+    """
+    # hexadecimal or number seed, case-insensitive
+    pattern = re.compile(r'np.random.default_rng\((0x[0-9A-F]+|\d+)\)', re.I)
+
+    def _wrapped(*args, **kwargs):
+        res = func(*args, **kwargs)
+        lines = [
+            re.sub(pattern, 'np.random.default_rng()', line)
+            for line in res
+        ]
+        return lines
+
+    return _wrapped
+
+
+def _argmin(a, keepdims=False, axis=None):
+    """
+    argmin with a `keepdims` parameter.
+
+    See https://github.com/numpy/numpy/issues/8710
+
+    If axis is not None, a.shape[axis] must be greater than 0.
+    """
+    res = np.argmin(a, axis=axis)
+    if keepdims and axis is not None:
+        res = np.expand_dims(res, axis=axis)
+    return res
+
+
+def _first_nonnan(a, axis):
+    """
+    Return the first non-nan value along the given axis.
+
+    If a slice is all nan, nan is returned for that slice.
+
+    The shape of the return value corresponds to ``keepdims=True``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> nan = np.nan
+    >>> a = np.array([[ 3.,  3., nan,  3.],
+                      [ 1., nan,  2.,  4.],
+                      [nan, nan,  9., -1.],
+                      [nan,  5.,  4.,  3.],
+                      [ 2.,  2.,  2.,  2.],
+                      [nan, nan, nan, nan]])
+    >>> _first_nonnan(a, axis=0)
+    array([[3., 3., 2., 3.]])
+    >>> _first_nonnan(a, axis=1)
+    array([[ 3.],
+           [ 1.],
+           [ 9.],
+           [ 5.],
+           [ 2.],
+           [nan]])
+    """
+    k = _argmin(np.isnan(a), axis=axis, keepdims=True)
+    return np.take_along_axis(a, k, axis=axis)
+
+
+def _nan_allsame(a, axis, keepdims=False):
+    """
+    Determine if the values along an axis are all the same.
+
+    nan values are ignored.
+
+    `a` must be a numpy array.
+
+    `axis` is assumed to be normalized; that is, 0 <= axis < a.ndim.
+
+    For an axis of length 0, the result is True.  That is, we adopt the
+    convention that ``allsame([])`` is True. (There are no values in the
+    input that are different.)
+
+    `True` is returned for slices that are all nan--not because all the
+    values are the same, but because this is equivalent to ``allsame([])``.
+
+    Examples
+    --------
+    >>> from numpy import nan, array
+    >>> a = array([[ 3.,  3., nan,  3.],
+    ...            [ 1., nan,  2.,  4.],
+    ...            [nan, nan,  9., -1.],
+    ...            [nan,  5.,  4.,  3.],
+    ...            [ 2.,  2.,  2.,  2.],
+    ...            [nan, nan, nan, nan]])
+    >>> _nan_allsame(a, axis=1, keepdims=True)
+    array([[ True],
+           [False],
+           [False],
+           [False],
+           [ True],
+           [ True]])
+    """
+    if axis is None:
+        if a.size == 0:
+            return True
+        a = a.ravel()
+        axis = 0
+    else:
+        shp = a.shape
+        if shp[axis] == 0:
+            shp = shp[:axis] + (1,)*keepdims + shp[axis + 1:]
+            return np.full(shp, fill_value=True, dtype=bool)
+    a0 = _first_nonnan(a, axis=axis)
+    return ((a0 == a) | np.isnan(a)).all(axis=axis, keepdims=keepdims)
+
+
+def _contains_nan(a, nan_policy='propagate', policies=None, *,
+                  xp_omit_okay=False, xp=None):
+    # Regarding `xp_omit_okay`: Temporarily, while `_axis_nan_policy` does not
+    # handle non-NumPy arrays, most functions that call `_contains_nan` want
+    # it to raise an error if `nan_policy='omit'` and `xp` is not `np`.
+    # Some functions support `nan_policy='omit'` natively, so setting this to
+    # `True` prevents the error from being raised.
+    if xp is None:
+        xp = array_namespace(a)
+    not_numpy = not is_numpy(xp)
+
+    if policies is None:
+        policies = {'propagate', 'raise', 'omit'}
+    if nan_policy not in policies:
+        raise ValueError(f"nan_policy must be one of {set(policies)}.")
+
+    if xp_size(a) == 0:
+        contains_nan = False
+    elif xp.isdtype(a.dtype, "real floating"):
+        # Faster and less memory-intensive than xp.any(xp.isnan(a)), and unlike other
+        # reductions, `max`/`min` won't return NaN unless there is a NaN in the data.
+        contains_nan = xp.isnan(xp.max(a))
+    elif xp.isdtype(a.dtype, "complex floating"):
+        # Typically `real` and `imag` produce views; otherwise, `xp.any(xp.isnan(a))`
+        # would be more efficient.
+        contains_nan = xp.isnan(xp.max(xp.real(a))) | xp.isnan(xp.max(xp.imag(a)))
+    elif is_numpy(xp) and np.issubdtype(a.dtype, object):
+        contains_nan = False
+        for el in a.ravel():
+            # isnan doesn't work on non-numeric elements
+            if np.issubdtype(type(el), np.number) and np.isnan(el):
+                contains_nan = True
+                break
+    else:
+        # Only `object` and `inexact` arrays can have NaNs
+        contains_nan = False
+
+    if contains_nan and nan_policy == 'raise':
+        raise ValueError("The input contains nan values")
+
+    if not xp_omit_okay and not_numpy and contains_nan and nan_policy=='omit':
+        message = "`nan_policy='omit' is incompatible with non-NumPy arrays."
+        raise ValueError(message)
+
+    return contains_nan, nan_policy
+
+
+def _rename_parameter(old_name, new_name, dep_version=None):
+    """
+    Generate decorator for backward-compatible keyword renaming.
+
+    Apply the decorator generated by `_rename_parameter` to functions with a
+    recently renamed parameter to maintain backward-compatibility.
+
+    After decoration, the function behaves as follows:
+    If only the new parameter is passed into the function, behave as usual.
+    If only the old parameter is passed into the function (as a keyword), raise
+    a DeprecationWarning if `dep_version` is provided, and behave as usual
+    otherwise.
+    If both old and new parameters are passed into the function, raise a
+    DeprecationWarning if `dep_version` is provided, and raise the appropriate
+    TypeError (function got multiple values for argument).
+
+    Parameters
+    ----------
+    old_name : str
+        Old name of parameter
+    new_name : str
+        New name of parameter
+    dep_version : str, optional
+        Version of SciPy in which old parameter was deprecated in the format
+        'X.Y.Z'. If supplied, the deprecation message will indicate that
+        support for the old parameter will be removed in version 'X.Y+2.Z'
+
+    Notes
+    -----
+    Untested with functions that accept *args. Probably won't work as written.
+
+    """
+    def decorator(fun):
+        @functools.wraps(fun)
+        def wrapper(*args, **kwargs):
+            if old_name in kwargs:
+                if dep_version:
+                    end_version = dep_version.split('.')
+                    end_version[1] = str(int(end_version[1]) + 2)
+                    end_version = '.'.join(end_version)
+                    message = (f"Use of keyword argument `{old_name}` is "
+                               f"deprecated and replaced by `{new_name}`.  "
+                               f"Support for `{old_name}` will be removed "
+                               f"in SciPy {end_version}.")
+                    warnings.warn(message, DeprecationWarning, stacklevel=2)
+                if new_name in kwargs:
+                    message = (f"{fun.__name__}() got multiple values for "
+                               f"argument now known as `{new_name}`")
+                    raise TypeError(message)
+                kwargs[new_name] = kwargs.pop(old_name)
+            return fun(*args, **kwargs)
+        return wrapper
+    return decorator
+
+
+def _rng_spawn(rng, n_children):
+    # spawns independent RNGs from a parent RNG
+    bg = rng._bit_generator
+    ss = bg._seed_seq
+    child_rngs = [np.random.Generator(type(bg)(child_ss))
+                  for child_ss in ss.spawn(n_children)]
+    return child_rngs
+
+
+def _get_nan(*data, xp=None):
+    xp = array_namespace(*data) if xp is None else xp
+    # Get NaN of appropriate dtype for data
+    data = [xp.asarray(item) for item in data]
+    try:
+        min_float = getattr(xp, 'float16', xp.float32)
+        dtype = xp.result_type(*data, min_float)  # must be at least a float
+    except DTypePromotionError:
+        # fallback to float64
+        dtype = xp.float64
+    return xp.asarray(xp.nan, dtype=dtype)[()]
+
+
+def normalize_axis_index(axis, ndim):
+    # Check if `axis` is in the correct range and normalize it
+    if axis < -ndim or axis >= ndim:
+        msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
+        raise AxisError(msg)
+
+    if axis < 0:
+        axis = axis + ndim
+    return axis
+
+
+def _call_callback_maybe_halt(callback, res):
+    """Call wrapped callback; return True if algorithm should stop.
+
+    Parameters
+    ----------
+    callback : callable or None
+        A user-provided callback wrapped with `_wrap_callback`
+    res : OptimizeResult
+        Information about the current iterate
+
+    Returns
+    -------
+    halt : bool
+        True if minimization should stop
+
+    """
+    if callback is None:
+        return False
+    try:
+        callback(res)
+        return False
+    except StopIteration:
+        callback.stop_iteration = True
+        return True
+
+
+class _RichResult(dict):
+    """ Container for multiple outputs with pretty-printing """
+    def __getattr__(self, name):
+        try:
+            return self[name]
+        except KeyError as e:
+            raise AttributeError(name) from e
+
+    __setattr__ = dict.__setitem__  # type: ignore[assignment]
+    __delattr__ = dict.__delitem__  # type: ignore[assignment]
+
+    def __repr__(self):
+        order_keys = ['message', 'success', 'status', 'fun', 'funl', 'x', 'xl',
+                      'col_ind', 'nit', 'lower', 'upper', 'eqlin', 'ineqlin',
+                      'converged', 'flag', 'function_calls', 'iterations',
+                      'root']
+        order_keys = getattr(self, '_order_keys', order_keys)
+        # 'slack', 'con' are redundant with residuals
+        # 'crossover_nit' is probably not interesting to most users
+        omit_keys = {'slack', 'con', 'crossover_nit', '_order_keys'}
+
+        def key(item):
+            try:
+                return order_keys.index(item[0].lower())
+            except ValueError:  # item not in list
+                return np.inf
+
+        def omit_redundant(items):
+            for item in items:
+                if item[0] in omit_keys:
+                    continue
+                yield item
+
+        def item_sorter(d):
+            return sorted(omit_redundant(d.items()), key=key)
+
+        if self.keys():
+            return _dict_formatter(self, sorter=item_sorter)
+        else:
+            return self.__class__.__name__ + "()"
+
+    def __dir__(self):
+        return list(self.keys())
+
+
+def _indenter(s, n=0):
+    """
+    Ensures that lines after the first are indented by the specified amount
+    """
+    split = s.split("\n")
+    indent = " "*n
+    return ("\n" + indent).join(split)
+
+
+def _float_formatter_10(x):
+    """
+    Returns a string representation of a float with exactly ten characters
+    """
+    if np.isposinf(x):
+        return "       inf"
+    elif np.isneginf(x):
+        return "      -inf"
+    elif np.isnan(x):
+        return "       nan"
+    return np.format_float_scientific(x, precision=3, pad_left=2, unique=False)
+
+
+def _dict_formatter(d, n=0, mplus=1, sorter=None):
+    """
+    Pretty printer for dictionaries
+
+    `n` keeps track of the starting indentation;
+    lines are indented by this much after a line break.
+    `mplus` is additional left padding applied to keys
+    """
+    if isinstance(d, dict):
+        m = max(map(len, list(d.keys()))) + mplus  # width to print keys
+        s = '\n'.join([k.rjust(m) + ': ' +  # right justified, width m
+                       _indenter(_dict_formatter(v, m+n+2, 0, sorter), m+2)
+                       for k, v in sorter(d)])  # +2 for ': '
+    else:
+        # By default, NumPy arrays print with linewidth=76. `n` is
+        # the indent at which a line begins printing, so it is subtracted
+        # from the default to avoid exceeding 76 characters total.
+        # `edgeitems` is the number of elements to include before and after
+        # ellipses when arrays are not shown in full.
+        # `threshold` is the maximum number of elements for which an
+        # array is shown in full.
+        # These values tend to work well for use with OptimizeResult.
+        with np.printoptions(linewidth=76-n, edgeitems=2, threshold=12,
+                             formatter={'float_kind': _float_formatter_10}):
+            s = str(d)
+    return s
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/__init__.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..30b1d852db016240f06f8a6dfb163b2e3edafba0
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/__init__.py
@@ -0,0 +1,22 @@
+"""
+NumPy Array API compatibility library
+
+This is a small wrapper around NumPy and CuPy that is compatible with the
+Array API standard https://data-apis.org/array-api/latest/. See also NEP 47
+https://numpy.org/neps/nep-0047-array-api-standard.html.
+
+Unlike array_api_strict, this is not a strict minimal implementation of the
+Array API, but rather just an extension of the main NumPy namespace with
+changes needed to be compliant with the Array API. See
+https://numpy.org/doc/stable/reference/array_api.html for a full list of
+changes. In particular, unlike array_api_strict, this package does not use a
+separate Array object, but rather just uses numpy.ndarray directly.
+
+Library authors using the Array API may wish to test against array_api_strict
+to ensure they are not using functionality outside of the standard, but prefer
+this implementation for the default when working with NumPy arrays.
+
+"""
+__version__ = '1.9.1'
+
+from .common import *  # noqa: F401, F403
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/_internal.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/_internal.py
new file mode 100644
index 0000000000000000000000000000000000000000..170a1ff9e6459a8cd76f8f6f9b4bca1e894e9883
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/_internal.py
@@ -0,0 +1,46 @@
+"""
+Internal helpers
+"""
+
+from functools import wraps
+from inspect import signature
+
+def get_xp(xp):
+    """
+    Decorator to automatically replace xp with the corresponding array module.
+
+    Use like
+
+    import numpy as np
+
+    @get_xp(np)
+    def func(x, /, xp, kwarg=None):
+        return xp.func(x, kwarg=kwarg)
+
+    Note that xp must be a keyword argument and come after all non-keyword
+    arguments.
+
+    """
+
+    def inner(f):
+        @wraps(f)
+        def wrapped_f(*args, **kwargs):
+            return f(*args, xp=xp, **kwargs)
+
+        sig = signature(f)
+        new_sig = sig.replace(
+            parameters=[sig.parameters[i] for i in sig.parameters if i != "xp"]
+        )
+
+        if wrapped_f.__doc__ is None:
+            wrapped_f.__doc__ = f"""\
+Array API compatibility wrapper for {f.__name__}.
+
+See the corresponding documentation in NumPy/CuPy and/or the array API
+specification for more details.
+
+"""
+        wrapped_f.__signature__ = new_sig
+        return wrapped_f
+
+    return inner
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__init__.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d86857618458bf422e7f16dab302b179e5d520d8
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__init__.py
@@ -0,0 +1,16 @@
+from cupy import * # noqa: F403
+
+# from cupy import * doesn't overwrite these builtin names
+from cupy import abs, max, min, round # noqa: F401
+
+# These imports may overwrite names from the import * above.
+from ._aliases import * # noqa: F403
+
+# See the comment in the numpy __init__.py
+__import__(__package__ + '.linalg')
+
+__import__(__package__ + '.fft')
+
+from ..common._helpers import * # noqa: F401,F403
+
+__array_api_version__ = '2023.12'
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/__init__.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..313a01ab796380c75fb506796ded1ccd24640a10
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/__init__.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_aliases.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_aliases.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4a52c9690872b65dbc65b04177f4c26f90dca14a
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_aliases.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_info.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_info.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..45282367d4b6a94eba20dda1dbccc3e890aab165
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_info.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_typing.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_typing.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f363c5596175e568c124b6addbe59dbc5f8235d5
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/_typing.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/fft.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/fft.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ae545cea6025ddd8ebc0fa3ee2c55bfb1b472cf4
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/fft.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/linalg.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/linalg.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5ed2757151e81829967755a8bea4c8580329cbeb
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/__pycache__/linalg.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_aliases.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_aliases.py
new file mode 100644
index 0000000000000000000000000000000000000000..3627fb6b97820c292c86d26b9aabcefd899cfed1
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_aliases.py
@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+import cupy as cp
+
+from ..common import _aliases
+from .._internal import get_xp
+
+from ._info import __array_namespace_info__
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from typing import Optional, Union
+    from ._typing import ndarray, Device, Dtype, NestedSequence, SupportsBufferProtocol
+
+bool = cp.bool_
+
+# Basic renames
+acos = cp.arccos
+acosh = cp.arccosh
+asin = cp.arcsin
+asinh = cp.arcsinh
+atan = cp.arctan
+atan2 = cp.arctan2
+atanh = cp.arctanh
+bitwise_left_shift = cp.left_shift
+bitwise_invert = cp.invert
+bitwise_right_shift = cp.right_shift
+concat = cp.concatenate
+pow = cp.power
+
+arange = get_xp(cp)(_aliases.arange)
+empty = get_xp(cp)(_aliases.empty)
+empty_like = get_xp(cp)(_aliases.empty_like)
+eye = get_xp(cp)(_aliases.eye)
+full = get_xp(cp)(_aliases.full)
+full_like = get_xp(cp)(_aliases.full_like)
+linspace = get_xp(cp)(_aliases.linspace)
+ones = get_xp(cp)(_aliases.ones)
+ones_like = get_xp(cp)(_aliases.ones_like)
+zeros = get_xp(cp)(_aliases.zeros)
+zeros_like = get_xp(cp)(_aliases.zeros_like)
+UniqueAllResult = get_xp(cp)(_aliases.UniqueAllResult)
+UniqueCountsResult = get_xp(cp)(_aliases.UniqueCountsResult)
+UniqueInverseResult = get_xp(cp)(_aliases.UniqueInverseResult)
+unique_all = get_xp(cp)(_aliases.unique_all)
+unique_counts = get_xp(cp)(_aliases.unique_counts)
+unique_inverse = get_xp(cp)(_aliases.unique_inverse)
+unique_values = get_xp(cp)(_aliases.unique_values)
+astype = _aliases.astype
+std = get_xp(cp)(_aliases.std)
+var = get_xp(cp)(_aliases.var)
+cumulative_sum = get_xp(cp)(_aliases.cumulative_sum)
+clip = get_xp(cp)(_aliases.clip)
+permute_dims = get_xp(cp)(_aliases.permute_dims)
+reshape = get_xp(cp)(_aliases.reshape)
+argsort = get_xp(cp)(_aliases.argsort)
+sort = get_xp(cp)(_aliases.sort)
+nonzero = get_xp(cp)(_aliases.nonzero)
+ceil = get_xp(cp)(_aliases.ceil)
+floor = get_xp(cp)(_aliases.floor)
+trunc = get_xp(cp)(_aliases.trunc)
+matmul = get_xp(cp)(_aliases.matmul)
+matrix_transpose = get_xp(cp)(_aliases.matrix_transpose)
+tensordot = get_xp(cp)(_aliases.tensordot)
+sign = get_xp(cp)(_aliases.sign)
+
+_copy_default = object()
+
+# asarray also adds the copy keyword, which is not present in numpy 1.0.
+def asarray(
+    obj: Union[
+        ndarray,
+        bool,
+        int,
+        float,
+        NestedSequence[bool | int | float],
+        SupportsBufferProtocol,
+    ],
+    /,
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+    copy: Optional[bool] = _copy_default,
+    **kwargs,
+) -> ndarray:
+    """
+    Array API compatibility wrapper for asarray().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    with cp.cuda.Device(device):
+        # cupy is like NumPy 1.26 (except without _CopyMode). See the comments
+        # in asarray in numpy/_aliases.py.
+        if copy is not _copy_default:
+            # A future version of CuPy will change the meaning of copy=False
+            # to mean no-copy. We don't know for certain what version it will
+            # be yet, so to avoid breaking that version, we use a different
+            # default value for copy so asarray(obj) with no copy kwarg will
+            # always do the copy-if-needed behavior.
+
+            # This will still need to be updated to remove the
+            # NotImplementedError for copy=False, but at least this won't
+            # break the default or existing behavior.
+            if copy is None:
+                copy = False
+            elif copy is False:
+                raise NotImplementedError("asarray(copy=False) is not yet supported in cupy")
+            kwargs['copy'] = copy
+
+        return cp.array(obj, dtype=dtype, **kwargs)
+
+# These functions are completely new here. If the library already has them
+# (i.e., numpy 2.0), use the library version instead of our wrapper.
+if hasattr(cp, 'vecdot'):
+    vecdot = cp.vecdot
+else:
+    vecdot = get_xp(cp)(_aliases.vecdot)
+
+if hasattr(cp, 'isdtype'):
+    isdtype = cp.isdtype
+else:
+    isdtype = get_xp(cp)(_aliases.isdtype)
+
+if hasattr(cp, 'unstack'):
+    unstack = cp.unstack
+else:
+    unstack = get_xp(cp)(_aliases.unstack)
+
+__all__ = _aliases.__all__ + ['__array_namespace_info__', 'asarray', 'bool',
+                              'acos', 'acosh', 'asin', 'asinh', 'atan',
+                              'atan2', 'atanh', 'bitwise_left_shift',
+                              'bitwise_invert', 'bitwise_right_shift',
+                              'concat', 'pow', 'sign']
+
+_all_ignore = ['cp', 'get_xp']
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_info.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..4440807d2240fb4125229b68a4af8b562b636753
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_info.py
@@ -0,0 +1,326 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+from cupy import (
+    dtype,
+    cuda,
+    bool_ as bool,
+    intp,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    complex64,
+    complex128,
+)
+
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for CuPy.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for CuPy.
+
+    Examples
+    --------
+    >>> info = np.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': cupy.float64,
+     'complex floating': cupy.complex128,
+     'integral': cupy.int64,
+     'indexing': cupy.int64}
+
+    """
+
+    __module__ = 'cupy'
+
+    def capabilities(self):
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing. Always ``True`` for CuPy.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes. Always ``True`` for
+          CuPy.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            # 'max rank' will be part of the 2024.12 standard
+            # "max rank": 64,
+        }
+
+    def default_device(self):
+        """
+        The default device used for new CuPy arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : str
+            The default device used for new CuPy arrays.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_device()
+        Device(0)
+
+        """
+        return cuda.Device(0)
+
+    def default_dtypes(self, *, device=None):
+        """
+        The default data types used for new CuPy arrays.
+
+        For CuPy, this always returns the following dictionary:
+
+        - **"real floating"**: ``cupy.float64``
+        - **"complex floating"**: ``cupy.complex128``
+        - **"integral"**: ``cupy.intp``
+        - **"indexing"**: ``cupy.intp``
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the default data types for.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new CuPy
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': cupy.float64,
+         'complex floating': cupy.complex128,
+         'integral': cupy.int64,
+         'indexing': cupy.int64}
+
+        """
+        # TODO: Does this depend on device?
+        return {
+            "real floating": dtype(float64),
+            "complex floating": dtype(complex128),
+            "integral": dtype(intp),
+            "indexing": dtype(intp),
+        }
+
+    def dtypes(self, *, device=None, kind=None):
+        """
+        The array API data types supported by CuPy.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the data types for.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            CuPy data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': cupy.int8,
+         'int16': cupy.int16,
+         'int32': cupy.int32,
+         'int64': cupy.int64}
+
+        """
+        # TODO: Does this depend on device?
+        if kind is None:
+            return {
+                "bool": dtype(bool),
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "bool":
+            return {"bool": bool}
+        if kind == "signed integer":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "integral":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "real floating":
+            return {
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "numeric":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if isinstance(kind, tuple):
+            res = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    def devices(self):
+        """
+        The devices supported by CuPy.
+
+        Returns
+        -------
+        devices : list of str
+            The devices supported by CuPy.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        """
+        return [cuda.Device(i) for i in range(cuda.runtime.getDeviceCount())]
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_typing.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3d9aab67e52f3300cd96c3d0e701d1604eaccbb
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/_typing.py
@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+__all__ = [
+    "ndarray",
+    "Device",
+    "Dtype",
+]
+
+import sys
+from typing import (
+    Union,
+    TYPE_CHECKING,
+)
+
+from cupy import (
+    ndarray,
+    dtype,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+)
+
+from cupy.cuda.device import Device
+
+if TYPE_CHECKING or sys.version_info >= (3, 9):
+    Dtype = dtype[Union[
+        int8,
+        int16,
+        int32,
+        int64,
+        uint8,
+        uint16,
+        uint32,
+        uint64,
+        float32,
+        float64,
+    ]]
+else:
+    Dtype = dtype
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/fft.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/fft.py
new file mode 100644
index 0000000000000000000000000000000000000000..307e0f7277710693063ef8c4d2cd7893275ad44a
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/fft.py
@@ -0,0 +1,36 @@
+from cupy.fft import * # noqa: F403
+# cupy.fft doesn't have __all__. If it is added, replace this with
+#
+# from cupy.fft import __all__ as linalg_all
+_n = {}
+exec('from cupy.fft import *', _n)
+del _n['__builtins__']
+fft_all = list(_n)
+del _n
+
+from ..common import _fft
+from .._internal import get_xp
+
+import cupy as cp
+
+fft = get_xp(cp)(_fft.fft)
+ifft = get_xp(cp)(_fft.ifft)
+fftn = get_xp(cp)(_fft.fftn)
+ifftn = get_xp(cp)(_fft.ifftn)
+rfft = get_xp(cp)(_fft.rfft)
+irfft = get_xp(cp)(_fft.irfft)
+rfftn = get_xp(cp)(_fft.rfftn)
+irfftn = get_xp(cp)(_fft.irfftn)
+hfft = get_xp(cp)(_fft.hfft)
+ihfft = get_xp(cp)(_fft.ihfft)
+fftfreq = get_xp(cp)(_fft.fftfreq)
+rfftfreq = get_xp(cp)(_fft.rfftfreq)
+fftshift = get_xp(cp)(_fft.fftshift)
+ifftshift = get_xp(cp)(_fft.ifftshift)
+
+__all__ = fft_all + _fft.__all__
+
+del get_xp
+del cp
+del fft_all
+del _fft
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/linalg.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fcdd498e0073ada094a20a9ae423e01cb0f8ceb
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/cupy/linalg.py
@@ -0,0 +1,49 @@
+from cupy.linalg import * # noqa: F403
+# cupy.linalg doesn't have __all__. If it is added, replace this with
+#
+# from cupy.linalg import __all__ as linalg_all
+_n = {}
+exec('from cupy.linalg import *', _n)
+del _n['__builtins__']
+linalg_all = list(_n)
+del _n
+
+from ..common import _linalg
+from .._internal import get_xp
+
+import cupy as cp
+
+# These functions are in both the main and linalg namespaces
+from ._aliases import matmul, matrix_transpose, tensordot, vecdot # noqa: F401
+
+cross = get_xp(cp)(_linalg.cross)
+outer = get_xp(cp)(_linalg.outer)
+EighResult = _linalg.EighResult
+QRResult = _linalg.QRResult
+SlogdetResult = _linalg.SlogdetResult
+SVDResult = _linalg.SVDResult
+eigh = get_xp(cp)(_linalg.eigh)
+qr = get_xp(cp)(_linalg.qr)
+slogdet = get_xp(cp)(_linalg.slogdet)
+svd = get_xp(cp)(_linalg.svd)
+cholesky = get_xp(cp)(_linalg.cholesky)
+matrix_rank = get_xp(cp)(_linalg.matrix_rank)
+pinv = get_xp(cp)(_linalg.pinv)
+matrix_norm = get_xp(cp)(_linalg.matrix_norm)
+svdvals = get_xp(cp)(_linalg.svdvals)
+diagonal = get_xp(cp)(_linalg.diagonal)
+trace = get_xp(cp)(_linalg.trace)
+
+# These functions are completely new here. If the library already has them
+# (i.e., numpy 2.0), use the library version instead of our wrapper.
+if hasattr(cp.linalg, 'vector_norm'):
+    vector_norm = cp.linalg.vector_norm
+else:
+    vector_norm = get_xp(cp)(_linalg.vector_norm)
+
+__all__ = linalg_all + _linalg.__all__
+
+del get_xp
+del cp
+del linalg_all
+del _linalg
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__init__.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfa3acf8945a84c8e3fcdc892edc19d4f674cd30
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__init__.py
@@ -0,0 +1,24 @@
+from torch import * # noqa: F403
+
+# Several names are not included in the above import *
+import torch
+for n in dir(torch):
+    if (n.startswith('_')
+        or n.endswith('_')
+        or 'cuda' in n
+        or 'cpu' in n
+        or 'backward' in n):
+        continue
+    exec(n + ' = torch.' + n)
+
+# These imports may overwrite names from the import * above.
+from ._aliases import * # noqa: F403
+
+# See the comment in the numpy __init__.py
+__import__(__package__ + '.linalg')
+
+__import__(__package__ + '.fft')
+
+from ..common._helpers import * # noqa: F403
+
+__array_api_version__ = '2023.12'
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/__init__.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..db2eeb2dcf85dfd1c30352fa1f7d74beafa23a79
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/__init__.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/_aliases.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/_aliases.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b81868ff3cad8d6fa1ec11eaabe7659ac807c6ed
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/_aliases.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/_info.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/_info.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8f0b81a510681e87337766f40a6d6d7c47bdaca4
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/_info.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/fft.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/fft.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..858e7fa6b8e1c5716f42fa2a6cddba65952dc182
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/fft.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/linalg.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/linalg.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b015f146158ed04d9c8d4d58c2024a9c82c1e92b
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/__pycache__/linalg.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/_aliases.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/_aliases.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ac66bcb17e6f13a51bd6c7fd345bee23c16140f
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/_aliases.py
@@ -0,0 +1,752 @@
+from __future__ import annotations
+
+from functools import wraps as _wraps
+from builtins import all as _builtin_all, any as _builtin_any
+
+from ..common._aliases import (matrix_transpose as _aliases_matrix_transpose,
+                               vecdot as _aliases_vecdot,
+                               clip as _aliases_clip,
+                               unstack as _aliases_unstack,
+                               cumulative_sum as _aliases_cumulative_sum,
+                               )
+from .._internal import get_xp
+
+from ._info import __array_namespace_info__
+
+import torch
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from typing import List, Optional, Sequence, Tuple, Union
+    from ..common._typing import Device
+    from torch import dtype as Dtype
+
+    array = torch.Tensor
+
+_int_dtypes = {
+    torch.uint8,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.int64,
+}
+
+_array_api_dtypes = {
+    torch.bool,
+    *_int_dtypes,
+    torch.float32,
+    torch.float64,
+    torch.complex64,
+    torch.complex128,
+}
+
+_promotion_table  = {
+    # bool
+    (torch.bool, torch.bool): torch.bool,
+    # ints
+    (torch.int8, torch.int8): torch.int8,
+    (torch.int8, torch.int16): torch.int16,
+    (torch.int8, torch.int32): torch.int32,
+    (torch.int8, torch.int64): torch.int64,
+    (torch.int16, torch.int8): torch.int16,
+    (torch.int16, torch.int16): torch.int16,
+    (torch.int16, torch.int32): torch.int32,
+    (torch.int16, torch.int64): torch.int64,
+    (torch.int32, torch.int8): torch.int32,
+    (torch.int32, torch.int16): torch.int32,
+    (torch.int32, torch.int32): torch.int32,
+    (torch.int32, torch.int64): torch.int64,
+    (torch.int64, torch.int8): torch.int64,
+    (torch.int64, torch.int16): torch.int64,
+    (torch.int64, torch.int32): torch.int64,
+    (torch.int64, torch.int64): torch.int64,
+    # uints
+    (torch.uint8, torch.uint8): torch.uint8,
+    # ints and uints (mixed sign)
+    (torch.int8, torch.uint8): torch.int16,
+    (torch.int16, torch.uint8): torch.int16,
+    (torch.int32, torch.uint8): torch.int32,
+    (torch.int64, torch.uint8): torch.int64,
+    (torch.uint8, torch.int8): torch.int16,
+    (torch.uint8, torch.int16): torch.int16,
+    (torch.uint8, torch.int32): torch.int32,
+    (torch.uint8, torch.int64): torch.int64,
+    # floats
+    (torch.float32, torch.float32): torch.float32,
+    (torch.float32, torch.float64): torch.float64,
+    (torch.float64, torch.float32): torch.float64,
+    (torch.float64, torch.float64): torch.float64,
+    # complexes
+    (torch.complex64, torch.complex64): torch.complex64,
+    (torch.complex64, torch.complex128): torch.complex128,
+    (torch.complex128, torch.complex64): torch.complex128,
+    (torch.complex128, torch.complex128): torch.complex128,
+    # Mixed float and complex
+    (torch.float32, torch.complex64): torch.complex64,
+    (torch.float32, torch.complex128): torch.complex128,
+    (torch.float64, torch.complex64): torch.complex128,
+    (torch.float64, torch.complex128): torch.complex128,
+}
+
+
+def _two_arg(f):
+    @_wraps(f)
+    def _f(x1, x2, /, **kwargs):
+        x1, x2 = _fix_promotion(x1, x2)
+        return f(x1, x2, **kwargs)
+    if _f.__doc__ is None:
+        _f.__doc__ = f"""\
+Array API compatibility wrapper for torch.{f.__name__}.
+
+See the corresponding PyTorch documentation and/or the array API specification
+for more details.
+
+"""
+    return _f
+
+def _fix_promotion(x1, x2, only_scalar=True):
+    if not isinstance(x1, torch.Tensor) or not isinstance(x2, torch.Tensor):
+        return x1, x2
+    if x1.dtype not in _array_api_dtypes or x2.dtype not in _array_api_dtypes:
+        return x1, x2
+    # If an argument is 0-D pytorch downcasts the other argument
+    if not only_scalar or x1.shape == ():
+        dtype = result_type(x1, x2)
+        x2 = x2.to(dtype)
+    if not only_scalar or x2.shape == ():
+        dtype = result_type(x1, x2)
+        x1 = x1.to(dtype)
+    return x1, x2
+
+def result_type(*arrays_and_dtypes: Union[array, Dtype]) -> Dtype:
+    if len(arrays_and_dtypes) == 0:
+        raise TypeError("At least one array or dtype must be provided")
+    if len(arrays_and_dtypes) == 1:
+        x = arrays_and_dtypes[0]
+        if isinstance(x, torch.dtype):
+            return x
+        return x.dtype
+    if len(arrays_and_dtypes) > 2:
+        return result_type(arrays_and_dtypes[0], result_type(*arrays_and_dtypes[1:]))
+
+    x, y = arrays_and_dtypes
+    xdt = x.dtype if not isinstance(x, torch.dtype) else x
+    ydt = y.dtype if not isinstance(y, torch.dtype) else y
+
+    if (xdt, ydt) in _promotion_table:
+        return _promotion_table[xdt, ydt]
+
+    # This doesn't result_type(dtype, dtype) for non-array API dtypes
+    # because torch.result_type only accepts tensors. This does however, allow
+    # cross-kind promotion.
+    x = torch.tensor([], dtype=x) if isinstance(x, torch.dtype) else x
+    y = torch.tensor([], dtype=y) if isinstance(y, torch.dtype) else y
+    return torch.result_type(x, y)
+
+def can_cast(from_: Union[Dtype, array], to: Dtype, /) -> bool:
+    if not isinstance(from_, torch.dtype):
+        from_ = from_.dtype
+    return torch.can_cast(from_, to)
+
+# Basic renames
+bitwise_invert = torch.bitwise_not
+newaxis = None
+# torch.conj sets the conjugation bit, which breaks conversion to other
+# libraries. See https://github.com/data-apis/array-api-compat/issues/173
+conj = torch.conj_physical
+
+# Two-arg elementwise functions
+# These require a wrapper to do the correct type promotion on 0-D tensors
+add = _two_arg(torch.add)
+atan2 = _two_arg(torch.atan2)
+bitwise_and = _two_arg(torch.bitwise_and)
+bitwise_left_shift = _two_arg(torch.bitwise_left_shift)
+bitwise_or = _two_arg(torch.bitwise_or)
+bitwise_right_shift = _two_arg(torch.bitwise_right_shift)
+bitwise_xor = _two_arg(torch.bitwise_xor)
+copysign = _two_arg(torch.copysign)
+divide = _two_arg(torch.divide)
+# Also a rename. torch.equal does not broadcast
+equal = _two_arg(torch.eq)
+floor_divide = _two_arg(torch.floor_divide)
+greater = _two_arg(torch.greater)
+greater_equal = _two_arg(torch.greater_equal)
+hypot = _two_arg(torch.hypot)
+less = _two_arg(torch.less)
+less_equal = _two_arg(torch.less_equal)
+logaddexp = _two_arg(torch.logaddexp)
+# logical functions are not included here because they only accept bool in the
+# spec, so type promotion is irrelevant.
+maximum = _two_arg(torch.maximum)
+minimum = _two_arg(torch.minimum)
+multiply = _two_arg(torch.multiply)
+not_equal = _two_arg(torch.not_equal)
+pow = _two_arg(torch.pow)
+remainder = _two_arg(torch.remainder)
+subtract = _two_arg(torch.subtract)
+
+# These wrappers are mostly based on the fact that pytorch uses 'dim' instead
+# of 'axis'.
+
+# torch.min and torch.max return a tuple and don't support multiple axes https://github.com/pytorch/pytorch/issues/58745
+def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.clone(x)
+    return torch.amax(x, axis, keepdims=keepdims)
+
+def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.clone(x)
+    return torch.amin(x, axis, keepdims=keepdims)
+
+clip = get_xp(torch)(_aliases_clip)
+unstack = get_xp(torch)(_aliases_unstack)
+cumulative_sum = get_xp(torch)(_aliases_cumulative_sum)
+
+# torch.sort also returns a tuple
+# https://github.com/pytorch/pytorch/issues/70921
+def sort(x: array, /, *, axis: int = -1, descending: bool = False, stable: bool = True, **kwargs) -> array:
+    return torch.sort(x, dim=axis, descending=descending, stable=stable, **kwargs).values
+
+def _normalize_axes(axis, ndim):
+    axes = []
+    if ndim == 0 and axis:
+        # Better error message in this case
+        raise IndexError(f"Dimension out of range: {axis[0]}")
+    lower, upper = -ndim, ndim - 1
+    for a in axis:
+        if a < lower or a > upper:
+            # Match torch error message (e.g., from sum())
+            raise IndexError(f"Dimension out of range (expected to be in range of [{lower}, {upper}], but got {a}")
+        if a < 0:
+            a = a + ndim
+        if a in axes:
+            # Use IndexError instead of RuntimeError, and "axis" instead of "dim"
+            raise IndexError(f"Axis {a} appears multiple times in the list of axes")
+        axes.append(a)
+    return sorted(axes)
+
+def _axis_none_keepdims(x, ndim, keepdims):
+    # Apply keepdims when axis=None
+    # (https://github.com/pytorch/pytorch/issues/71209)
+    # Note that this is only valid for the axis=None case.
+    if keepdims:
+        for i in range(ndim):
+            x = torch.unsqueeze(x, 0)
+    return x
+
+def _reduce_multiple_axes(f, x, axis, keepdims=False, **kwargs):
+    # Some reductions don't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    axes = _normalize_axes(axis, x.ndim)
+    for a in reversed(axes):
+        x = torch.movedim(x, a, -1)
+    x = torch.flatten(x, -len(axes))
+
+    out = f(x, -1, **kwargs)
+
+    if keepdims:
+        for a in axes:
+            out = torch.unsqueeze(out, a)
+    return out
+
+def prod(x: array,
+         /,
+         *,
+         axis: Optional[Union[int, Tuple[int, ...]]] = None,
+         dtype: Optional[Dtype] = None,
+         keepdims: bool = False,
+         **kwargs) -> array:
+    x = torch.asarray(x)
+    ndim = x.ndim
+
+    # https://github.com/pytorch/pytorch/issues/29137. Separate from the logic
+    # below because it still needs to upcast.
+    if axis == ():
+        if dtype is None:
+            # We can't upcast uint8 according to the spec because there is no
+            # torch.uint64, so at least upcast to int64 which is what sum does
+            # when axis=None.
+            if x.dtype in [torch.int8, torch.int16, torch.int32, torch.uint8]:
+                return x.to(torch.int64)
+            return x.clone()
+        return x.to(dtype)
+
+    # torch.prod doesn't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    if isinstance(axis, tuple):
+        return _reduce_multiple_axes(torch.prod, x, axis, keepdims=keepdims, dtype=dtype, **kwargs)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.prod(x, dtype=dtype, **kwargs)
+        res = _axis_none_keepdims(res, ndim, keepdims)
+        return res
+
+    return torch.prod(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
+
+
+def sum(x: array,
+         /,
+         *,
+         axis: Optional[Union[int, Tuple[int, ...]]] = None,
+         dtype: Optional[Dtype] = None,
+         keepdims: bool = False,
+         **kwargs) -> array:
+    x = torch.asarray(x)
+    ndim = x.ndim
+
+    # https://github.com/pytorch/pytorch/issues/29137.
+    # Make sure it upcasts.
+    if axis == ():
+        if dtype is None:
+            # We can't upcast uint8 according to the spec because there is no
+            # torch.uint64, so at least upcast to int64 which is what sum does
+            # when axis=None.
+            if x.dtype in [torch.int8, torch.int16, torch.int32, torch.uint8]:
+                return x.to(torch.int64)
+            return x.clone()
+        return x.to(dtype)
+
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.sum(x, dtype=dtype, **kwargs)
+        res = _axis_none_keepdims(res, ndim, keepdims)
+        return res
+
+    return torch.sum(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
+
+def any(x: array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        keepdims: bool = False,
+        **kwargs) -> array:
+    x = torch.asarray(x)
+    ndim = x.ndim
+    if axis == ():
+        return x.to(torch.bool)
+    # torch.any doesn't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    if isinstance(axis, tuple):
+        res = _reduce_multiple_axes(torch.any, x, axis, keepdims=keepdims, **kwargs)
+        return res.to(torch.bool)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.any(x, **kwargs)
+        res = _axis_none_keepdims(res, ndim, keepdims)
+        return res.to(torch.bool)
+
+    # torch.any doesn't return bool for uint8
+    return torch.any(x, axis, keepdims=keepdims).to(torch.bool)
+
+def all(x: array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        keepdims: bool = False,
+        **kwargs) -> array:
+    x = torch.asarray(x)
+    ndim = x.ndim
+    if axis == ():
+        return x.to(torch.bool)
+    # torch.all doesn't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    if isinstance(axis, tuple):
+        res = _reduce_multiple_axes(torch.all, x, axis, keepdims=keepdims, **kwargs)
+        return res.to(torch.bool)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.all(x, **kwargs)
+        res = _axis_none_keepdims(res, ndim, keepdims)
+        return res.to(torch.bool)
+
+    # torch.all doesn't return bool for uint8
+    return torch.all(x, axis, keepdims=keepdims).to(torch.bool)
+
+def mean(x: array,
+         /,
+         *,
+         axis: Optional[Union[int, Tuple[int, ...]]] = None,
+         keepdims: bool = False,
+         **kwargs) -> array:
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.clone(x)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.mean(x, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+    return torch.mean(x, axis, keepdims=keepdims, **kwargs)
+
+def std(x: array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        correction: Union[int, float] = 0.0,
+        keepdims: bool = False,
+        **kwargs) -> array:
+    # Note, float correction is not supported
+    # https://github.com/pytorch/pytorch/issues/61492. We don't try to
+    # implement it here for now.
+
+    if isinstance(correction, float):
+        _correction = int(correction)
+        if correction != _correction:
+            raise NotImplementedError("float correction in torch std() is not yet supported")
+    else:
+        _correction = correction
+
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.zeros_like(x)
+    if isinstance(axis, int):
+        axis = (axis,)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.std(x, tuple(range(x.ndim)), correction=_correction, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+    return torch.std(x, axis, correction=_correction, keepdims=keepdims, **kwargs)
+
+def var(x: array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        correction: Union[int, float] = 0.0,
+        keepdims: bool = False,
+        **kwargs) -> array:
+    # Note, float correction is not supported
+    # https://github.com/pytorch/pytorch/issues/61492. We don't try to
+    # implement it here for now.
+
+    # if isinstance(correction, float):
+    #     correction = int(correction)
+
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.zeros_like(x)
+    if isinstance(axis, int):
+        axis = (axis,)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.var(x, tuple(range(x.ndim)), correction=correction, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+    return torch.var(x, axis, correction=correction, keepdims=keepdims, **kwargs)
+
+# torch.concat doesn't support dim=None
+# https://github.com/pytorch/pytorch/issues/70925
+def concat(arrays: Union[Tuple[array, ...], List[array]],
+           /,
+           *,
+           axis: Optional[int] = 0,
+           **kwargs) -> array:
+    if axis is None:
+        arrays = tuple(ar.flatten() for ar in arrays)
+        axis = 0
+    return torch.concat(arrays, axis, **kwargs)
+
+# torch.squeeze only accepts int dim and doesn't require it
+# https://github.com/pytorch/pytorch/issues/70924. Support for tuple dim was
+# added at https://github.com/pytorch/pytorch/pull/89017.
+def squeeze(x: array, /, axis: Union[int, Tuple[int, ...]]) -> array:
+    if isinstance(axis, int):
+        axis = (axis,)
+    for a in axis:
+        if x.shape[a] != 1:
+            raise ValueError("squeezed dimensions must be equal to 1")
+    axes = _normalize_axes(axis, x.ndim)
+    # Remove this once pytorch 1.14 is released with the above PR #89017.
+    sequence = [a - i for i, a in enumerate(axes)]
+    for a in sequence:
+        x = torch.squeeze(x, a)
+    return x
+
+# torch.broadcast_to uses size instead of shape
+def broadcast_to(x: array, /, shape: Tuple[int, ...], **kwargs) -> array:
+    return torch.broadcast_to(x, shape, **kwargs)
+
+# torch.permute uses dims instead of axes
+def permute_dims(x: array, /, axes: Tuple[int, ...]) -> array:
+    return torch.permute(x, axes)
+
+# The axis parameter doesn't work for flip() and roll()
+# https://github.com/pytorch/pytorch/issues/71210. Also torch.flip() doesn't
+# accept axis=None
+def flip(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> array:
+    if axis is None:
+        axis = tuple(range(x.ndim))
+    # torch.flip doesn't accept dim as an int but the method does
+    # https://github.com/pytorch/pytorch/issues/18095
+    return x.flip(axis, **kwargs)
+
+def roll(x: array, /, shift: Union[int, Tuple[int, ...]], *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> array:
+    return torch.roll(x, shift, axis, **kwargs)
+
+def nonzero(x: array, /, **kwargs) -> Tuple[array, ...]:
+    if x.ndim == 0:
+        raise ValueError("nonzero() does not support zero-dimensional arrays")
+    return torch.nonzero(x, as_tuple=True, **kwargs)
+
+def where(condition: array, x1: array, x2: array, /) -> array:
+    x1, x2 = _fix_promotion(x1, x2)
+    return torch.where(condition, x1, x2)
+
+# torch.reshape doesn't have the copy keyword
+def reshape(x: array,
+            /,
+            shape: Tuple[int, ...],
+            copy: Optional[bool] = None,
+            **kwargs) -> array:
+    if copy is not None:
+        raise NotImplementedError("torch.reshape doesn't yet support the copy keyword")
+    return torch.reshape(x, shape, **kwargs)
+
+# torch.arange doesn't support returning empty arrays
+# (https://github.com/pytorch/pytorch/issues/70915), and doesn't support some
+# keyword argument combinations
+# (https://github.com/pytorch/pytorch/issues/70914)
+def arange(start: Union[int, float],
+           /,
+           stop: Optional[Union[int, float]] = None,
+           step: Union[int, float] = 1,
+           *,
+           dtype: Optional[Dtype] = None,
+           device: Optional[Device] = None,
+           **kwargs) -> array:
+    if stop is None:
+        start, stop = 0, start
+    if step > 0 and stop <= start or step < 0 and stop >= start:
+        if dtype is None:
+            if _builtin_all(isinstance(i, int) for i in [start, stop, step]):
+                dtype = torch.int64
+            else:
+                dtype = torch.float32
+        return torch.empty(0, dtype=dtype, device=device, **kwargs)
+    return torch.arange(start, stop, step, dtype=dtype, device=device, **kwargs)
+
+# torch.eye does not accept None as a default for the second argument and
+# doesn't support off-diagonals (https://github.com/pytorch/pytorch/issues/70910)
+def eye(n_rows: int,
+        n_cols: Optional[int] = None,
+        /,
+        *,
+        k: int = 0,
+        dtype: Optional[Dtype] = None,
+        device: Optional[Device] = None,
+        **kwargs) -> array:
+    if n_cols is None:
+        n_cols = n_rows
+    z = torch.zeros(n_rows, n_cols, dtype=dtype, device=device, **kwargs)
+    if abs(k) <= n_rows + n_cols:
+        z.diagonal(k).fill_(1)
+    return z
+
+# torch.linspace doesn't have the endpoint parameter
+def linspace(start: Union[int, float],
+             stop: Union[int, float],
+             /,
+             num: int,
+             *,
+             dtype: Optional[Dtype] = None,
+             device: Optional[Device] = None,
+             endpoint: bool = True,
+             **kwargs) -> array:
+    if not endpoint:
+        return torch.linspace(start, stop, num+1, dtype=dtype, device=device, **kwargs)[:-1]
+    return torch.linspace(start, stop, num, dtype=dtype, device=device, **kwargs)
+
+# torch.full does not accept an int size
+# https://github.com/pytorch/pytorch/issues/70906
+def full(shape: Union[int, Tuple[int, ...]],
+         fill_value: Union[bool, int, float, complex],
+         *,
+         dtype: Optional[Dtype] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> array:
+    if isinstance(shape, int):
+        shape = (shape,)
+
+    return torch.full(shape, fill_value, dtype=dtype, device=device, **kwargs)
+
+# ones, zeros, and empty do not accept shape as a keyword argument
+def ones(shape: Union[int, Tuple[int, ...]],
+         *,
+         dtype: Optional[Dtype] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> array:
+    return torch.ones(shape, dtype=dtype, device=device, **kwargs)
+
+def zeros(shape: Union[int, Tuple[int, ...]],
+         *,
+         dtype: Optional[Dtype] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> array:
+    return torch.zeros(shape, dtype=dtype, device=device, **kwargs)
+
+def empty(shape: Union[int, Tuple[int, ...]],
+         *,
+         dtype: Optional[Dtype] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> array:
+    return torch.empty(shape, dtype=dtype, device=device, **kwargs)
+
+# tril and triu do not call the keyword argument k
+
+def tril(x: array, /, *, k: int = 0) -> array:
+    return torch.tril(x, k)
+
+def triu(x: array, /, *, k: int = 0) -> array:
+    return torch.triu(x, k)
+
+# Functions that aren't in torch https://github.com/pytorch/pytorch/issues/58742
+def expand_dims(x: array, /, *, axis: int = 0) -> array:
+    return torch.unsqueeze(x, axis)
+
+def astype(x: array, dtype: Dtype, /, *, copy: bool = True) -> array:
+    return x.to(dtype, copy=copy)
+
+def broadcast_arrays(*arrays: array) -> List[array]:
+    shape = torch.broadcast_shapes(*[a.shape for a in arrays])
+    return [torch.broadcast_to(a, shape) for a in arrays]
+
+# Note that these named tuples aren't actually part of the standard namespace,
+# but I don't see any issue with exporting the names here regardless.
+from ..common._aliases import (UniqueAllResult, UniqueCountsResult,
+                               UniqueInverseResult)
+
+# https://github.com/pytorch/pytorch/issues/70920
+def unique_all(x: array) -> UniqueAllResult:
+    # torch.unique doesn't support returning indices.
+    # https://github.com/pytorch/pytorch/issues/36748. The workaround
+    # suggested in that issue doesn't actually function correctly (it relies
+    # on non-deterministic behavior of scatter()).
+    raise NotImplementedError("unique_all() not yet implemented for pytorch (see https://github.com/pytorch/pytorch/issues/36748)")
+
+    # values, inverse_indices, counts = torch.unique(x, return_counts=True, return_inverse=True)
+    # # torch.unique incorrectly gives a 0 count for nan values.
+    # # https://github.com/pytorch/pytorch/issues/94106
+    # counts[torch.isnan(values)] = 1
+    # return UniqueAllResult(values, indices, inverse_indices, counts)
+
+def unique_counts(x: array) -> UniqueCountsResult:
+    values, counts = torch.unique(x, return_counts=True)
+
+    # torch.unique incorrectly gives a 0 count for nan values.
+    # https://github.com/pytorch/pytorch/issues/94106
+    counts[torch.isnan(values)] = 1
+    return UniqueCountsResult(values, counts)
+
+def unique_inverse(x: array) -> UniqueInverseResult:
+    values, inverse = torch.unique(x, return_inverse=True)
+    return UniqueInverseResult(values, inverse)
+
+def unique_values(x: array) -> array:
+    return torch.unique(x)
+
+def matmul(x1: array, x2: array, /, **kwargs) -> array:
+    # torch.matmul doesn't type promote (but differently from _fix_promotion)
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    return torch.matmul(x1, x2, **kwargs)
+
+matrix_transpose = get_xp(torch)(_aliases_matrix_transpose)
+_vecdot = get_xp(torch)(_aliases_vecdot)
+
+def vecdot(x1: array, x2: array, /, *, axis: int = -1) -> array:
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    return _vecdot(x1, x2, axis=axis)
+
+# torch.tensordot uses dims instead of axes
+def tensordot(x1: array, x2: array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2, **kwargs) -> array:
+    # Note: torch.tensordot fails with integer dtypes when there is only 1
+    # element in the axis (https://github.com/pytorch/pytorch/issues/84530).
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    return torch.tensordot(x1, x2, dims=axes, **kwargs)
+
+
+def isdtype(
+    dtype: Dtype, kind: Union[Dtype, str, Tuple[Union[Dtype, str], ...]],
+    *, _tuple=True, # Disallow nested tuples
+) -> bool:
+    """
+    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
+
+    Note that outside of this function, this compat library does not yet fully
+    support complex numbers.
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
+    for more details
+    """
+    if isinstance(kind, tuple) and _tuple:
+        return _builtin_any(isdtype(dtype, k, _tuple=False) for k in kind)
+    elif isinstance(kind, str):
+        if kind == 'bool':
+            return dtype == torch.bool
+        elif kind == 'signed integer':
+            return dtype in _int_dtypes and dtype.is_signed
+        elif kind == 'unsigned integer':
+            return dtype in _int_dtypes and not dtype.is_signed
+        elif kind == 'integral':
+            return dtype in _int_dtypes
+        elif kind == 'real floating':
+            return dtype.is_floating_point
+        elif kind == 'complex floating':
+            return dtype.is_complex
+        elif kind == 'numeric':
+            return isdtype(dtype, ('integral', 'real floating', 'complex floating'))
+        else:
+            raise ValueError(f"Unrecognized data type kind: {kind!r}")
+    else:
+        return dtype == kind
+
+def take(x: array, indices: array, /, *, axis: Optional[int] = None, **kwargs) -> array:
+    if axis is None:
+        if x.ndim != 1:
+            raise ValueError("axis must be specified when ndim > 1")
+        axis = 0
+    return torch.index_select(x, axis, indices, **kwargs)
+
+def sign(x: array, /) -> array:
+    # torch sign() does not support complex numbers and does not propagate
+    # nans. See https://github.com/data-apis/array-api-compat/issues/136
+    if x.dtype.is_complex:
+        out = x/torch.abs(x)
+        # sign(0) = 0 but the above formula would give nan
+        out[x == 0+0j] = 0+0j
+        return out
+    else:
+        out = torch.sign(x)
+        if x.dtype.is_floating_point:
+            out[torch.isnan(x)] = torch.nan
+        return out
+
+
+__all__ = ['__array_namespace_info__', 'result_type', 'can_cast',
+           'permute_dims', 'bitwise_invert', 'newaxis', 'conj', 'add',
+           'atan2', 'bitwise_and', 'bitwise_left_shift', 'bitwise_or',
+           'bitwise_right_shift', 'bitwise_xor', 'copysign', 'divide',
+           'equal', 'floor_divide', 'greater', 'greater_equal', 'hypot',
+           'less', 'less_equal', 'logaddexp', 'maximum', 'minimum',
+           'multiply', 'not_equal', 'pow', 'remainder', 'subtract', 'max',
+           'min', 'clip', 'unstack', 'cumulative_sum', 'sort', 'prod', 'sum',
+           'any', 'all', 'mean', 'std', 'var', 'concat', 'squeeze',
+           'broadcast_to', 'flip', 'roll', 'nonzero', 'where', 'reshape',
+           'arange', 'eye', 'linspace', 'full', 'ones', 'zeros', 'empty',
+           'tril', 'triu', 'expand_dims', 'astype', 'broadcast_arrays',
+           'UniqueAllResult', 'UniqueCountsResult', 'UniqueInverseResult',
+           'unique_all', 'unique_counts', 'unique_inverse', 'unique_values',
+           'matmul', 'matrix_transpose', 'vecdot', 'tensordot', 'isdtype',
+           'take', 'sign']
+
+_all_ignore = ['torch', 'get_xp']
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/_info.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..264caa9e5fbbe9da3d9b9594b8d11f313d8536ef
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/_info.py
@@ -0,0 +1,358 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+import torch
+
+from functools import cache
+
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for PyTorch.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for PyTorch.
+
+    Examples
+    --------
+    >>> info = np.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': numpy.float64,
+     'complex floating': numpy.complex128,
+     'integral': numpy.int64,
+     'indexing': numpy.int64}
+
+    """
+
+    __module__ = 'torch'
+
+    def capabilities(self):
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing. Always ``True`` for PyTorch.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes. Always ``True`` for
+          PyTorch.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            # 'max rank' will be part of the 2024.12 standard
+            # "max rank": 64,
+        }
+
+    def default_device(self):
+        """
+        The default device used for new PyTorch arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : str
+            The default device used for new PyTorch arrays.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_device()
+        'cpu'
+
+        """
+        return torch.device("cpu")
+
+    def default_dtypes(self, *, device=None):
+        """
+        The default data types used for new PyTorch arrays.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the default data types for. For PyTorch, only
+            ``'cpu'`` is allowed.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new PyTorch
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': torch.float32,
+         'complex floating': torch.complex64,
+         'integral': torch.int64,
+         'indexing': torch.int64}
+
+        """
+        # Note: if the default is set to float64, the devices like MPS that
+        # don't support float64 will error. We still return the default_dtype
+        # value here because this error doesn't represent a different default
+        # per-device.
+        default_floating = torch.get_default_dtype()
+        default_complex = torch.complex64 if default_floating == torch.float32 else torch.complex128
+        default_integral = torch.int64
+        return {
+            "real floating": default_floating,
+            "complex floating": default_complex,
+            "integral": default_integral,
+            "indexing": default_integral,
+        }
+
+
+    def _dtypes(self, kind):
+        bool = torch.bool
+        int8 = torch.int8
+        int16 = torch.int16
+        int32 = torch.int32
+        int64 = torch.int64
+        uint8 = torch.uint8
+        # uint16, uint32, and uint64 are present in newer versions of pytorch,
+        # but they aren't generally supported by the array API functions, so
+        # we omit them from this function.
+        float32 = torch.float32
+        float64 = torch.float64
+        complex64 = torch.complex64
+        complex128 = torch.complex128
+
+        if kind is None:
+            return {
+                "bool": bool,
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+                "uint8": uint8,
+                "float32": float32,
+                "float64": float64,
+                "complex64": complex64,
+                "complex128": complex128,
+            }
+        if kind == "bool":
+            return {"bool": bool}
+        if kind == "signed integer":
+            return {
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": uint8,
+            }
+        if kind == "integral":
+            return {
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+                "uint8": uint8,
+            }
+        if kind == "real floating":
+            return {
+                "float32": float32,
+                "float64": float64,
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": complex64,
+                "complex128": complex128,
+            }
+        if kind == "numeric":
+            return {
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+                "uint8": uint8,
+                "float32": float32,
+                "float64": float64,
+                "complex64": complex64,
+                "complex128": complex128,
+            }
+        if isinstance(kind, tuple):
+            res = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    @cache
+    def dtypes(self, *, device=None, kind=None):
+        """
+        The array API data types supported by PyTorch.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the data types for.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            PyTorch data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': numpy.int8,
+         'int16': numpy.int16,
+         'int32': numpy.int32,
+         'int64': numpy.int64}
+
+        """
+        res = self._dtypes(kind)
+        for k, v in res.copy().items():
+            try:
+                torch.empty((0,), dtype=v, device=device)
+            except:
+                del res[k]
+        return res
+
+    @cache
+    def devices(self):
+        """
+        The devices supported by PyTorch.
+
+        Returns
+        -------
+        devices : list of str
+            The devices supported by PyTorch.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.devices()
+        [device(type='cpu'), device(type='mps', index=0), device(type='meta')]
+
+        """
+        # Torch doesn't have a straightforward way to get the list of all
+        # currently supported devices. To do this, we first parse the error
+        # message of torch.device to get the list of all possible types of
+        # device:
+        try:
+            torch.device('notadevice')
+        except RuntimeError as e:
+            # The error message is something like:
+            # "Expected one of cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone device type at start of device string: notadevice"
+            devices_names = e.args[0].split('Expected one of ')[1].split(' device type')[0].split(', ')
+
+        # Next we need to check for different indices for different devices.
+        # device(device_name, index=index) doesn't actually check if the
+        # device name or index is valid. We have to try to create a tensor
+        # with it (which is why this function is cached).
+        devices = []
+        for device_name in devices_names:
+            i = 0
+            while True:
+                try:
+                    a = torch.empty((0,), device=torch.device(device_name, index=i))
+                    if a.device in devices:
+                        break
+                    devices.append(a.device)
+                except:
+                    break
+                i += 1
+
+        return devices
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/fft.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/fft.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c9117ee57d3534e3e72329d740632c02e936200
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/fft.py
@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    import torch
+    array = torch.Tensor
+    from typing import Union, Sequence, Literal
+
+from torch.fft import * # noqa: F403
+import torch.fft
+
+# Several torch fft functions do not map axes to dim
+
+def fftn(
+    x: array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> array:
+    return torch.fft.fftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def ifftn(
+    x: array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> array:
+    return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def rfftn(
+    x: array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> array:
+    return torch.fft.rfftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def irfftn(
+    x: array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> array:
+    return torch.fft.irfftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def fftshift(
+    x: array,
+    /,
+    *,
+    axes: Union[int, Sequence[int]] = None,
+    **kwargs,
+) -> array:
+    return torch.fft.fftshift(x, dim=axes, **kwargs)
+
+def ifftshift(
+    x: array,
+    /,
+    *,
+    axes: Union[int, Sequence[int]] = None,
+    **kwargs,
+) -> array:
+    return torch.fft.ifftshift(x, dim=axes, **kwargs)
+
+
+__all__ = torch.fft.__all__ + [
+    "fftn",
+    "ifftn",
+    "rfftn",
+    "irfftn",
+    "fftshift",
+    "ifftshift",
+]
+
+_all_ignore = ['torch']
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/linalg.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e26198b9b562ed307206dd08dd9de7c8aa2a918b
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_compat/torch/linalg.py
@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    import torch
+    array = torch.Tensor
+    from torch import dtype as Dtype
+    from typing import Optional, Union, Tuple, Literal
+    inf = float('inf')
+
+from ._aliases import _fix_promotion, sum
+
+from torch.linalg import * # noqa: F403
+
+# torch.linalg doesn't define __all__
+# from torch.linalg import __all__ as linalg_all
+from torch import linalg as torch_linalg
+linalg_all = [i for i in dir(torch_linalg) if not i.startswith('_')]
+
+# outer is implemented in torch but aren't in the linalg namespace
+from torch import outer
+# These functions are in both the main and linalg namespaces
+from ._aliases import matmul, matrix_transpose, tensordot
+
+# Note: torch.linalg.cross does not default to axis=-1 (it defaults to the
+# first axis with size 3), see https://github.com/pytorch/pytorch/issues/58743
+
+# torch.cross also does not support broadcasting when it would add new
+# dimensions https://github.com/pytorch/pytorch/issues/39656
+def cross(x1: array, x2: array, /, *, axis: int = -1) -> array:
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    if not (-min(x1.ndim, x2.ndim) <= axis < max(x1.ndim, x2.ndim)):
+        raise ValueError(f"axis {axis} out of bounds for cross product of arrays with shapes {x1.shape} and {x2.shape}")
+    if not (x1.shape[axis] == x2.shape[axis] == 3):
+        raise ValueError(f"cross product axis must have size 3, got {x1.shape[axis]} and {x2.shape[axis]}")
+    x1, x2 = torch.broadcast_tensors(x1, x2)
+    return torch_linalg.cross(x1, x2, dim=axis)
+
+def vecdot(x1: array, x2: array, /, *, axis: int = -1, **kwargs) -> array:
+    from ._aliases import isdtype
+
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+
+    # torch.linalg.vecdot incorrectly allows broadcasting along the contracted dimension
+    if x1.shape[axis] != x2.shape[axis]:
+        raise ValueError("x1 and x2 must have the same size along the given axis")
+
+    # torch.linalg.vecdot doesn't support integer dtypes
+    if isdtype(x1.dtype, 'integral') or isdtype(x2.dtype, 'integral'):
+        if kwargs:
+            raise RuntimeError("vecdot kwargs not supported for integral dtypes")
+
+        x1_ = torch.moveaxis(x1, axis, -1)
+        x2_ = torch.moveaxis(x2, axis, -1)
+        x1_, x2_ = torch.broadcast_tensors(x1_, x2_)
+
+        res = x1_[..., None, :] @ x2_[..., None]
+        return res[..., 0, 0]
+    return torch.linalg.vecdot(x1, x2, dim=axis, **kwargs)
+
+def solve(x1: array, x2: array, /, **kwargs) -> array:
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    # Torch tries to emulate NumPy 1 solve behavior by using batched 1-D solve
+    # whenever
+    # 1. x1.ndim - 1 == x2.ndim
+    # 2. x1.shape[:-1] == x2.shape
+    #
+    # See linalg_solve_is_vector_rhs in
+    # aten/src/ATen/native/LinearAlgebraUtils.h and
+    # TORCH_META_FUNC(_linalg_solve_ex) in
+    # aten/src/ATen/native/BatchLinearAlgebra.cpp in the PyTorch source code.
+    #
+    # The easiest way to work around this is to prepend a size 1 dimension to
+    # x2, since x2 is already one dimension less than x1.
+    #
+    # See https://github.com/pytorch/pytorch/issues/52915
+    if x2.ndim != 1 and x1.ndim - 1 == x2.ndim and x1.shape[:-1] == x2.shape:
+        x2 = x2[None]
+    return torch.linalg.solve(x1, x2, **kwargs)
+
+# torch.trace doesn't support the offset argument and doesn't support stacking
+def trace(x: array, /, *, offset: int = 0, dtype: Optional[Dtype] = None) -> array:
+    # Use our wrapped sum to make sure it does upcasting correctly
+    return sum(torch.diagonal(x, offset=offset, dim1=-2, dim2=-1), axis=-1, dtype=dtype)
+
+def vector_norm(
+    x: array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+    ord: Union[int, float, Literal[inf, -inf]] = 2,
+    **kwargs,
+) -> array:
+    # torch.vector_norm incorrectly treats axis=() the same as axis=None
+    if axis == ():
+        out = kwargs.get('out')
+        if out is None:
+            dtype = None
+            if x.dtype == torch.complex64:
+                dtype = torch.float32
+            elif x.dtype == torch.complex128:
+                dtype = torch.float64
+
+            out = torch.zeros_like(x, dtype=dtype)
+
+        # The norm of a single scalar works out to abs(x) in every case except
+        # for ord=0, which is x != 0.
+        if ord == 0:
+            out[:] = (x != 0)
+        else:
+            out[:] = torch.abs(x)
+        return out
+    return torch.linalg.vector_norm(x, ord=ord, axis=axis, keepdim=keepdims, **kwargs)
+
+__all__ = linalg_all + ['outer', 'matmul', 'matrix_transpose', 'tensordot',
+                        'cross', 'vecdot', 'solve', 'trace', 'vector_norm']
+
+_all_ignore = ['torch_linalg', 'sum']
+
+del linalg_all
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__init__.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2062f7d5d6a4d9f5a3556164720a6abc4da456bc
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__init__.py
@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+from ._funcs import atleast_nd, cov, create_diagonal, expand_dims, kron, sinc
+
+__version__ = "0.2.0"
+
+__all__ = [
+    "__version__",
+    "atleast_nd",
+    "cov",
+    "create_diagonal",
+    "expand_dims",
+    "kron",
+    "sinc",
+]
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/__init__.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3d4c1aad742828144b626898eb7fef6f2e93cbbf
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/__init__.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/_funcs.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/_funcs.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2d58cceab3de51d824dc5f2473b1f3c68e9589ee
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/_funcs.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/_typing.cpython-310.pyc b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/_typing.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9a0aafe5d130336b0a3c2c9b10d67307372c8d54
Binary files /dev/null and b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/__pycache__/_typing.cpython-310.pyc differ
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/_funcs.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/_funcs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce800189b46d25316c3123a22ce4ff2e7e1e81ce
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/_funcs.py
@@ -0,0 +1,484 @@
+from __future__ import annotations
+
+import warnings
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from ._typing import Array, ModuleType
+
+__all__ = ["atleast_nd", "cov", "create_diagonal", "expand_dims", "kron", "sinc"]
+
+
+def atleast_nd(x: Array, /, *, ndim: int, xp: ModuleType) -> Array:
+    """
+    Recursively expand the dimension of an array to at least `ndim`.
+
+    Parameters
+    ----------
+    x : array
+    ndim : int
+        The minimum number of dimensions for the result.
+    xp : array_namespace
+        The standard-compatible namespace for `x`.
+
+    Returns
+    -------
+    res : array
+        An array with ``res.ndim`` >= `ndim`.
+        If ``x.ndim`` >= `ndim`, `x` is returned.
+        If ``x.ndim`` < `ndim`, `x` is expanded by prepending new axes
+        until ``res.ndim`` equals `ndim`.
+
+    Examples
+    --------
+    >>> import array_api_strict as xp
+    >>> import array_api_extra as xpx
+    >>> x = xp.asarray([1])
+    >>> xpx.atleast_nd(x, ndim=3, xp=xp)
+    Array([[[1]]], dtype=array_api_strict.int64)
+
+    >>> x = xp.asarray([[[1, 2],
+    ...                  [3, 4]]])
+    >>> xpx.atleast_nd(x, ndim=1, xp=xp) is x
+    True
+
+    """
+    if x.ndim < ndim:
+        x = xp.expand_dims(x, axis=0)
+        x = atleast_nd(x, ndim=ndim, xp=xp)
+    return x
+
+
+def cov(m: Array, /, *, xp: ModuleType) -> Array:
+    """
+    Estimate a covariance matrix.
+
+    Covariance indicates the level to which two variables vary together.
+    If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,
+    then the covariance matrix element :math:`C_{ij}` is the covariance of
+    :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance
+    of :math:`x_i`.
+
+    This provides a subset of the functionality of ``numpy.cov``.
+
+    Parameters
+    ----------
+    m : array
+        A 1-D or 2-D array containing multiple variables and observations.
+        Each row of `m` represents a variable, and each column a single
+        observation of all those variables.
+    xp : array_namespace
+        The standard-compatible namespace for `m`.
+
+    Returns
+    -------
+    res : array
+        The covariance matrix of the variables.
+
+    Examples
+    --------
+    >>> import array_api_strict as xp
+    >>> import array_api_extra as xpx
+
+    Consider two variables, :math:`x_0` and :math:`x_1`, which
+    correlate perfectly, but in opposite directions:
+
+    >>> x = xp.asarray([[0, 2], [1, 1], [2, 0]]).T
+    >>> x
+    Array([[0, 1, 2],
+           [2, 1, 0]], dtype=array_api_strict.int64)
+
+    Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance
+    matrix shows this clearly:
+
+    >>> xpx.cov(x, xp=xp)
+    Array([[ 1., -1.],
+           [-1.,  1.]], dtype=array_api_strict.float64)
+
+
+    Note that element :math:`C_{0,1}`, which shows the correlation between
+    :math:`x_0` and :math:`x_1`, is negative.
+
+    Further, note how `x` and `y` are combined:
+
+    >>> x = xp.asarray([-2.1, -1,  4.3])
+    >>> y = xp.asarray([3,  1.1,  0.12])
+    >>> X = xp.stack((x, y), axis=0)
+    >>> xpx.cov(X, xp=xp)
+    Array([[11.71      , -4.286     ],
+           [-4.286     ,  2.14413333]], dtype=array_api_strict.float64)
+
+    >>> xpx.cov(x, xp=xp)
+    Array(11.71, dtype=array_api_strict.float64)
+
+    >>> xpx.cov(y, xp=xp)
+    Array(2.14413333, dtype=array_api_strict.float64)
+
+    """
+    m = xp.asarray(m, copy=True)
+    dtype = (
+        xp.float64 if xp.isdtype(m.dtype, "integral") else xp.result_type(m, xp.float64)
+    )
+
+    m = atleast_nd(m, ndim=2, xp=xp)
+    m = xp.astype(m, dtype)
+
+    avg = _mean(m, axis=1, xp=xp)
+    fact = m.shape[1] - 1
+
+    if fact <= 0:
+        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2)
+        fact = 0.0
+
+    m -= avg[:, None]
+    m_transpose = m.T
+    if xp.isdtype(m_transpose.dtype, "complex floating"):
+        m_transpose = xp.conj(m_transpose)
+    c = m @ m_transpose
+    c /= fact
+    axes = tuple(axis for axis, length in enumerate(c.shape) if length == 1)
+    return xp.squeeze(c, axis=axes)
+
+
+def create_diagonal(x: Array, /, *, offset: int = 0, xp: ModuleType) -> Array:
+    """
+    Construct a diagonal array.
+
+    Parameters
+    ----------
+    x : array
+        A 1-D array
+    offset : int, optional
+        Offset from the leading diagonal (default is ``0``).
+        Use positive ints for diagonals above the leading diagonal,
+        and negative ints for diagonals below the leading diagonal.
+    xp : array_namespace
+        The standard-compatible namespace for `x`.
+
+    Returns
+    -------
+    res : array
+        A 2-D array with `x` on the diagonal (offset by `offset`).
+
+    Examples
+    --------
+    >>> import array_api_strict as xp
+    >>> import array_api_extra as xpx
+    >>> x = xp.asarray([2, 4, 8])
+
+    >>> xpx.create_diagonal(x, xp=xp)
+    Array([[2, 0, 0],
+           [0, 4, 0],
+           [0, 0, 8]], dtype=array_api_strict.int64)
+
+    >>> xpx.create_diagonal(x, offset=-2, xp=xp)
+    Array([[0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0],
+           [2, 0, 0, 0, 0],
+           [0, 4, 0, 0, 0],
+           [0, 0, 8, 0, 0]], dtype=array_api_strict.int64)
+
+    """
+    if x.ndim != 1:
+        err_msg = "`x` must be 1-dimensional."
+        raise ValueError(err_msg)
+    n = x.shape[0] + abs(offset)
+    diag = xp.zeros(n**2, dtype=x.dtype)
+    i = offset if offset >= 0 else abs(offset) * n
+    diag[i : min(n * (n - offset), diag.shape[0]) : n + 1] = x
+    return xp.reshape(diag, (n, n))
+
+
+def _mean(
+    x: Array,
+    /,
+    *,
+    axis: int | tuple[int, ...] | None = None,
+    keepdims: bool = False,
+    xp: ModuleType,
+) -> Array:
+    """
+    Complex mean, https://github.com/data-apis/array-api/issues/846.
+    """
+    if xp.isdtype(x.dtype, "complex floating"):
+        x_real = xp.real(x)
+        x_imag = xp.imag(x)
+        mean_real = xp.mean(x_real, axis=axis, keepdims=keepdims)
+        mean_imag = xp.mean(x_imag, axis=axis, keepdims=keepdims)
+        return mean_real + (mean_imag * xp.asarray(1j))
+    return xp.mean(x, axis=axis, keepdims=keepdims)
+
+
+def expand_dims(
+    a: Array, /, *, axis: int | tuple[int, ...] = (0,), xp: ModuleType
+) -> Array:
+    """
+    Expand the shape of an array.
+
+    Insert (a) new axis/axes that will appear at the position(s) specified by
+    `axis` in the expanded array shape.
+
+    This is ``xp.expand_dims`` for `axis` an int *or a tuple of ints*.
+    Roughly equivalent to ``numpy.expand_dims`` for NumPy arrays.
+
+    Parameters
+    ----------
+    a : array
+    axis : int or tuple of ints, optional
+        Position(s) in the expanded axes where the new axis (or axes) is/are placed.
+        If multiple positions are provided, they should be unique (note that a position
+        given by a positive index could also be referred to by a negative index -
+        that will also result in an error).
+        Default: ``(0,)``.
+    xp : array_namespace
+        The standard-compatible namespace for `a`.
+
+    Returns
+    -------
+    res : array
+        `a` with an expanded shape.
+
+    Examples
+    --------
+    >>> import array_api_strict as xp
+    >>> import array_api_extra as xpx
+    >>> x = xp.asarray([1, 2])
+    >>> x.shape
+    (2,)
+
+    The following is equivalent to ``x[xp.newaxis, :]`` or ``x[xp.newaxis]``:
+
+    >>> y = xpx.expand_dims(x, axis=0, xp=xp)
+    >>> y
+    Array([[1, 2]], dtype=array_api_strict.int64)
+    >>> y.shape
+    (1, 2)
+
+    The following is equivalent to ``x[:, xp.newaxis]``:
+
+    >>> y = xpx.expand_dims(x, axis=1, xp=xp)
+    >>> y
+    Array([[1],
+           [2]], dtype=array_api_strict.int64)
+    >>> y.shape
+    (2, 1)
+
+    ``axis`` may also be a tuple:
+
+    >>> y = xpx.expand_dims(x, axis=(0, 1), xp=xp)
+    >>> y
+    Array([[[1, 2]]], dtype=array_api_strict.int64)
+
+    >>> y = xpx.expand_dims(x, axis=(2, 0), xp=xp)
+    >>> y
+    Array([[[1],
+            [2]]], dtype=array_api_strict.int64)
+
+    """
+    if not isinstance(axis, tuple):
+        axis = (axis,)
+    ndim = a.ndim + len(axis)
+    if axis != () and (min(axis) < -ndim or max(axis) >= ndim):
+        err_msg = (
+            f"a provided axis position is out of bounds for array of dimension {a.ndim}"
+        )
+        raise IndexError(err_msg)
+    axis = tuple(dim % ndim for dim in axis)
+    if len(set(axis)) != len(axis):
+        err_msg = "Duplicate dimensions specified in `axis`."
+        raise ValueError(err_msg)
+    for i in sorted(axis):
+        a = xp.expand_dims(a, axis=i)
+    return a
+
+
+def kron(a: Array, b: Array, /, *, xp: ModuleType) -> Array:
+    """
+    Kronecker product of two arrays.
+
+    Computes the Kronecker product, a composite array made of blocks of the
+    second array scaled by the first.
+
+    Equivalent to ``numpy.kron`` for NumPy arrays.
+
+    Parameters
+    ----------
+    a, b : array
+    xp : array_namespace
+        The standard-compatible namespace for `a` and `b`.
+
+    Returns
+    -------
+    res : array
+        The Kronecker product of `a` and `b`.
+
+    Notes
+    -----
+    The function assumes that the number of dimensions of `a` and `b`
+    are the same, if necessary prepending the smallest with ones.
+    If ``a.shape = (r0,r1,..,rN)`` and ``b.shape = (s0,s1,...,sN)``,
+    the Kronecker product has shape ``(r0*s0, r1*s1, ..., rN*SN)``.
+    The elements are products of elements from `a` and `b`, organized
+    explicitly by::
+
+        kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]
+
+    where::
+
+        kt = it * st + jt,  t = 0,...,N
+
+    In the common 2-D case (N=1), the block structure can be visualized::
+
+        [[ a[0,0]*b,   a[0,1]*b,  ... , a[0,-1]*b  ],
+         [  ...                              ...   ],
+         [ a[-1,0]*b,  a[-1,1]*b, ... , a[-1,-1]*b ]]
+
+
+    Examples
+    --------
+    >>> import array_api_strict as xp
+    >>> import array_api_extra as xpx
+    >>> xpx.kron(xp.asarray([1, 10, 100]), xp.asarray([5, 6, 7]), xp=xp)
+    Array([  5,   6,   7,  50,  60,  70, 500,
+           600, 700], dtype=array_api_strict.int64)
+
+    >>> xpx.kron(xp.asarray([5, 6, 7]), xp.asarray([1, 10, 100]), xp=xp)
+    Array([  5,  50, 500,   6,  60, 600,   7,
+            70, 700], dtype=array_api_strict.int64)
+
+    >>> xpx.kron(xp.eye(2), xp.ones((2, 2)), xp=xp)
+    Array([[1., 1., 0., 0.],
+           [1., 1., 0., 0.],
+           [0., 0., 1., 1.],
+           [0., 0., 1., 1.]], dtype=array_api_strict.float64)
+
+
+    >>> a = xp.reshape(xp.arange(100), (2, 5, 2, 5))
+    >>> b = xp.reshape(xp.arange(24), (2, 3, 4))
+    >>> c = xpx.kron(a, b, xp=xp)
+    >>> c.shape
+    (2, 10, 6, 20)
+    >>> I = (1, 3, 0, 2)
+    >>> J = (0, 2, 1)
+    >>> J1 = (0,) + J             # extend to ndim=4
+    >>> S1 = (1,) + b.shape
+    >>> K = tuple(xp.asarray(I) * xp.asarray(S1) + xp.asarray(J1))
+    >>> c[K] == a[I]*b[J]
+    Array(True, dtype=array_api_strict.bool)
+
+    """
+
+    b = xp.asarray(b)
+    singletons = (1,) * (b.ndim - a.ndim)
+    a = xp.broadcast_to(xp.asarray(a), singletons + a.shape)
+
+    nd_b, nd_a = b.ndim, a.ndim
+    nd_max = max(nd_b, nd_a)
+    if nd_a == 0 or nd_b == 0:
+        return xp.multiply(a, b)
+
+    a_shape = a.shape
+    b_shape = b.shape
+
+    # Equalise the shapes by prepending smaller one with 1s
+    a_shape = (1,) * max(0, nd_b - nd_a) + a_shape
+    b_shape = (1,) * max(0, nd_a - nd_b) + b_shape
+
+    # Insert empty dimensions
+    a_arr = expand_dims(a, axis=tuple(range(nd_b - nd_a)), xp=xp)
+    b_arr = expand_dims(b, axis=tuple(range(nd_a - nd_b)), xp=xp)
+
+    # Compute the product
+    a_arr = expand_dims(a_arr, axis=tuple(range(1, nd_max * 2, 2)), xp=xp)
+    b_arr = expand_dims(b_arr, axis=tuple(range(0, nd_max * 2, 2)), xp=xp)
+    result = xp.multiply(a_arr, b_arr)
+
+    # Reshape back and return
+    a_shape = xp.asarray(a_shape)
+    b_shape = xp.asarray(b_shape)
+    return xp.reshape(result, tuple(xp.multiply(a_shape, b_shape)))
+
+
+def sinc(x: Array, /, *, xp: ModuleType) -> Array:
+    r"""
+    Return the normalized sinc function.
+
+    The sinc function is equal to :math:`\sin(\pi x)/(\pi x)` for any argument
+    :math:`x\ne 0`. ``sinc(0)`` takes the limit value 1, making ``sinc`` not
+    only everywhere continuous but also infinitely differentiable.
+
+    .. note::
+
+        Note the normalization factor of ``pi`` used in the definition.
+        This is the most commonly used definition in signal processing.
+        Use ``sinc(x / xp.pi)`` to obtain the unnormalized sinc function
+        :math:`\sin(x)/x` that is more common in mathematics.
+
+    Parameters
+    ----------
+    x : array
+        Array (possibly multi-dimensional) of values for which to calculate
+        ``sinc(x)``. Must have a real floating point dtype.
+    xp : array_namespace
+        The standard-compatible namespace for `x`.
+
+    Returns
+    -------
+    res : array
+        ``sinc(x)`` calculated elementwise, which has the same shape as the input.
+
+    Notes
+    -----
+    The name sinc is short for "sine cardinal" or "sinus cardinalis".
+
+    The sinc function is used in various signal processing applications,
+    including in anti-aliasing, in the construction of a Lanczos resampling
+    filter, and in interpolation.
+
+    For bandlimited interpolation of discrete-time signals, the ideal
+    interpolation kernel is proportional to the sinc function.
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web
+           Resource. https://mathworld.wolfram.com/SincFunction.html
+    .. [2] Wikipedia, "Sinc function",
+           https://en.wikipedia.org/wiki/Sinc_function
+
+    Examples
+    --------
+    >>> import array_api_strict as xp
+    >>> import array_api_extra as xpx
+    >>> x = xp.linspace(-4, 4, 41)
+    >>> xpx.sinc(x, xp=xp)
+    Array([-3.89817183e-17, -4.92362781e-02,
+           -8.40918587e-02, -8.90384387e-02,
+           -5.84680802e-02,  3.89817183e-17,
+            6.68206631e-02,  1.16434881e-01,
+            1.26137788e-01,  8.50444803e-02,
+           -3.89817183e-17, -1.03943254e-01,
+           -1.89206682e-01, -2.16236208e-01,
+           -1.55914881e-01,  3.89817183e-17,
+            2.33872321e-01,  5.04551152e-01,
+            7.56826729e-01,  9.35489284e-01,
+            1.00000000e+00,  9.35489284e-01,
+            7.56826729e-01,  5.04551152e-01,
+            2.33872321e-01,  3.89817183e-17,
+           -1.55914881e-01, -2.16236208e-01,
+           -1.89206682e-01, -1.03943254e-01,
+           -3.89817183e-17,  8.50444803e-02,
+            1.26137788e-01,  1.16434881e-01,
+            6.68206631e-02,  3.89817183e-17,
+           -5.84680802e-02, -8.90384387e-02,
+           -8.40918587e-02, -4.92362781e-02,
+           -3.89817183e-17], dtype=array_api_strict.float64)
+
+    """
+    if not xp.isdtype(x.dtype, "real floating"):
+        err_msg = "`x` must have a real floating data type."
+        raise ValueError(err_msg)
+    # no scalars in `where` - array-api#807
+    y = xp.pi * xp.where(
+        x, x, xp.asarray(xp.finfo(x.dtype).smallest_normal, dtype=x.dtype)
+    )
+    return xp.sin(y) / y
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/_typing.py b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ffa13f23fc8c52abf5c65206ec1ff5a8481832c
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/array_api_extra/_typing.py
@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from types import ModuleType
+from typing import Any
+
+Array = Any  # To be changed to a Protocol later (see array-api#589)
+
+__all__ = ["Array", "ModuleType"]
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/decorator.py b/phi4/lib/python3.10/site-packages/scipy/_lib/decorator.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c4ab90e3d52db448b6381bc7860f55ac8789c9c
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/decorator.py
@@ -0,0 +1,399 @@
+# #########################     LICENSE     ############################ #
+
+# Copyright (c) 2005-2015, Michele Simionato
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are
+# met:
+
+#   Redistributions of source code must retain the above copyright
+#   notice, this list of conditions and the following disclaimer.
+#   Redistributions in bytecode form must reproduce the above copyright
+#   notice, this list of conditions and the following disclaimer in
+#   the documentation and/or other materials provided with the
+#   distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
+# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
+# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+# DAMAGE.
+
+"""
+Decorator module, see https://pypi.python.org/pypi/decorator
+for the documentation.
+"""
+import re
+import sys
+import inspect
+import operator
+import itertools
+import collections
+
+from inspect import getfullargspec
+
+__version__ = '4.0.5'
+
+
+def get_init(cls):
+    return cls.__init__
+
+
+# getargspec has been deprecated in Python 3.5
+ArgSpec = collections.namedtuple(
+    'ArgSpec', 'args varargs varkw defaults')
+
+
+def getargspec(f):
+    """A replacement for inspect.getargspec"""
+    spec = getfullargspec(f)
+    return ArgSpec(spec.args, spec.varargs, spec.varkw, spec.defaults)
+
+
+DEF = re.compile(r'\s*def\s*([_\w][_\w\d]*)\s*\(')
+
+
+# basic functionality
+class FunctionMaker:
+    """
+    An object with the ability to create functions with a given signature.
+    It has attributes name, doc, module, signature, defaults, dict, and
+    methods update and make.
+    """
+
+    # Atomic get-and-increment provided by the GIL
+    _compile_count = itertools.count()
+
+    def __init__(self, func=None, name=None, signature=None,
+                 defaults=None, doc=None, module=None, funcdict=None):
+        self.shortsignature = signature
+        if func:
+            # func can be a class or a callable, but not an instance method
+            self.name = func.__name__
+            if self.name == '':  # small hack for lambda functions
+                self.name = '_lambda_'
+            self.doc = func.__doc__
+            self.module = func.__module__
+            if inspect.isfunction(func):
+                argspec = getfullargspec(func)
+                self.annotations = getattr(func, '__annotations__', {})
+                for a in ('args', 'varargs', 'varkw', 'defaults', 'kwonlyargs',
+                          'kwonlydefaults'):
+                    setattr(self, a, getattr(argspec, a))
+                for i, arg in enumerate(self.args):
+                    setattr(self, 'arg%d' % i, arg)
+                allargs = list(self.args)
+                allshortargs = list(self.args)
+                if self.varargs:
+                    allargs.append('*' + self.varargs)
+                    allshortargs.append('*' + self.varargs)
+                elif self.kwonlyargs:
+                    allargs.append('*')  # single star syntax
+                for a in self.kwonlyargs:
+                    allargs.append(f'{a}=None')
+                    allshortargs.append(f'{a}={a}')
+                if self.varkw:
+                    allargs.append('**' + self.varkw)
+                    allshortargs.append('**' + self.varkw)
+                self.signature = ', '.join(allargs)
+                self.shortsignature = ', '.join(allshortargs)
+                self.dict = func.__dict__.copy()
+        # func=None happens when decorating a caller
+        if name:
+            self.name = name
+        if signature is not None:
+            self.signature = signature
+        if defaults:
+            self.defaults = defaults
+        if doc:
+            self.doc = doc
+        if module:
+            self.module = module
+        if funcdict:
+            self.dict = funcdict
+        # check existence required attributes
+        assert hasattr(self, 'name')
+        if not hasattr(self, 'signature'):
+            raise TypeError(f'You are decorating a non-function: {func}')
+
+    def update(self, func, **kw):
+        "Update the signature of func with the data in self"
+        func.__name__ = self.name
+        func.__doc__ = getattr(self, 'doc', None)
+        func.__dict__ = getattr(self, 'dict', {})
+        func.__defaults__ = getattr(self, 'defaults', ())
+        func.__kwdefaults__ = getattr(self, 'kwonlydefaults', None)
+        func.__annotations__ = getattr(self, 'annotations', None)
+        try:
+            frame = sys._getframe(3)
+        except AttributeError:  # for IronPython and similar implementations
+            callermodule = '?'
+        else:
+            callermodule = frame.f_globals.get('__name__', '?')
+        func.__module__ = getattr(self, 'module', callermodule)
+        func.__dict__.update(kw)
+
+    def make(self, src_templ, evaldict=None, addsource=False, **attrs):
+        "Make a new function from a given template and update the signature"
+        src = src_templ % vars(self)  # expand name and signature
+        evaldict = evaldict or {}
+        mo = DEF.match(src)
+        if mo is None:
+            raise SyntaxError(f'not a valid function template\n{src}')
+        name = mo.group(1)  # extract the function name
+        names = set([name] + [arg.strip(' *') for arg in
+                              self.shortsignature.split(',')])
+        for n in names:
+            if n in ('_func_', '_call_'):
+                raise NameError(f'{n} is overridden in\n{src}')
+        if not src.endswith('\n'):  # add a newline just for safety
+            src += '\n'  # this is needed in old versions of Python
+
+        # Ensure each generated function has a unique filename for profilers
+        # (such as cProfile) that depend on the tuple of (,
+        # , ) being unique.
+        filename = '' % (next(self._compile_count),)
+        try:
+            code = compile(src, filename, 'single')
+            exec(code, evaldict)
+        except:  # noqa: E722
+            print('Error in generated code:', file=sys.stderr)
+            print(src, file=sys.stderr)
+            raise
+        func = evaldict[name]
+        if addsource:
+            attrs['__source__'] = src
+        self.update(func, **attrs)
+        return func
+
+    @classmethod
+    def create(cls, obj, body, evaldict, defaults=None,
+               doc=None, module=None, addsource=True, **attrs):
+        """
+        Create a function from the strings name, signature, and body.
+        evaldict is the evaluation dictionary. If addsource is true, an
+        attribute __source__ is added to the result. The attributes attrs
+        are added, if any.
+        """
+        if isinstance(obj, str):  # "name(signature)"
+            name, rest = obj.strip().split('(', 1)
+            signature = rest[:-1]  # strip a right parens
+            func = None
+        else:  # a function
+            name = None
+            signature = None
+            func = obj
+        self = cls(func, name, signature, defaults, doc, module)
+        ibody = '\n'.join('    ' + line for line in body.splitlines())
+        return self.make('def %(name)s(%(signature)s):\n' + ibody,
+                         evaldict, addsource, **attrs)
+
+
+def decorate(func, caller):
+    """
+    decorate(func, caller) decorates a function using a caller.
+    """
+    evaldict = func.__globals__.copy()
+    evaldict['_call_'] = caller
+    evaldict['_func_'] = func
+    fun = FunctionMaker.create(
+        func, "return _call_(_func_, %(shortsignature)s)",
+        evaldict, __wrapped__=func)
+    if hasattr(func, '__qualname__'):
+        fun.__qualname__ = func.__qualname__
+    return fun
+
+
+def decorator(caller, _func=None):
+    """decorator(caller) converts a caller function into a decorator"""
+    if _func is not None:  # return a decorated function
+        # this is obsolete behavior; you should use decorate instead
+        return decorate(_func, caller)
+    # else return a decorator function
+    if inspect.isclass(caller):
+        name = caller.__name__.lower()
+        callerfunc = get_init(caller)
+        doc = (f'decorator({caller.__name__}) converts functions/generators into ' 
+               f'factories of {caller.__name__} objects')
+    elif inspect.isfunction(caller):
+        if caller.__name__ == '':
+            name = '_lambda_'
+        else:
+            name = caller.__name__
+        callerfunc = caller
+        doc = caller.__doc__
+    else:  # assume caller is an object with a __call__ method
+        name = caller.__class__.__name__.lower()
+        callerfunc = caller.__call__.__func__
+        doc = caller.__call__.__doc__
+    evaldict = callerfunc.__globals__.copy()
+    evaldict['_call_'] = caller
+    evaldict['_decorate_'] = decorate
+    return FunctionMaker.create(
+        f'{name}(func)', 'return _decorate_(func, _call_)',
+        evaldict, doc=doc, module=caller.__module__,
+        __wrapped__=caller)
+
+
+# ####################### contextmanager ####################### #
+
+try:  # Python >= 3.2
+    from contextlib import _GeneratorContextManager
+except ImportError:  # Python >= 2.5
+    from contextlib import GeneratorContextManager as _GeneratorContextManager
+
+
+class ContextManager(_GeneratorContextManager):
+    def __call__(self, func):
+        """Context manager decorator"""
+        return FunctionMaker.create(
+            func, "with _self_: return _func_(%(shortsignature)s)",
+            dict(_self_=self, _func_=func), __wrapped__=func)
+
+
+init = getfullargspec(_GeneratorContextManager.__init__)
+n_args = len(init.args)
+if n_args == 2 and not init.varargs:  # (self, genobj) Python 2.7
+    def __init__(self, g, *a, **k):
+        return _GeneratorContextManager.__init__(self, g(*a, **k))
+    ContextManager.__init__ = __init__
+elif n_args == 2 and init.varargs:  # (self, gen, *a, **k) Python 3.4
+    pass
+elif n_args == 4:  # (self, gen, args, kwds) Python 3.5
+    def __init__(self, g, *a, **k):
+        return _GeneratorContextManager.__init__(self, g, a, k)
+    ContextManager.__init__ = __init__
+
+contextmanager = decorator(ContextManager)
+
+
+# ############################ dispatch_on ############################ #
+
+def append(a, vancestors):
+    """
+    Append ``a`` to the list of the virtual ancestors, unless it is already
+    included.
+    """
+    add = True
+    for j, va in enumerate(vancestors):
+        if issubclass(va, a):
+            add = False
+            break
+        if issubclass(a, va):
+            vancestors[j] = a
+            add = False
+    if add:
+        vancestors.append(a)
+
+
+# inspired from simplegeneric by P.J. Eby and functools.singledispatch
+def dispatch_on(*dispatch_args):
+    """
+    Factory of decorators turning a function into a generic function
+    dispatching on the given arguments.
+    """
+    assert dispatch_args, 'No dispatch args passed'
+    dispatch_str = f"({', '.join(dispatch_args)},)"
+
+    def check(arguments, wrong=operator.ne, msg=''):
+        """Make sure one passes the expected number of arguments"""
+        if wrong(len(arguments), len(dispatch_args)):
+            raise TypeError(f'Expected {len(dispatch_args)} arguments, '
+                            'got {len(arguments)}{msg}')
+
+    def gen_func_dec(func):
+        """Decorator turning a function into a generic function"""
+
+        # first check the dispatch arguments
+        argset = set(getfullargspec(func).args)
+        if not set(dispatch_args) <= argset:
+            raise NameError(f'Unknown dispatch arguments {dispatch_str}')
+
+        typemap = {}
+
+        def vancestors(*types):
+            """
+            Get a list of sets of virtual ancestors for the given types
+            """
+            check(types)
+            ras = [[] for _ in range(len(dispatch_args))]
+            for types_ in typemap:
+                for t, type_, ra in zip(types, types_, ras):
+                    if issubclass(t, type_) and type_ not in t.__mro__:
+                        append(type_, ra)
+            return [set(ra) for ra in ras]
+
+        def ancestors(*types):
+            """
+            Get a list of virtual MROs, one for each type
+            """
+            check(types)
+            lists = []
+            for t, vas in zip(types, vancestors(*types)):
+                n_vas = len(vas)
+                if n_vas > 1:
+                    raise RuntimeError(
+                        f'Ambiguous dispatch for {t}: {vas}')
+                elif n_vas == 1:
+                    va, = vas
+                    mro = type('t', (t, va), {}).__mro__[1:]
+                else:
+                    mro = t.__mro__
+                lists.append(mro[:-1])  # discard t and object
+            return lists
+
+        def register(*types):
+            """
+            Decorator to register an implementation for the given types
+            """
+            check(types)
+
+            def dec(f):
+                check(getfullargspec(f).args, operator.lt, ' in ' + f.__name__)
+                typemap[types] = f
+                return f
+            return dec
+
+        def dispatch_info(*types):
+            """
+            An utility to introspect the dispatch algorithm
+            """
+            check(types)
+            lst = [tuple(a.__name__ for a in anc)
+                   for anc in itertools.product(*ancestors(*types))]
+            return lst
+
+        def _dispatch(dispatch_args, *args, **kw):
+            types = tuple(type(arg) for arg in dispatch_args)
+            try:  # fast path
+                f = typemap[types]
+            except KeyError:
+                pass
+            else:
+                return f(*args, **kw)
+            combinations = itertools.product(*ancestors(*types))
+            next(combinations)  # the first one has been already tried
+            for types_ in combinations:
+                f = typemap.get(types_)
+                if f is not None:
+                    return f(*args, **kw)
+
+            # else call the default implementation
+            return func(*args, **kw)
+
+        return FunctionMaker.create(
+            func, f'return _f_({dispatch_str}, %%(shortsignature)s)',
+            dict(_f_=_dispatch), register=register, default=func,
+            typemap=typemap, vancestors=vancestors, ancestors=ancestors,
+            dispatch_info=dispatch_info, __wrapped__=func)
+
+    gen_func_dec.__name__ = 'dispatch_on' + dispatch_str
+    return gen_func_dec
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/deprecation.py b/phi4/lib/python3.10/site-packages/scipy/_lib/deprecation.py
new file mode 100644
index 0000000000000000000000000000000000000000..82a6ef8f39ba764b46fdc01de9281cdbf72f4736
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/deprecation.py
@@ -0,0 +1,274 @@
+from inspect import Parameter, signature
+import functools
+import warnings
+from importlib import import_module
+from scipy._lib._docscrape import FunctionDoc
+
+
+__all__ = ["_deprecated"]
+
+
+# Object to use as default value for arguments to be deprecated. This should
+# be used over 'None' as the user could parse 'None' as a positional argument
+_NoValue = object()
+
+def _sub_module_deprecation(*, sub_package, module, private_modules, all,
+                            attribute, correct_module=None, dep_version="1.16.0"):
+    """Helper function for deprecating modules that are public but were
+    intended to be private.
+
+    Parameters
+    ----------
+    sub_package : str
+        Subpackage the module belongs to eg. stats
+    module : str
+        Public but intended private module to deprecate
+    private_modules : list
+        Private replacement(s) for `module`; should contain the
+        content of ``all``, possibly spread over several modules.
+    all : list
+        ``__all__`` belonging to `module`
+    attribute : str
+        The attribute in `module` being accessed
+    correct_module : str, optional
+        Module in `sub_package` that `attribute` should be imported from.
+        Default is that `attribute` should be imported from ``scipy.sub_package``.
+    dep_version : str, optional
+        Version in which deprecated attributes will be removed.
+    """
+    if correct_module is not None:
+        correct_import = f"scipy.{sub_package}.{correct_module}"
+    else:
+        correct_import = f"scipy.{sub_package}"
+
+    if attribute not in all:
+        raise AttributeError(
+            f"`scipy.{sub_package}.{module}` has no attribute `{attribute}`; "
+            f"furthermore, `scipy.{sub_package}.{module}` is deprecated "
+            f"and will be removed in SciPy 2.0.0."
+        )
+
+    attr = getattr(import_module(correct_import), attribute, None)
+
+    if attr is not None:
+        message = (
+            f"Please import `{attribute}` from the `{correct_import}` namespace; "
+            f"the `scipy.{sub_package}.{module}` namespace is deprecated "
+            f"and will be removed in SciPy 2.0.0."
+        )
+    else:
+        message = (
+            f"`scipy.{sub_package}.{module}.{attribute}` is deprecated along with "
+            f"the `scipy.{sub_package}.{module}` namespace. "
+            f"`scipy.{sub_package}.{module}.{attribute}` will be removed "
+            f"in SciPy {dep_version}, and the `scipy.{sub_package}.{module}` namespace "
+            f"will be removed in SciPy 2.0.0."
+        )
+
+    warnings.warn(message, category=DeprecationWarning, stacklevel=3)
+
+    for module in private_modules:
+        try:
+            return getattr(import_module(f"scipy.{sub_package}.{module}"), attribute)
+        except AttributeError as e:
+            # still raise an error if the attribute isn't in any of the expected
+            # private modules
+            if module == private_modules[-1]:
+                raise e
+            continue
+    
+
+def _deprecated(msg, stacklevel=2):
+    """Deprecate a function by emitting a warning on use."""
+    def wrap(fun):
+        if isinstance(fun, type):
+            warnings.warn(
+                f"Trying to deprecate class {fun!r}",
+                category=RuntimeWarning, stacklevel=2)
+            return fun
+
+        @functools.wraps(fun)
+        def call(*args, **kwargs):
+            warnings.warn(msg, category=DeprecationWarning,
+                          stacklevel=stacklevel)
+            return fun(*args, **kwargs)
+        call.__doc__ = fun.__doc__
+        return call
+
+    return wrap
+
+
+class _DeprecationHelperStr:
+    """
+    Helper class used by deprecate_cython_api
+    """
+    def __init__(self, content, message):
+        self._content = content
+        self._message = message
+
+    def __hash__(self):
+        return hash(self._content)
+
+    def __eq__(self, other):
+        res = (self._content == other)
+        if res:
+            warnings.warn(self._message, category=DeprecationWarning,
+                          stacklevel=2)
+        return res
+
+
+def deprecate_cython_api(module, routine_name, new_name=None, message=None):
+    """
+    Deprecate an exported cdef function in a public Cython API module.
+
+    Only functions can be deprecated; typedefs etc. cannot.
+
+    Parameters
+    ----------
+    module : module
+        Public Cython API module (e.g. scipy.linalg.cython_blas).
+    routine_name : str
+        Name of the routine to deprecate. May also be a fused-type
+        routine (in which case its all specializations are deprecated).
+    new_name : str
+        New name to include in the deprecation warning message
+    message : str
+        Additional text in the deprecation warning message
+
+    Examples
+    --------
+    Usually, this function would be used in the top-level of the
+    module ``.pyx`` file:
+
+    >>> from scipy._lib.deprecation import deprecate_cython_api
+    >>> import scipy.linalg.cython_blas as mod
+    >>> deprecate_cython_api(mod, "dgemm", "dgemm_new",
+    ...                      message="Deprecated in Scipy 1.5.0")
+    >>> del deprecate_cython_api, mod
+
+    After this, Cython modules that use the deprecated function emit a
+    deprecation warning when they are imported.
+
+    """
+    old_name = f"{module.__name__}.{routine_name}"
+
+    if new_name is None:
+        depdoc = f"`{old_name}` is deprecated!"
+    else:
+        depdoc = f"`{old_name}` is deprecated, use `{new_name}` instead!"
+
+    if message is not None:
+        depdoc += "\n" + message
+
+    d = module.__pyx_capi__
+
+    # Check if the function is a fused-type function with a mangled name
+    j = 0
+    has_fused = False
+    while True:
+        fused_name = f"__pyx_fuse_{j}{routine_name}"
+        if fused_name in d:
+            has_fused = True
+            d[_DeprecationHelperStr(fused_name, depdoc)] = d.pop(fused_name)
+            j += 1
+        else:
+            break
+
+    # If not, apply deprecation to the named routine
+    if not has_fused:
+        d[_DeprecationHelperStr(routine_name, depdoc)] = d.pop(routine_name)
+
+
+# taken from scikit-learn, see
+# https://github.com/scikit-learn/scikit-learn/blob/1.3.0/sklearn/utils/validation.py#L38
+def _deprecate_positional_args(func=None, *, version=None,
+                               deprecated_args=None, custom_message=""):
+    """Decorator for methods that issues warnings for positional arguments.
+
+    Using the keyword-only argument syntax in pep 3102, arguments after the
+    * will issue a warning when passed as a positional argument.
+
+    Parameters
+    ----------
+    func : callable, default=None
+        Function to check arguments on.
+    version : callable, default=None
+        The version when positional arguments will result in error.
+    deprecated_args : set of str, optional
+        Arguments to deprecate - whether passed by position or keyword.
+    custom_message : str, optional
+        Custom message to add to deprecation warning and documentation.
+    """
+    if version is None:
+        msg = "Need to specify a version where signature will be changed"
+        raise ValueError(msg)
+
+    deprecated_args = set() if deprecated_args is None else set(deprecated_args)
+
+    def _inner_deprecate_positional_args(f):
+        sig = signature(f)
+        kwonly_args = []
+        all_args = []
+
+        for name, param in sig.parameters.items():
+            if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
+                all_args.append(name)
+            elif param.kind == Parameter.KEYWORD_ONLY:
+                kwonly_args.append(name)
+
+        def warn_deprecated_args(kwargs):
+            intersection = deprecated_args.intersection(kwargs)
+            if intersection:
+                message = (f"Arguments {intersection} are deprecated, whether passed "
+                           "by position or keyword. They will be removed in SciPy "
+                           f"{version}. ")
+                message += custom_message
+                warnings.warn(message, category=DeprecationWarning, stacklevel=3)
+
+        @functools.wraps(f)
+        def inner_f(*args, **kwargs):
+
+            extra_args = len(args) - len(all_args)
+            if extra_args <= 0:
+                warn_deprecated_args(kwargs)
+                return f(*args, **kwargs)
+
+            # extra_args > 0
+            kwonly_extra_args = set(kwonly_args[:extra_args]) - deprecated_args
+            args_msg = ", ".join(kwonly_extra_args)
+            warnings.warn(
+                (
+                    f"You are passing as positional arguments: {args_msg}. "
+                    "Please change your invocation to use keyword arguments. "
+                    f"From SciPy {version}, passing these as positional "
+                    "arguments will result in an error."
+                ),
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            kwargs.update(zip(sig.parameters, args))
+            warn_deprecated_args(kwargs)
+            return f(**kwargs)
+
+        doc = FunctionDoc(inner_f)
+        kwonly_extra_args = set(kwonly_args) - deprecated_args
+        admonition = f"""
+.. deprecated:: {version}
+    Use of argument(s) ``{kwonly_extra_args}`` by position is deprecated; beginning in 
+    SciPy {version}, these will be keyword-only. """
+        if deprecated_args:
+            admonition += (f"Argument(s) ``{deprecated_args}`` are deprecated, whether "
+                           "passed by position or keyword; they will be removed in "
+                           f"SciPy {version}. ")
+        admonition += custom_message
+        doc['Extended Summary'] += [admonition]
+
+        doc = str(doc).split("\n", 1)[1]  # remove signature
+        inner_f.__doc__ = str(doc)
+
+        return inner_f
+
+    if func is not None:
+        return _inner_deprecate_positional_args(func)
+
+    return _inner_deprecate_positional_args
diff --git a/phi4/lib/python3.10/site-packages/scipy/_lib/uarray.py b/phi4/lib/python3.10/site-packages/scipy/_lib/uarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..b29fc713efb3e836cc179ac87ce41f87b51870ef
--- /dev/null
+++ b/phi4/lib/python3.10/site-packages/scipy/_lib/uarray.py
@@ -0,0 +1,31 @@
+"""`uarray` provides functions for generating multimethods that dispatch to
+multiple different backends
+
+This should be imported, rather than `_uarray` so that an installed version could
+be used instead, if available. This means that users can call
+`uarray.set_backend` directly instead of going through SciPy.
+
+"""
+
+
+# Prefer an installed version of uarray, if available
+try:
+    import uarray as _uarray
+except ImportError:
+    _has_uarray = False
+else:
+    from scipy._lib._pep440 import Version as _Version
+
+    _has_uarray = _Version(_uarray.__version__) >= _Version("0.8")
+    del _uarray
+    del _Version
+
+
+if _has_uarray:
+    from uarray import *  # noqa: F403
+    from uarray import _Function
+else:
+    from ._uarray import *  # noqa: F403
+    from ._uarray import _Function  # noqa: F401
+
+del _has_uarray