demo + utils venv

utils/venv/lib/python3.6/site-packages/scipy/spatial/__init__.py

@@ -0,0 +1,113 @@
+"""
+=============================================================
+Spatial algorithms and data structures (:mod:`scipy.spatial`)
+=============================================================
+
+.. currentmodule:: scipy.spatial
+
+Spatial Transformations
+=======================
+Contained in the `scipy.spatial.transform` submodule.
+
+Nearest-neighbor Queries
+========================
+.. autosummary::
+   :toctree: generated/
+
+   KDTree      -- class for efficient nearest-neighbor queries
+   cKDTree     -- class for efficient nearest-neighbor queries (faster impl.)
+   Rectangle
+
+Distance metrics are contained in the :mod:`scipy.spatial.distance` submodule.
+
+Delaunay Triangulation, Convex Hulls and Voronoi Diagrams
+=========================================================
+
+.. autosummary::
+   :toctree: generated/
+
+   Delaunay    -- compute Delaunay triangulation of input points
+   ConvexHull  -- compute a convex hull for input points
+   Voronoi     -- compute a Voronoi diagram hull from input points
+   SphericalVoronoi -- compute a Voronoi diagram from input points on the surface of a sphere
+   HalfspaceIntersection -- compute the intersection points of input halfspaces
+
+Plotting Helpers
+================
+
+.. autosummary::
+   :toctree: generated/
+
+   delaunay_plot_2d     -- plot 2-D triangulation
+   convex_hull_plot_2d  -- plot 2-D convex hull
+   voronoi_plot_2d      -- plot 2-D voronoi diagram
+
+.. seealso:: :ref:`Tutorial <qhulltutorial>`
+
+
+Simplex representation
+======================
+The simplices (triangles, tetrahedra, ...) appearing in the Delaunay
+tessellation (N-dim simplices), convex hull facets, and Voronoi ridges
+(N-1 dim simplices) are represented in the following scheme::
+
+    tess = Delaunay(points)
+    hull = ConvexHull(points)
+    voro = Voronoi(points)
+
+    # coordinates of the j-th vertex of the i-th simplex
+    tess.points[tess.simplices[i, j], :]        # tessellation element
+    hull.points[hull.simplices[i, j], :]        # convex hull facet
+    voro.vertices[voro.ridge_vertices[i, j], :] # ridge between Voronoi cells
+
+For Delaunay triangulations and convex hulls, the neighborhood
+structure of the simplices satisfies the condition:
+
+    ``tess.neighbors[i,j]`` is the neighboring simplex of the i-th
+    simplex, opposite to the j-vertex. It is -1 in case of no
+    neighbor.
+
+Convex hull facets also define a hyperplane equation::
+
+    (hull.equations[i,:-1] * coord).sum() + hull.equations[i,-1] == 0
+
+Similar hyperplane equations for the Delaunay triangulation correspond
+to the convex hull facets on the corresponding N+1 dimensional
+paraboloid.
+
+The Delaunay triangulation objects offer a method for locating the
+simplex containing a given point, and barycentric coordinate
+computations.
+
+Functions
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   tsearch
+   distance_matrix
+   minkowski_distance
+   minkowski_distance_p
+   procrustes
+
+"""
+
+from __future__ import division, print_function, absolute_import
+
+from .kdtree import *
+from .ckdtree import *
+from .qhull import *
+from ._spherical_voronoi import SphericalVoronoi
+from ._plotutils import *
+from ._procrustes import procrustes
+from . import transform
+
+__all__ = [s for s in dir() if not s.startswith('_')]
+__all__ += ['distance']
+
+from . import distance
+
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester

utils/venv/lib/python3.6/site-packages/scipy/spatial/_plotutils.py

@@ -0,0 +1,262 @@
+from __future__ import division, print_function, absolute_import
+
+import numpy as np
+from scipy._lib.decorator import decorator as _decorator
+
+__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
+
+
+@_decorator
+def _held_figure(func, obj, ax=None, **kw):
+    import matplotlib.pyplot as plt
+
+    if ax is None:
+        fig = plt.figure()
+        ax = fig.gca()
+        return func(obj, ax=ax, **kw)
+
+    # As of matplotlib 2.0, the "hold" mechanism is deprecated.
+    # When matplotlib 1.x is no longer supported, this check can be removed.
+    was_held = getattr(ax, 'ishold', lambda: True)()
+    if was_held:
+        return func(obj, ax=ax, **kw)
+    try:
+        ax.hold(True)
+        return func(obj, ax=ax, **kw)
+    finally:
+        ax.hold(was_held)
+
+
+def _adjust_bounds(ax, points):
+    margin = 0.1 * points.ptp(axis=0)
+    xy_min = points.min(axis=0) - margin
+    xy_max = points.max(axis=0) + margin
+    ax.set_xlim(xy_min[0], xy_max[0])
+    ax.set_ylim(xy_min[1], xy_max[1])
+
+
+@_held_figure
+def delaunay_plot_2d(tri, ax=None):
+    """
+    Plot the given Delaunay triangulation in 2-D
+
+    Parameters
+    ----------
+    tri : scipy.spatial.Delaunay instance
+        Triangulation to plot
+    ax : matplotlib.axes.Axes instance, optional
+        Axes to plot on
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure instance
+        Figure for the plot
+
+    See Also
+    --------
+    Delaunay
+    matplotlib.pyplot.triplot
+
+    Notes
+    -----
+    Requires Matplotlib.
+
+    Examples
+    --------
+
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.spatial import Delaunay, delaunay_plot_2d
+
+    The Delaunay triangulation of a set of random points:
+
+    >>> points = np.random.rand(30, 2)
+    >>> tri = Delaunay(points)
+
+    Plot it:
+
+    >>> _ = delaunay_plot_2d(tri)
+    >>> plt.show()
+
+    """
+    if tri.points.shape[1] != 2:
+        raise ValueError("Delaunay triangulation is not 2-D")
+
+    x, y = tri.points.T
+    ax.plot(x, y, 'o')
+    ax.triplot(x, y, tri.simplices.copy())
+
+    _adjust_bounds(ax, tri.points)
+
+    return ax.figure
+
+
+@_held_figure
+def convex_hull_plot_2d(hull, ax=None):
+    """
+    Plot the given convex hull diagram in 2-D
+
+    Parameters
+    ----------
+    hull : scipy.spatial.ConvexHull instance
+        Convex hull to plot
+    ax : matplotlib.axes.Axes instance, optional
+        Axes to plot on
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure instance
+        Figure for the plot
+
+    See Also
+    --------
+    ConvexHull
+
+    Notes
+    -----
+    Requires Matplotlib.
+
+
+    Examples
+    --------
+
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.spatial import ConvexHull, convex_hull_plot_2d
+
+    The convex hull of a random set of points:
+
+    >>> points = np.random.rand(30, 2)
+    >>> hull = ConvexHull(points)
+
+    Plot it:
+
+    >>> _ = convex_hull_plot_2d(hull)
+    >>> plt.show()
+
+    """
+    from matplotlib.collections import LineCollection
+
+    if hull.points.shape[1] != 2:
+        raise ValueError("Convex hull is not 2-D")
+
+    ax.plot(hull.points[:,0], hull.points[:,1], 'o')
+    line_segments = [hull.points[simplex] for simplex in hull.simplices]
+    ax.add_collection(LineCollection(line_segments,
+                                     colors='k',
+                                     linestyle='solid'))
+    _adjust_bounds(ax, hull.points)
+
+    return ax.figure
+
+
+@_held_figure
+def voronoi_plot_2d(vor, ax=None, **kw):
+    """
+    Plot the given Voronoi diagram in 2-D
+
+    Parameters
+    ----------
+    vor : scipy.spatial.Voronoi instance
+        Diagram to plot
+    ax : matplotlib.axes.Axes instance, optional
+        Axes to plot on
+    show_points: bool, optional
+        Add the Voronoi points to the plot.
+    show_vertices : bool, optional
+        Add the Voronoi vertices to the plot.
+    line_colors : string, optional
+        Specifies the line color for polygon boundaries
+    line_width : float, optional
+        Specifies the line width for polygon boundaries
+    line_alpha: float, optional
+        Specifies the line alpha for polygon boundaries
+    point_size: float, optional
+        Specifies the size of points
+
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure instance
+        Figure for the plot
+
+    See Also
+    --------
+    Voronoi
+
+    Notes
+    -----
+    Requires Matplotlib.
+
+    Examples
+    --------
+    Set of point:
+
+    >>> import matplotlib.pyplot as plt
+    >>> points = np.random.rand(10,2) #random
+
+    Voronoi diagram of the points:
+
+    >>> from scipy.spatial import Voronoi, voronoi_plot_2d
+    >>> vor = Voronoi(points)
+
+    using `voronoi_plot_2d` for visualisation:
+
+    >>> fig = voronoi_plot_2d(vor)
+
+    using `voronoi_plot_2d` for visualisation with enhancements:
+
+    >>> fig = voronoi_plot_2d(vor, show_vertices=False, line_colors='orange',
+    ...                 line_width=2, line_alpha=0.6, point_size=2)
+    >>> plt.show()
+
+    """
+    from matplotlib.collections import LineCollection
+
+    if vor.points.shape[1] != 2:
+        raise ValueError("Voronoi diagram is not 2-D")
+
+    if kw.get('show_points', True):
+        point_size = kw.get('point_size', None)
+        ax.plot(vor.points[:,0], vor.points[:,1], '.', markersize=point_size)
+    if kw.get('show_vertices', True):
+        ax.plot(vor.vertices[:,0], vor.vertices[:,1], 'o')
+
+    line_colors = kw.get('line_colors', 'k')
+    line_width = kw.get('line_width', 1.0)
+    line_alpha = kw.get('line_alpha', 1.0)
+
+    center = vor.points.mean(axis=0)
+    ptp_bound = vor.points.ptp(axis=0)
+
+    finite_segments = []
+    infinite_segments = []
+    for pointidx, simplex in zip(vor.ridge_points, vor.ridge_vertices):
+        simplex = np.asarray(simplex)
+        if np.all(simplex >= 0):
+            finite_segments.append(vor.vertices[simplex])
+        else:
+            i = simplex[simplex >= 0][0]  # finite end Voronoi vertex
+
+            t = vor.points[pointidx[1]] - vor.points[pointidx[0]]  # tangent
+            t /= np.linalg.norm(t)
+            n = np.array([-t[1], t[0]])  # normal
+
+            midpoint = vor.points[pointidx].mean(axis=0)
+            direction = np.sign(np.dot(midpoint - center, n)) * n
+            far_point = vor.vertices[i] + direction * ptp_bound.max()
+
+            infinite_segments.append([vor.vertices[i], far_point])
+
+    ax.add_collection(LineCollection(finite_segments,
+                                     colors=line_colors,
+                                     lw=line_width,
+                                     alpha=line_alpha,
+                                     linestyle='solid'))
+    ax.add_collection(LineCollection(infinite_segments,
+                                     colors=line_colors,
+                                     lw=line_width,
+                                     alpha=line_alpha,
+                                     linestyle='dashed'))
+
+    _adjust_bounds(ax, vor.points)
+
+    return ax.figure

utils/venv/lib/python3.6/site-packages/scipy/spatial/_procrustes.py

@@ -0,0 +1,133 @@
+"""
+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.
+"""
+
+from __future__ import absolute_import, division, print_function
+
+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
+    --------
+    >>> 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.double, copy=True)
+    mtx2 = np.array(data2, dtype=np.double, 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
+

utils/venv/lib/python3.6/site-packages/scipy/spatial/_spherical_voronoi.py

@@ -0,0 +1,337 @@
+"""
+Spherical Voronoi Code
+
+.. versionadded:: 0.18.0
+
+"""
+#
+# Copyright (C)  Tyler Reddy, Ross Hemsley, Edd Edmondson,
+#                Nikolai Nowaczyk, Joe Pitt-Francis, 2015.
+#
+# Distributed under the same BSD license as Scipy.
+#
+
+import numpy as np
+import scipy
+import itertools
+from . import _voronoi
+from scipy.spatial.distance import pdist
+
+__all__ = ['SphericalVoronoi']
+
+def sphere_check(points, radius, center):
+    """ Determines distance of generators from theoretical sphere
+    surface.
+
+    """
+    actual_squared_radii = (((points[...,0] - center[0]) ** 2) +
+                            ((points[...,1] - center[1]) ** 2) +
+                            ((points[...,2] - center[2]) ** 2))
+    max_discrepancy = (np.sqrt(actual_squared_radii) - radius).max()
+    return abs(max_discrepancy)
+
+def calc_circumcenters(tetrahedrons):
+    """ Calculates the cirumcenters of the circumspheres of tetrahedrons.
+
+    An implementation based on
+    http://mathworld.wolfram.com/Circumsphere.html
+
+    Parameters
+    ----------
+    tetrahedrons : an array of shape (N, 4, 3)
+        consisting of N tetrahedrons defined by 4 points in 3D
+
+    Returns
+    ----------
+    circumcenters : an array of shape (N, 3)
+        consisting of the N circumcenters of the tetrahedrons in 3D
+
+    """
+
+    num = tetrahedrons.shape[0]
+    a = np.concatenate((tetrahedrons, np.ones((num, 4, 1))), axis=2)
+
+    sums = np.sum(tetrahedrons ** 2, axis=2)
+    d = np.concatenate((sums[:, :, np.newaxis], a), axis=2)
+
+    dx = np.delete(d, 1, axis=2)
+    dy = np.delete(d, 2, axis=2)
+    dz = np.delete(d, 3, axis=2)
+
+    dx = np.linalg.det(dx)
+    dy = -np.linalg.det(dy)
+    dz = np.linalg.det(dz)
+    a = np.linalg.det(a)
+
+    nominator = np.vstack((dx, dy, dz))
+    denominator = 2*a
+    return (nominator / denominator).T
+
+
+def project_to_sphere(points, center, radius):
+    """
+    Projects the elements of points onto the sphere defined
+    by center and radius.
+
+    Parameters
+    ----------
+    points : array of floats of shape (npoints, ndim)
+             consisting of the points in a space of dimension ndim
+    center : array of floats of shape (ndim,)
+            the center of the sphere to project on
+    radius : float
+            the radius of the sphere to project on
+
+    returns: array of floats of shape (npoints, ndim)
+            the points projected onto the sphere
+    """
+
+    lengths = scipy.spatial.distance.cdist(points, np.array([center]))
+    return (points - center) / lengths * radius + center
+
+
+class SphericalVoronoi:
+    """ Voronoi diagrams on the surface of a sphere.
+
+    .. versionadded:: 0.18.0
+
+    Parameters
+    ----------
+    points : ndarray of floats, shape (npoints, 3)
+        Coordinates of points to construct a spherical
+        Voronoi diagram from
+    radius : float, optional
+        Radius of the sphere (Default: 1)
+    center : ndarray of floats, shape (3,)
+        Center of sphere (Default: origin)
+    threshold : float
+        Threshold for detecting duplicate points and
+        mismatches between points and sphere parameters.
+        (Default: 1e-06)
+
+    Attributes
+    ----------
+    points : double array of shape (npoints, 3)
+            the points in 3D to generate the Voronoi diagram from
+    radius : double
+            radius of the sphere
+            Default: None (forces estimation, which is less precise)
+    center : double array of shape (3,)
+            center of the sphere
+            Default: None (assumes sphere is centered at origin)
+    vertices : double array of shape (nvertices, 3)
+            Voronoi vertices corresponding to points
+    regions : list of list of integers of shape (npoints, _ )
+            the n-th entry is a list consisting of the indices
+            of the vertices belonging to the n-th point in points
+
+    Raises
+    ------
+    ValueError
+        If there are duplicates in `points`.
+        If the provided `radius` is not consistent with `points`.
+
+    Notes
+    ----------
+    The spherical Voronoi diagram algorithm proceeds as follows. The Convex
+    Hull of the input points (generators) is calculated, and is equivalent to
+    their Delaunay triangulation on the surface of the sphere [Caroli]_.
+    A 3D Delaunay tetrahedralization is obtained by including the origin of
+    the coordinate system as the fourth vertex of each simplex of the Convex
+    Hull. The circumcenters of all tetrahedra in the system are calculated and
+    projected to the surface of the sphere, producing the Voronoi vertices.
+    The Delaunay tetrahedralization neighbour information is then used to
+    order the Voronoi region vertices around each generator. The latter
+    approach is substantially less sensitive to floating point issues than
+    angle-based methods of Voronoi region vertex sorting.
+
+    The surface area of spherical polygons is calculated by decomposing them
+    into triangles and using L'Huilier's Theorem to calculate the spherical
+    excess of each triangle [Weisstein]_. The sum of the spherical excesses is
+    multiplied by the square of the sphere radius to obtain the surface area
+    of the spherical polygon. For nearly-degenerate spherical polygons an area
+    of approximately 0 is returned by default, rather than attempting the
+    unstable calculation.
+
+    Empirical assessment of spherical Voronoi algorithm performance suggests
+    quadratic time complexity (loglinear is optimal, but algorithms are more
+    challenging to implement). The reconstitution of the surface area of the
+    sphere, measured as the sum of the surface areas of all Voronoi regions,
+    is closest to 100 % for larger (>> 10) numbers of generators.
+
+    References
+    ----------
+
+    .. [Caroli] Caroli et al. Robust and Efficient Delaunay triangulations of
+                points on or close to a sphere. Research Report RR-7004, 2009.
+    .. [Weisstein] "L'Huilier's Theorem." From MathWorld -- A Wolfram Web
+                Resource. http://mathworld.wolfram.com/LHuiliersTheorem.html
+
+    See Also
+    --------
+    Voronoi : Conventional Voronoi diagrams in N dimensions.
+
+    Examples
+    --------
+
+    >>> from matplotlib import colors
+    >>> from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.spatial import SphericalVoronoi
+    >>> from mpl_toolkits.mplot3d import proj3d
+    >>> # set input data
+    >>> points = np.array([[0, 0, 1], [0, 0, -1], [1, 0, 0],
+    ...                    [0, 1, 0], [0, -1, 0], [-1, 0, 0], ])
+    >>> center = np.array([0, 0, 0])
+    >>> radius = 1
+    >>> # calculate spherical Voronoi diagram
+    >>> sv = SphericalVoronoi(points, radius, center)
+    >>> # sort vertices (optional, helpful for plotting)
+    >>> sv.sort_vertices_of_regions()
+    >>> # generate plot
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111, projection='3d')
+    >>> # plot the unit sphere for reference (optional)
+    >>> u = np.linspace(0, 2 * np.pi, 100)
+    >>> v = np.linspace(0, np.pi, 100)
+    >>> x = np.outer(np.cos(u), np.sin(v))
+    >>> y = np.outer(np.sin(u), np.sin(v))
+    >>> z = np.outer(np.ones(np.size(u)), np.cos(v))
+    >>> ax.plot_surface(x, y, z, color='y', alpha=0.1)
+    >>> # plot generator points
+    >>> ax.scatter(points[:, 0], points[:, 1], points[:, 2], c='b')
+    >>> # plot Voronoi vertices
+    >>> ax.scatter(sv.vertices[:, 0], sv.vertices[:, 1], sv.vertices[:, 2],
+    ...                    c='g')
+    >>> # indicate Voronoi regions (as Euclidean polygons)
+    >>> for region in sv.regions:
+    ...    random_color = colors.rgb2hex(np.random.rand(3))
+    ...    polygon = Poly3DCollection([sv.vertices[region]], alpha=1.0)
+    ...    polygon.set_color(random_color)
+    ...    ax.add_collection3d(polygon)
+    >>> plt.show()
+
+    """
+
+    def __init__(self, points, radius=None, center=None, threshold=1e-06):
+        """
+        Initializes the object and starts the computation of the Voronoi
+        diagram.
+
+        points : The generator points of the Voronoi diagram assumed to be
+         all on the sphere with radius supplied by the radius parameter and
+         center supplied by the center parameter.
+        radius : The radius of the sphere. Will default to 1 if not supplied.
+        center : The center of the sphere. Will default to the origin if not
+         supplied.
+        """
+
+        self.points = points
+        if np.any(center):
+            self.center = center
+        else:
+            self.center = np.zeros(3)
+        if radius:
+            self.radius = radius
+        else:
+            self.radius = 1
+
+        if pdist(self.points).min() <= threshold * self.radius:
+            raise ValueError("Duplicate generators present.")
+
+        max_discrepancy = sphere_check(self.points,
+                                       self.radius,
+                                       self.center)
+        if max_discrepancy >= threshold * self.radius:
+            raise ValueError("Radius inconsistent with generators.")
+        self.vertices = None
+        self.regions = None
+        self._tri = None
+        self._calc_vertices_regions()
+
+    def _calc_vertices_regions(self):
+        """
+        Calculates the Voronoi vertices and regions of the generators stored
+        in self.points. The vertices will be stored in self.vertices and the
+        regions in self.regions.
+
+        This algorithm was discussed at PyData London 2015 by
+        Tyler Reddy, Ross Hemsley and Nikolai Nowaczyk
+        """
+
+        # perform 3D Delaunay triangulation on data set
+        # (here ConvexHull can also be used, and is faster)
+        self._tri = scipy.spatial.ConvexHull(self.points)
+
+        # add the center to each of the simplices in tri to get the same
+        # tetrahedrons we'd have gotten from Delaunay tetrahedralization
+        # tetrahedrons will have shape: (2N-4, 4, 3)
+        tetrahedrons = self._tri.points[self._tri.simplices]
+        tetrahedrons = np.insert(
+            tetrahedrons,
+            3,
+            np.array([self.center]),
+            axis=1
+        )
+
+        # produce circumcenters of tetrahedrons from 3D Delaunay
+        # circumcenters will have shape: (2N-4, 3)
+        circumcenters = calc_circumcenters(tetrahedrons)
+
+        # project tetrahedron circumcenters to the surface of the sphere
+        # self.vertices will have shape: (2N-4, 3)
+        self.vertices = project_to_sphere(
+            circumcenters,
+            self.center,
+            self.radius
+        )
+
+        # calculate regions from triangulation
+        # simplex_indices will have shape: (2N-4,)
+        simplex_indices = np.arange(self._tri.simplices.shape[0])
+        # tri_indices will have shape: (6N-12,)
+        tri_indices = np.column_stack([simplex_indices, simplex_indices,
+            simplex_indices]).ravel()
+        # point_indices will have shape: (6N-12,)
+        point_indices = self._tri.simplices.ravel()
+
+        # array_associations will have shape: (6N-12, 2)
+        array_associations = np.dstack((point_indices, tri_indices))[0]
+        array_associations = array_associations[np.lexsort((
+                                                array_associations[...,1],
+                                                array_associations[...,0]))]
+        array_associations = array_associations.astype(np.intp)
+
+        # group by generator indices to produce
+        # unsorted regions in nested list
+        groups = []
+        for k, g in itertools.groupby(array_associations,
+                                      lambda t: t[0]):
+            groups.append(list(list(zip(*list(g)))[1]))
+
+        self.regions = groups
+
+    def sort_vertices_of_regions(self):
+        """
+         For each region in regions, it sorts the indices of the Voronoi
+         vertices such that the resulting points are in a clockwise or
+         counterclockwise order around the generator point.
+
+         This is done as follows: Recall that the n-th region in regions
+         surrounds the n-th generator in points and that the k-th
+         Voronoi vertex in vertices is the projected circumcenter of the
+         tetrahedron obtained by the k-th triangle in _tri.simplices (and the
+         origin). For each region n, we choose the first triangle (=Voronoi
+         vertex) in _tri.simplices and a vertex of that triangle not equal to
+         the center n. These determine a unique neighbor of that triangle,
+         which is then chosen as the second triangle. The second triangle
+         will have a unique vertex not equal to the current vertex or the
+         center. This determines a unique neighbor of the second triangle,
+         which is then chosen as the third triangle and so forth. We proceed
+         through all the triangles (=Voronoi vertices) belonging to the
+         generator in points and obtain a sorted version of the vertices
+         of its surrounding region.
+        """
+
+        _voronoi.sort_vertices_of_regions(self._tri.simplices,
+                                                   self.regions)

utils/venv/lib/python3.6/site-packages/scipy/spatial/kdtree.py

@@ -0,0 +1,987 @@
+# Copyright Anne M. Archibald 2008
+# Released under the scipy license
+from __future__ import division, print_function, absolute_import
+
+import sys
+import numpy as np
+from heapq import heappush, heappop
+import scipy.sparse
+
+__all__ = ['minkowski_distance_p', 'minkowski_distance',
+           'distance_matrix',
+           'Rectangle', 'KDTree']
+
+
+def minkowski_distance_p(x, y, p=2):
+    """
+    Compute the p-th 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.
+
+    Parameters
+    ----------
+    x : (M, K) array_like
+        Input array.
+    y : (N, K) array_like
+        Input array.
+    p : float, 1 <= p <= infinity
+        Which Minkowski p-norm to use.
+
+    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)
+    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.
+
+    Parameters
+    ----------
+    x : (M, K) array_like
+        Input array.
+    y : (N, K) array_like
+        Input array.
+    p : float, 1 <= p <= infinity
+        Which Minkowski p-norm to use.
+
+    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(object):
+    """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 "<Rectangle %s>" % 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(object):
+    """
+    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 : (N,K) array_like
+        The data points to be indexed. This array is not copied, and
+        so modifying this data will result in bogus results.
+    leafsize : int, optional
+        The number of points at which the algorithm switches over to
+        brute-force.  Has to be positive.
+
+    Raises
+    ------
+    RuntimeError
+        The maximum recursion limit can be exceeded for large data
+        sets.  If this happens, either increase the value for the `leafsize`
+        parameter or increase the recursion limit by::
+
+            >>> import sys
+            >>> sys.setrecursionlimit(10000)
+
+    See Also
+    --------
+    cKDTree : Implementation of `KDTree` in Cython
+
+    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.
+
+    The tree also supports all-neighbors queries, both with arrays of points
+    and with other kd-trees. These do use a reasonably efficient algorithm,
+    but the kd-tree is not necessarily the best data structure for this
+    sort of calculation.
+
+    """
+    def __init__(self, data, leafsize=10):
+        self.data = np.asarray(data)
+        self.n, self.m = np.shape(self.data)
+        self.leafsize = int(leafsize)
+        if self.leafsize < 1:
+            raise ValueError("leafsize must be at least 1")
+        self.maxes = np.amax(self.data,axis=0)
+        self.mins = np.amin(self.data,axis=0)
+
+        self.tree = self.__build(np.arange(self.n), self.maxes, self.mins)
+
+    class node(object):
+        if sys.version_info[0] >= 3:
+            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):
+        def __init__(self, idx):
+            self.idx = idx
+            self.children = len(idx)
+
+    class innernode(node):
+        def __init__(self, split_dim, split, less, greater):
+            self.split_dim = split_dim
+            self.split = split
+            self.less = less
+            self.greater = greater
+            self.children = less.children+greater.children
+
+    def __build(self, idx, maxes, mins):
+        if len(idx) <= self.leafsize:
+            return KDTree.leafnode(idx)
+        else:
+            data = self.data[idx]
+            # maxes = np.amax(data,axis=0)
+            # mins = np.amin(data,axis=0)
+            d = np.argmax(maxes-mins)
+            maxval = maxes[d]
+            minval = mins[d]
+            if maxval == minval:
+                # all points are identical; warn user?
+                return KDTree.leafnode(idx)
+            data = data[:,d]
+
+            # sliding midpoint rule; see Maneewongvatana and Mount 1999
+            # for arguments that this is a good idea.
+            split = (maxval+minval)/2
+            less_idx = np.nonzero(data <= split)[0]
+            greater_idx = np.nonzero(data > split)[0]
+            if len(less_idx) == 0:
+                split = np.amin(data)
+                less_idx = np.nonzero(data <= split)[0]
+                greater_idx = np.nonzero(data > split)[0]
+            if len(greater_idx) == 0:
+                split = np.amax(data)
+                less_idx = np.nonzero(data < split)[0]
+                greater_idx = np.nonzero(data >= split)[0]
+            if len(less_idx) == 0:
+                # _still_ zero? all must have the same value
+                if not np.all(data == data[0]):
+                    raise ValueError("Troublesome data array: %s" % data)
+                split = data[0]
+                less_idx = np.arange(len(data)-1)
+                greater_idx = np.array([len(data)-1])
+
+            lessmaxes = np.copy(maxes)
+            lessmaxes[d] = split
+            greatermins = np.copy(mins)
+            greatermins[d] = split
+            return KDTree.innernode(d, split,
+                    self.__build(idx[less_idx],lessmaxes,mins),
+                    self.__build(idx[greater_idx],maxes,greatermins))
+
+    def __query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
+
+        side_distances = np.maximum(0,np.maximum(x-self.maxes,self.mins-x))
+        if p != np.inf:
+            side_distances **= p
+            min_distance = np.sum(side_distances)
+        else:
+            min_distance = np.amax(side_distances)
+
+        # priority queue for chasing nodes
+        # entries are:
+        #  minimum distance between the cell and the target
+        #  distances between the nearest side of the cell and the target
+        #  the head node of the cell
+        q = [(min_distance,
+              tuple(side_distances),
+              self.tree)]
+        # priority queue for the nearest neighbors
+        # furthest known neighbor first
+        # entries are (-distance**p, i)
+        neighbors = []
+
+        if eps == 0:
+            epsfac = 1
+        elif p == np.inf:
+            epsfac = 1/(1+eps)
+        else:
+            epsfac = 1/(1+eps)**p
+
+        if p != np.inf and distance_upper_bound != np.inf:
+            distance_upper_bound = distance_upper_bound**p
+
+        while q:
+            min_distance, side_distances, node = heappop(q)
+            if isinstance(node, KDTree.leafnode):
+                # brute-force
+                data = self.data[node.idx]
+                ds = minkowski_distance_p(data,x[np.newaxis,:],p)
+                for i in range(len(ds)):
+                    if ds[i] < distance_upper_bound:
+                        if len(neighbors) == k:
+                            heappop(neighbors)
+                        heappush(neighbors, (-ds[i], node.idx[i]))
+                        if len(neighbors) == k:
+                            distance_upper_bound = -neighbors[0][0]
+            else:
+                # we don't push cells that are too far onto the queue at all,
+                # but since the distance_upper_bound decreases, we might get
+                # here even if the cell's too far
+                if min_distance > distance_upper_bound*epsfac:
+                    # since this is the nearest cell, we're done, bail out
+                    break
+                # compute minimum distances to the children and push them on
+                if x[node.split_dim] < node.split:
+                    near, far = node.less, node.greater
+                else:
+                    near, far = node.greater, node.less
+
+                # near child is at the same distance as the current node
+                heappush(q,(min_distance, side_distances, near))
+
+                # far child is further by an amount depending only
+                # on the split value
+                sd = list(side_distances)
+                if p == np.inf:
+                    min_distance = max(min_distance, abs(node.split-x[node.split_dim]))
+                elif p == 1:
+                    sd[node.split_dim] = np.abs(node.split-x[node.split_dim])
+                    min_distance = min_distance - side_distances[node.split_dim] + sd[node.split_dim]
+                else:
+                    sd[node.split_dim] = np.abs(node.split-x[node.split_dim])**p
+                    min_distance = min_distance - side_distances[node.split_dim] + sd[node.split_dim]
+
+                # far child might be too far, if so, don't bother pushing it
+                if min_distance <= distance_upper_bound*epsfac:
+                    heappush(q,(min_distance, tuple(sd), far))
+
+        if p == np.inf:
+            return sorted([(-d,i) for (d,i) in neighbors])
+        else:
+            return sorted([((-d)**(1./p),i) for (d,i) in neighbors])
+
+    def query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
+        """
+        Query the kd-tree for nearest neighbors
+
+        Parameters
+        ----------
+        x : array_like, last dimension self.m
+            An array of points to query.
+        k : int, optional
+            The number of nearest neighbors to return.
+        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 "Manhattan" distance
+            2 is the usual Euclidean distance
+            infinity is the maximum-coordinate-difference distance
+        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.
+
+        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 if
+            k is one, or tuple+(k,) if k is larger than one. Missing
+            neighbors (e.g. when k > n or distance_upper_bound is
+            given) are indicated with infinite distances.  If k is None,
+            then d is an object array of shape tuple, containing lists
+            of distances. In either case the hits are sorted by distance
+            (nearest first).
+        i : integer or array of integers
+            The locations of the neighbors in self.data. i is the same
+            shape as d.
+
+        Examples
+        --------
+        >>> from scipy import spatial
+        >>> x, y = np.mgrid[0:5, 2:8]
+        >>> tree = spatial.KDTree(list(zip(x.ravel(), y.ravel())))
+        >>> tree.data
+        array([[0, 2],
+               [0, 3],
+               [0, 4],
+               [0, 5],
+               [0, 6],
+               [0, 7],
+               [1, 2],
+               [1, 3],
+               [1, 4],
+               [1, 5],
+               [1, 6],
+               [1, 7],
+               [2, 2],
+               [2, 3],
+               [2, 4],
+               [2, 5],
+               [2, 6],
+               [2, 7],
+               [3, 2],
+               [3, 3],
+               [3, 4],
+               [3, 5],
+               [3, 6],
+               [3, 7],
+               [4, 2],
+               [4, 3],
+               [4, 4],
+               [4, 5],
+               [4, 6],
+               [4, 7]])
+        >>> pts = np.array([[0, 0], [2.1, 2.9]])
+        >>> tree.query(pts)
+        (array([ 2.        ,  0.14142136]), array([ 0, 13]))
+        >>> tree.query(pts[0])
+        (2.0, 0)
+
+        """
+        x = np.asarray(x)
+        if np.shape(x)[-1] != self.m:
+            raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x)))
+        if p < 1:
+            raise ValueError("Only p-norms with 1<=p<=infinity permitted")
+        retshape = np.shape(x)[:-1]
+        if retshape != ():
+            if k is None:
+                dd = np.empty(retshape,dtype=object)
+                ii = np.empty(retshape,dtype=object)
+            elif k > 1:
+                dd = np.empty(retshape+(k,),dtype=float)
+                dd.fill(np.inf)
+                ii = np.empty(retshape+(k,),dtype=int)
+                ii.fill(self.n)
+            elif k == 1:
+                dd = np.empty(retshape,dtype=float)
+                dd.fill(np.inf)
+                ii = np.empty(retshape,dtype=int)
+                ii.fill(self.n)
+            else:
+                raise ValueError("Requested %s nearest neighbors; acceptable numbers are integers greater than or equal to one, or None")
+            for c in np.ndindex(retshape):
+                hits = self.__query(x[c], k=k, eps=eps, p=p, distance_upper_bound=distance_upper_bound)
+                if k is None:
+                    dd[c] = [d for (d,i) in hits]
+                    ii[c] = [i for (d,i) in hits]
+                elif k > 1:
+                    for j in range(len(hits)):
+                        dd[c+(j,)], ii[c+(j,)] = hits[j]
+                elif k == 1:
+                    if len(hits) > 0:
+                        dd[c], ii[c] = hits[0]
+                    else:
+                        dd[c] = np.inf
+                        ii[c] = self.n
+            return dd, ii
+        else:
+            hits = self.__query(x, k=k, eps=eps, p=p, distance_upper_bound=distance_upper_bound)
+            if k is None:
+                return [d for (d,i) in hits], [i for (d,i) in hits]
+            elif k == 1:
+                if len(hits) > 0:
+                    return hits[0]
+                else:
+                    return np.inf, self.n
+            elif k > 1:
+                dd = np.empty(k,dtype=float)
+                dd.fill(np.inf)
+                ii = np.empty(k,dtype=int)
+                ii.fill(self.n)
+                for j in range(len(hits)):
+                    dd[j], ii[j] = hits[j]
+                return dd, ii
+            else:
+                raise ValueError("Requested %s nearest neighbors; acceptable numbers are integers greater than or equal to one, or None")
+
+    def __query_ball_point(self, x, r, p=2., eps=0):
+        R = Rectangle(self.maxes, self.mins)
+
+        def traverse_checking(node, rect):
+            if rect.min_distance_point(x, p) > r / (1. + eps):
+                return []
+            elif rect.max_distance_point(x, p) < r * (1. + eps):
+                return traverse_no_checking(node)
+            elif isinstance(node, KDTree.leafnode):
+                d = self.data[node.idx]
+                return node.idx[minkowski_distance(d, x, p) <= r].tolist()
+            else:
+                less, greater = rect.split(node.split_dim, node.split)
+                return traverse_checking(node.less, less) + \
+                       traverse_checking(node.greater, greater)
+
+        def traverse_no_checking(node):
+            if isinstance(node, KDTree.leafnode):
+                return node.idx.tolist()
+            else:
+                return traverse_no_checking(node.less) + \
+                       traverse_no_checking(node.greater)
+
+        return traverse_checking(self.tree, R)
+
+    def query_ball_point(self, x, r, p=2., eps=0):
+        """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 : positive float
+            The radius of points to return.
+        p : float, optional
+            Which Minkowski p-norm to use.  Should be in the range [1, inf].
+        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)``.
+
+        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
+        --------
+        >>> from scipy import spatial
+        >>> x, y = np.mgrid[0:5, 0:5]
+        >>> points = np.c_[x.ravel(), y.ravel()]
+        >>> tree = spatial.KDTree(points)
+        >>> 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.shape[-1] != self.m:
+            raise ValueError("Searching for a %d-dimensional point in a "
+                             "%d-dimensional KDTree" % (x.shape[-1], self.m))
+        if len(x.shape) == 1:
+            return self.__query_ball_point(x, r, p, eps)
+        else:
+            retshape = x.shape[:-1]
+            result = np.empty(retshape, dtype=object)
+            for c in np.ndindex(retshape):
+                result[c] = self.__query_ball_point(x[c], r, p=p, eps=eps)
+            return result
+
+    def query_ball_tree(self, other, r, p=2., eps=0):
+        """Find all pairs of points 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``.
+
+        """
+        results = [[] for i in range(self.n)]
+
+        def traverse_checking(node1, rect1, node2, rect2):
+            if rect1.min_distance_rectangle(rect2, p) > r/(1.+eps):
+                return
+            elif rect1.max_distance_rectangle(rect2, p) < r*(1.+eps):
+                traverse_no_checking(node1, node2)
+            elif isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    d = other.data[node2.idx]
+                    for i in node1.idx:
+                        results[i] += node2.idx[minkowski_distance(d,self.data[i],p) <= r].tolist()
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse_checking(node1,rect1,node2.less,less)
+                    traverse_checking(node1,rect1,node2.greater,greater)
+            elif isinstance(node2, KDTree.leafnode):
+                less, greater = rect1.split(node1.split_dim, node1.split)
+                traverse_checking(node1.less,less,node2,rect2)
+                traverse_checking(node1.greater,greater,node2,rect2)
+            else:
+                less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                traverse_checking(node1.less,less1,node2.less,less2)
+                traverse_checking(node1.less,less1,node2.greater,greater2)
+                traverse_checking(node1.greater,greater1,node2.less,less2)
+                traverse_checking(node1.greater,greater1,node2.greater,greater2)
+
+        def traverse_no_checking(node1, node2):
+            if isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    for i in node1.idx:
+                        results[i] += node2.idx.tolist()
+                else:
+                    traverse_no_checking(node1, node2.less)
+                    traverse_no_checking(node1, node2.greater)
+            else:
+                traverse_no_checking(node1.less, node2)
+                traverse_no_checking(node1.greater, node2)
+
+        traverse_checking(self.tree, Rectangle(self.maxes, self.mins),
+                          other.tree, Rectangle(other.maxes, other.mins))
+        return results
+
+    def query_pairs(self, r, p=2., eps=0):
+        """
+        Find all pairs of points within a distance.
+
+        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.
+
+        Returns
+        -------
+        results : set
+            Set of pairs ``(i,j)``, with ``i < j``, for which the corresponding
+            positions are close.
+
+        """
+        results = set()
+
+        def traverse_checking(node1, rect1, node2, rect2):
+            if rect1.min_distance_rectangle(rect2, p) > r/(1.+eps):
+                return
+            elif rect1.max_distance_rectangle(rect2, p) < r*(1.+eps):
+                traverse_no_checking(node1, node2)
+            elif isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    # Special care to avoid duplicate pairs
+                    if id(node1) == id(node2):
+                        d = self.data[node2.idx]
+                        for i in node1.idx:
+                            for j in node2.idx[minkowski_distance(d,self.data[i],p) <= r]:
+                                if i < j:
+                                    results.add((i,j))
+                    else:
+                        d = self.data[node2.idx]
+                        for i in node1.idx:
+                            for j in node2.idx[minkowski_distance(d,self.data[i],p) <= r]:
+                                if i < j:
+                                    results.add((i,j))
+                                elif j < i:
+                                    results.add((j,i))
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse_checking(node1,rect1,node2.less,less)
+                    traverse_checking(node1,rect1,node2.greater,greater)
+            elif isinstance(node2, KDTree.leafnode):
+                less, greater = rect1.split(node1.split_dim, node1.split)
+                traverse_checking(node1.less,less,node2,rect2)
+                traverse_checking(node1.greater,greater,node2,rect2)
+            else:
+                less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                traverse_checking(node1.less,less1,node2.less,less2)
+                traverse_checking(node1.less,less1,node2.greater,greater2)
+
+                # Avoid traversing (node1.less, node2.greater) and
+                # (node1.greater, node2.less) (it's the same node pair twice
+                # over, which is the source of the complication in the
+                # original KDTree.query_pairs)
+                if id(node1) != id(node2):
+                    traverse_checking(node1.greater,greater1,node2.less,less2)
+
+                traverse_checking(node1.greater,greater1,node2.greater,greater2)
+
+        def traverse_no_checking(node1, node2):
+            if isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    # Special care to avoid duplicate pairs
+                    if id(node1) == id(node2):
+                        for i in node1.idx:
+                            for j in node2.idx:
+                                if i < j:
+                                    results.add((i,j))
+                    else:
+                        for i in node1.idx:
+                            for j in node2.idx:
+                                if i < j:
+                                    results.add((i,j))
+                                elif j < i:
+                                    results.add((j,i))
+                else:
+                    traverse_no_checking(node1, node2.less)
+                    traverse_no_checking(node1, node2.greater)
+            else:
+                # Avoid traversing (node1.less, node2.greater) and
+                # (node1.greater, node2.less) (it's the same node pair twice
+                # over, which is the source of the complication in the
+                # original KDTree.query_pairs)
+                if id(node1) == id(node2):
+                    traverse_no_checking(node1.less, node2.less)
+                    traverse_no_checking(node1.less, node2.greater)
+                    traverse_no_checking(node1.greater, node2.greater)
+                else:
+                    traverse_no_checking(node1.less, node2)
+                    traverse_no_checking(node1.greater, node2)
+
+        traverse_checking(self.tree, Rectangle(self.maxes, self.mins),
+                          self.tree, Rectangle(self.maxes, self.mins))
+        return results
+
+    def count_neighbors(self, other, r, p=2.):
+        """
+        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``.
+        This is the "two-point correlation" described in Gray and Moore 2000,
+        "N-body problems in statistical learning", and the code here is based
+        on their algorithm.
+
+        Parameters
+        ----------
+        other : KDTree instance
+            The other tree to draw points from.
+        r : float or one-dimensional array of floats
+            The radius to produce a count for. Multiple radii are searched with
+            a single tree traversal.
+        p : float, 1<=p<=infinity, optional
+            Which Minkowski p-norm to use
+
+        Returns
+        -------
+        result : int or 1-D array of ints
+            The number of pairs. Note that this is internally stored in a numpy
+            int, and so may overflow if very large (2e9).
+
+        """
+        def traverse(node1, rect1, node2, rect2, idx):
+            min_r = rect1.min_distance_rectangle(rect2,p)
+            max_r = rect1.max_distance_rectangle(rect2,p)
+            c_greater = r[idx] > max_r
+            result[idx[c_greater]] += node1.children*node2.children
+            idx = idx[(min_r <= r[idx]) & (r[idx] <= max_r)]
+            if len(idx) == 0:
+                return
+
+            if isinstance(node1,KDTree.leafnode):
+                if isinstance(node2,KDTree.leafnode):
+                    ds = minkowski_distance(self.data[node1.idx][:,np.newaxis,:],
+                                  other.data[node2.idx][np.newaxis,:,:],
+                                  p).ravel()
+                    ds.sort()
+                    result[idx] += np.searchsorted(ds,r[idx],side='right')
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse(node1, rect1, node2.less, less, idx)
+                    traverse(node1, rect1, node2.greater, greater, idx)
+            else:
+                if isinstance(node2,KDTree.leafnode):
+                    less, greater = rect1.split(node1.split_dim, node1.split)
+                    traverse(node1.less, less, node2, rect2, idx)
+                    traverse(node1.greater, greater, node2, rect2, idx)
+                else:
+                    less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                    less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                    traverse(node1.less,less1,node2.less,less2,idx)
+                    traverse(node1.less,less1,node2.greater,greater2,idx)
+                    traverse(node1.greater,greater1,node2.less,less2,idx)
+                    traverse(node1.greater,greater1,node2.greater,greater2,idx)
+
+        R1 = Rectangle(self.maxes, self.mins)
+        R2 = Rectangle(other.maxes, other.mins)
+        if np.shape(r) == ():
+            r = np.array([r])
+            result = np.zeros(1,dtype=int)
+            traverse(self.tree, R1, other.tree, R2, np.arange(1))
+            return result[0]
+        elif len(np.shape(r)) == 1:
+            r = np.asarray(r)
+            n, = r.shape
+            result = np.zeros(n,dtype=int)
+            traverse(self.tree, R1, other.tree, R2, np.arange(n))
+            return result
+        else:
+            raise ValueError("r must be either a single value or a one-dimensional array of values")
+
+    def sparse_distance_matrix(self, other, max_distance, p=2.):
+        """
+        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, optional
+
+        Returns
+        -------
+        result : dok_matrix
+            Sparse matrix representing the results in "dictionary of keys" format.
+
+        """
+        result = scipy.sparse.dok_matrix((self.n,other.n))
+
+        def traverse(node1, rect1, node2, rect2):
+            if rect1.min_distance_rectangle(rect2, p) > max_distance:
+                return
+            elif isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    for i in node1.idx:
+                        for j in node2.idx:
+                            d = minkowski_distance(self.data[i],other.data[j],p)
+                            if d <= max_distance:
+                                result[i,j] = d
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse(node1,rect1,node2.less,less)
+                    traverse(node1,rect1,node2.greater,greater)
+            elif isinstance(node2, KDTree.leafnode):
+                less, greater = rect1.split(node1.split_dim, node1.split)
+                traverse(node1.less,less,node2,rect2)
+                traverse(node1.greater,greater,node2,rect2)
+            else:
+                less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                traverse(node1.less,less1,node2.less,less2)
+                traverse(node1.less,less1,node2.greater,greater2)
+                traverse(node1.greater,greater1,node2.less,less2)
+                traverse(node1.greater,greater1,node2.greater,greater2)
+        traverse(self.tree, Rectangle(self.maxes, self.mins),
+                 other.tree, Rectangle(other.maxes, other.mins))
+
+        return result
+
+
+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("x contains %d-dimensional vectors but y contains %d-dimensional vectors" % (k, kk))
+
+    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

utils/venv/lib/python3.6/site-packages/scipy/spatial/qhull_src/COPYING.txt

@@ -0,0 +1,38 @@
+                    Qhull, Copyright (c) 1993-2015
+                    
+                            C.B. Barber
+                           Arlington, MA 
+                          
+                               and
+
+       The National Science and Technology Research Center for
+        Computation and Visualization of Geometric Structures
+                        (The Geometry Center)
+                       University of Minnesota
+
+                       email: qhull@qhull.org
+
+This software includes Qhull from C.B. Barber and The Geometry Center.  
+Qhull is copyrighted as noted above.  Qhull is free software and may 
+be obtained via http from www.qhull.org.  It may be freely copied, modified, 
+and redistributed under the following conditions:
+
+1. All copyright notices must remain intact in all files.
+
+2. A copy of this text file must be distributed along with any copies 
+   of Qhull that you redistribute; this includes copies that you have 
+   modified, or copies of programs or other software products that 
+   include Qhull.
+
+3. If you modify Qhull, you must include a notice giving the
+   name of the person performing the modification, the date of
+   modification, and the reason for such modification.
+
+4. When distributing modified versions of Qhull, or other software 
+   products that include Qhull, you must provide notice that the original 
+   source code may be obtained as noted above.
+
+5. There is no warranty or other guarantee of fitness for Qhull, it is 
+   provided solely "as is".  Bug reports or fixes may be sent to 
+   qhull_bug@qhull.org; the authors may or may not act on them as 
+   they desire.

utils/venv/lib/python3.6/site-packages/scipy/spatial/setup.py

@@ -0,0 +1,88 @@
+from __future__ import division, print_function, absolute_import
+
+from os.path import join, dirname
+import glob
+
+
+def configuration(parent_package='', top_path=None):
+    from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs
+    from numpy.distutils.misc_util import get_info as get_misc_info
+    from scipy._build_utils.system_info import get_info as get_sys_info
+    from distutils.sysconfig import get_python_inc
+
+    config = Configuration('spatial', parent_package, top_path)
+
+    config.add_data_dir('tests')
+
+    # spatial.transform
+    config.add_subpackage('transform')
+
+    # qhull
+    qhull_src = sorted(glob.glob(join(dirname(__file__), 'qhull_src',
+                                    'src', '*.c')))
+
+    inc_dirs = [get_python_inc()]
+    if inc_dirs[0] != get_python_inc(plat_specific=1):
+        inc_dirs.append(get_python_inc(plat_specific=1))
+    inc_dirs.append(get_numpy_include_dirs())
+    inc_dirs.append(join(dirname(dirname(__file__)), '_lib'))
+
+    cfg = dict(get_sys_info('lapack_opt'))
+    cfg.setdefault('include_dirs', []).extend(inc_dirs)
+    config.add_extension('qhull',
+                         sources=['qhull.c'] + qhull_src,
+                         **cfg)
+
+    # cKDTree
+    ckdtree_src = ['query.cxx',
+                   'build.cxx',
+                   'globals.cxx',
+                   'cpp_exc.cxx',
+                   'query_pairs.cxx',
+                   'count_neighbors.cxx',
+                   'query_ball_point.cxx',
+                   'query_ball_tree.cxx',
+                   'sparse_distances.cxx']
+
+    ckdtree_src = [join('ckdtree', 'src', x) for x in ckdtree_src]
+
+    ckdtree_headers = ['ckdtree_decl.h',
+                       'ckdtree_methods.h',
+                       'coo_entries.h',
+                       'cpp_exc.h',
+                       'cpp_utils.h',
+                       'distance_base.h',
+                       'distance.h',
+                       'ordered_pair.h',
+                       'partial_sort.h',
+                       'rectangle.h']
+
+    ckdtree_headers = [join('ckdtree', 'src', x) for x in ckdtree_headers]
+
+    ckdtree_dep = ['ckdtree.cxx'] + ckdtree_headers + ckdtree_src
+    config.add_extension('ckdtree',
+                         sources=['ckdtree.cxx'] + ckdtree_src,
+                         depends=ckdtree_dep,
+                         include_dirs=inc_dirs + [join('ckdtree', 'src')])
+    # _distance_wrap
+    config.add_extension('_distance_wrap',
+                         sources=[join('src', 'distance_wrap.c')],
+                         depends=[join('src', 'distance_impl.h')],
+                         include_dirs=[get_numpy_include_dirs()],
+                         extra_info=get_misc_info("npymath"))
+
+    config.add_extension('_voronoi',
+                         sources=['_voronoi.c'])
+
+    config.add_extension('_hausdorff',
+                         sources=['_hausdorff.c'])
+
+    # Add license files
+    config.add_data_files('qhull_src/COPYING.txt')
+
+    return config
+
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(**configuration(top_path='').todict())

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/cdist-X1.txt

@@ -0,0 +1,10 @@
+1.147593763490969421e-01 8.926156143344999849e-01 1.437758624645746330e-02 1.803435962879929022e-02 5.533046214065578949e-01 5.554315640747428118e-01 4.497546637814608950e-02 4.438089247948049376e-01 7.984582810220538507e-01 2.752880789161644692e-01 1.344667112315823809e-01 9.230479561452992199e-01 6.040471462941819913e-01 3.797251652770228247e-01 4.316042735592399149e-01 5.312356915348823705e-01 4.348143005129563310e-01 3.111531488508799681e-01 9.531194313908697424e-04 8.212995023500069269e-02 6.689953269869852726e-01 9.914864535288493430e-01 8.037556036341153565e-01
+9.608925123801395074e-01 2.974451233678974127e-01 9.001110330654185088e-01 5.824163330415995654e-01 7.308574928293812834e-01 2.276154562412870952e-01 7.306791076039623745e-01 8.677244866905511333e-01 9.160806456176984192e-01 6.157216959991280714e-01 5.149053524695440531e-01 3.056427344890983999e-01 9.790557366933895223e-01 4.484995861076724877e-01 4.776550391081165747e-01 7.210436977670631187e-01 9.136399501661039979e-01 4.260275733550000776e-02 5.943900041968954717e-01 3.864571606342745991e-01 9.442027665110838131e-01 4.779949058608601309e-02 6.107551944250865228e-01
+3.297286578103622023e-01 5.980207401936733502e-01 3.673301293561567205e-01 2.585830520887681949e-01 4.660558746104259686e-01 6.083795956610364986e-01 4.535206368070313632e-01 6.873989778785424276e-01 5.130152688495458468e-01 7.665877846542720198e-01 3.444402973525138023e-01 3.583658123644906102e-02 7.924818220986856732e-01 8.746685720522412444e-01 3.010105569182431884e-01 6.012239357385538163e-01 6.233737362204671006e-01 4.830438698668915176e-01 2.317286885842551047e-02 7.585989958123050547e-01 7.108257632278830451e-01 1.551024884178199281e-01 2.665485998155288083e-01
+2.456278068903017253e-02 4.148739837711815648e-01 1.986372227934196655e-01 6.920408530298168825e-01 1.003067576685774398e-01 7.421560456480125190e-01 1.808453980608998313e-01 4.251297882537475870e-01 6.773002683522370004e-01 4.084108792570182445e-01 7.462888013191590897e-01 8.069930220529277776e-01 9.211110587681808903e-01 4.141491046181076108e-01 7.486318689260342829e-01 9.515405507589296263e-01 4.634288892577109742e-03 8.027593488166355762e-01 3.010346805217798405e-01 8.663248877242523127e-01 2.479968181181605447e-01 5.619851096054278017e-01 3.903886764590250857e-01
+7.122019976035700584e-01 6.188878051047785878e-01 7.290897087051201320e-01 6.334802157757637442e-01 5.523084734954342156e-01 5.614937129563645213e-01 2.496741051791574462e-01 5.972227939599233926e-01 1.786590597761109622e-01 2.609525984850900038e-01 7.210438943286010538e-01 2.211429064605652250e-01 9.140497572472672250e-02 1.430242193668443962e-01 7.856446942916397447e-01 4.635256358156553125e-01 5.278744289813760426e-01 3.702808015407184072e-01 5.527073830480792038e-01 6.370732917599846168e-01 9.953487928925482953e-01 3.021789770611936765e-01 3.354901923998221402e-02
+6.509638560895427695e-01 8.387598220902757751e-01 7.761375971745763103e-01 1.481627639227802717e-01 3.529474982902305324e-01 4.883093646287851586e-01 9.652923033658690199e-01 9.500680513565308294e-01 3.061885005078281985e-01 7.271902818906019750e-01 2.358962978196710303e-03 7.359889703223099211e-01 8.988893768074724955e-01 4.135279653937307121e-02 8.516441856688283796e-01 4.889597623270667270e-01 5.575909822114655245e-01 9.010853652261575641e-01 2.912844516556202246e-01 9.088759383368658629e-01 8.104351227460024898e-01 8.080695436776826890e-01 1.430530913253185155e-01
+8.048001196608134400e-01 3.066089444418462762e-02 9.021887554292090661e-01 6.154331491807940591e-02 1.378912575206647784e-02 5.775720193142440673e-01 1.219298963069791464e-01 1.883270243412101808e-01 5.569262398688379356e-02 8.964817777510125651e-02 7.977092785346929782e-01 4.878149375226197293e-01 4.511973131518809410e-02 1.858690046801604323e-01 6.947686471083162063e-01 5.884058794291086025e-01 8.638884676612634816e-01 3.855470871341656336e-01 3.495049047300468059e-01 2.767740932353948136e-01 4.731087031714035218e-01 6.679001673437914288e-01 7.502944200696660682e-01
+6.527328264244687261e-01 8.289483383553154505e-01 9.179741348282299818e-01 1.065639864466713105e-01 6.253616929058514184e-01 5.927750325266062381e-01 3.039157425463192563e-01 2.452766763359194302e-01 6.514027700704632107e-01 5.529218485487964463e-01 4.941158239308394151e-01 6.605306467722642516e-01 2.273688037050677346e-01 4.282616592244774534e-01 2.956128257930247250e-01 1.154803628237965896e-01 9.228220410235263849e-01 6.663525307676617659e-01 1.908852615936970087e-01 9.921383408926374159e-01 4.988716450388516188e-01 1.014900352736023414e-01 3.363930180244284474e-01
+2.914369076275757919e-01 5.196673601143533272e-01 7.420144907858341465e-01 1.768984185504740569e-01 5.296766993228564369e-01 5.922023566159900776e-01 5.965161262020234334e-01 3.810272333046110793e-01 8.368797246118340194e-01 7.896422363801189892e-01 9.655797561098209414e-01 4.430034032346981121e-01 2.780869795706976122e-01 3.047310845416009162e-01 8.051138863500326703e-01 6.731468634690835895e-01 4.743383036815584930e-01 9.530709614322225853e-01 7.753587619850917934e-01 2.801137109357491051e-01 6.182543660889736614e-01 5.005218857766725593e-01 9.071447804755052857e-01
+2.075071644012620453e-01 4.834950086973934802e-01 3.037011473860764532e-01 6.476084284887700937e-01 8.107195771564194020e-01 7.869075869075803364e-01 6.851234019375299633e-01 3.544187468104398331e-02 4.847673235908021017e-01 5.690262846164507726e-01 1.663354142616256803e-01 9.692796809752548537e-01 4.133441725866372485e-01 6.729167604487583665e-01 3.998813427407297283e-01 8.272617414104491695e-01 2.129248316324727774e-01 6.517004761357130249e-01 7.363013506605019520e-01 4.072375306356985636e-01 4.463336683526665238e-01 5.485059309728204102e-01 1.981745754527846071e-01

utils/venv/lib/python3.6/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

utils/venv/lib/python3.6/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

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-boolean-inp.txt

@@ -0,0 +1,20 @@
+1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00
+1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
+0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00
+1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00
+1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00

utils/venv/lib/python3.6/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

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-cityblock-ml.txt

@@ -0,0 +1 @@
+   3.2420590e+01   3.3246607e+01   3.0526910e+01   3.5166573e+01   3.1868301e+01   3.6025002e+01   3.2513623e+01   3.6557796e+01   3.3752212e+01   3.4422130e+01   3.2526018e+01   3.2581161e+01   3.3743555e+01   3.6960777e+01   3.4225270e+01   3.2965308e+01   3.4591031e+01   3.4204203e+01   3.4678123e+01   3.5728720e+01   3.0830047e+01   3.1550681e+01   3.3304790e+01   3.2676753e+01   3.2742330e+01   3.1684556e+01   3.2830915e+01   3.2956614e+01   2.7365639e+01   3.3207307e+01   3.3420925e+01   3.4357941e+01   2.8280126e+01   3.4523458e+01   3.2705274e+01   3.2455891e+01   3.1636060e+01   3.1594957e+01   3.1805202e+01   3.3886574e+01   3.3438829e+01   3.3330030e+01   3.4168514e+01   3.0637353e+01   4.2149167e+01   3.6340559e+01   2.9315308e+01   3.5778314e+01   3.7693050e+01   3.2598714e+01   3.2990836e+01   3.4967659e+01   3.9748920e+01   3.6745043e+01   2.7117550e+01   3.6014760e+01   2.9367558e+01   3.3845350e+01   3.5477339e+01   3.1513372e+01   3.2517953e+01   2.4755097e+01   3.0229897e+01   3.4799343e+01   3.3371710e+01   2.9600910e+01   3.3275088e+01   3.3567110e+01   3.4527016e+01   3.4942320e+01   3.2359383e+01   3.2607100e+01   3.1467914e+01   2.9032039e+01   3.3122878e+01   2.8496709e+01   2.9908448e+01   2.9962886e+01   3.0345299e+01   3.1737613e+01   2.8551485e+01   3.2610551e+01   3.3082660e+01   3.3719298e+01   3.6434018e+01   3.6589278e+01   3.3889586e+01   3.8036774e+01   3.1483497e+01   3.4196794e+01   3.5154035e+01   3.5488608e+01   3.6143183e+01   3.3473491e+01   3.4686446e+01   2.8687495e+01   3.5725742e+01   3.0188298e+01   3.3084534e+01   3.3538519e+01   3.6226849e+01   2.9052099e+01   3.6032733e+01   3.0811503e+01   3.2616190e+01   3.3888566e+01   3.3074570e+01   2.9683515e+01   3.0600771e+01   3.4345247e+01   3.6983843e+01   3.3692824e+01   3.3762461e+01   3.4024582e+01   3.3698854e+01   3.1238613e+01   3.4978833e+01   3.4991078e+01   3.4577741e+01   3.3749227e+01   3.4982272e+01   3.0487868e+01   3.2317632e+01   3.1125588e+01   3.4413791e+01   3.1881871e+01   3.1373821e+01   3.0416864e+01   3.2066187e+01   3.1128313e+01   3.0240249e+01   3.0125198e+01   3.1343454e+01   3.5479092e+01   3.4450767e+01   3.2953507e+01   3.4456795e+01   3.0136375e+01   3.3462150e+01   2.9894274e+01   3.1367432e+01   3.2839320e+01   3.1440398e+01   2.9400374e+01   3.1106338e+01   3.1242624e+01   3.5537892e+01   3.3056459e+01   2.8610281e+01   3.4296217e+01   3.5819772e+01   3.2503922e+01   3.0963029e+01   3.4762112e+01   3.4796284e+01   2.9645345e+01   3.4468088e+01   2.6975590e+01   3.3738555e+01   2.8825009e+01   3.2663999e+01   3.2547878e+01   3.2308091e+01   3.2489966e+01   3.0868597e+01   3.2974220e+01   3.0866111e+01   3.8197342e+01   3.0609568e+01   3.5478978e+01   2.9249184e+01   3.6185622e+01   3.1948258e+01   3.2649719e+01   3.3305650e+01   3.4643955e+01   3.6566241e+01   3.4968484e+01   3.2632218e+01   3.6741383e+01   3.5700008e+01   3.1962468e+01   3.1410623e+01   3.0412061e+01   3.3749077e+01   3.5649661e+01   3.7649263e+01   3.2832574e+01   3.1783914e+01   2.8264292e+01

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-correlation-ml.txt

@@ -0,0 +1 @@
+   9.2507465e-01   9.6528566e-01   8.7255441e-01   1.1287379e+00   8.7318727e-01   1.0767102e+00   9.1419676e-01   1.1503304e+00   9.8074509e-01   1.0135025e+00   1.0495025e+00   9.4794536e-01   9.6829273e-01   1.1345767e+00   1.1048008e+00   9.2407796e-01   1.0228634e+00   9.3853195e-01   9.9377619e-01   1.0407662e+00   9.5048989e-01   9.0465688e-01   9.8056930e-01   8.9777156e-01   9.6357127e-01   9.3864452e-01   9.9754613e-01   9.7271356e-01   8.4383151e-01   9.6981983e-01   9.7510267e-01   1.0112663e+00   7.8730400e-01   1.0299498e+00   9.9307979e-01   9.0239520e-01   8.5428231e-01   8.8972742e-01   8.5933162e-01   9.6625934e-01   9.4175449e-01   9.9120729e-01   1.0503963e+00   8.8223053e-01   1.3261434e+00   1.1063209e+00   8.4058398e-01   1.0844267e+00   1.1153093e+00   1.0092643e+00   8.9585237e-01   1.0599818e+00   1.2321707e+00   1.1359624e+00   8.3503556e-01   1.1792243e+00   7.9159781e-01   1.0830419e+00   1.2181870e+00   9.9888500e-01   1.0227144e+00   6.8557277e-01   9.6836193e-01   1.1061227e+00   1.0883453e+00   9.5681974e-01   9.9436299e-01   1.0304323e+00   1.1273949e+00   1.0735563e+00   1.0582583e+00   9.6040272e-01   1.0032137e+00   8.4900547e-01   1.1035351e+00   8.7867480e-01   9.6433176e-01   9.1850122e-01   8.9337435e-01   1.0449390e+00   8.9639384e-01   9.6704971e-01   1.0084258e+00   1.0528587e+00   1.1764481e+00   1.0913280e+00   1.0136672e+00   1.2737156e+00   9.5130359e-01   1.0367909e+00   1.1983402e+00   1.1319901e+00   1.1117462e+00   1.0343695e+00   1.0838628e+00   7.5266057e-01   1.0763316e+00   8.8067924e-01   9.6734383e-01   9.8800551e-01   1.2265742e+00   7.8833055e-01   1.0338670e+00   8.6666625e-01   9.9039950e-01   9.7142684e-01   9.3138616e-01   8.5849977e-01   8.5486301e-01   1.0516028e+00   1.1105313e+00   9.5943505e-01   9.8845171e-01   1.0566288e+00   9.9712198e-01   9.5545756e-01   1.1817974e+00   9.9128482e-01   1.0117892e+00   1.0979115e+00   1.0493943e+00   9.1318848e-01   9.3157311e-01   8.7073304e-01   1.2459441e+00   9.3412689e-01   1.0482297e+00   9.4224032e-01   9.5134153e-01   9.0857493e-01   9.7264161e-01   8.2900820e-01   9.3140549e-01   1.1330242e+00   1.0333002e+00   1.0117861e+00   1.2053255e+00   8.5291396e-01   1.0148928e+00   8.6641379e-01   9.7080819e-01   9.5457159e-01   9.5207457e-01   9.3539674e-01   9.0769069e-01   9.5322590e-01   1.1181803e+00   9.9765614e-01   7.5370610e-01   1.0807114e+00   1.0804601e+00   9.0214124e-01   8.7101998e-01   1.0167435e+00   1.2045936e+00   8.7300539e-01   1.1054300e+00   7.9145574e-01   1.0279340e+00   8.7623462e-01   1.0034756e+00   1.0386933e+00   9.3910970e-01   1.0028455e+00   9.9868824e-01   9.8752945e-01   9.8319327e-01   1.3110209e+00   8.6180633e-01   1.0993856e+00   8.5912563e-01   1.1303979e+00   9.8690459e-01   9.6910090e-01   9.1456819e-01   1.1525339e+00   1.1064552e+00   1.1062255e+00   9.7226683e-01   1.1091447e+00   1.1072238e+00   9.6544444e-01   9.6681036e-01   9.3247685e-01   9.6854634e-01   1.1035119e+00   1.1317148e+00   9.5557793e-01   9.8908485e-01   7.4873648e-01

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-cosine-ml.txt

@@ -0,0 +1 @@
+   2.5695885e-01   2.6882042e-01   2.3470353e-01   2.9299329e-01   2.2742702e-01   3.1253572e-01   2.4986352e-01   3.0770122e-01   2.5191977e-01   2.7931567e-01   2.8133743e-01   2.6316239e-01   2.6067201e-01   3.2982339e-01   2.8993002e-01   2.5506356e-01   2.8728051e-01   2.4952121e-01   2.8613379e-01   2.6894157e-01   2.3606353e-01   2.1670935e-01   2.3470242e-01   2.4294172e-01   2.4376454e-01   2.3228195e-01   2.3554918e-01   2.4851241e-01   2.0917546e-01   2.4971488e-01   2.4264224e-01   2.7405461e-01   1.9086415e-01   2.6346574e-01   2.5908801e-01   2.2138495e-01   2.2910721e-01   2.2169919e-01   2.0660065e-01   2.3207102e-01   2.5554688e-01   2.5153751e-01   2.6073682e-01   2.0919640e-01   3.3984433e-01   2.7503792e-01   2.1709889e-01   2.7068095e-01   3.0307041e-01   2.4529612e-01   2.2987015e-01   2.7736967e-01   3.0310708e-01   3.0544316e-01   1.9205388e-01   2.7098021e-01   2.0722466e-01   2.6387343e-01   2.8998308e-01   2.2633010e-01   2.5177075e-01   1.6347011e-01   2.4036389e-01   2.6485871e-01   2.8491965e-01   2.2273619e-01   2.4511873e-01   2.5930533e-01   2.6589995e-01   2.7797191e-01   2.3357373e-01   2.4279909e-01   2.3544532e-01   1.9447286e-01   2.3993534e-01   2.0856243e-01   2.2125251e-01   2.1988206e-01   2.0590152e-01   2.6441952e-01   2.0052739e-01   2.2978496e-01   2.4483670e-01   2.3879510e-01   2.9398425e-01   2.7541852e-01   2.3777469e-01   2.9151131e-01   2.0672752e-01   2.4584031e-01   2.7475025e-01   2.7064343e-01   2.5603684e-01   2.6165327e-01   2.4233155e-01   1.7892657e-01   2.6111203e-01   1.9965682e-01   2.4201634e-01   2.6281353e-01   3.1928221e-01   1.9731963e-01   2.7752862e-01   2.2633080e-01   2.6783167e-01   2.5447186e-01   2.6424243e-01   2.1960672e-01   2.2984242e-01   2.8788736e-01   2.8681630e-01   2.6949787e-01   2.3993685e-01   2.4440073e-01   2.5010397e-01   2.3230769e-01   2.9879682e-01   2.4200592e-01   2.6957748e-01   2.6073240e-01   2.6355347e-01   2.3403674e-01   2.2411413e-01   2.2956729e-01   2.8105976e-01   2.2913304e-01   2.4898608e-01   2.3304000e-01   2.2692988e-01   2.3728251e-01   2.2552243e-01   2.0364084e-01   2.3359511e-01   2.6619167e-01   2.6666588e-01   2.3666880e-01   2.7239113e-01   2.0146697e-01   2.3045559e-01   2.1695523e-01   2.1387991e-01   2.2366404e-01   2.2809635e-01   2.0901297e-01   2.2441100e-01   2.3418882e-01   2.8552218e-01   2.4609015e-01   2.0282492e-01   2.5940295e-01   2.7407006e-01   2.3344890e-01   2.1179142e-01   2.7047821e-01   2.9832768e-01   2.0859082e-01   2.8881331e-01   1.8384598e-01   2.5286491e-01   2.2012615e-01   2.3615775e-01   2.6845565e-01   2.3356355e-01   2.7164193e-01   2.4179380e-01   2.5247973e-01   2.5637548e-01   3.2126483e-01   2.3100774e-01   2.8832546e-01   2.0043257e-01   2.7918333e-01   2.4884522e-01   2.2904723e-01   2.3738940e-01   2.9461278e-01   2.9782005e-01   3.0332073e-01   2.5175971e-01   3.1203784e-01   2.6611535e-01   2.3713507e-01   2.2203585e-01   2.3602325e-01   2.5093670e-01   2.6860434e-01   3.0137874e-01   2.3759606e-01   2.6840346e-01   1.9200556e-01

utils/venv/lib/python3.6/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

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-euclidean-ml.txt

@@ -0,0 +1 @@
+   4.0515260e+00   4.2121458e+00   3.7357405e+00   4.2313317e+00   3.9136009e+00   4.3843298e+00   3.9811426e+00   4.3624182e+00   4.0642508e+00   4.2105933e+00   4.0747226e+00   3.9068586e+00   4.1637004e+00   4.4303203e+00   4.1841564e+00   4.1063279e+00   4.1862390e+00   4.0719925e+00   4.2227579e+00   4.3173531e+00   3.8811067e+00   3.7577567e+00   4.0623722e+00   3.9882453e+00   4.0432671e+00   3.9085109e+00   4.0283414e+00   4.0846110e+00   3.6459235e+00   3.9544001e+00   4.1134244e+00   4.1805752e+00   3.5121011e+00   4.2747789e+00   4.1048323e+00   3.9269426e+00   3.8932032e+00   3.8281172e+00   3.7288430e+00   4.0863477e+00   4.1527428e+00   4.1646409e+00   4.2027433e+00   3.8441594e+00   4.8419117e+00   4.2455384e+00   3.7622220e+00   4.3967923e+00   4.4663183e+00   4.0435853e+00   4.0421692e+00   4.3124625e+00   4.6499961e+00   4.5595743e+00   3.4230430e+00   4.2612266e+00   3.5676603e+00   4.0866580e+00   4.2307103e+00   3.8521940e+00   3.9951183e+00   3.1022409e+00   3.7290193e+00   4.1931517e+00   4.1127027e+00   3.6633651e+00   4.0235815e+00   3.9729858e+00   4.1980132e+00   4.1579993e+00   3.9948955e+00   3.9081966e+00   3.9031152e+00   3.5069036e+00   4.0015727e+00   3.6763496e+00   3.6614339e+00   3.6227109e+00   3.7357992e+00   4.0170026e+00   3.5216829e+00   3.9322227e+00   3.9094621e+00   4.0170286e+00   4.3264246e+00   4.3435483e+00   4.0788635e+00   4.4761765e+00   3.8468186e+00   4.1490333e+00   4.2800007e+00   4.2260191e+00   4.3031858e+00   4.1897413e+00   4.0530244e+00   3.5893641e+00   4.2186615e+00   3.7979503e+00   4.0915473e+00   4.1343073e+00   4.5063851e+00   3.6394889e+00   4.2508448e+00   3.7160826e+00   4.0105262e+00   4.1578269e+00   4.0290590e+00   3.6971819e+00   3.9414087e+00   4.2522313e+00   4.4091714e+00   4.1542292e+00   3.9594691e+00   4.0923600e+00   4.0855497e+00   3.8253075e+00   4.3034717e+00   4.0976731e+00   4.1316523e+00   4.0872717e+00   4.2643353e+00   3.8887280e+00   3.9411273e+00   3.8848001e+00   4.3481996e+00   3.8716733e+00   3.9084684e+00   3.7546361e+00   3.9354816e+00   3.8293694e+00   3.7568515e+00   3.7184961e+00   3.8404278e+00   4.2570811e+00   4.1423777e+00   4.0291411e+00   4.2094682e+00   3.6127418e+00   4.0459839e+00   3.7737985e+00   3.7647653e+00   3.9762006e+00   3.8999512e+00   3.8509090e+00   3.8975941e+00   3.8432839e+00   4.2109046e+00   4.1339124e+00   3.5898873e+00   4.0794519e+00   4.3504966e+00   3.8862612e+00   3.8332931e+00   4.2190310e+00   4.1366595e+00   3.7220268e+00   4.1250795e+00   3.3169452e+00   4.0757181e+00   3.6487114e+00   3.9513724e+00   4.0735549e+00   3.9137880e+00   3.9656942e+00   3.7724953e+00   4.0505153e+00   3.9062302e+00   4.5671852e+00   3.7542175e+00   4.3731708e+00   3.6733907e+00   4.4667545e+00   4.1004635e+00   4.0530038e+00   4.0346958e+00   4.2145752e+00   4.4298637e+00   4.2982360e+00   4.0878239e+00   4.4061563e+00   4.2115971e+00   3.8263277e+00   3.8603258e+00   3.8572375e+00   4.1051910e+00   4.3787786e+00   4.5309659e+00   4.0047055e+00   4.1308854e+00   3.6283561e+00

utils/venv/lib/python3.6/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

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-jaccard-ml.txt

@@ -0,0 +1 @@
+   6.5714286e-01   6.0563380e-01   6.3235294e-01   7.3972603e-01   6.0294118e-01   7.3611111e-01   6.4179104e-01   7.7631579e-01   6.4000000e-01   6.6197183e-01   6.6666667e-01   7.0000000e-01   6.4285714e-01   7.7464789e-01   7.1621622e-01   6.4285714e-01   6.8571429e-01   6.4383562e-01   6.6666667e-01   6.5384615e-01   6.6216216e-01   6.1971831e-01   6.5333333e-01   6.5277778e-01   6.7123288e-01   6.4383562e-01   6.5000000e-01   6.3513514e-01   6.0000000e-01   6.7123288e-01   6.3513514e-01   7.4324324e-01   5.5714286e-01   7.0512821e-01   6.3888889e-01   6.0000000e-01   5.6338028e-01   6.3157895e-01   6.0810811e-01   6.2337662e-01   6.4000000e-01   6.5789474e-01   6.3157895e-01   5.6962025e-01   7.5294118e-01   7.1250000e-01   6.2162162e-01   6.7500000e-01   7.2727273e-01   6.2337662e-01   6.2337662e-01   6.7948718e-01   6.5853659e-01   6.6250000e-01   6.3380282e-01   7.3417722e-01   6.0869565e-01   7.2000000e-01   7.5949367e-01   6.4556962e-01   6.3013699e-01   5.9420290e-01   6.2857143e-01   7.1794872e-01   7.3972603e-01   6.4864865e-01   6.4864865e-01   6.8918919e-01   6.6666667e-01   7.0512821e-01   6.2500000e-01   6.2318841e-01   6.6197183e-01   6.5277778e-01   6.9135802e-01   6.6216216e-01   6.6666667e-01   6.4285714e-01   6.6216216e-01   6.8115942e-01   6.2500000e-01   6.2500000e-01   7.3684211e-01   6.4473684e-01   7.3417722e-01   7.1052632e-01   6.3888889e-01   7.3417722e-01   6.5432099e-01   6.9230769e-01   7.1428571e-01   6.7567568e-01   6.7532468e-01   6.7605634e-01   6.5789474e-01   5.4285714e-01   6.9736842e-01   6.2337662e-01   6.6233766e-01   6.7605634e-01   7.0270270e-01   6.1842105e-01   6.7567568e-01   6.2318841e-01   6.7605634e-01   6.9333333e-01   7.1428571e-01   6.0000000e-01   6.0000000e-01   6.6197183e-01   6.9230769e-01   6.8000000e-01   7.2000000e-01   6.5384615e-01   6.5753425e-01   6.6197183e-01   7.1232877e-01   6.9333333e-01   7.5000000e-01   7.1052632e-01   6.7567568e-01   6.4285714e-01   6.0273973e-01   5.8571429e-01   6.9512195e-01   6.3013699e-01   6.8918919e-01   7.0270270e-01   6.6666667e-01   6.8571429e-01   6.6666667e-01   6.1111111e-01   7.0666667e-01   6.6666667e-01   6.5333333e-01   6.8674699e-01   7.0731707e-01   6.3636364e-01   6.3750000e-01   6.1643836e-01   6.5432099e-01   5.8441558e-01   5.8666667e-01   4.7297297e-01   5.5263158e-01   6.9736842e-01   6.9333333e-01   6.5789474e-01   5.7575758e-01   6.7532468e-01   7.0886076e-01   6.4383562e-01   5.8666667e-01   6.6233766e-01   7.5000000e-01   6.2500000e-01   7.7027027e-01   6.0563380e-01   6.8000000e-01   5.6716418e-01   6.7948718e-01   6.4864865e-01   6.1971831e-01   7.1428571e-01   6.5753425e-01   6.7567568e-01   6.6197183e-01   7.7108434e-01   6.6216216e-01   7.1232877e-01   6.4000000e-01   7.0886076e-01   6.0563380e-01   6.2337662e-01   6.2666667e-01   7.7922078e-01   7.2972973e-01   7.5342466e-01   5.7971014e-01   7.3333333e-01   7.0886076e-01   6.6216216e-01   6.4102564e-01   5.8904110e-01   7.3076923e-01   6.4102564e-01   7.1250000e-01   6.4473684e-01   5.9154930e-01   5.3424658e-01

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-jensenshannon-ml.txt

@@ -0,0 +1 @@
+0.320369972991 0.338972466 0.308199372323 0.3452431902 0.310024768313 0.357115225615 0.311131096357 0.357391534414 0.329718053755 0.347365921475 0.335272625287 0.336451560653 0.33015370606 0.369628769749 0.344499490029 0.321622508707 0.345377707016 0.321007207534 0.350728979121 0.32809430086 0.30207071308 0.291663252492 0.30760470102 0.315976639534 0.308132467187 0.313014586878 0.310463895925 0.321091616502 0.290044394125 0.322213459935 0.315509196522 0.3331114403 0.281071919202 0.320854431887 0.332190658438 0.299342730178 0.313528775154 0.310049073937 0.288821516545 0.307662081954 0.328387688508 0.317185603454 0.332046170365 0.291912213887 0.37870970117 0.336080073379 0.304593343921 0.330138983604 0.355071759299 0.311946140607 0.302025400768 0.330940761586 0.351140062502 0.354772884287 0.272605322053 0.327957349848 0.28871110366 0.320821172951 0.340976919806 0.30757488831 0.320975346884 0.252776262329 0.314549731907 0.326876483 0.337684418756 0.296520013735 0.31493077245 0.327721982167 0.325802862624 0.341908184107 0.300481749419 0.312499767894 0.301061762121 0.27665157989 0.3082566692 0.287466396145 0.288313694552 0.296629698731 0.283556095025 0.322489360684 0.280765581604 0.297958166613 0.313189657041 0.303470399659 0.348652898212 0.331594734387 0.299446687464 0.339047458559 0.286979246044 0.316326095312 0.321618884109 0.330065896317 0.324500638067 0.328300795872 0.309002568222 0.262587468469 0.31974123777 0.286316182293 0.321162329165 0.328160620315 0.356618051635 0.289733970648 0.344507756538 0.301485561986 0.335785898715 0.322635066518 0.331480718646 0.297897604494 0.306942928189 0.350843442517 0.342585296966 0.341311053315 0.306780105123 0.313401804298 0.319978145568 0.302460397612 0.346105758567 0.312802351189 0.331552275517 0.321624157344 0.318798118247 0.301906095501 0.301585920138 0.314556178985 0.333215221158 0.306929663844 0.317083256901 0.309667679181 0.306529028004 0.30865993751 0.296031907986 0.28742420979 0.311584483038 0.319043629504 0.330278008622 0.314466433681 0.327937382021 0.296448162218 0.307033121385 0.296391953011 0.292691206116 0.297146209653 0.307929858983 0.291863681454 0.307300188104 0.306597817799 0.34718100163 0.317436210259 0.29952626739 0.330762834707 0.334951064852 0.323806678898 0.296203706701 0.33398466797 0.344931265559 0.293948734727 0.332764639313 0.272651853935 0.317324315923 0.300493570867 0.307008231016 0.333263322802 0.31390648462 0.332416491248 0.314766869708 0.321015549211 0.322909289307 0.356882966656 0.310596945263 0.343939748528 0.286269629586 0.33173459898 0.323848483719 0.305841388975 0.319266258167 0.34012363898 0.3443280395 0.353885654057 0.320544729867 0.353280499623 0.315621795536 0.312176062734 0.301562130879 0.312061680573 0.312642847966 0.326222109701 0.357417912858 0.313083593142 0.334033412713 0.295630506074

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-minkowski-3.2-ml.txt

@@ -0,0 +1 @@
+   2.0215050e+00   2.0988154e+00   1.8614681e+00   2.0510161e+00   1.9210911e+00   2.1323516e+00   1.9565454e+00   2.1029889e+00   1.9617871e+00   2.0544792e+00   2.0357408e+00   1.8811414e+00   2.0694693e+00   2.1245977e+00   2.0632165e+00   2.0452823e+00   2.0249330e+00   1.9635489e+00   2.0508580e+00   2.0838578e+00   1.9324052e+00   1.8224609e+00   1.9795343e+00   1.9536534e+00   1.9694910e+00   1.9075569e+00   1.9590397e+00   2.0022087e+00   1.8814000e+00   1.8884208e+00   1.9961121e+00   2.0215351e+00   1.7515769e+00   2.0756437e+00   2.0109476e+00   1.9234849e+00   1.9160076e+00   1.8550862e+00   1.7733640e+00   2.0071906e+00   2.0209542e+00   2.0616569e+00   2.0565503e+00   1.9083573e+00   2.2732431e+00   1.9975503e+00   1.9080072e+00   2.1437809e+00   2.1296295e+00   1.9739085e+00   1.9834166e+00   2.1078664e+00   2.2016840e+00   2.2080962e+00   1.7340579e+00   2.0549287e+00   1.7331748e+00   1.9559688e+00   2.0343364e+00   1.8736929e+00   1.9730416e+00   1.5308944e+00   1.8421831e+00   2.0174240e+00   2.0137378e+00   1.7956151e+00   1.9606596e+00   1.9074857e+00   2.0413879e+00   2.0070305e+00   1.9584677e+00   1.8977851e+00   1.9176239e+00   1.7067419e+00   1.9461927e+00   1.8431700e+00   1.8284576e+00   1.7778704e+00   1.8350329e+00   2.0175415e+00   1.7459063e+00   1.9242505e+00   1.8757370e+00   1.9312506e+00   2.0574808e+00   2.0894636e+00   1.9780203e+00   2.1374036e+00   1.8900436e+00   2.0273032e+00   2.0681953e+00   2.0234699e+00   2.0666449e+00   2.0663485e+00   1.9281402e+00   1.7846314e+00   2.0372479e+00   1.8831230e+00   2.0186015e+00   2.0193231e+00   2.2022665e+00   1.8145737e+00   2.0466545e+00   1.8092421e+00   1.9600687e+00   2.0322961e+00   1.9556364e+00   1.8266422e+00   1.9950345e+00   2.1038429e+00   2.1164145e+00   2.0188062e+00   1.8863331e+00   2.0006971e+00   1.9971068e+00   1.8771862e+00   2.1148855e+00   1.9570638e+00   1.9859615e+00   2.0030854e+00   2.0737344e+00   1.9739259e+00   1.9266524e+00   1.9200535e+00   2.1376689e+00   1.8944425e+00   1.9330553e+00   1.8561590e+00   1.9422954e+00   1.8874178e+00   1.8624808e+00   1.8265563e+00   1.8840519e+00   2.0515092e+00   2.0174226e+00   1.9771196e+00   2.0635988e+00   1.7334466e+00   1.9912604e+00   1.8915711e+00   1.8262636e+00   1.9369173e+00   1.9560446e+00   1.9549934e+00   1.9279230e+00   1.9021073e+00   2.0113391e+00   2.0305786e+00   1.8066806e+00   1.9656739e+00   2.1219217e+00   1.8820250e+00   1.8936826e+00   2.0565131e+00   1.9839441e+00   1.8553479e+00   1.9923760e+00   1.6393276e+00   1.9786440e+00   1.8274394e+00   1.9322611e+00   2.0404318e+00   1.9216532e+00   1.9361171e+00   1.8401373e+00   1.9908059e+00   1.9495117e+00   2.1975655e+00   1.8413913e+00   2.1528773e+00   1.8434374e+00   2.1668863e+00   2.0429273e+00   1.9980016e+00   1.9790129e+00   2.0264829e+00   2.1478843e+00   2.0899600e+00   2.0280670e+00   2.1210881e+00   1.9993891e+00   1.8646871e+00   1.9099983e+00   1.9263353e+00   2.0042495e+00   2.1365919e+00   2.1830279e+00   1.9631961e+00   2.0880004e+00   1.8348369e+00

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-seuclidean-ml.txt

@@ -0,0 +1 @@
+   1.4330520e+01   1.4635426e+01   1.3450855e+01   1.4761140e+01   1.3508642e+01   1.5434417e+01   1.3887693e+01   1.5166776e+01   1.3966038e+01   1.4950451e+01   1.4564587e+01   1.3834201e+01   1.4347008e+01   1.5641962e+01   1.4689053e+01   1.4418720e+01   1.4545856e+01   1.4151822e+01   1.4669017e+01   1.5150750e+01   1.3770166e+01   1.3288969e+01   1.4048191e+01   1.4049959e+01   1.4164158e+01   1.3727834e+01   1.4074687e+01   1.4321303e+01   1.2497330e+01   1.3820273e+01   1.4441030e+01   1.4780222e+01   1.2504339e+01   1.5022245e+01   1.4263650e+01   1.3704507e+01   1.3694385e+01   1.3667517e+01   1.3177468e+01   1.4391931e+01   1.4893903e+01   1.4475753e+01   1.4440707e+01   1.3603096e+01   1.6889651e+01   1.4731174e+01   1.3337775e+01   1.5187532e+01   1.5667271e+01   1.4226037e+01   1.4203554e+01   1.5272898e+01   1.6031460e+01   1.5991549e+01   1.1855060e+01   1.4844776e+01   1.2475182e+01   1.4408126e+01   1.4836870e+01   1.3472986e+01   1.4089281e+01   1.1018298e+01   1.3183296e+01   1.4590802e+01   1.4404230e+01   1.2717623e+01   1.3983283e+01   1.4017133e+01   1.4608005e+01   1.4402553e+01   1.3977803e+01   1.4091040e+01   1.3977459e+01   1.2630449e+01   1.4160109e+01   1.3029417e+01   1.2654432e+01   1.2794946e+01   1.3194978e+01   1.4378745e+01   1.2431908e+01   1.3852651e+01   1.3748358e+01   1.4003568e+01   1.5066681e+01   1.5192826e+01   1.4370013e+01   1.5792545e+01   1.3547546e+01   1.4411543e+01   1.4794215e+01   1.4924312e+01   1.4789153e+01   1.4875055e+01   1.4208537e+01   1.2786148e+01   1.4882476e+01   1.3302010e+01   1.4354774e+01   1.4542129e+01   1.5889633e+01   1.2928185e+01   1.4877868e+01   1.2890902e+01   1.4406165e+01   1.4498123e+01   1.4303273e+01   1.3207002e+01   1.3954732e+01   1.4841248e+01   1.5427799e+01   1.4363463e+01   1.3976277e+01   1.4284878e+01   1.4457991e+01   1.3369469e+01   1.5246610e+01   1.4487573e+01   1.4525176e+01   1.4505865e+01   1.5037347e+01   1.3834927e+01   1.3758988e+01   1.3424987e+01   1.4914766e+01   1.3783923e+01   1.3434291e+01   1.2895927e+01   1.3870360e+01   1.3342977e+01   1.3094322e+01   1.3057847e+01   1.3322375e+01   1.4940650e+01   1.4476829e+01   1.4197503e+01   1.4597035e+01   1.2963234e+01   1.4011414e+01   1.3181409e+01   1.3339615e+01   1.3928735e+01   1.3508015e+01   1.3170749e+01   1.3529133e+01   1.3454724e+01   1.4883437e+01   1.4564565e+01   1.2474313e+01   1.4435790e+01   1.5285703e+01   1.3701736e+01   1.3578312e+01   1.4807311e+01   1.4281072e+01   1.2920213e+01   1.4427803e+01   1.1408611e+01   1.4097334e+01   1.2868115e+01   1.3903683e+01   1.3800332e+01   1.3439339e+01   1.4062651e+01   1.3242107e+01   1.4400424e+01   1.3826132e+01   1.5991146e+01   1.3118258e+01   1.5377390e+01   1.2858378e+01   1.5249567e+01   1.4081585e+01   1.4458052e+01   1.4175623e+01   1.4850069e+01   1.5506668e+01   1.5014770e+01   1.4337030e+01   1.5214705e+01   1.4803729e+01   1.3188675e+01   1.3437739e+01   1.3409394e+01   1.4607386e+01   1.5394271e+01   1.5946451e+01   1.3769364e+01   1.4181208e+01   1.2551765e+01

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/pdist-spearman-ml.txt

@@ -0,0 +1 @@
+   9.3540954e-01   9.7904590e-01   8.6703870e-01   1.1569997e+00   8.7174317e-01   1.0627183e+00   9.1272727e-01   1.1593999e+00   9.7573357e-01   1.0072127e+00   1.0536814e+00   9.6276028e-01   9.7700570e-01   1.1513951e+00   1.0719592e+00   9.2178818e-01   1.0004680e+00   9.3689769e-01   9.8205821e-01   1.0332673e+00   9.4517852e-01   8.9437744e-01   9.7556556e-01   9.0460246e-01   9.7210921e-01   9.2230423e-01   9.9605161e-01   9.6852085e-01   8.4162016e-01   9.6667267e-01   9.7759376e-01   9.9757576e-01   7.6992499e-01   1.0151695e+00   9.8691869e-01   9.0325833e-01   8.6665467e-01   8.8844884e-01   8.4553255e-01   9.7700570e-01   9.5159916e-01   9.8906691e-01   1.0551935e+00   9.1973597e-01   1.3266247e+00   1.0982778e+00   8.4531653e-01   1.0887369e+00   1.0984938e+00   9.9851185e-01   9.0701470e-01   1.0639304e+00   1.2392919e+00   1.1422502e+00   8.1725773e-01   1.1844944e+00   7.8219022e-01   1.0817162e+00   1.2196100e+00   1.0003120e+00   1.0164536e+00   7.0724272e-01   9.7981398e-01   1.1134953e+00   1.0671107e+00   9.3600960e-01   9.9984398e-01   1.0356916e+00   1.1248005e+00   1.0696310e+00   1.0634263e+00   9.6472847e-01   9.9365137e-01   8.5724572e-01   1.1257846e+00   8.9930993e-01   9.4903090e-01   9.0667867e-01   9.1231923e-01   1.0573777e+00   9.0105011e-01   9.5255926e-01   1.0177978e+00   1.0606901e+00   1.1966997e+00   1.0891929e+00   1.0085089e+00   1.2640264e+00   9.3246925e-01   1.0198020e+00   1.2055806e+00   1.1237924e+00   1.1060666e+00   1.0517252e+00   1.0684668e+00   7.6844884e-01   1.0572697e+00   8.7373537e-01   9.6283228e-01   9.9350735e-01   1.2412601e+00   7.6322832e-01   1.0298950e+00   8.6148215e-01   1.0042724e+00   9.7012901e-01   9.3712571e-01   8.5845785e-01   8.5862586e-01   1.0336634e+00   1.0955536e+00   9.5302730e-01   9.8696670e-01   1.0633063e+00   1.0026643e+00   9.6380438e-01   1.1711251e+00   9.9273927e-01   1.0260906e+00   1.0863966e+00   1.0482808e+00   9.0361836e-01   9.2358836e-01   8.7794779e-01   1.2461206e+00   9.2985299e-01   1.0418962e+00   9.4660666e-01   9.5636364e-01   9.0646265e-01   9.9113111e-01   8.3027903e-01   9.3341734e-01   1.1378938e+00   1.0548215e+00   1.0086889e+00   1.1998920e+00   8.6063006e-01   1.0255506e+00   8.4786079e-01   1.0090729e+00   9.2542454e-01   9.5176718e-01   9.3477348e-01   9.0091809e-01   9.6404440e-01   1.1158716e+00   9.9614761e-01   7.7682568e-01   1.0605461e+00   1.0895650e+00   9.0065407e-01   8.7173117e-01   9.9821182e-01   1.2165617e+00   8.6127813e-01   1.1111071e+00   7.9015902e-01   1.0433843e+00   8.6510651e-01   1.0019202e+00   1.0154815e+00   9.4381038e-01   9.8646265e-01   1.0062526e+00   9.7426943e-01   9.8191419e-01   1.3038944e+00   8.6277828e-01   1.0830243e+00   8.6851485e-01   1.1192559e+00   9.9120312e-01   9.6540054e-01   9.1072307e-01   1.1775698e+00   1.1139154e+00   1.1083468e+00   9.9593159e-01   1.0825923e+00   1.1115032e+00   9.7430543e-01   9.5605161e-01   9.2800480e-01   9.4369037e-01   1.1136034e+00   1.1382898e+00   9.5937594e-01   9.8843084e-01   7.4563456e-01

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/random-bool-data.txt

@@ -0,0 +1,100 @@
+0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 1
+1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1
+0 1 0 1 1 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 0 1
+1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0
+1 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 0 0
+1 0 1 1 0 0 0 1 1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1
+0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 1 0 1 1 1 1 0 1 0
+1 0 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 0 1 0
+1 1 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1
+1 1 0 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 0 0
+1 0 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0
+1 1 1 1 0 1 0 0 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1
+1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1
+0 1 1 0 0 1 1 0 0 0 0 1 0 1 1 0 1 0 1 0 1 1 0 1 0 0 1 1 1 1
+1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 1 1
+1 0 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 0
+1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 0 1 1 1 1 1
+0 0 0 1 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 1 0 1 0 0 0
+1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 1
+0 0 0 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1
+0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 1 0
+1 0 1 0 1 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1
+0 0 1 0 0 0 0 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1 0
+0 1 0 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 0 1 1 1 0 0 1 1 0 0 1
+0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 1 1 0 0
+1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 1 0 1 0 1 1 1 0 0 0 1 1 1 0
+1 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 1 1
+0 1 0 0 0 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 1 1 0 1 1
+0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 1 0 1 0 1 0 1 0 1
+0 0 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1 0 1 0
+1 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1
+0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1
+1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 0 1
+1 1 1 0 1 0 0 1 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 1 1 1 1 1
+0 1 0 0 1 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 0 1 0
+1 1 1 1 0 1 0 1 0 1 1 1 1 0 1 0 1 0 1 0 1 1 0 0 1 0 1 0 0 0
+0 0 0 0 1 1 1 0 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0
+0 1 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 1 1
+0 1 0 0 1 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0
+1 1 0 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 0 1 1 1 0 1 0 0 1
+0 1 1 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1
+0 0 1 1 1 0 1 0 0 1 1 0 0 0 1 1 1 0 1 0 0 0 0 1 1 0 1 1 0 0
+1 0 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0
+1 0 1 1 1 0 1 1 1 1 0 0 1 0 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0 0
+1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 1 0 0 1 0 1 0 1 1 1 0 0 0 1
+1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 0 0 1 0 1 1 0 1 0 0 1 0 0
+0 1 1 0 1 0 1 1 1 1 1 0 0 0 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1
+0 1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1
+1 0 1 1 1 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1
+1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 0 0 1 1 1 1 0 1 0 1
+1 1 1 1 0 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0
+1 0 1 0 0 1 1 1 1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0
+0 1 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 0
+0 0 0 1 0 0 0 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 1 0 1 1 1 0
+1 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0
+1 0 1 1 1 0 1 0 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 0 1
+0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 0 1 0 0
+0 0 1 1 1 1 1 0 1 0 1 0 0 1 1 1 1 0 0 0 1 0 1 1 0 1 1 1 0 0
+0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 0 0 1 0 0 1 0 1 0
+1 0 0 1 0 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 0 1 0 0 0 1 1 1 1
+0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0
+1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 1 0 1 0
+0 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1
+1 0 1 1 1 0 0 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 0 0 0 1
+0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1
+0 0 1 0 1 1 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1
+0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1
+1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0
+1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 1 1 1 1 1 0 0 1 1 1 1 1 0
+0 0 0 0 1 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 0 1 0 1 0 0 0 1 0 0
+1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 1 0
+1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 0 0 1 1 0 0 1 1 0 1 0 1
+1 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1
+0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 1 1 1 1 1
+1 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 0 1 0 0 1 1
+1 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0
+1 0 1 1 0 0 1 1 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 1 1 0
+1 1 1 1 1 0 0 1 0 0 1 1 1 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1
+1 0 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 0
+0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 1 0 0 1 0 0
+1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 1 0
+1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1
+1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 1 0 1 1 0 1
+0 1 0 0 0 0 1 0 1 1 1 0 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0 1 1 1
+0 1 0 0 1 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0
+0 1 0 1 1 1 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0
+0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0
+1 0 0 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0
+1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0
+0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0
+1 0 0 0 0 0 1 0 1 0 1 0 0 1 1 1 0 1 1 1 0 0 1 0 1 1 1 0 1 0
+0 1 0 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 1 1 0 1
+0 0 0 1 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1
+1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 1 1 0 1 0 1 0
+0 1 1 0 0 0 1 1 0 0 1 1 0 1 1 1 1 1 0 1 0 0 0 0 1 0 1 0 0 0
+1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 0 1 1 0 0 1
+0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 1 0 0 0 1 1 0
+1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 1 1 0 1 1
+0 0 1 1 1 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1
+0 0 0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 0

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/random-double-data.txt

@@ -0,0 +1,100 @@
+1.172993630434470589e+02 1.905532343119886605e+02 2.613653823499444115e+02 1.570270816248337269e+02 2.373767637129340642e+02 2.175366144750510671e+02 2.609909144757107242e+02 2.086671686166440622e+02 2.674986450118991002e+02 1.395992762090408235e+02 1.115453060949917159e+02 1.531034842395609701e+02 2.621042034264289668e+02 2.958729454449504033e+02 2.137960368830719062e+02 2.606436280968571282e+02 2.492136530687155869e+02 2.770806237064748530e+02 2.667325121892417883e+02 2.909243437665674037e+02 1.570328417294508085e+02 1.738762543815240917e+02 1.514157955792608163e+02 2.264748814818163396e+02 1.911869834397498380e+02 2.083706054660671043e+02 2.778115921852293013e+02 1.330374814391803397e+02 2.988697222234711717e+02 2.534167825404447001e+02
+2.090964891529778242e+02 1.322006104643973003e+02 1.443415945355371832e+02 2.991388772264980389e+02 1.649302344777914868e+02 2.839528641910463875e+02 1.677159709681393736e+02 2.597553832458208944e+02 2.861055547321268477e+02 1.866431671806918189e+02 2.131812134614140177e+02 1.881465139477683124e+02 1.271865139985419262e+02 1.821608865941132649e+02 2.793653846657656459e+02 1.745982981552271838e+02 2.440893905635109888e+02 1.926469151980868446e+02 2.752453852984189098e+02 1.333479229516146347e+02 1.756311805755703404e+02 2.039367615619088383e+02 2.441861159155101575e+02 2.136111324500645594e+02 2.893808960992043922e+02 2.723220466017930335e+02 2.367879096909125565e+02 2.831541206793258425e+02 2.017643187924728068e+02 1.293072046241175030e+02
+2.311242818257193221e+02 2.180694109009666306e+02 2.728791416531455525e+02 1.239345918565636993e+02 2.885729762050686418e+02 2.082619393005260804e+02 2.331416004257805525e+02 1.003112528445347778e+02 2.796331120515330895e+02 2.804679740148056339e+02 2.466936828597247597e+02 1.422398585800914361e+02 1.312115029632765015e+02 1.324417143647877708e+02 2.161716508991076466e+02 1.791489656100356171e+02 2.239038785146145472e+02 2.456511993086799919e+02 2.885023077068626662e+02 2.127338775308419940e+02 2.468090724782538246e+02 2.704135008577740109e+02 1.144148504575758665e+02 1.641571759150080538e+02 2.473349551308716343e+02 2.366620528761779667e+02 1.208143167141831498e+02 1.403705034199327599e+02 2.061073908129479548e+02 1.482034962693051057e+02
+1.938319500339997035e+02 2.000523826243218650e+02 1.356134735235139317e+02 1.224357428573656250e+02 1.262840705282213918e+02 1.112797762573139977e+02 1.727826315738305993e+02 2.199559683100150664e+02 1.817290208723558180e+02 2.185579898773881951e+02 1.772844462934412491e+02 1.589145011846130728e+02 1.017520743541414703e+02 2.836990856171782980e+02 1.265544072638776640e+02 2.503473341476423855e+02 2.178539278172635534e+02 2.063574432066289432e+02 1.473169457524925861e+02 1.112719632489760784e+02 1.195996070145015722e+02 1.345099678548529312e+02 2.992645259487585463e+02 2.692242364540683752e+02 2.139649193607747861e+02 2.313659165106297451e+02 2.524185025119667785e+02 2.678714004815313388e+02 1.111457754393238702e+02 1.296443575800298902e+02
+1.183944097426736306e+02 2.750477277868330930e+02 1.688558971333346790e+02 1.432283295687057034e+02 2.226043174503911359e+02 1.825124733235978169e+02 1.806485153578007612e+02 2.270256019866706936e+02 2.852913053786990076e+02 2.867562520175486043e+02 2.795056496733417362e+02 1.142488895870292822e+02 1.502985045661773427e+02 2.246907359526948937e+02 2.051158858061974115e+02 2.663351441156772808e+02 2.864853431806749882e+02 2.276548949573071070e+02 2.678087640355958001e+02 2.266463576941352187e+02 1.886763304826383774e+02 1.150603609957262563e+02 1.596187994714221929e+02 1.844565420383776484e+02 1.730173420200940768e+02 1.427940137102308995e+02 1.774757620992130001e+02 2.563086691508434001e+02 1.666317348809653822e+02 1.878143419608473437e+02
+1.642344698640436036e+02 1.591648429561690818e+02 1.561851029939521140e+02 1.854367091922420059e+02 1.494951311500319093e+02 2.443780767043579942e+02 2.741090240793212160e+02 1.519200656263381006e+02 1.391711947382712538e+02 1.482414334940778815e+02 2.574425018646875287e+02 1.455120022089010945e+02 1.620904376421240727e+02 2.098493186451893848e+02 2.377904829227144887e+02 2.881187570801528750e+02 1.785609418793050054e+02 1.500483139796714340e+02 1.697371065898091729e+02 1.824143324642365087e+02 2.329862749140337712e+02 1.372006180078979298e+02 2.250666134242961789e+02 1.760894707637434067e+02 1.874161150869196035e+02 2.860410495381969440e+02 1.539271628213176086e+02 1.051658254213322152e+02 1.501619097950496666e+02 1.205717364486104515e+02
+1.275638286377957371e+02 2.620802183565458563e+02 2.290828196339760723e+02 2.591630015014513333e+02 2.102568650793322149e+02 2.385080320420775593e+02 2.683788150825365619e+02 1.808700201925492763e+02 1.972184450648797451e+02 2.382313686117472287e+02 1.733526990293641177e+02 2.369802981553972074e+02 1.835652530901061823e+02 1.274084560526275141e+02 2.403488205519001326e+02 2.713515297463850402e+02 1.455311801633137065e+02 1.889430214806582171e+02 1.676324321357484735e+02 2.327799977696781184e+02 2.846419393176552148e+02 1.510702433968490936e+02 1.361559014852734606e+02 1.732199851325496525e+02 2.451323003571785364e+02 1.833444866660036894e+02 2.451280287301300405e+02 1.669088211440060832e+02 2.768492228383354359e+02 2.445882168033535038e+02
+2.905092787520428601e+02 2.948076984760371033e+02 1.731080208454208673e+02 2.825532355845657548e+02 1.108820315678514845e+02 2.862013985457700755e+02 2.111453776876104769e+02 2.614428154999528147e+02 1.461523265575596042e+02 2.304914832379158156e+02 2.502987607420118934e+02 2.474276046141548875e+02 1.739607960146905725e+02 2.098700376203710789e+02 2.373226438948917121e+02 1.258493219462072119e+02 2.692932028872633055e+02 2.819145908444669999e+02 1.941653933285864468e+02 1.666395497972145847e+02 2.371919109091950588e+02 1.978302896313488191e+02 1.951483674191611613e+02 2.694357972099330141e+02 2.387068160427941450e+02 2.826084316255729618e+02 1.350954172043159929e+02 1.414479610501084039e+02 1.407657276334374501e+02 2.725513503737778365e+02
+2.055761393809777360e+02 1.070553196069381556e+02 1.045726024365074096e+02 1.611577217417760153e+02 1.258091705742062629e+02 1.038769334534844120e+02 2.956016304760584035e+02 1.586570076132481972e+02 1.636816353299032585e+02 2.375674325770941095e+02 2.085436646116971531e+02 2.088922128397473443e+02 2.316234644183506930e+02 2.623581653234684268e+02 1.714245300492981698e+02 2.844387943099641234e+02 1.469270259610659650e+02 1.157700922187784727e+02 2.367694595159086361e+02 1.548671738744121740e+02 2.013687686570863207e+02 1.860374943080277887e+02 1.733446602950305930e+02 2.488507085609763010e+02 2.929099979257852056e+02 1.825615338506695480e+02 1.338575452835397925e+02 1.491478381149757979e+02 1.116052925520655066e+02 2.341983606431906537e+02
+1.014445800974648222e+02 2.539987638010908597e+02 1.871788778457793399e+02 1.454231386314719998e+02 2.284640297096368045e+02 1.174773591296971915e+02 1.395683165851895637e+02 1.137193571402578414e+02 2.370662356797280950e+02 1.767292649815032064e+02 2.688513591587910696e+02 2.913902923086397436e+02 1.122392290694582897e+02 1.366157623619356229e+02 2.667409125457835444e+02 1.834435599491967537e+02 1.437174343391732236e+02 1.130622879516462120e+02 2.898543289046954214e+02 1.559795378531963479e+02 1.765577834073310157e+02 2.422955620302867885e+02 2.384835032255701321e+02 1.708163174135501094e+02 2.012159081107001839e+02 2.825663186839160517e+02 2.627299211659199045e+02 2.173916205317264883e+02 1.878835852278120910e+02 2.578733373077019451e+02
+2.843897417914848802e+02 2.685865547709703378e+02 2.810255710736182664e+02 2.572690897085278152e+02 2.416998564827035523e+02 1.770932574976374099e+02 2.021652319180342943e+02 1.414744641219446351e+02 1.464677002516696405e+02 1.831165552459343644e+02 1.157177632931430651e+02 2.625289386264841482e+02 2.972225480003540952e+02 1.024156386789293265e+02 2.305099741095138768e+02 2.241903749843916671e+02 1.157222019118702292e+02 1.533205318359311775e+02 1.179505454242311799e+02 2.666741766563739020e+02 2.792728900733587238e+02 1.222170248460037811e+02 2.573772727215269924e+02 1.535874607134987286e+02 1.231830862844115728e+02 2.584552954023608891e+02 2.541883057030129862e+02 1.001259630352790566e+02 2.332879439260797767e+02 2.240027888381033563e+02
+1.537092645679641123e+02 1.737278083620151392e+02 1.736358049797527201e+02 2.251608985235982630e+02 1.812387130195175473e+02 1.605621432944637377e+02 1.880655312831545700e+02 2.234500385148787700e+02 1.156918728696038272e+02 2.243685096423413654e+02 1.934342626327970720e+02 1.850952349553267027e+02 2.629944548485545965e+02 1.410418270562070973e+02 1.442479234012843960e+02 2.244518961458842909e+02 1.350755563946989923e+02 1.207094763037939913e+02 1.849900977633715797e+02 1.712315707730903398e+02 1.136025349108833495e+02 2.266901327137990734e+02 2.049289406654929735e+02 2.168279721613268407e+02 2.802488024880154285e+02 2.288593244920211873e+02 2.512942787545493957e+02 1.605416563468323261e+02 1.449848598254574483e+02 1.444073785399158396e+02
+1.576600406756634243e+02 1.316580100950168912e+02 2.530050469343043460e+02 1.319013133578224028e+02 2.708693079386434306e+02 1.256852413190491689e+02 1.471714019119002046e+02 1.119112141125198576e+02 1.482405279774543772e+02 2.151504825709631064e+02 1.449998801809978488e+02 2.163638771503673581e+02 1.272949254250747657e+02 2.476027791419436141e+02 2.891208457332292028e+02 2.642744540427622724e+02 1.972643066216432999e+02 2.480891057982425423e+02 1.265454595896786003e+02 2.957735252703171227e+02 1.831389323451852533e+02 2.674516147697771657e+02 1.404389674972707667e+02 1.350952754772052913e+02 2.169062951790871807e+02 2.445227715623778408e+02 1.771545655819627427e+02 2.729961759152714649e+02 2.655105689521545855e+02 1.887977700062222084e+02
+1.336462666694000632e+02 1.333709897858500995e+02 2.263366393511863350e+02 1.847175439991091821e+02 1.121699721143812383e+02 1.985314153845103533e+02 2.097626398761568396e+02 1.994292542548276970e+02 2.119822099620050722e+02 1.121578896112172430e+02 2.285640262135607372e+02 1.530452060058861719e+02 2.280757825791220625e+02 1.002584314437652893e+02 1.549763597162410349e+02 1.962603185897801836e+02 1.520023734031539107e+02 2.188357004065238129e+02 2.078620274892635678e+02 2.253215106546470281e+02 1.707542413836397373e+02 2.818584030117174279e+02 2.256862624833151472e+02 1.123882683852972377e+02 2.188298604829752776e+02 1.623779544769217296e+02 2.272253780943444212e+02 1.236449568833132560e+02 1.456708971140968174e+02 2.173334506159979753e+02
+1.355111076933105210e+02 2.882277378633141325e+02 1.458332953325788139e+02 2.038461345794760007e+02 2.077052275373579278e+02 2.430957456359013804e+02 2.398926697516154150e+02 1.861334604823129553e+02 1.056851094080089695e+02 1.250491536199931772e+02 1.475324860190441427e+02 2.446126161547439324e+02 2.283994822545897705e+02 1.411463500178549850e+02 1.017206978570942510e+02 2.805514386584911790e+02 1.128847993259780083e+02 2.326583828053989862e+02 1.968387029218569069e+02 2.013375618903088480e+02 2.981010702857409456e+02 1.018614681114941902e+02 1.799507821883679526e+02 1.133741465580100396e+02 1.235533581072856038e+02 1.980629645203880500e+02 2.289642287691829097e+02 1.596082722591768288e+02 1.905110471998515322e+02 1.789448781159623820e+02
+2.588286452268601465e+02 1.978130463173739599e+02 1.052689337312009599e+02 1.316763830509305251e+02 2.659236586726388509e+02 1.637014132384438767e+02 1.416031833329826668e+02 2.638665530652568236e+02 1.007257384115875425e+02 1.143900271701907769e+02 2.977834670475828602e+02 1.589765734727692745e+02 1.903975572290986520e+02 2.371635535037608804e+02 1.840341975670916668e+02 2.047003785265828242e+02 2.798969769773281655e+02 2.731706896262927557e+02 1.266878907904394254e+02 1.882415083052244427e+02 2.273996647906652129e+02 1.051754139634791869e+02 1.949647447346334843e+02 2.153583447980240919e+02 2.763468452623635585e+02 1.126493843527773322e+02 1.566047572050934491e+02 1.655928523150526246e+02 1.733528322945315949e+02 1.292815908595541146e+02
+1.453195062153936874e+02 1.443849872704900008e+02 2.393030362110915519e+02 2.203850914291498668e+02 2.628192548589183275e+02 1.142161203389242132e+02 2.954875947743198594e+02 1.914138981839176950e+02 1.956478457154231023e+02 1.282875398486639824e+02 2.801001077571227142e+02 2.478095646281364566e+02 2.467477848581343949e+02 2.819656424464902784e+02 2.951823714077539194e+02 1.777239847229775478e+02 1.197979896746704185e+02 1.481181033052623661e+02 1.906710229153984528e+02 2.142395628283543658e+02 2.300980272040501973e+02 2.228884003748859186e+02 2.473330601440014220e+02 1.391193242835927322e+02 2.836257563055140736e+02 1.510096324299383923e+02 2.202302141125946946e+02 1.931468179284185851e+02 1.332427495686727639e+02 2.591048546650930575e+02
+1.878681542531208208e+02 1.576240359584147654e+02 2.653849736815447500e+02 2.963544993865212973e+02 2.044592436730770828e+02 2.022626486161902903e+02 2.692262675681025144e+02 2.660999355751699227e+02 2.275843495473382347e+02 1.090849337992742818e+02 2.095602584555617227e+02 1.896271059113536808e+02 1.103822849104477513e+02 2.916911739044173260e+02 1.131212278363718582e+02 2.998892666268029643e+02 2.476782245756396605e+02 2.259689579913920738e+02 1.853942231198421950e+02 1.358270117521841200e+02 1.538630682720535674e+02 1.002148317174243601e+02 2.538393939061405433e+02 1.631649956267838206e+02 2.086654853664906000e+02 2.065167771482954322e+02 2.184161808630845485e+02 2.204789814939956045e+02 2.876785893506615821e+02 2.415299687386639675e+02
+2.578989465605797591e+02 2.309888943086805853e+02 2.139372792253111584e+02 1.438019921733897775e+02 2.686852572045135616e+02 1.347038004304963579e+02 2.662658866335509060e+02 2.378358170108797367e+02 2.901455078003721155e+02 2.653867524737770509e+02 1.011162296015096302e+02 1.236447329941733528e+02 2.440241295351771669e+02 1.285889645706482725e+02 1.234088480316093808e+02 2.765916670935633874e+02 1.132915304101479421e+02 2.967043774237617413e+02 2.960414394814537786e+02 1.923965028192617410e+02 2.177448618307050765e+02 2.328047369831131732e+02 1.702256773965170282e+02 2.320080409490440729e+02 2.962065584958517093e+02 1.421971909775941185e+02 1.416181340866144183e+02 2.318260414882616374e+02 1.990521696869427046e+02 1.291045564046920333e+02
+1.562042774178686386e+02 1.004265446278790392e+02 2.987714610921041185e+02 1.843637355858842284e+02 1.975513718825063165e+02 2.869996482942455032e+02 1.598134132589713943e+02 1.814921031876193638e+02 2.433389905907341983e+02 2.220363745053336970e+02 1.548306942100590504e+02 2.274512269554506361e+02 2.173006200058655963e+02 2.139515436667214772e+02 1.820439741095771353e+02 2.954110718222074183e+02 2.706126458816278273e+02 2.546812106115172583e+02 1.499899738326257363e+02 1.498010641912065921e+02 1.897725780579399668e+02 2.531561160917130167e+02 2.568891780637028432e+02 2.223136077092870551e+02 1.518604819103856585e+02 1.610422120589223027e+02 1.090455809489133259e+02 1.950503873748027388e+02 1.235704160644129388e+02 2.711492093024702967e+02
+2.039597038432034424e+02 2.026680584622021684e+02 1.365818873512059213e+02 2.909476552420245525e+02 1.721994194158640425e+02 1.854386667051114443e+02 2.287109571295530372e+02 1.912591665763447963e+02 1.607322994166321450e+02 2.949516230628389053e+02 2.522065912002103403e+02 1.869433122585654701e+02 1.235797649248940644e+02 1.522422059501078024e+02 2.738245135411146975e+02 1.059681837441489307e+02 1.013027238331489173e+02 1.660100598156148237e+02 2.454471731623151243e+02 2.467503196183328100e+02 2.584564749953993896e+02 2.079587352810677316e+02 1.650926041957846451e+02 2.269719270682073784e+02 2.376254891983122093e+02 1.510146656008620596e+02 2.672848371954185041e+02 2.692845974117340688e+02 2.180714754246087921e+02 2.186797802447831884e+02
+1.704231257711912519e+02 1.993416036368699906e+02 2.293703655438095268e+02 1.494582642918422266e+02 1.988970317734676030e+02 2.329763291241497711e+02 2.594871448385057420e+02 2.168089936885102134e+02 1.825320854593447280e+02 1.816754553181755796e+02 2.164740515812325725e+02 2.676208645391697019e+02 1.298365075936954725e+02 1.802664596093496243e+02 1.015344620621038132e+02 1.955048336384612639e+02 1.938953913674110083e+02 2.716932071347151805e+02 2.391085978949223829e+02 1.852300387899809380e+02 2.933293185307651356e+02 2.502753353909542966e+02 1.326128348575908262e+02 1.132638325194699433e+02 1.382024010322260494e+02 1.899310337488860796e+02 2.577639546186944699e+02 2.130234590296898887e+02 2.056292296528304746e+02 2.070746044453983927e+02
+2.712524956603344890e+02 1.103212761114690750e+02 1.501201791543782917e+02 1.588084859702673555e+02 1.780379814134324192e+02 1.938691258391782810e+02 1.322057441019641146e+02 1.105823874551086590e+02 2.879365916037821194e+02 2.457617763012990224e+02 1.036189749330240488e+02 1.682919366264929124e+02 2.271749409116763161e+02 2.468308259697249127e+02 2.530034131464132088e+02 2.481420904342841709e+02 1.546080547019561209e+02 1.278414739842506265e+02 2.234886960240669111e+02 2.535365186455997843e+02 1.599130733896959669e+02 1.151371295028686035e+02 2.378656188176093451e+02 2.901072209563180877e+02 2.524076257924749882e+02 2.849501171254129304e+02 1.802791659856764568e+02 1.527418387706650833e+02 2.578820596338672431e+02 1.208856989199291263e+02
+1.884906470590645711e+02 2.304295185581007672e+02 1.035923344330140736e+02 1.647061655195892627e+02 1.910201770870304472e+02 1.752788518438422614e+02 2.763014227316762117e+02 2.545709641405486252e+02 1.642694881393259152e+02 1.850698110761380804e+02 2.423689469305483328e+02 2.821007056776016384e+02 1.440765548977453250e+02 1.082195827231368952e+02 1.292487205530619008e+02 2.136496853657876613e+02 2.268509220579896635e+02 2.999629735037570981e+02 2.135306905316524535e+02 2.807718279523737692e+02 1.079256111018183759e+02 2.233050677333321801e+02 1.960571416898615951e+02 2.930642308139058514e+02 1.350490077967585307e+02 2.626074042719769750e+02 2.812196827814445328e+02 2.812753678081913336e+02 1.893738913514469004e+02 1.237248675858835725e+02
+2.024005284879252144e+02 2.663611407988397559e+02 2.687079844301063076e+02 1.583164038086077312e+02 1.451019436850150441e+02 1.100558451420041450e+02 2.083655450975085159e+02 2.034012033819327598e+02 2.745375932717230398e+02 1.454718097055225599e+02 1.519068131933423729e+02 2.522666952972969625e+02 2.409340029943109300e+02 1.697386944425205115e+02 1.092659514648129289e+02 2.785598218078254149e+02 1.404092026094307357e+02 2.152301424167146990e+02 1.170396027347833723e+02 2.495323893679063474e+02 2.070836095469416591e+02 2.187978925167305135e+02 1.478606128149070855e+02 1.189323178954538207e+02 2.012925160284665651e+02 2.080878545398990127e+02 1.510128433840351647e+02 1.657302151838663065e+02 2.177026636795220043e+02 1.221198981216710422e+02
+1.411258561955272341e+02 1.419717097672817374e+02 2.247481951315160984e+02 2.805973971111802712e+02 2.755562061324142178e+02 2.039769327420251557e+02 2.994080883760036045e+02 2.417843309736466040e+02 1.023751441731232319e+02 1.491356884971497152e+02 2.542464200475323821e+02 1.496044144381669128e+02 2.829129207809560285e+02 2.479316882407134699e+02 2.441205876677642550e+02 2.045492313770996020e+02 2.855582203360229414e+02 2.884005586284110336e+02 2.039668453101600676e+02 1.690279206477617890e+02 2.136822090795746760e+02 1.254275901194574772e+02 1.084851042192170922e+02 1.656011685190305229e+02 1.415195951026897774e+02 1.578115814760412263e+02 2.619737257057257693e+02 1.492347147839753347e+02 1.627213988646173561e+02 1.343297485726322691e+02
+2.544675070683062756e+02 1.367461330002975899e+02 2.928364121110963652e+02 2.024865028281971036e+02 2.758937379397792142e+02 1.293527538914390220e+02 1.003170531204512059e+02 1.514803620238746760e+02 2.603616046431354789e+02 1.790387290949859960e+02 1.954717187769221027e+02 1.325226280128280223e+02 1.522166198122710625e+02 1.162911821325583048e+02 2.798489406348742250e+02 2.521718932296424498e+02 2.622327475379161115e+02 1.027798265388270949e+02 2.437256510683693023e+02 1.911771820917219884e+02 2.722604457055863350e+02 2.850557929858495640e+02 1.953760157441756746e+02 2.473572905253965644e+02 1.891404804097296051e+02 1.514672503279451803e+02 2.213565012031598940e+02 2.253356064978207769e+02 2.044629345029305227e+02 2.805872739342098612e+02
+2.859142434488251183e+02 1.016009480575973356e+02 1.779351649172412522e+02 2.205171340775500539e+02 2.104472905774927369e+02 1.755755724600441567e+02 2.751836189782782185e+02 2.820692049982218350e+02 1.337557428916256015e+02 1.569761138230965969e+02 1.991757527032745543e+02 2.615974376894962461e+02 1.944849272958306017e+02 1.868411694165790777e+02 2.994394032068257729e+02 2.802783326794233290e+02 2.693871918204162625e+02 1.750293298802730249e+02 1.468161278725061720e+02 1.272003326865558108e+02 2.233103517167062932e+02 2.103066399402185027e+02 2.720825853079193735e+02 2.728915492341989193e+02 2.160004538807991992e+02 1.325145501710478015e+02 2.549827549782140466e+02 2.921469675413995901e+02 1.846231529604695822e+02 1.391152989663993651e+02
+2.538717579982014456e+02 1.450483481068324352e+02 2.720200816305956550e+02 1.120834821105324011e+02 1.703801876168104741e+02 1.091293661435919233e+02 1.410263490040598526e+02 1.910022197757120352e+02 2.505223413771657022e+02 2.069613533172621374e+02 1.367200764291426935e+02 1.269156762039037574e+02 1.459486945063737267e+02 1.585863332989725905e+02 1.433846106215619329e+02 2.893202513225785424e+02 1.754070497414596730e+02 1.678900237854272746e+02 2.363821059303507752e+02 1.088858921730617908e+02 1.962435837543239927e+02 2.151311182954276831e+02 1.943029551670006754e+02 1.670799798236046172e+02 1.348235227224938910e+02 2.005836112104490212e+02 2.601588534628079969e+02 1.194827586439497935e+02 2.131891535893303740e+02 1.835674362703964277e+02
+2.872207377280434457e+02 1.680389491751975299e+02 2.268072198735419533e+02 1.324343035526375729e+02 2.746241572770433095e+02 2.142161570690199710e+02 1.852290440736100550e+02 1.772431485621305285e+02 1.144750125154023266e+02 2.162070901557998468e+02 1.490690769171257557e+02 2.904041493178549445e+02 2.673617561413327621e+02 2.904362235840736730e+02 1.438791831406123833e+02 2.596893065528289526e+02 2.617155941751458386e+02 2.388486986717779246e+02 2.718819501315180105e+02 1.265484539827731680e+02 2.508989305854047700e+02 1.677208481362706323e+02 1.527665277518251230e+02 2.069026506407369084e+02 2.223100964495413336e+02 2.859845330217733022e+02 1.430291068893224349e+02 1.186508486537613436e+02 2.043257492072551713e+02 2.909823892985461953e+02
+2.385945641230763670e+02 2.011887933217761031e+02 1.622448188725907983e+02 1.738874847453056987e+02 1.669498482708885376e+02 1.853462372214463016e+02 1.514500885098960907e+02 1.569159134451362547e+02 2.521399095730983504e+02 1.246878140446721659e+02 1.758330561641313352e+02 2.722601647479554003e+02 1.679012078705679869e+02 1.710944469563905272e+02 2.012619557548435978e+02 2.130692925302264200e+02 2.489118511754019778e+02 1.553758318484749452e+02 2.531318516516165857e+02 1.895498740333992487e+02 2.010265603399928409e+02 1.805605111948569856e+02 2.471772127430102159e+02 2.822665908577009759e+02 1.256656757093761314e+02 1.218957078832023626e+02 2.851942693987446660e+02 2.434079459678487751e+02 2.183256665756584312e+02 1.881473862468819220e+02
+2.878274557836845133e+02 1.654481949983921254e+02 1.215681808546938214e+02 2.567820905945674781e+02 2.104106688330284101e+02 2.960796083414018085e+02 2.020680111052573693e+02 2.328934707961639106e+02 1.081575190462602336e+02 1.003340046261853189e+02 2.009697278729638299e+02 2.231963192062537757e+02 1.203849639323555323e+02 1.187994179134823156e+02 2.211937485225296030e+02 1.667300587261732119e+02 1.727379541915926211e+02 2.085029285798690353e+02 2.440827389167183981e+02 2.864522928573259151e+02 2.974890568790378893e+02 2.102945085846974393e+02 1.972598274048171447e+02 1.762889209976547136e+02 1.346946323322499666e+02 1.554434255958064170e+02 2.915634104756007901e+02 1.434053307556222876e+02 1.055800565037633163e+02 2.043924431141962259e+02
+1.494596010135965116e+02 1.369114048625681335e+02 1.414146701131132886e+02 1.383970135097982848e+02 1.734304788623498155e+02 1.594301265610334610e+02 1.040146208229407137e+02 2.208381597698417806e+02 2.904998286250861383e+02 1.300157615397056929e+02 2.667076669416877621e+02 1.062418844419948556e+02 2.717657999079561364e+02 1.054097765488278640e+02 2.401074677516734823e+02 1.045408432466875297e+02 1.330046749931937882e+02 2.297648034226271534e+02 1.488059718063634307e+02 1.725671935994615183e+02 1.330818497812682608e+02 2.341687919103425770e+02 2.983144736799429211e+02 2.798846823197050071e+02 2.218705077010061473e+02 2.681931695329894865e+02 2.339384973461015420e+02 2.893058480095726281e+02 1.539801301873031321e+02 2.746688360458649640e+02
+1.330701439354522222e+02 1.727884450558678395e+02 2.309082669627648272e+02 2.027633892073664299e+02 2.725503026364725656e+02 1.999882667367585896e+02 1.904108867169430255e+02 2.952458047945178805e+02 2.903769421220866320e+02 1.593020200554085477e+02 1.236139458806368623e+02 2.670862420061573062e+02 2.910830183895285472e+02 1.860711175093342149e+02 2.161724988935532963e+02 2.564488756979296795e+02 1.231566645138573648e+02 1.554206254375235403e+02 1.148558104746345521e+02 1.512714227454516163e+02 1.953024826710307025e+02 1.296022137194406127e+02 1.500450396815122076e+02 2.611742573447975246e+02 1.601671705158374550e+02 2.391666762859087214e+02 2.566415095930981352e+02 1.923304801412870404e+02 1.194174883996373353e+02 1.970722090829630986e+02
+1.912113734453868688e+02 1.498407015577022605e+02 2.038188614169363007e+02 1.315017316695561647e+02 2.564290419741012101e+02 1.890015309531812022e+02 2.451565642315005960e+02 2.794356592632736920e+02 2.286941218755985972e+02 1.959549984609147941e+02 1.183834182035568716e+02 2.102820643179567242e+02 1.748108698585573393e+02 1.534379248653211221e+02 1.919662859034699522e+02 1.273611408042816464e+02 1.848163823983119585e+02 1.719445827292381637e+02 1.098466009889928898e+02 2.781108902268393877e+02 2.089286134506138524e+02 2.324518337977864348e+02 1.983840049195213169e+02 1.897881971862217370e+02 1.057077761008814605e+02 2.693629461665184408e+02 1.359710117509105487e+02 2.191184409971657487e+02 1.295811391257115304e+02 1.272165218667991553e+02
+1.987244486959793903e+02 1.516360617950651317e+02 2.198509518241761498e+02 2.494181713303175911e+02 2.903223989223247372e+02 2.847249789220907132e+02 1.747037051964282171e+02 1.610307305098726829e+02 1.866621867053561061e+02 1.016530888490581503e+02 2.606194448419089440e+02 1.820037020201941402e+02 2.650669443765450524e+02 1.137210849453726098e+02 1.329244106101075715e+02 1.741312140090854257e+02 2.301425980066611885e+02 1.051708772384664030e+02 1.994040172335078864e+02 1.874773290907829733e+02 2.745616984783777070e+02 2.354781865911449756e+02 1.598287033335407159e+02 2.650689470710170212e+02 1.643692352330562017e+02 2.991199217036622713e+02 2.713535332162406348e+02 2.516280148665988463e+02 1.124367393830256532e+02 1.725070309959049837e+02
+1.637875882282461077e+02 1.407642428016634426e+02 2.759741260511348173e+02 1.982469453863400304e+02 2.966736241669494802e+02 2.756530253528777052e+02 1.426661371226006167e+02 1.585144634205103102e+02 2.836415355000413001e+02 2.468213340046699784e+02 2.898204535963063790e+02 1.711408259966125343e+02 1.900542569026269177e+02 1.112151031999617032e+02 2.679918109779015936e+02 2.737346364036235400e+02 2.597479311885246602e+02 1.719445390286030886e+02 2.361360157374418236e+02 1.123330408578339785e+02 1.214203690485689719e+02 2.552722899309185891e+02 2.436705678248840456e+02 1.596697357728296254e+02 2.533254006866929444e+02 2.066863222258713790e+02 1.194370826184286329e+02 2.943584774485435673e+02 1.636272134478143130e+02 1.191267138602315185e+02
+2.350924626651462006e+02 2.263138093076711357e+02 2.206572605284771385e+02 1.704171521239532296e+02 2.000250897638135257e+02 2.966317084215347109e+02 1.350543763227695138e+02 1.248113195978286285e+02 1.480602782771696297e+02 2.391913401309390679e+02 1.908758915801345779e+02 2.476074601271855045e+02 2.408834383325319095e+02 1.009169451940341560e+02 2.567526834523320645e+02 1.791854948779896688e+02 1.412277552146151152e+02 2.660711025781407670e+02 2.073940326990519054e+02 2.509760072499196610e+02 1.358593750308925223e+02 2.127422683140523532e+02 1.874643773621423293e+02 2.844455725631112273e+02 2.197223292953194118e+02 2.049519862750077266e+02 1.674367936692717365e+02 2.806316262053937294e+02 2.040091003350897836e+02 2.675290975004411962e+02
+1.483513543637005796e+02 2.384008274111940011e+02 2.834409911154408519e+02 1.344593118283445392e+02 2.346883831968173979e+02 1.381882879805813218e+02 1.241165074750676638e+02 2.186327911062819567e+02 2.466602279029802673e+02 1.573094529523951906e+02 1.568918412618390903e+02 2.289205163045023710e+02 1.170165333644822283e+02 1.742406104080407658e+02 2.082974381484526702e+02 1.600869123712819260e+02 2.399160913983472199e+02 2.877189278027444743e+02 2.845252294036096146e+02 2.342337907657317544e+02 1.496264758341107779e+02 2.905797831387872066e+02 2.824703799011629144e+02 1.047015685176013307e+02 1.056531628249932169e+02 2.778559625738202499e+02 1.693549799118289343e+02 1.654193764711911570e+02 1.062077606699500762e+02 1.159643419206647792e+02
+2.694780377267857716e+02 2.229138360502907403e+02 2.407432883969363218e+02 1.240072643521201741e+02 2.128611568148922970e+02 2.114050669978733481e+02 1.042337934877265297e+02 1.044783539591350490e+02 2.706611056394938259e+02 1.972285130309975898e+02 1.959046941044780681e+02 2.915493579522836853e+02 1.131994346897827342e+02 1.197362406389762839e+02 2.877593780027675621e+02 1.089470964294721824e+02 1.996015695685267417e+02 2.185569019121031999e+02 2.102686704320404374e+02 2.955299037924150980e+02 2.987478446256551479e+02 2.517129931888254646e+02 1.552463625479420557e+02 2.295020326441428153e+02 2.886454895961533111e+02 1.869792800456660871e+02 2.703426621835664037e+02 1.873514421416134326e+02 2.714620374401066556e+02 1.623625260081516331e+02
+1.457420078291350194e+02 1.926195242081234369e+02 1.841639049563959247e+02 1.397830290030836125e+02 1.287503203163068406e+02 1.684614546803193775e+02 2.820658047345126533e+02 2.986548244924653090e+02 2.631399932039782925e+02 2.870930868530864473e+02 1.141938207690214426e+02 2.868552010662050407e+02 2.019110175402121286e+02 2.840219745246005232e+02 2.848478851173646262e+02 1.902287203163165259e+02 2.696968940302964484e+02 1.690355482825476656e+02 2.171695948786692725e+02 1.960363641465239652e+02 2.930566891688549731e+02 1.380341365242818483e+02 1.769912313914243214e+02 1.164985277343077996e+02 2.079184380436491324e+02 2.871364788135472850e+02 1.796231479741346391e+02 1.115892945700443875e+02 1.922852518794877028e+02 1.851500906627327083e+02
+2.894943401361737187e+02 1.972990286414578804e+02 2.801948561309920933e+02 1.993490085147259947e+02 2.539099743775018112e+02 2.972486389690005240e+02 1.162404922698449354e+02 1.801898545246462504e+02 1.283416456049016858e+02 2.289248555429664407e+02 2.419505668531598985e+02 2.755101537543703216e+02 2.786083442131507013e+02 2.461931811431258552e+02 2.699066237266536064e+02 1.088542193903703179e+02 2.302113104476973149e+02 2.158136503417114227e+02 2.797451432348925096e+02 2.832754349673875822e+02 2.207567008139471909e+02 2.920947868166995249e+02 1.300092217647513735e+02 2.953259288980694350e+02 2.539624465668687492e+02 1.304833679125420645e+02 1.051395153781939484e+02 1.855592224876973830e+02 2.160289702497469477e+02 1.227895712666205981e+02
+1.029685235386965587e+02 1.410297052380113882e+02 1.832105986621241982e+02 1.016727951098498579e+02 2.130361696974732126e+02 1.817578553203918830e+02 2.644724203174304193e+02 1.713346250427240420e+02 1.297164370175517547e+02 1.072810924841072193e+02 1.083932811014470161e+02 2.860684171745337494e+02 2.893854146138399983e+02 1.677808320623732925e+02 2.343535290724524600e+02 1.209564642240636090e+02 1.329537830609780542e+02 2.924542956964438645e+02 2.733376468658280487e+02 1.397146179999238598e+02 1.103570089598620285e+02 2.231457082965310690e+02 1.056672424832338635e+02 2.887779644840117612e+02 1.127167878193751704e+02 1.387640376146708263e+02 1.791595456124304633e+02 2.709107895779202408e+02 2.238624693992912569e+02 1.773395240564728397e+02
+2.317578772498348769e+02 1.294950944138938667e+02 1.126253428029936572e+02 1.371351849575549693e+02 1.785990678455200964e+02 1.021081186758702444e+02 1.471984209931611360e+02 2.907355141803875540e+02 1.881128962816476644e+02 2.776434621780599628e+02 2.231668573818950279e+02 1.905362514139340817e+02 1.921875823712000226e+02 1.027725913116546792e+02 2.939602582690168902e+02 1.776540079128602656e+02 2.761214484196684111e+02 1.042033722248946646e+02 1.812858538041361385e+02 1.739774673118114663e+02 2.626640185867897799e+02 1.702975408841979288e+02 2.558138050153142729e+02 1.733257751657050392e+02 2.918973111180089859e+02 2.499103812623473857e+02 1.210050998380505973e+02 2.819910650801346605e+02 1.887952629909842699e+02 1.910084514453274380e+02
+2.212539479167726029e+02 2.774434360961662378e+02 2.337566454731646104e+02 2.345785537275947661e+02 2.365459264006348405e+02 1.983982238092833086e+02 2.030822332599765332e+02 1.995891111618029186e+02 2.834365683300363798e+02 1.036872616932399609e+02 2.192093181482490252e+02 2.601252995545215754e+02 2.498786393235831724e+02 2.102914196276636858e+02 1.344974807588668000e+02 2.319076536245909210e+02 2.769341510052834110e+02 2.705990780330756138e+02 1.679097240924248240e+02 2.394521666103182724e+02 2.042111123157340842e+02 1.679545908808316028e+02 1.638112120198904051e+02 2.498667640522866407e+02 1.298749690282424183e+02 2.953546510122243944e+02 2.420377599473625025e+02 1.972281420856064642e+02 1.511153679243939223e+02 1.785899871179086063e+02
+2.568297621323404201e+02 2.469847896802298237e+02 2.766623631158322496e+02 2.476135901735717937e+02 1.788596740963971570e+02 1.849716544556056874e+02 2.568516536462929594e+02 1.692762419184084877e+02 1.468834240718183537e+02 2.716053370235183593e+02 1.674083895790932957e+02 2.340636951853666687e+02 1.637725360284847227e+02 1.316562872243186177e+02 2.850086566701365882e+02 2.066513343106022944e+02 2.990778363456342390e+02 1.780020440519503495e+02 2.906711993591478631e+02 2.149926413975278479e+02 2.151504627144789765e+02 1.458362697904619836e+02 2.339644011324822657e+02 1.740513991402896181e+02 1.804876886135730842e+02 1.706585538790989176e+02 1.113370339871644603e+02 2.032819788543359039e+02 1.225434838619497526e+02 1.558188197132453183e+02
+2.752385657001058803e+02 1.704994416021052643e+02 1.607090409105587696e+02 2.031247490318933444e+02 1.333383797740430339e+02 1.922643047184382112e+02 2.665685682619526915e+02 2.611043497447243453e+02 2.444450591022788615e+02 1.012899678037660181e+02 2.236752860048796947e+02 1.164606756896235993e+02 1.768812782093617955e+02 2.532808672341815850e+02 1.308823477633827395e+02 1.683394957344131626e+02 1.787390150786144716e+02 1.962681762314343530e+02 1.178176219749694980e+02 2.151624908275416885e+02 2.951256579216935734e+02 2.058583926262361388e+02 2.348769662163374790e+02 2.500118096543036472e+02 2.065978549387351109e+02 1.732426267043477139e+02 2.575950640438621804e+02 1.826939497339359946e+02 1.586062531006688801e+02 1.141086110094916819e+02
+2.107478059550890066e+02 1.212326460542207940e+02 2.154852140069355073e+02 2.624147598788578648e+02 1.169795422214265699e+02 1.682202484364929660e+02 2.987700686247625299e+02 2.259973608163532504e+02 1.912690930240648015e+02 1.896338093439390775e+02 2.747727757049322008e+02 2.388804299971102978e+02 2.538821160842531128e+02 1.839990833334872491e+02 2.839611350159472067e+02 2.953225980324958755e+02 1.674336071760058076e+02 1.609172697163818953e+02 2.902596210806400450e+02 1.513824951234124114e+02 1.873458283487339600e+02 1.695960935104061491e+02 2.116215526550050470e+02 1.849422962892989233e+02 1.434256749723924713e+02 1.304784783123307079e+02 2.632948417544853328e+02 1.656472047377057777e+02 2.303125851744007377e+02 1.681993961373014486e+02
+1.104191565760665128e+02 1.750924257030650040e+02 1.242494131306669090e+02 1.541741282893887899e+02 2.585460716706878657e+02 2.286423505464783261e+02 1.890990979891397501e+02 2.707781238779197679e+02 2.619171833457787670e+02 2.695823002806438353e+02 1.941989480397771786e+02 1.389058748786196134e+02 1.283479072532797431e+02 2.347481590897206729e+02 1.518985431591505630e+02 1.757095590143896402e+02 2.225334593093496096e+02 2.231309387578290568e+02 1.039310896134069395e+02 2.614149485334186238e+02 2.212890027388380076e+02 1.425609106790709859e+02 1.376620423520403733e+02 2.403640719649376933e+02 1.152284694789922526e+02 2.108068210397188409e+02 2.526640691383259991e+02 2.323633859683563969e+02 2.720522122905912283e+02 2.498034621012949685e+02
+2.223449436042899947e+02 2.823923482876032267e+02 1.728419664392092727e+02 1.542710015610415724e+02 2.699062389875002737e+02 1.776741825057288793e+02 1.800001384193664080e+02 1.819433000632012636e+02 1.436484983468620840e+02 2.344086094824976954e+02 2.824459866922626361e+02 1.860318500101035681e+02 1.749968777772715498e+02 2.792448396035428004e+02 2.134719239619671498e+02 2.649346822194891047e+02 2.535109715864082602e+02 1.651109960016319178e+02 2.407385671793928736e+02 2.276937454871455770e+02 2.965404491761371446e+02 1.771850291606413634e+02 2.317902380753697855e+02 2.233400563607936817e+02 2.471010629200553694e+02 2.999085009765063319e+02 1.263611681933084725e+02 2.954593528043474180e+02 2.279026703099021915e+02 2.630592311905735414e+02
+1.662671322607742752e+02 1.600442354914371208e+02 2.476541290397616137e+02 1.471310870365195740e+02 2.302232198157895198e+02 2.833854716762933776e+02 1.464787719165046553e+02 1.913553080525503560e+02 1.014594285276723156e+02 2.182963956218923158e+02 1.629807715448000636e+02 2.692152036144454428e+02 2.287521686048013976e+02 2.982465613581407524e+02 1.646080094271899839e+02 1.685350412843276899e+02 2.638506951547767585e+02 2.931520510309920837e+02 1.395453733045734168e+02 2.192750645467382355e+02 1.118562057344099543e+02 2.210439168983162972e+02 1.977199388190010438e+02 2.248771354041466566e+02 2.967583759675493411e+02 1.144799677712354793e+02 2.877369511761256149e+02 2.831237961244747225e+02 2.909105411130262269e+02 2.550977837950437390e+02
+1.519738194711488006e+02 1.042788193386050608e+02 1.298121344332743377e+02 1.827398187867084971e+02 2.371985543371917800e+02 1.647119082252074236e+02 2.792046599520904238e+02 1.737333830141970452e+02 2.019611337599129968e+02 2.402390448779260623e+02 2.107045415433176174e+02 2.447101973248666411e+02 1.584507446746840174e+02 2.877533155913679366e+02 1.209142860803932251e+02 1.903846717728129931e+02 1.485923447895592631e+02 1.040627746119376695e+02 2.329784390325348795e+02 1.136264746597146882e+02 1.019818146651219024e+02 2.395077159260278847e+02 2.571474008697522322e+02 2.507839876514990465e+02 2.649762964978717719e+02 1.398370322453145889e+02 1.116668292809188614e+02 1.262068209877756289e+02 2.561228606182183967e+02 1.019925993853918413e+02
+2.525550526067758881e+02 2.649927164229666232e+02 1.457764901336312846e+02 1.519121804298574148e+02 1.112983565335166247e+02 2.979018464293943680e+02 2.517559946611144142e+02 1.257251989750113239e+02 2.377842966816966737e+02 2.692916709774201536e+02 1.558791612193160745e+02 2.988101508442036334e+02 1.264682305510686575e+02 2.586186621657187743e+02 2.397705732393993969e+02 1.799773948514575750e+02 2.289212202830902072e+02 2.551439950194432242e+02 2.270410183155361210e+02 2.624250216967006395e+02 2.894508375480465361e+02 1.106681053253299183e+02 1.696755343387707171e+02 2.302155275158106917e+02 1.445113211107399138e+02 1.886794441144848236e+02 2.129906512422033131e+02 2.340704769023953986e+02 1.082933010325512981e+02 1.977265970892881626e+02
+2.874406426475449052e+02 1.913451373833616742e+02 2.647704607931181044e+02 1.881279366057496532e+02 2.840067538093052804e+02 2.179159896935567247e+02 1.839859875309309132e+02 1.189702187115672132e+02 2.794517441847542614e+02 2.815599370853284427e+02 1.258259904677427699e+02 1.428483537633051412e+02 2.541426109645265967e+02 1.338781623221585164e+02 2.877181693280556374e+02 2.041742222547631513e+02 2.429167887622087392e+02 1.861891141000048435e+02 2.815058357304060337e+02 2.932279451804108703e+02 1.428092602118218792e+02 1.129541128601477595e+02 1.104970415865426503e+02 1.361068733124779726e+02 1.702082770497633533e+02 1.583852379729134157e+02 1.614070717213254511e+02 1.054529192214523476e+02 1.116913943762218366e+02 1.806474879921846366e+02
+1.904583320230821926e+02 1.477903225290235980e+02 2.926623631581093150e+02 2.267002240281469199e+02 1.643763662729302268e+02 2.199235242233247902e+02 1.853923849032223359e+02 2.941726936508506469e+02 2.665966841434134835e+02 1.199566433868006357e+02 2.951991052054676175e+02 1.594510101065885124e+02 1.458298791153635534e+02 1.532145001211049475e+02 1.411023254500616133e+02 2.140513226665028128e+02 1.678784758049908419e+02 1.708308530430679184e+02 2.099440033407245778e+02 2.664570659333852518e+02 2.959905162222905801e+02 2.829445582187913715e+02 2.588706049990775000e+02 1.722199615074994483e+02 2.869184560072056343e+02 1.681559218785307053e+02 1.503240659973911306e+02 2.588597461006905291e+02 2.678295026364270939e+02 2.154561503934444886e+02
+2.071927904539387839e+02 2.171736003654224305e+02 1.593735315924418785e+02 2.947356579175152547e+02 1.742775794491871011e+02 2.184611101357660914e+02 2.225198306238390842e+02 2.168369296352294668e+02 1.755672175076374231e+02 2.252214925755263835e+02 1.563369877784152209e+02 2.085332604119019209e+02 2.572482649031854862e+02 2.951800051631508950e+02 1.079183556031880329e+02 1.218838648771928774e+02 2.685371616407055626e+02 2.419162624723466877e+02 1.022244855205179022e+02 1.101224552326326602e+02 2.597819405832950679e+02 1.134555412120959517e+02 2.870491931154815575e+02 1.374365654160442318e+02 2.645641258978021142e+02 2.531141673781916666e+02 2.361747183362105886e+02 1.893108861581111171e+02 1.539026912190118139e+02 2.501170032332128415e+02
+2.547888423116186232e+02 1.853670755857669974e+02 1.389074705955763420e+02 2.709929622842061008e+02 1.228800068832790515e+02 2.778321736112652616e+02 1.309641642706778555e+02 1.156980811627219055e+02 1.431313378740429982e+02 1.646591400066212714e+02 1.920182917083556049e+02 2.178001706163468043e+02 2.235489712948179886e+02 1.079088316874027242e+02 2.447091545393394370e+02 2.320303973549428065e+02 2.359105911115680101e+02 2.382951907588607128e+02 1.062067779247245483e+02 2.905379355334102911e+02 2.023335418134440715e+02 2.128348219019524095e+02 2.865957710750057004e+02 1.782427960783044796e+02 2.856139874187100531e+02 1.139905905655008098e+02 2.264676166669663360e+02 2.479179013019825675e+02 1.746165350218777803e+02 2.255842464851874070e+02
+1.883869033800616819e+02 1.965817072065136699e+02 1.890868666652849015e+02 1.898737766004000491e+02 2.779218373710688184e+02 2.134628932560298722e+02 1.100835458783813436e+02 2.768750976313177148e+02 2.547073561014202880e+02 2.728160162818061281e+02 1.733645011505617504e+02 1.625036971255624394e+02 2.977754324167240156e+02 1.632372616873928450e+02 2.174045665187836107e+02 2.606964806055048030e+02 1.625508452643421720e+02 1.715067940576683441e+02 1.218481476549646629e+02 2.842560845538128547e+02 1.928678337146606623e+02 2.708765321293922739e+02 2.077020047066411621e+02 2.923591890868326004e+02 2.230876482822842206e+02 2.689925468225608256e+02 1.036588336737814586e+02 2.052618530546818363e+02 2.648220111560104897e+02 1.868396012623422280e+02
+1.785937212608853315e+02 2.973454718025594161e+02 2.368986004504845084e+02 1.146953890760472348e+02 1.265905165006724644e+02 2.255973396401841455e+02 2.163675674740596264e+02 1.527913853500098185e+02 2.283358642424602465e+02 2.759303134283557597e+02 2.876072117803540777e+02 2.029362495845153944e+02 1.212425121544320490e+02 1.100001317370093830e+02 2.335268996183764330e+02 2.375268130741384027e+02 2.336339660612213436e+02 2.462747325703657282e+02 2.841981652294566061e+02 1.081959034831858446e+02 1.291296469376330833e+02 2.602425849072438950e+02 2.575669438145553727e+02 2.135342654708205714e+02 2.294373105308322067e+02 2.706502840281193016e+02 2.928412927772634475e+02 1.330151104176747765e+02 1.533759962548247131e+02 2.744006234275867655e+02
+2.257735103076358882e+02 2.728385269717355186e+02 2.290872800510813363e+02 2.330934692803050154e+02 1.037274604992595215e+02 2.674079561164307961e+02 1.195755645916240866e+02 1.402804464035359047e+02 2.170516922702277611e+02 2.744725918691634661e+02 2.930458735600458908e+02 1.496408395971007224e+02 1.595562419103408729e+02 2.835538666488008630e+02 1.780163567793609332e+02 2.906408145890961237e+02 1.133853019218590248e+02 1.494630592331960770e+02 1.214592101712915451e+02 2.263015460193574881e+02 2.598100406717117608e+02 1.963383361449393192e+02 2.235083985338561376e+02 2.946475410923074492e+02 1.758055989844200724e+02 2.637780439251395137e+02 2.875400021086666698e+02 1.577781508415756662e+02 2.146553072676672684e+02 1.798181279868336446e+02
+2.620574340171276617e+02 2.153711882285265915e+02 2.245961661539886904e+02 2.054509343172356921e+02 2.926008719008261210e+02 2.432564531143420652e+02 2.303655720936658611e+02 1.615953803481287991e+02 2.918921003884012748e+02 2.760746977013722017e+02 1.909442200188182710e+02 1.596536528765051060e+02 2.491411570718119037e+02 2.924629085319008936e+02 2.587604848561293807e+02 1.524605619386706792e+02 2.737599884275671798e+02 2.090365453766356723e+02 1.610548024559351461e+02 1.018774121963877803e+02 2.410901898572944049e+02 1.875862586601133444e+02 2.588626077539996686e+02 2.579873618626863845e+02 2.838744453525392828e+02 2.580071516854936817e+02 2.114887112935771256e+02 2.675506009048368696e+02 1.260391751775616029e+02 1.858866479221875920e+02
+1.963224789638335892e+02 2.444908535968891954e+02 1.962779352478895589e+02 1.553096436749702889e+02 2.483662294276224429e+02 1.067992874414757978e+02 2.633849667942634483e+02 2.454321751613854588e+02 1.854433418739394028e+02 2.562889653665436072e+02 2.506342746416453622e+02 1.900819942764665598e+02 1.704565979131312474e+02 2.916979173024495822e+02 1.898592592817412310e+02 2.687872145548625440e+02 1.525347862509104004e+02 2.786582104923993484e+02 2.310813531087783872e+02 1.166208530157265386e+02 2.602471623613457723e+02 2.102772607982462034e+02 2.183751071150112466e+02 1.065011561509572999e+02 2.813176394708128782e+02 1.792292558016025623e+02 2.804083600455996361e+02 1.557890480883644102e+02 2.439522159916458861e+02 2.652201783594097719e+02
+1.425266334964659904e+02 2.075049705342416928e+02 1.704914602333145126e+02 1.886474594627911756e+02 1.252313163849750595e+02 2.836097447326676502e+02 1.406399617929505439e+02 2.414245225193989768e+02 2.576349788827002385e+02 1.486724691707949262e+02 1.092388214497626961e+02 1.685935770192617724e+02 2.033388664740227227e+02 1.390809359458484948e+02 1.056188661648174758e+02 2.350581131530574055e+02 1.964295662906907012e+02 2.578831766420791496e+02 1.109952979966328144e+02 2.027546721440710940e+02 2.501377690830167637e+02 2.111868593440530617e+02 2.324728205186171692e+02 2.453971856382445935e+02 1.723822394524685819e+02 2.872924628066301693e+02 1.140766727214026446e+02 2.221345013854892159e+02 1.728173248741775296e+02 2.676400838220500873e+02
+1.711571121866394947e+02 1.085759247733173396e+02 2.001753766691515750e+02 2.760446855018309407e+02 2.056587091496190567e+02 1.121827347031253197e+02 2.274644480946081444e+02 2.571858980756533128e+02 2.945439217283808375e+02 1.913312305877045674e+02 1.500446430731354894e+02 1.650397772114545489e+02 2.581660073502400792e+02 2.094009769144933273e+02 1.731816092302842094e+02 2.727903589313663133e+02 2.606648610353666982e+02 1.460656197586831695e+02 2.016951883706858268e+02 1.247477859691891240e+02 1.732157361502286221e+02 1.195560196858487245e+02 1.253893910664414904e+02 2.455457677441618216e+02 1.778732818035962850e+02 2.490436815297808266e+02 1.487573988963908960e+02 1.937302250034929898e+02 1.502426775501600389e+02 1.110841009912817583e+02
+2.382535443835092508e+02 1.972031918916456732e+02 2.576267295349729807e+02 1.730194312205534288e+02 1.301593684828995094e+02 1.624008376323430127e+02 2.060036399923972681e+02 1.233366573394677630e+02 2.194763391620297739e+02 1.701495187616251314e+02 1.223397596968992218e+02 1.987622577877627350e+02 2.511738650001373117e+02 2.130204435763062634e+02 1.993899817227978133e+02 1.597764561560970265e+02 1.205224890815559604e+02 2.184250491898233690e+02 1.755709834516516139e+02 2.741081010321077542e+02 2.104755291992826187e+02 2.698148014221883386e+02 1.299106544858947814e+02 2.008369880697999292e+02 2.938716155581552130e+02 2.671516623028076083e+02 1.332347035771324215e+02 1.291435420390463378e+02 1.835021202063177554e+02 2.002866194329941720e+02
+2.554906544300547182e+02 2.365682876454178540e+02 2.924004211094360244e+02 1.662852505275750730e+02 1.123350814405425808e+02 1.910015128879867632e+02 1.341551373493250594e+02 1.313122940860927770e+02 2.397311819484906152e+02 1.559268654058377024e+02 1.407120959783594003e+02 2.371419051640040152e+02 2.217591327496910480e+02 1.881187811266301537e+02 1.632462641154496907e+02 2.970940639140721373e+02 2.422917505999918433e+02 1.356966040631749593e+02 1.702398486895437486e+02 2.608644720933497183e+02 2.783751927848827563e+02 2.951746624002826138e+02 1.720706565846523688e+02 1.275268866601749096e+02 1.880990845238362681e+02 1.129502795714700625e+02 2.919985401845127626e+02 2.747497807112307555e+02 2.667734033775608395e+02 1.373740617490475699e+02
+2.115416415080857746e+02 1.431719947715498336e+02 1.718744824503889674e+02 1.075365968452523902e+02 2.220100335193473029e+02 1.965127222891928795e+02 1.062726056237197838e+02 2.631794488147562561e+02 1.658640190278337627e+02 1.169182569761068464e+02 1.645780782039788619e+02 2.940728738870184316e+02 2.979920277570993790e+02 2.125849825405138631e+02 1.533327700316632161e+02 2.655551337415409421e+02 1.329075684859120088e+02 2.686536376777100941e+02 2.299223677315555676e+02 2.123135030200585334e+02 1.474417961566917654e+02 2.899688778344954017e+02 1.439992490259426461e+02 1.606165457016644780e+02 2.854253601360321682e+02 2.837928223954166924e+02 1.868865943198568402e+02 1.809928275876523571e+02 1.583918020284682484e+02 2.384217495701244331e+02
+1.181670050605631417e+02 1.525653020190297582e+02 2.615084872177121724e+02 1.755024420886775829e+02 2.989795566898581001e+02 1.573585789513378188e+02 1.903575226478752711e+02 1.641861715477102166e+02 2.943146494922903003e+02 2.038802368327418719e+02 2.581560000437879694e+02 1.504995935930718076e+02 1.095655891680627008e+02 2.628623226127134558e+02 1.069018430130149255e+02 2.750818506761686422e+02 1.121786007219489818e+02 1.106710601660877415e+02 1.217291564359016149e+02 2.915199334459504144e+02 1.325859381653097557e+02 1.737237090326784141e+02 1.036075961875061751e+02 2.392327113385031510e+02 2.486092083099548233e+02 1.259492139939950306e+02 2.665249241620523435e+02 2.103119814995928039e+02 2.718465347096271216e+02 2.018653364759854298e+02
+2.085808638159350608e+02 2.977621083099649582e+02 1.394173606621695285e+02 2.232898484647512873e+02 1.347812725162832521e+02 1.574683348766579627e+02 1.827258429860655724e+02 2.827887224427595356e+02 2.608349632236463549e+02 2.370910079389979046e+02 2.033290260845359398e+02 1.566531500677691042e+02 2.982287288081304837e+02 2.998057140577807900e+02 1.906108269451214596e+02 2.023344526730545851e+02 1.717672594576409040e+02 2.093320563180507747e+02 2.649028095061802333e+02 2.840422446800275793e+02 2.111868958418739908e+02 1.803076798272542760e+02 2.311954915496957312e+02 1.563425451766251513e+02 2.610066662710300989e+02 1.855286443040786537e+02 1.478912573842241045e+02 2.544380211258828410e+02 2.799416317427427430e+02 2.238937193404353252e+02
+1.269470316997365131e+02 1.895539822645488357e+02 2.443421824114378467e+02 2.632321641240823737e+02 2.164919638664115951e+02 1.042697198382110884e+02 2.896061632271033659e+02 2.068164163046922681e+02 2.059671371408958294e+02 2.352532326493898722e+02 1.046233655847859296e+02 2.755187319279126541e+02 2.344641322699609987e+02 1.434858288567621969e+02 1.255438908126368176e+02 2.548141480364848803e+02 1.466719626681152704e+02 2.020892715394597872e+02 1.195107046056347713e+02 2.012968701954913797e+02 1.996902768982717191e+02 1.560547951636197013e+02 2.162555170020900164e+02 1.483278604161245084e+02 2.615607136845001151e+02 2.424344777210258997e+02 2.524090919470299070e+02 1.726167614603126026e+02 2.199373130240069258e+02 2.318614758097714912e+02
+1.590143031424979370e+02 1.933970326403360502e+02 1.227042846200323112e+02 2.107086401017011781e+02 2.844049872407889552e+02 1.420899421875644464e+02 1.736571760246831673e+02 1.130876049831349661e+02 1.470306210908964317e+02 2.959723384067232246e+02 1.438030965279091049e+02 1.685928342779160403e+02 1.351720793691902713e+02 1.909711091249450590e+02 1.477005416416634205e+02 1.010528808923594681e+02 2.205493627613245167e+02 2.367352422049318079e+02 1.224997665062844305e+02 1.620949451166091251e+02 1.270634404764108467e+02 2.673321646154778932e+02 1.618882934467209225e+02 1.208967331765591524e+02 2.073956586593529607e+02 1.223277950209799059e+02 2.625820210851194361e+02 2.262632377752408672e+02 2.222881433937307349e+02 1.716205611551696961e+02
+2.376094214038359667e+02 2.287867757784330820e+02 2.035778067022395703e+02 2.546588007138803391e+02 1.514832565507949198e+02 1.736683542684334327e+02 1.991020520349750598e+02 1.873563480883249213e+02 1.589186331386689801e+02 1.042563150975229149e+02 2.019924784676414902e+02 1.136537158101241971e+02 1.091264020137841158e+02 1.352770409719844054e+02 2.178414513482917414e+02 1.831380105899948489e+02 1.114225947990316570e+02 1.736029819106907439e+02 1.354612112967272424e+02 1.996055424300992627e+02 2.905125217944571432e+02 2.980326934372309893e+02 1.560898949881966473e+02 1.943286005606112212e+02 2.429797193518882636e+02 2.652714760000731076e+02 2.863852813340179182e+02 1.838252831614893239e+02 1.814799327205894315e+02 2.338290144642930954e+02
+2.526381992552952340e+02 2.089745531365245483e+02 1.869938021147821701e+02 2.864405091884094645e+02 1.736924996547539877e+02 1.479914815134324613e+02 2.132537252074255321e+02 1.830098172980584934e+02 2.476607236946428827e+02 1.066503395377639265e+02 1.405219898965278276e+02 2.743866427972425299e+02 2.269305408710248173e+02 2.791638036143738191e+02 1.824422387811073634e+02 1.852994662516045423e+02 2.777032940597408128e+02 2.109153407914434126e+02 2.214759900082639490e+02 1.857033490029854761e+02 1.302118293337227328e+02 1.889562709124264188e+02 1.844813915245081546e+02 2.875482403705134402e+02 2.022892465111445404e+02 2.230217175841083872e+02 2.843056043891419904e+02 2.350834055358549222e+02 2.080929758762673032e+02 2.770814576487081240e+02
+2.389430507965955428e+02 2.463651891862864147e+02 2.369578462650186452e+02 1.902366989508459199e+02 2.003468797600664004e+02 2.681735461841141728e+02 2.362787745532336601e+02 2.323782975776413480e+02 2.525302892415198812e+02 2.828059530799229151e+02 2.840327053185673662e+02 1.223941816187275435e+02 1.056255174412387134e+02 1.386503050117574105e+02 1.384325506562210535e+02 1.176641636239777426e+02 1.670688688422628161e+02 2.506322552784647826e+02 1.181229702988334083e+02 2.607048520072489737e+02 1.667476448166365515e+02 1.310085831735554223e+02 1.553111545647699927e+02 2.907454039462255651e+02 2.844644695877585718e+02 1.989933906493695019e+02 2.662036190025202131e+02 1.792754658114438371e+02 1.073664330563030944e+02 2.793141822468826376e+02
+2.640306978448612654e+02 2.458161373226257069e+02 1.015510894380497575e+02 1.527048938693112916e+02 2.893334394723561900e+02 2.994916089563248534e+02 1.054055716033572452e+02 2.278819528330843127e+02 1.890909183007994443e+02 2.134436011261824433e+02 2.654189934957544210e+02 1.780852604264427725e+02 2.222277079756825628e+02 2.689688042831336361e+02 2.232046857529678050e+02 1.778434593737022169e+02 1.336418515516146783e+02 2.739064893378349552e+02 2.065065746675076355e+02 1.329712924393647313e+02 2.176938186185978736e+02 1.918043587714230114e+02 2.280421349429639122e+02 1.182282112372680842e+02 1.370131137248831692e+02 1.716251366233928195e+02 2.412427837766657888e+02 2.738208811966829899e+02 1.471415247536169488e+02 1.638288393831292353e+02
+2.669085627842696908e+02 2.477147782526785136e+02 1.718200513884793565e+02 2.299346472745743597e+02 2.016242169414389309e+02 1.631378839470685307e+02 1.859938403107781255e+02 1.609729169019194330e+02 1.536303039404505171e+02 2.234728543554556950e+02 1.953401084257108096e+02 2.920381588589057174e+02 2.034966688752892310e+02 1.019427894404581139e+02 2.980736970140829953e+02 1.738263823108001418e+02 1.531314323312329293e+02 1.400030133312995702e+02 1.802287961283190043e+02 1.719909696301723443e+02 1.974918793689569725e+02 1.666882741246514001e+02 2.879569025675030502e+02 1.334044307903087088e+02 1.016937569869423896e+02 1.660343944328368764e+02 2.214967229035601974e+02 2.539424882366704992e+02 1.211914878013190133e+02 2.835892388637473687e+02
+1.704109091340931741e+02 1.337843054639438378e+02 1.570106251098002588e+02 2.123587857442842335e+02 2.788290802167920219e+02 2.795601449888932848e+02 1.220747715539721696e+02 1.179984498565524405e+02 1.552783750686872963e+02 1.257256444039083192e+02 2.312614004137946893e+02 1.971625968209403084e+02 1.208837070227885135e+02 2.231693789143681386e+02 2.332576722664892941e+02 1.659208209363902711e+02 1.979623049620595907e+02 2.497459328714609512e+02 2.540243570817084446e+02 1.309045902221261599e+02 2.376613837929333499e+02 2.140333351750954023e+02 2.231625169053620539e+02 2.869160136215916737e+02 1.282002159167354023e+02 1.029173927424986488e+02 2.432034421383394545e+02 1.495648010251883306e+02 1.971910657968611247e+02 1.358409247687675361e+02
+1.833826243837603442e+02 2.960483510370855811e+02 2.343723986770386318e+02 1.560358896543934293e+02 2.499669478251469172e+02 1.762005778153444169e+02 1.918050503412152921e+02 2.089352602085182866e+02 2.770127170480132008e+02 1.268157216157417224e+02 2.670673189640755822e+02 1.547628252866769287e+02 2.602514896343354849e+02 1.557532905756793866e+02 2.574076233589491949e+02 2.646855654359934533e+02 1.749681240869035719e+02 2.465698370051858035e+02 1.076897610845538082e+02 2.337637497458482301e+02 1.791847918196868932e+02 1.967068388721293104e+02 2.340964493346380095e+02 2.762770912600988140e+02 1.174465260954359564e+02 2.950490567997024982e+02 1.354710376622284116e+02 2.342233227246520642e+02 1.617966271393036379e+02 2.107879984327653915e+02
+2.493754578342164336e+02 2.275093847135933061e+02 1.466148442335522191e+02 2.261697123059220189e+02 1.213252451599347950e+02 1.628949300801819504e+02 2.100466501082228206e+02 1.508908296808102989e+02 1.488199564735201079e+02 1.727131563468088302e+02 2.306747713688439205e+02 2.570279850661015644e+02 2.309125192178541113e+02 2.422081718543400370e+02 1.769407234272878782e+02 2.688532243604371956e+02 2.276780878660686085e+02 1.065345319601523641e+02 1.535069430280279050e+02 1.717902253122074967e+02 2.876755354986605084e+02 1.683056100689713332e+02 1.120105413679224569e+02 1.755508096146901664e+02 2.095863991316655870e+02 1.523590730880595174e+02 2.944635547123552897e+02 1.444697311944634066e+02 2.165062978405008494e+02 1.410128743297030098e+02
+1.434402193906418006e+02 2.368914090178307106e+02 1.963465933374949941e+02 1.914557752364961516e+02 2.870767419320768568e+02 2.044699144835463187e+02 1.223520556576680036e+02 2.352284247043744472e+02 2.917945011866975165e+02 2.225925999946875322e+02 2.240309397680480288e+02 2.048455962243571093e+02 1.188048963943729035e+02 2.200553599997707579e+02 1.885605934416515765e+02 2.863412817843446874e+02 2.913876692311304737e+02 2.446563674684449552e+02 2.981153955140326843e+02 1.111775924383378253e+02 2.239868361016714857e+02 2.540473271011064469e+02 1.343930974769885438e+02 2.368686732696482409e+02 1.175691554116390591e+02 1.014879352562223715e+02 1.330784448687188046e+02 2.045426156006566885e+02 1.168174380391246245e+02 1.704438548713551995e+02
+2.696784010384477597e+02 2.991318545155386346e+02 2.120364825583467336e+02 1.950895785161033018e+02 1.216112431291165592e+02 2.438998438799096391e+02 1.588292735755803733e+02 2.347670069791354024e+02 1.862846309471772770e+02 2.258642611266068343e+02 1.423367506635381119e+02 2.692888471853933083e+02 2.950212092401994255e+02 2.331327670110776467e+02 1.542291422318579635e+02 2.809064569107727038e+02 2.358857646534314654e+02 2.378124255062788563e+02 2.664164586086786812e+02 1.387157904298663880e+02 2.297158046581682243e+02 2.386372312695162634e+02 1.246509391338716171e+02 2.338956320284196408e+02 1.820257170558419944e+02 1.957425768708682767e+02 1.680974560138464540e+02 1.288235048549348676e+02 1.483029350020115089e+02 1.744880718659300669e+02
+2.512494238114035738e+02 1.112846425403449615e+02 2.472643304237797395e+02 1.241745840646870818e+02 1.808849124644312099e+02 2.524760780760417731e+02 1.836118621524309447e+02 1.408362492891266982e+02 1.099623406752946693e+02 2.383967522197594064e+02 2.436606913384966049e+02 2.770699525768120566e+02 2.597573569531676867e+02 2.935649366424795517e+02 2.702790297508025219e+02 2.563597369995835606e+02 2.279477293752616447e+02 2.477470305460766440e+02 1.962131167814513333e+02 2.859744526791636190e+02 2.703401534622389590e+02 2.763052603711840902e+02 2.934416645125817809e+02 2.193475948646207030e+02 2.822891098008749395e+02 1.085391177109117820e+02 1.782208012387337703e+02 2.335496863699061976e+02 1.715066387390946829e+02 1.948062204233656303e+02
+2.879262290016004613e+02 1.676743911135137068e+02 1.403503828589753937e+02 2.744454339345198832e+02 2.935124358491533485e+02 2.920282649929100671e+02 1.390240222956847447e+02 2.426642861805074745e+02 1.217336684570653489e+02 1.311823750440439085e+02 1.647679902066092836e+02 2.962811279981685288e+02 2.945746172932865647e+02 2.005257587949587332e+02 2.072045953580022228e+02 2.893049469033056766e+02 1.913962360581630833e+02 1.823675529874825543e+02 1.830342103129283373e+02 1.222396004373517400e+02 2.248239872372262482e+02 1.170253438297526429e+02 2.853825568202013301e+02 2.214973458763422514e+02 2.563932510909227176e+02 2.144837192650675206e+02 1.793062298958048473e+02 2.920176466690815005e+02 1.515607839109829627e+02 1.981203765908239802e+02
+1.733053660232129403e+02 1.312183264386245583e+02 1.276233157677672807e+02 2.020942572504836789e+02 2.314817368496994732e+02 2.242589617101967008e+02 2.160504620978007893e+02 2.360595788588375399e+02 2.952977074031120992e+02 2.334652590044975682e+02 1.243453875174208747e+02 1.916144242306085630e+02 1.092365115042800596e+02 1.478765005471206280e+02 2.191946613400726278e+02 2.879274886834762697e+02 2.733443652356662597e+02 1.858481832262083344e+02 2.193747651131673706e+02 2.695165737089945424e+02 2.960753121523491700e+02 1.890691006834304631e+02 2.638343907584013550e+02 1.510492177865631334e+02 1.878288206285384661e+02 2.726561149875388992e+02 1.704246795027074199e+02 1.006381753343381718e+02 2.153734239260733148e+02 2.551451126036402854e+02
+1.591849792872858984e+02 1.304671215023752779e+02 1.427456440770346831e+02 2.882324895344759170e+02 1.680635293254793510e+02 1.205800311663507642e+02 2.861305963205076637e+02 1.219224106654408928e+02 2.467003871618023538e+02 2.830287806498602095e+02 1.445950870572595193e+02 2.496562286252286640e+02 1.464987579205844099e+02 2.848280464142704318e+02 2.785616857190397013e+02 1.837468579783306950e+02 1.246964377230690673e+02 1.251791080124520050e+02 1.496399061799681363e+02 1.375936265087168522e+02 2.547928467777094852e+02 2.554856419260690927e+02 1.285559318166884850e+02 2.092144446410586909e+02 2.868951534942014518e+02 1.178319347908447270e+02 1.347784205269015274e+02 2.851299399919766984e+02 1.754694686670390809e+02 1.016886128619324694e+02
+2.606618423405234353e+02 2.125366732076933545e+02 2.822772640751277322e+02 1.096405633955119185e+02 2.437561663288932721e+02 2.129146561548243994e+02 1.148823764090175530e+02 1.516868774610028368e+02 2.090025176018670265e+02 1.817684320186263562e+02 1.584667226055155709e+02 1.501973711988126468e+02 2.530199923706828713e+02 1.847948752811591930e+02 1.778871618489498303e+02 1.664551902511519188e+02 1.100020157933824265e+02 1.352000835393275509e+02 1.710981737682794801e+02 1.530513645967782566e+02 2.588476693974693035e+02 1.775587245068043956e+02 2.006331886716666588e+02 1.389709403689849694e+02 2.489553638298030194e+02 1.673604491791948021e+02 1.991154502489720812e+02 2.423848982654565418e+02 2.882603768001737308e+02 1.620650086718309240e+02
+2.723642490909132903e+02 1.680927290528325670e+02 1.005734627393615455e+02 1.598916606218045047e+02 1.672547346703738071e+02 2.361420151042074451e+02 2.741857058408131707e+02 2.533004150866734392e+02 2.036092771261417340e+02 1.091915011443997230e+02 1.145604210422382323e+02 1.209982156413156247e+02 2.749595368914399387e+02 2.177794513808643160e+02 2.054163746311436967e+02 2.185860861470465579e+02 1.504022045473846845e+02 1.713704456854883347e+02 2.175221629008602804e+02 1.230663148243889253e+02 2.419648244223723168e+02 1.383010418990747326e+02 2.040260833828849059e+02 2.966316994044250919e+02 1.630596872908637351e+02 2.562534082821714492e+02 2.549425872735235998e+02 1.983522705781282127e+02 1.524860865223137694e+02 2.736848821358530586e+02
+1.277021385004174192e+02 2.448445434866889343e+02 1.296687360965440803e+02 1.874271582575348702e+02 1.145742775945452792e+02 1.884744688522491742e+02 1.336298647132909423e+02 1.523816963142488419e+02 2.658270705367647224e+02 1.781637174983711134e+02 1.154610011723892171e+02 2.005342781476718415e+02 1.303166615041172918e+02 2.397284110571510496e+02 1.612912854182502542e+02 2.821645080329541315e+02 2.544831471501324813e+02 2.622237400581972224e+02 1.417212269902922230e+02 2.054005404298748658e+02 1.092142219674599062e+02 1.652051184306486107e+02 2.825679563619778492e+02 2.056286073102957630e+02 1.772062144904277545e+02 1.163520479257007310e+02 1.006186351926139366e+02 1.734025793931427586e+02 1.446958902579306709e+02 2.025820689614877779e+02
+1.798382687901162740e+02 1.604629760861514001e+02 2.668981169240885265e+02 2.763242846779806996e+02 1.318105471716862098e+02 2.191362245125996537e+02 2.770758446308884686e+02 2.308910816293108326e+02 2.956895796828827656e+02 1.566426856848869988e+02 2.326210561246332418e+02 1.206555816723871715e+02 2.603144096756907970e+02 1.172571782204154829e+02 2.219493974369055991e+02 2.385109304229506790e+02 2.599678734377965839e+02 2.850516346518521686e+02 1.472948582444382168e+02 2.234296740595885922e+02 1.427895312415343199e+02 2.848238578369252423e+02 2.260232767550441508e+02 1.544648385858973541e+02 1.163971462755376791e+02 1.762731012775239492e+02 1.089523563056807660e+02 1.663966154222005116e+02 1.342495772836978745e+02 2.922401077696804350e+02
+2.806557294060240224e+02 1.077657131130299604e+02 1.622983596366119059e+02 1.723469481204717795e+02 2.678046848873893850e+02 1.442059922525422451e+02 2.629931208031973711e+02 2.741083495447689415e+02 1.194142462414748707e+02 1.688961325073638022e+02 2.967954354880449728e+02 1.822107331135221671e+02 1.292333403080546645e+02 1.856814508383810391e+02 2.103923137448445573e+02 2.517859299913771451e+02 2.551152596962431574e+02 2.077883190793959898e+02 2.986930461834413677e+02 1.196764061335889551e+02 2.378823960447958257e+02 1.692017967083341432e+02 1.471250494556689432e+02 2.608355254883699672e+02 1.757172426071724942e+02 2.629426236813185369e+02 1.040244734248400533e+02 1.533558690719498827e+02 2.011860465194789072e+02 1.720545334339216765e+02
+2.966488050331527688e+02 1.809989340563203086e+02 1.871527370563514978e+02 2.315558973515319394e+02 2.657682292004950000e+02 2.237816732699509998e+02 2.282045922056215090e+02 1.846236325909775928e+02 1.644827554373339353e+02 2.760250360653360531e+02 2.492622345937652995e+02 1.483432536002697191e+02 1.527550390024584601e+02 1.573429964258168070e+02 2.090721206423400247e+02 2.535819867756219708e+02 2.420536340362719159e+02 1.691914404667937788e+02 2.388696721384086459e+02 2.593840245957078423e+02 1.331872961625781500e+02 1.116342264469163581e+02 1.680964276125217793e+02 1.555020753508222526e+02 2.422052215908822177e+02 2.626184375196450560e+02 2.674230788003709449e+02 1.948146659156083729e+02 2.663681889818526543e+02 2.795342087705012659e+02
+1.674728956867265310e+02 2.635505920196726493e+02 1.395353777027027604e+02 1.883233466008314565e+02 1.249441512057495913e+02 2.512189370435067417e+02 2.719913755602378842e+02 1.237326636617429614e+02 2.939951219495833357e+02 1.686366002602222807e+02 1.800181056076297068e+02 2.288525977776352818e+02 2.717306800175948638e+02 1.565292507387619594e+02 1.445460932655216766e+02 2.092313282690445249e+02 2.370375511382032698e+02 2.880525812713749474e+02 1.172567175017127141e+02 1.112412797274302250e+02 2.246954385922853135e+02 2.812359340959551446e+02 1.004168603505609241e+02 1.005387863078678805e+02 1.815971195408835683e+02 2.811251817522295937e+02 2.605765849402707772e+02 2.298114360271968621e+02 2.557293814584297706e+02 2.542416589790913122e+02
+2.943583269632734414e+02 1.442274778682184717e+02 2.700917391987959491e+02 2.527420049761408904e+02 1.527279900348522688e+02 1.841979337126335281e+02 2.902442440856567600e+02 2.889101481258517765e+02 1.828125218264408716e+02 1.133179379993730862e+02 1.484787634874768116e+02 2.676352293304336740e+02 1.452118425579454311e+02 2.636966617786087568e+02 1.313546620759107100e+02 1.834019443937838787e+02 2.892465421328221282e+02 2.575015388377624959e+02 1.970702343003932242e+02 2.507528167727347181e+02 1.724897096143170074e+02 2.664268628760375464e+02 1.365257050051324370e+02 1.198011035974838308e+02 1.176831988053894520e+02 1.070946883963453899e+02 1.964638491125322446e+02 2.570844982939356100e+02 1.593905150913052466e+02 1.202569936867807598e+02
+2.734271498156417692e+02 2.352133531486530842e+02 2.590835237087205769e+02 2.260994493040042528e+02 1.805421354394846105e+02 2.728408805160995598e+02 2.367263522625478913e+02 2.580210451062748689e+02 1.204524877415260562e+02 2.946465680607327613e+02 1.547220269335912803e+02 1.186203172746691337e+02 1.923878728892914864e+02 1.094127410697402354e+02 2.222837240826847278e+02 1.529333599077602628e+02 1.861450256630199647e+02 2.125583079944122176e+02 1.527591657960447264e+02 2.694001797345342766e+02 1.986063989766776388e+02 2.192493126389772442e+02 2.986827335637019587e+02 2.790660387254000625e+02 2.781487003899754313e+02 2.564198676846006606e+02 2.597551240338123648e+02 2.358970425952163907e+02 1.951628676328612357e+02 1.078208269500064347e+02
+1.190762776130697205e+02 2.951075493308472346e+02 1.091043363430719069e+02 2.824365312299846664e+02 2.445811468414383398e+02 2.538090805786315514e+02 1.230092364266577363e+02 2.633887649939744051e+02 1.865216093980499181e+02 1.540388898662323243e+02 2.047343894245035756e+02 1.431412534309083640e+02 2.857794001060171922e+02 1.492366175285521592e+02 1.380934567887849198e+02 1.331831467466375898e+02 1.149412013934811796e+02 2.205070844660474734e+02 2.939252657951740844e+02 2.049464694042562769e+02 2.047902832862141054e+02 1.810793422252176015e+02 2.005356992447976836e+02 1.381400138775680375e+02 2.582445444487385657e+02 1.698212931623984616e+02 2.252085951830697468e+02 1.808378144669676999e+02 1.307311344108444473e+02 1.050024101356033697e+02
+1.722314120162143354e+02 2.530014253763471856e+02 1.298340795948372772e+02 2.948664870226410812e+02 2.383106068289312702e+02 1.822969205106659558e+02 2.285226769051377005e+02 2.759417691711663565e+02 2.120970517474504220e+02 2.831046044310812704e+02 2.320579821788242612e+02 1.286125039667014960e+02 1.609837368065715282e+02 2.931112965353385107e+02 1.441758663366052531e+02 2.810263276191118962e+02 1.239857273771131077e+02 2.399447548605567988e+02 1.460208836055017514e+02 1.205325462037979491e+02 2.112513935912650993e+02 1.036793750016967692e+02 1.113202625217208777e+02 1.646612561683649574e+02 1.018350908838390581e+02 1.263835026124204859e+02 2.766683711501553944e+02 1.682407929561517506e+02 2.677103056024840271e+02 2.147294480454548307e+02
+2.763536852866382105e+02 1.511976958084401872e+02 1.026794659371155944e+02 1.805990415690671398e+02 2.442493962549426385e+02 1.881796213041043018e+02 1.028768312506858535e+02 2.787706953534510603e+02 2.589640601731795755e+02 1.730107396932538677e+02 2.218419822849910190e+02 2.651646152747807719e+02 1.476149140151474342e+02 1.986450675254654072e+02 1.050693447352362853e+02 1.819666738706916931e+02 2.873544952103893593e+02 1.472060704631180954e+02 1.297023844405691761e+02 2.824778443572924971e+02 2.918073394139615289e+02 2.128134400148996974e+02 2.223096450508596149e+02 2.761940547406351811e+02 1.348708672340777639e+02 1.857009592938832441e+02 1.062906640064134649e+02 2.104442283262811202e+02 2.812954268214299418e+02 2.739038950945439979e+02
+1.837264129055918147e+02 2.399207190527903322e+02 2.843910623120511900e+02 1.773207161532972975e+02 2.056581469496123873e+02 1.558029517788254168e+02 1.458438122541016924e+02 1.893030782939712253e+02 1.139027557376393673e+02 2.228775749423569437e+02 1.367670384452707140e+02 2.854480456674787092e+02 2.424985140340279202e+02 2.940521113211518696e+02 1.330693282221190259e+02 1.212599008475133076e+02 2.754747741586869552e+02 1.062856492128348549e+02 1.212724485003486166e+02 2.100514698158626743e+02 2.547262582240854272e+02 1.999488755181088777e+02 2.578561029518564283e+02 2.784200494851090752e+02 2.728829168298310606e+02 2.071711407548560544e+02 1.708729380756020362e+02 2.726254883308487251e+02 1.104364015278258364e+02 1.175773277008901090e+02
+2.554381337818412305e+02 1.634513906120204183e+02 2.309962436793083214e+02 2.460443770945291249e+02 1.618890365991254896e+02 1.046310291743186980e+02 2.772116654811295575e+02 2.098555252827713957e+02 2.309383801112169863e+02 2.845300950466865402e+02 1.268119123926061320e+02 1.697885006171669602e+02 1.901887742560337529e+02 2.605757830463372215e+02 2.755463791239279772e+02 1.771647294768940810e+02 2.403902735905423356e+02 1.774352552408031443e+02 1.796883744424403631e+02 2.736192366006921475e+02 2.118505050785533967e+02 1.873353967662169453e+02 1.802980863638028950e+02 1.869858546159753132e+02 1.200946851663063342e+02 2.350811068219035178e+02 2.018941614745772313e+02 1.010158706413519525e+02 1.661546933057649937e+02 2.570882207683835077e+02
+2.856134023048114159e+02 1.356279054667102741e+02 1.225310201562991494e+02 1.529777144242077327e+02 2.936506440162480658e+02 2.589580133771784176e+02 1.864782805190425279e+02 1.931182124516369640e+02 2.913608028278327993e+02 1.555662042949096531e+02 1.173676742008071301e+02 2.242990267171766732e+02 2.651338851871976203e+02 1.128980005738893482e+02 1.283582653966309408e+02 2.071495534530326097e+02 1.241509031508031740e+02 2.393403040292282640e+02 2.829812266966206380e+02 2.294799861563923287e+02 2.129576840814710295e+02 2.165539860914115877e+02 1.357366103660294243e+02 2.396252028023287153e+02 1.395106368224716107e+02 1.700689743264745744e+02 1.253435651632085950e+02 1.508112259783626428e+02 2.310267786371028933e+02 2.311667616985857876e+02

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/random-int-data.txt

@@ -0,0 +1,100 @@
+-67 65 82 64 51 1 -12 2 -84 -52 12 82 -45 -84 -41 31 -49 36 -70 40 -74 -99 32 64 -6 43 -53 -43 43 96
+-58 20 25 99 -25 78 -6 59 -23 30 36 25 -8 83 -43 -7 -8 42 -90 96 46 88 31 12 68 -21 -6 7 78 -19
+-66 -51 0 13 42 -43 -30 -29 20 10 -24 -5 -42 38 -56 6 1 -80 -65 -91 89 64 -21 49 -84 41 6 -78 71 -2
+-50 -84 -50 -66 46 -88 -10 -28 -25 6 -7 10 -35 86 41 -17 72 -67 13 -67 -76 -84 -15 35 67 40 90 38 -1 -47
+-51 27 -48 26 -73 -46 -68 -56 -38 -4 49 -64 57 -86 -80 70 50 34 84 97 -76 3 -54 -89 -7 -53 15 36 -28 85
+2 -59 4 30 70 -42 -26 -1 27 -90 -18 95 -10 -36 43 24 86 -8 -100 92 80 -40 17 -93 -81 54 -8 84 -53 38
+-80 0 -71 -41 -33 9 -61 0 26 80 64 67 74 68 -72 78 -72 -52 -19 37 -33 -24 -11 -71 -53 -16 25 56 -74 0
+71 -23 49 -36 -43 -70 82 69 -100 -27 50 20 30 84 -33 90 49 39 -52 -51 -86 -76 -72 -88 12 91 -96 -61 -87 -47
+21 39 1 78 68 -80 -54 71 17 -94 34 -20 14 -5 -24 55 -84 -50 -90 -24 -79 -81 53 -50 22 -13 -92 78 -22 -50
+-47 -73 77 -93 -20 51 -37 -14 -37 -18 -8 -14 -71 29 -27 -5 54 77 -7 -2 15 -23 98 -34 -65 -78 -77 -90 -5 -35
+92 -33 71 24 43 -19 50 -40 -48 -33 -51 -14 23 40 -78 -14 -76 1 52 69 93 5 -13 30 -60 -20 -54 49 -52 93
+32 -86 21 -41 -86 -38 97 -35 -37 -89 -15 -18 -46 -37 8 63 -63 -61 57 50 43 -27 -45 98 -56 -81 16 -38 -25 -28
+-18 19 -52 -86 92 -72 23 35 20 57 69 -22 52 -66 -74 -29 -1 -10 -97 22 -97 -93 -70 87 85 -31 42 -29 -10 -36
+78 80 -93 68 41 84 -37 -62 38 -9 99 -60 90 47 -33 -40 -59 97 -28 9 35 -6 -60 -83 -39 -97 -25 -78 95 40
+79 -35 -45 -46 69 10 29 -88 98 -44 66 11 45 -58 -11 -25 51 -44 54 30 59 98 35 -28 93 86 99 19 -27 -83
+80 77 -72 57 -35 -27 86 -67 11 77 -28 -89 -30 -31 -72 64 -95 -75 92 -32 -96 -14 6 -83 -66 -58 71 -17 58 -53
+-1 17 -72 82 -57 -48 -7 -44 -80 85 -99 -9 27 -11 24 13 86 18 67 -9 12 77 98 49 49 12 -82 45 31 -68
+-13 -75 -26 17 91 12 -95 -62 -54 -60 22 50 86 58 -11 -11 -21 31 16 -15 67 90 1 80 -57 -98 35 -54 51 91
+28 -75 -31 49 0 73 75 -66 50 -77 -20 82 -40 -90 -28 32 -44 89 -75 -33 -11 -19 -55 79 18 2 -39 -49 78 -72
+14 56 78 69 -40 -20 -39 71 99 -89 60 -82 -1 -77 -42 94 -41 35 72 11 -13 89 -52 -41 -93 43 -39 -61 68 -4
+88 18 -90 -75 -49 46 -28 -48 -69 -64 77 -8 91 -65 62 -27 -19 34 10 78 82 49 -34 63 78 -88 -17 -37 -85 91
+4 36 -77 -75 -12 70 42 8 7 -31 -69 -74 -65 18 85 -92 91 16 -15 24 -74 -56 71 -70 -90 20 13 73 -68 -65
+92 22 -31 -73 -59 -78 -20 -11 -61 36 -40 34 -96 -12 51 -45 -12 12 -3 -42 -71 68 -8 -91 50 -73 -96 -46 -38 -4
+-87 44 -58 -83 70 -81 32 29 -79 45 -64 -52 57 73 -80 69 7 -22 31 -71 -34 -33 47 79 -17 6 -77 -89 3 50
+85 2 73 -88 -99 -13 -76 1 -90 51 30 -52 75 -2 -8 10 -83 -40 -5 -79 82 19 79 94 49 4 66 -76 6 -48
+29 -34 66 -93 45 -1 -98 92 -92 29 -10 64 -23 -81 -73 -62 -18 37 -29 -50 -52 90 -28 24 -4 -67 -33 25 -78 93
+57 -46 36 -16 34 -59 -96 -86 64 2 28 42 -32 6 -17 37 38 -40 -92 55 -22 -42 11 -77 12 81 -89 -39 -30 -39
+-72 -68 -41 -5 93 55 24 -6 84 77 30 33 -51 -62 6 -5 -83 60 -1 -64 7 -7 -92 31 5 -21 -34 -14 21 -33
+26 -75 -36 -54 -21 -38 -49 -20 82 73 -84 -5 -69 84 -87 12 7 -67 -40 -50 -35 -65 80 -83 -2 1 34 -16 91 82
+61 -21 1 -64 -56 -61 74 16 0 38 51 34 -35 37 -28 -52 -14 61 14 58 50 27 -43 -27 14 56 -16 -78 50 -89
+45 -47 -61 68 -41 -70 14 -51 49 -84 64 -65 88 -39 -88 28 -55 -18 81 -2 -1 -45 65 -6 62 16 71 71 -1 47
+47 60 22 -42 -5 -74 12 66 89 -82 -85 65 74 0 -18 56 -39 84 -65 -42 -33 -60 23 33 -8 -72 3 -64 -3 -25
+-70 11 -19 -12 -1 -50 -89 -61 78 28 55 92 -17 86 -17 -45 -31 68 -24 -99 -59 27 79 -2 21 -80 54 9 14 -70
+-38 52 -99 50 -46 -63 -74 -41 -43 -62 -81 38 -99 17 -94 -6 44 -20 -13 -30 71 -43 43 -28 -8 57 -93 98 4 42
+-17 -27 -60 -22 86 -49 39 -83 72 -16 82 74 73 -29 16 -59 81 -60 -96 51 -62 -55 -79 -31 -15 -67 -18 -83 -61 -86
+28 37 -44 7 -17 -10 -65 8 -78 -17 -46 -5 -35 -86 13 -16 27 24 60 -12 -48 -45 16 -33 70 -45 -63 -60 21 70
+-75 -89 -93 -93 62 -44 -39 46 31 57 72 30 -65 29 66 -53 2 -2 71 -90 -73 -40 -63 32 68 30 25 98 38 92
+88 3 5 73 -2 -61 -94 79 99 94 71 -83 -40 80 -79 -14 -34 -99 -52 27 23 13 13 -35 -74 13 43 -19 2 -62
+92 -47 -27 9 -68 -86 -57 43 9 -81 -9 69 52 -28 80 -13 -6 -44 -81 -89 -10 30 -64 86 -76 -11 -100 15 12 -62
+76 -42 39 70 74 79 84 -52 18 -58 78 53 89 58 -32 20 -51 35 12 37 -70 -21 5 97 67 -25 -25 -10 2 30
+-84 26 -60 -34 11 -27 47 85 -89 29 54 -53 66 -9 12 4 92 70 2 -12 -55 72 -62 -79 -8 68 -19 12 -8 -100
+78 -97 -76 86 -47 42 99 -3 9 49 -84 86 26 43 -26 90 23 -66 -37 -35 25 -12 -42 -12 96 -15 48 87 -95 -12
+-60 57 -30 -4 -84 24 -82 -5 34 56 76 81 -64 23 32 34 -41 -48 -6 77 -42 64 87 92 82 59 9 -71 -56 -45
+-74 -90 -27 93 33 15 -35 -73 78 23 17 -28 9 63 9 35 15 32 0 -4 -32 54 -76 14 -14 -8 16 -43 -81 57
+-2 22 85 -33 -48 74 64 -59 -27 17 -65 27 -50 -81 41 -69 -26 -29 -83 48 -81 51 58 -62 -63 -55 -63 39 32 -34
+98 -99 13 25 -10 43 -62 50 82 -90 -51 40 -71 82 27 -73 19 -62 37 10 -21 45 -94 -45 -41 -3 44 86 -2 27
+-80 -89 -57 87 -42 19 32 -49 37 -4 -30 54 46 -3 -92 89 60 37 -86 38 61 93 45 -45 -86 54 21 45 50 -53
+7 -68 71 -6 41 -72 67 45 15 46 85 59 82 19 65 75 -62 -35 47 -51 23 41 -54 27 -99 14 9 69 60 62
+99 -51 83 -47 -19 -57 -22 51 -52 52 92 80 69 1 -31 0 -19 -54 73 -5 3 82 -86 -84 -95 -83 -92 -52 -90 -79
+43 -75 62 99 66 -43 -38 -21 23 35 -63 -61 -46 5 3 -90 -28 55 87 89 -29 -46 23 -61 -5 10 -70 -63 50 -14
+39 38 10 66 -24 -45 55 -33 31 29 44 31 73 44 6 69 -21 -58 -3 93 -51 86 -16 -88 88 -30 75 78 -20 -12
+-11 11 -19 40 82 6 10 22 90 -78 -88 -49 72 69 -62 42 -23 22 -38 -98 0 -3 -43 20 9 18 -67 -7 22 21
+99 80 -55 74 43 -31 60 -26 -29 -6 75 60 92 -42 85 18 1 1 -74 -44 -12 72 -57 -98 99 62 45 -40 -39 -75
+50 30 -18 -29 -80 -59 -96 46 -99 -76 -13 -75 -93 -95 -45 62 -37 53 -96 57 -40 3 14 -45 -84 58 75 16 37 -6
+1 -47 87 -99 -22 -22 -20 71 -91 13 35 -80 75 65 -87 16 -37 99 -60 49 52 18 55 -11 18 24 -65 -80 8 -79
+-8 -87 86 -9 -64 -76 59 -52 -89 18 13 70 44 93 99 62 39 49 83 28 72 -71 -13 -71 -22 44 -87 73 -68 80
+41 -26 44 -63 -26 -83 -44 63 -51 -48 52 -8 55 73 -45 84 40 45 32 -34 -78 -46 -79 57 -40 11 34 -75 -20 91
+94 9 -35 -5 3 59 -63 2 -7 -72 -34 -70 78 99 -29 37 11 91 61 29 85 -15 59 79 47 41 19 -18 -92 47
+-59 -89 57 -72 -79 88 -85 18 -35 -96 -57 33 83 70 -55 -16 -21 72 -53 89 -44 -86 9 -44 -26 78 2 -93 -75 6
+55 73 89 80 -69 -93 -39 -88 62 49 91 -68 87 -26 40 16 -49 -53 -57 23 -97 39 -78 44 -15 1 60 -87 43 -42
+-2 -23 -74 -80 -59 52 -58 68 64 97 -86 -41 -88 35 49 3 -40 90 34 -2 3 13 -95 8 -1 6 75 92 19 -31
+57 76 65 3 37 -72 -43 57 64 -23 41 87 26 76 -18 -32 28 47 11 47 -33 -12 4 81 -92 -47 -81 43 -2 5
+68 74 66 -89 -95 -40 -78 -58 -54 -20 2 20 94 -35 58 -20 41 77 0 95 39 14 36 -40 -85 -60 -63 82 0 58
+-61 -99 61 10 -2 -31 -70 37 -77 -10 85 95 -28 70 -81 -78 -68 -33 -77 77 -6 42 -100 -68 -59 -86 -42 -74 35 -32
+64 -1 -1 -64 51 11 -65 47 -87 -8 5 58 22 -80 68 -25 24 59 -25 -75 95 -22 -73 27 86 -39 -98 -1 -17 -32
+94 -50 -53 -62 -53 46 50 38 -95 -77 40 -38 -23 -14 -68 -20 -47 23 -8 -12 -92 -69 -97 30 94 -45 47 -81 82 -60
+28 67 -48 4 74 27 -30 12 -32 35 91 -83 30 -55 -7 79 97 11 93 -45 -79 31 78 65 84 -23 -26 17 -61 43
+44 60 -88 72 31 98 55 -4 66 -14 10 -81 -40 66 -15 21 69 -98 34 3 75 18 98 -6 47 -39 31 -19 30 -51
+-6 18 -93 31 51 -20 -16 -33 -38 -19 71 4 -53 23 97 1 -28 -72 -44 -48 45 33 -76 86 64 49 -45 -34 -9 -76
+-19 8 28 -27 -51 -58 -36 63 -92 -95 70 41 -38 -49 -95 -100 43 97 -60 -5 -56 45 -13 -3 20 -10 -21 -85 -5 63
+-74 -74 -74 -39 -57 -12 51 11 -11 -22 -26 -54 71 24 -37 77 -90 77 75 86 -53 3 69 -99 -82 -59 30 81 -21 -86
+67 63 87 -15 60 -82 87 51 -39 -49 -16 74 51 17 6 47 98 89 -20 -98 97 -61 18 34 37 -36 37 -96 90 44
+53 -8 37 -76 -61 70 -77 -11 98 -80 12 -80 6 -89 8 -59 -69 -100 -52 -30 95 -58 61 29 52 -64 -51 10 16 -58
+54 -10 49 62 76 -25 80 36 13 5 59 -65 14 41 26 -78 23 -45 -51 -85 91 -43 -61 -37 94 27 -11 49 98 48
+53 -51 27 34 28 -53 18 17 31 -31 59 71 -34 25 54 -84 -34 -24 76 38 -36 15 -1 56 2 -12 0 26 -38 -62
+4 -94 -63 -21 -95 -42 -12 86 14 -86 -1 80 -48 62 -47 -52 3 91 -86 11 79 32 -24 -33 -54 19 -17 28 -33 -97
+-18 41 84 1 -83 48 -99 -64 26 -52 3 -64 68 -98 93 -79 -97 11 88 74 41 -31 -42 -35 -66 18 97 -30 19 -93
+-19 42 61 -91 -20 59 -11 -64 -60 85 -6 -71 33 -52 46 51 -86 -77 74 -4 74 -81 1 -39 -30 12 -12 20 66 60
+86 1 -67 -91 -92 -22 91 -90 -45 26 53 -6 99 46 -29 -40 -99 57 -45 -47 -3 -86 90 -78 -33 73 90 -51 -75 2
+88 -34 -2 30 -18 35 -23 90 99 -49 90 -79 94 -38 48 67 -35 -58 81 -24 18 -54 83 65 -58 -12 13 89 -59 57
+92 -99 94 -73 97 -78 -93 98 -78 95 -21 -17 -11 -92 69 -60 86 9 -36 -18 -33 -39 -65 74 -65 37 -49 87 -28 -81
+-95 2 -18 20 93 54 86 -63 -5 -89 17 -9 75 -66 -64 -82 -46 -48 82 5 -89 19 -32 -45 53 -47 21 -9 40 34
+86 87 55 -41 49 -10 -6 -7 -99 23 90 -50 -9 -81 77 65 29 -21 22 -82 19 48 -24 -72 75 -66 -69 -17 72 6
+13 37 96 31 -65 -54 -91 -27 84 52 -9 -28 85 96 14 63 -34 -29 -85 78 -75 -44 -30 -5 4 72 -45 6 13 71
+96 -69 67 59 69 46 80 42 81 30 89 -45 -10 -44 25 31 89 16 -36 86 31 92 1 5 -2 92 -11 77 20 40
+-48 98 -100 30 54 9 84 -88 5 48 93 56 -94 -89 81 33 44 -30 -95 -98 29 -33 13 -26 -59 -80 -68 -40 12 11
+82 -63 -30 -67 54 -68 50 -63 -91 -68 -45 -66 -58 16 -25 9 -50 -59 -55 4 -2 0 -63 67 30 -21 -8 55 21 -68
+9 -8 56 -6 84 81 -63 -35 81 56 -50 -54 96 -51 86 0 66 -4 -18 65 -26 -57 8 78 -54 17 18 86 21 68
+9 38 33 16 3 86 -57 28 -6 -44 -42 -2 3 -71 -86 23 34 -29 33 -30 67 63 -11 76 -65 92 30 -66 61 1
+-72 -85 -1 64 -79 -78 -1 15 -35 -32 80 33 -36 -82 24 -65 -23 29 38 -31 87 55 -18 -52 -77 -22 -11 54 62 -48
+65 -77 50 16 41 -94 -21 16 85 24 60 86 -78 -13 69 46 55 5 -27 -18 -6 -1 59 -62 -58 -99 -49 -84 89 18
+-21 -15 -55 60 78 98 67 94 58 -5 -36 42 36 73 13 72 -78 -68 41 -37 -33 -46 -80 40 13 -44 -71 -8 15 -77
+16 -93 -42 -10 14 57 -54 -3 -44 -21 30 -93 71 25 -60 -94 93 5 -94 -84 -72 1 -50 -34 23 -15 15 18 72 -29
+-22 -82 -30 -87 -88 -25 46 32 -30 -55 -79 -85 71 -89 -57 -88 21 53 -100 -64 -92 -97 56 -51 -17 -34 -31 6 -68 84
+-53 -51 90 -38 -61 57 -63 67 22 22 70 44 43 97 20 -62 -74 72 83 -32 35 -66 -29 5 -88 55 -94 94 -19 55
+57 51 29 -42 -21 63 -57 7 -48 -87 -60 -55 -77 -53 -1 -85 64 60 53 71 41 59 -61 -73 -12 86 90 10 -60 -38
+2 -9 14 67 -2 70 11 -78 26 -55 -86 -25 99 66 63 64 46 59 66 -37 -78 -70 63 1 -20 2 46 50 34 19
+-87 -40 75 -11 -88 -80 -95 -20 -92 -28 83 24 88 -39 83 -36 -61 56 99 -73 -59 -85 -49 -10 91 12 -79 -18 -15 6
+35 -74 -4 -15 40 -87 81 -22 -12 -46 14 9 98 -35 -2 -12 57 -74 -52 71 70 -70 -61 -47 89 44 33 -100 54 42
+-4 -34 80 -12 -15 -9 -8 -29 89 -55 -33 89 16 -33 -73 -82 98 27 88 59 48 20 -67 -21 -86 11 -50 46 64 -8

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/random-uint-data.txt

@@ -0,0 +1,100 @@
+52 34 59 34 64 20 89 69 26 93 95 32 17 93 77 49 51 60 51 27 60 10 61 2 16 30 41 68 65 0
+43 74 11 37 32 61 72 29 47 21 7 47 68 58 22 33 29 37 14 45 71 1 67 79 69 9 6 6 95 78
+86 20 68 67 43 5 77 70 96 37 79 71 35 30 22 4 56 28 33 50 97 17 85 52 21 5 57 19 35 97
+15 21 99 4 54 39 15 29 68 21 50 76 64 51 79 0 24 5 65 95 90 51 99 82 9 80 61 32 2 38
+46 97 53 96 51 84 18 42 30 52 82 77 72 59 1 67 72 16 14 63 70 94 20 27 38 70 86 95 41 75
+2 35 45 63 92 76 81 60 62 72 90 46 47 33 1 30 54 22 50 85 63 61 22 79 45 53 45 33 8 28
+43 41 14 79 2 77 95 16 74 19 17 78 47 12 68 55 3 2 77 10 35 86 52 33 47 26 98 42 48 86
+18 32 85 4 91 10 69 68 15 42 58 77 88 64 91 43 56 30 92 11 52 23 43 92 65 50 68 8 80 81
+20 57 38 44 62 10 80 25 32 11 70 32 13 50 41 55 44 0 28 83 5 1 34 94 55 52 56 24 76 21
+36 43 59 28 10 59 4 41 64 98 54 66 44 3 37 41 67 10 85 23 58 35 58 34 35 79 46 18 1 51
+72 63 85 51 23 91 3 56 35 72 38 26 91 0 68 98 27 10 12 71 30 1 14 47 47 88 17 68 78 46
+53 47 1 89 95 53 11 45 46 6 91 20 57 35 58 79 60 3 21 45 4 18 59 96 36 12 13 83 52 46
+33 91 82 24 97 28 50 43 65 22 14 44 32 57 33 10 34 77 58 6 27 90 26 77 62 81 87 96 0 32
+96 44 59 3 47 18 0 91 83 68 48 26 67 82 39 18 88 47 80 0 57 40 30 7 57 74 49 37 57 65
+18 44 0 46 47 30 65 79 53 8 26 42 80 76 30 61 82 93 78 25 89 49 55 15 86 63 35 74 41 11
+18 14 40 90 91 79 80 36 33 72 25 56 73 28 65 27 62 17 60 84 23 70 32 26 77 97 47 94 72 1
+82 36 68 10 83 83 40 42 51 55 82 6 37 69 93 82 64 13 54 30 45 36 87 59 1 80 39 93 11 61
+78 34 53 39 64 52 52 22 33 69 71 82 57 37 78 52 62 31 87 68 70 5 85 94 41 75 38 45 84 22
+36 23 51 15 61 76 88 85 36 96 21 60 34 61 72 60 69 81 5 17 16 82 30 61 39 96 40 70 42 71
+45 30 60 50 78 90 36 40 11 85 42 14 61 3 66 53 68 14 41 30 97 74 79 91 64 8 1 53 52 33
+55 24 35 4 49 51 44 70 93 78 25 65 1 29 96 12 93 94 13 65 4 47 84 10 90 12 36 48 21 36
+17 74 61 54 21 83 35 97 47 90 57 11 16 39 95 78 23 40 23 55 17 51 20 73 98 93 50 32 58 4
+84 76 78 33 50 29 11 20 5 93 63 22 91 92 44 85 62 25 63 92 36 26 57 33 8 74 69 64 78 91
+58 34 91 71 37 84 28 90 28 37 97 7 26 44 59 18 58 64 31 83 16 17 50 36 65 81 19 63 66 64
+20 71 1 35 87 5 47 27 6 95 86 75 74 9 94 93 26 5 61 3 97 88 0 57 21 64 46 24 86 12
+23 53 31 39 37 77 29 51 85 10 41 91 67 82 50 91 53 72 75 81 50 63 52 92 83 49 92 50 26 9
+38 43 13 87 11 45 28 16 27 61 70 52 77 9 57 42 73 22 32 95 23 91 93 63 16 44 26 9 93 83
+77 68 21 96 44 45 9 2 14 2 67 90 55 82 67 21 18 64 31 16 2 27 86 42 34 72 22 98 91 33
+89 66 87 76 0 32 81 39 55 76 23 56 51 53 75 79 30 86 1 66 64 14 46 84 92 19 95 47 77 97
+88 79 61 26 66 92 54 22 15 25 26 0 76 27 17 59 48 4 42 61 65 91 0 62 55 79 29 88 10 11
+24 89 91 39 56 36 16 86 41 31 14 35 7 71 77 74 33 11 49 7 96 83 31 63 90 49 96 22 58 86
+45 7 93 44 50 54 83 80 3 36 11 38 14 17 10 84 96 94 26 34 26 75 72 0 41 89 96 47 39 88
+0 95 2 22 68 38 0 3 51 6 13 10 14 49 75 69 25 39 63 67 12 80 37 77 10 90 60 35 84 37
+98 56 99 75 49 66 3 33 65 86 1 79 91 23 69 98 91 73 95 45 64 26 99 75 49 77 71 55 42 18
+80 39 26 94 85 42 91 27 14 57 36 34 10 44 38 77 23 39 54 25 32 5 17 9 66 3 67 94 20 11
+88 80 30 77 72 67 16 75 84 87 60 89 21 94 24 11 63 8 79 89 37 18 6 82 76 70 81 95 67 95
+92 36 55 55 43 18 76 94 30 74 95 38 45 95 54 87 22 57 4 65 15 90 90 38 73 24 67 24 36 25
+98 30 34 68 11 48 42 38 80 23 12 91 77 22 65 2 88 31 70 12 46 63 17 63 27 76 21 71 70 7
+76 29 56 12 41 66 22 96 8 6 7 13 27 10 77 90 2 76 30 24 81 88 19 16 93 13 30 24 98 96
+45 94 89 41 52 14 71 88 80 74 7 85 44 69 65 88 4 15 84 97 86 5 53 15 39 34 9 10 45 20
+95 47 45 96 71 10 36 10 90 49 7 68 14 46 97 89 82 58 69 34 93 77 90 9 27 91 29 27 22 17
+80 6 29 26 34 59 10 55 32 53 18 72 39 40 29 35 52 64 2 64 38 83 16 46 53 20 19 8 10 67
+47 44 79 32 58 82 26 69 0 26 4 73 95 98 61 96 20 38 3 92 6 5 25 24 42 49 15 92 80 16
+74 37 86 84 47 15 56 36 43 59 72 72 74 73 49 54 26 5 40 80 78 48 4 65 31 70 14 91 88 72
+91 45 73 62 83 40 49 3 27 79 80 90 3 3 58 44 7 66 77 42 37 25 20 91 47 63 71 7 72 22
+51 3 36 90 45 84 18 55 75 78 42 62 86 63 65 67 46 75 1 79 2 85 85 60 36 92 34 89 66 99
+36 99 0 63 89 65 54 58 52 28 98 27 67 1 45 71 35 52 55 55 44 23 46 89 83 37 8 2 92 75
+51 13 71 2 9 95 23 60 24 98 86 43 32 16 75 70 92 78 26 84 29 14 35 55 61 89 73 59 76 44
+59 57 28 92 33 50 70 94 89 67 70 38 53 16 35 70 35 92 39 78 88 80 71 1 93 21 87 64 49 84
+29 6 17 45 38 65 41 48 81 69 34 12 2 14 41 71 16 92 69 27 61 74 58 20 75 19 39 66 57 82
+12 8 14 85 97 31 58 31 20 76 6 42 29 95 60 94 15 84 86 69 73 52 73 57 12 66 89 65 60 84
+20 74 96 34 83 41 8 37 22 36 30 25 20 8 58 73 9 75 76 73 84 38 16 24 95 95 68 66 43 19
+33 15 25 80 48 69 63 39 16 45 6 77 14 46 38 15 64 85 49 5 59 28 9 4 23 68 59 26 1 75
+35 45 3 6 34 59 55 51 81 59 59 93 18 41 8 44 88 7 86 4 88 90 24 54 73 62 89 13 44 92
+72 60 68 83 39 32 30 15 98 92 69 94 51 48 9 0 4 1 30 92 40 1 61 82 66 4 39 10 93 87
+12 20 34 72 33 31 67 71 67 47 98 76 53 29 17 17 13 31 43 76 25 37 8 39 9 5 96 41 87 66
+96 30 2 57 57 10 14 17 86 76 35 94 42 54 18 24 19 34 12 42 18 11 83 65 86 38 45 17 60 70
+19 62 71 99 35 60 96 30 44 80 78 15 14 5 32 43 10 26 81 72 41 98 30 87 75 8 53 33 25 95
+22 0 38 57 88 7 47 83 49 41 52 1 14 93 41 3 18 42 15 57 28 74 97 2 18 48 64 25 77 69
+36 95 65 81 44 41 6 74 62 16 72 81 15 72 31 5 22 17 19 6 7 15 82 10 31 93 11 45 41 11
+22 76 14 62 34 65 82 5 57 51 51 5 1 6 17 43 28 31 90 99 48 14 96 49 95 40 87 85 40 51
+95 13 99 46 52 80 4 18 95 94 0 46 10 80 3 34 60 15 86 10 28 59 6 35 14 93 18 8 3 65
+57 37 6 31 45 85 42 34 47 92 48 40 7 17 5 74 67 62 0 74 58 21 23 3 5 24 50 54 99 19
+24 14 10 4 36 33 88 51 40 66 40 56 65 23 43 13 82 62 27 88 89 91 36 37 19 11 50 39 96 68
+82 7 39 80 52 90 57 17 61 15 51 71 82 15 21 44 4 46 75 50 78 18 63 75 98 45 6 16 57 25
+0 26 56 74 62 84 71 42 25 86 68 10 73 0 71 6 15 99 1 51 45 42 5 49 3 35 84 29 15 36
+60 78 76 3 95 73 36 57 35 44 50 42 85 57 18 69 37 42 75 79 15 12 74 72 51 36 79 3 58 71
+69 24 16 96 17 25 21 94 71 78 74 39 7 96 3 12 13 16 7 99 65 72 12 28 75 44 55 8 75 67
+3 13 92 9 92 83 69 91 65 92 29 63 46 1 4 62 29 85 47 93 81 3 15 23 63 50 17 9 13 13
+9 18 46 53 0 86 10 41 87 89 24 25 70 73 8 23 27 76 66 46 58 39 28 1 99 64 59 13 7 68
+72 57 90 50 47 57 34 27 94 39 23 31 74 77 45 74 18 49 96 8 95 50 20 81 73 55 72 2 32 15
+87 77 74 5 99 86 5 65 97 39 17 74 48 87 20 66 28 2 18 58 49 22 79 23 36 30 64 20 71 32
+35 43 66 96 63 77 18 90 47 86 94 19 88 79 23 12 38 4 56 42 36 2 77 1 3 17 64 52 31 24
+80 2 4 39 61 60 74 83 28 28 61 10 71 82 44 29 55 30 1 58 81 79 34 41 85 82 84 55 22 12
+76 77 58 92 90 0 54 28 77 68 58 12 1 81 37 28 19 60 71 59 25 83 8 49 52 11 28 65 59 70
+14 1 92 90 5 48 28 78 1 42 54 43 60 83 72 19 28 33 12 52 18 15 56 95 39 33 37 70 53 23
+53 76 26 31 18 81 83 79 25 1 82 43 50 24 63 49 5 23 66 37 80 41 63 77 2 28 15 21 32 93
+80 41 81 7 37 95 19 42 57 30 12 25 29 34 41 45 87 8 20 95 63 16 99 55 16 61 16 36 81 25
+32 30 2 81 23 25 88 30 37 76 52 77 79 58 21 58 10 0 13 32 72 80 3 75 75 25 21 9 79 18
+26 13 36 63 43 2 50 41 65 18 88 44 82 75 73 24 1 30 54 68 15 18 22 50 41 99 27 96 51 53
+22 4 76 11 85 88 28 75 1 2 92 66 63 3 58 43 53 5 1 24 99 90 87 87 41 1 85 37 98 92
+16 39 13 88 60 55 35 11 34 23 23 85 79 41 79 87 65 78 47 83 88 78 35 84 30 61 37 58 25 55
+27 33 15 76 82 79 73 92 93 78 18 38 22 96 63 92 41 9 50 96 14 55 8 60 15 61 97 56 43 22
+42 34 94 11 35 70 50 49 36 34 59 14 87 84 88 83 4 69 29 99 35 24 2 18 97 97 74 88 91 49
+33 25 71 12 60 2 48 22 81 33 27 95 54 25 53 14 20 43 26 96 98 37 64 27 72 33 78 45 22 61
+61 21 91 38 92 47 26 90 78 96 58 41 21 72 81 61 55 9 55 60 28 25 25 74 73 81 64 16 49 39
+90 89 12 93 91 23 82 36 63 58 73 81 49 32 60 39 4 84 73 16 18 26 58 85 46 28 82 91 72 7
+79 41 28 76 33 70 47 6 18 64 40 54 45 61 28 63 87 83 38 9 65 68 62 45 80 63 89 29 20 40
+20 59 58 23 61 79 35 19 78 2 26 48 90 34 69 31 31 42 92 33 18 74 28 47 45 52 36 89 19 40
+58 13 72 24 31 26 73 72 84 29 85 99 20 32 54 92 8 80 86 58 23 80 59 21 76 75 90 76 92 57
+74 53 80 51 8 88 84 63 82 99 97 77 38 9 51 61 37 20 68 47 65 21 53 82 85 96 62 65 35 4
+71 82 14 18 88 79 38 76 66 27 10 10 62 54 80 21 6 57 83 33 52 10 97 37 6 38 12 51 0 84
+95 30 75 92 84 30 55 57 32 44 53 24 77 81 34 84 69 85 91 33 50 72 62 79 62 12 59 75 99 81
+38 42 47 1 11 34 27 77 70 85 89 84 79 15 14 54 78 93 72 68 63 39 98 72 55 32 93 0 13 21
+3 15 10 15 3 31 84 89 53 5 60 41 66 77 45 12 68 68 50 68 99 64 46 54 30 56 2 90 99 78
+66 10 27 89 42 16 9 98 16 2 68 51 0 22 73 60 69 96 37 69 30 36 20 21 51 26 65 13 74 86
+94 58 34 97 77 88 90 75 47 30 6 36 89 66 48 9 20 6 52 45 0 37 99 46 11 53 53 72 94 40
+5 71 50 96 89 71 80 43 27 95 49 9 74 28 62 65 64 97 2 55 58 11 69 0 31 22 73 20 66 11
+63 39 84 62 64 5 56 92 26 86 19 20 56 85 42 48 56 51 54 29 26 95 72 38 70 61 16 54 57 19
+76 97 40 99 73 68 98 92 97 62 73 1 29 72 18 70 90 4 98 95 70 36 65 45 86 36 88 38 64 54

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/data/selfdual-4d-polytope.txt

@@ -0,0 +1,27 @@
+# The facets of a self-dual 4-dim regular polytope
+# with 24 octahedron facets. Taken from cddlib.
+# Format b + Ax >= 0
+ 1  1  1  1  1
+ 1  1  1  1 -1
+ 1  1  1 -1  1
+ 1  1  1 -1 -1
+ 1  1 -1  1  1
+ 1  1 -1  1 -1
+ 1  1 -1 -1  1
+ 1  1 -1 -1 -1
+ 1 -1  1  1  1
+ 1 -1  1  1 -1
+ 1 -1  1 -1  1
+ 1 -1  1 -1 -1
+ 1 -1 -1  1  1
+ 1 -1 -1  1 -1
+ 1 -1 -1 -1  1
+ 1 -1 -1 -1 -1
+ 1  2  0  0  0
+ 1  0  2  0  0
+ 1  0  0  2  0
+ 1  0  0  0  2
+ 1 -2  0  0  0
+ 1  0 -2  0  0
+ 1  0  0 -2  0
+ 1  0  0  0 -2

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/test__plotutils.py

@@ -0,0 +1,60 @@
+from __future__ import division, print_function, absolute_import
+
+import pytest
+from numpy.testing import assert_, assert_array_equal
+from scipy._lib._numpy_compat import suppress_warnings
+
+try:
+    import matplotlib
+    matplotlib.rcParams['backend'] = 'Agg'
+    import matplotlib.pyplot as plt
+    from matplotlib.collections import LineCollection
+    from matplotlib import MatplotlibDeprecationWarning
+    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()
+        with suppress_warnings() as sup:
+            # filter can be removed when matplotlib 1.x is dropped
+            sup.filter(message="The ishold function was deprecated in version")
+            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)
+        with suppress_warnings() as sup:
+            # filter can be removed when matplotlib 1.x is dropped
+            sup.filter(message="The ishold function was deprecated in version")
+            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)
+        with suppress_warnings() as sup:
+            # filter can be removed when matplotlib 1.x is dropped
+            sup.filter(message="The ishold function was deprecated in version")
+            r = convex_hull_plot_2d(tri, ax=fig.gca())
+        assert_(r is fig)
+        convex_hull_plot_2d(tri)

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/test__procrustes.py

@@ -0,0 +1,118 @@
+from __future__ import absolute_import, division, print_function
+
+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(object):
+    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]]]))
+

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/test_hausdorff.py

@@ -0,0 +1,115 @@
+from __future__ import division, absolute_import, print_function
+
+import numpy as np
+from numpy.testing import (assert_almost_equal,
+                           assert_array_equal,
+                           assert_equal,
+                           assert_)
+from scipy.spatial.distance import directed_hausdorff
+from scipy.spatial import distance
+from scipy._lib._util import check_random_state
+
+class TestHausdorff(object):
+    # Test various properties of the directed Hausdorff code.
+
+    def setup_method(self):
+        np.random.seed(1234)
+        random_angles = np.random.random(100) * np.pi * 2
+        random_columns = np.column_stack(
+            (random_angles, random_angles, np.zeros(100)))
+        random_columns[..., 0] = np.cos(random_columns[..., 0])
+        random_columns[..., 1] = np.sin(random_columns[..., 1])
+        random_columns_2 = np.column_stack(
+            (random_angles, random_angles, np.zeros(100)))
+        random_columns_2[1:, 0] = np.cos(random_columns_2[1:, 0]) * 2.0
+        random_columns_2[1:, 1] = np.sin(random_columns_2[1:, 1]) * 2.0
+        # move one point farther out so we don't have two perfect circles
+        random_columns_2[0, 0] = np.cos(random_columns_2[0, 0]) * 3.3
+        random_columns_2[0, 1] = np.sin(random_columns_2[0, 1]) * 3.3
+        self.path_1 = random_columns
+        self.path_2 = random_columns_2
+        self.path_1_4d = np.insert(self.path_1, 3, 5, axis=1)
+        self.path_2_4d = np.insert(self.path_2, 3, 27, axis=1)
+
+    def test_symmetry(self):
+        # Ensure that the directed (asymmetric) Hausdorff distance is
+        # actually asymmetric
+
+        forward = directed_hausdorff(self.path_1, self.path_2)[0]
+        reverse = directed_hausdorff(self.path_2, self.path_1)[0]
+        assert_(forward != reverse)
+
+    def test_brute_force_comparison_forward(self):
+        # Ensure that the algorithm for directed_hausdorff gives the
+        # same result as the simple / brute force approach in the
+        # forward direction.
+        actual = directed_hausdorff(self.path_1, self.path_2)[0]
+        # brute force over rows:
+        expected = max(np.amin(distance.cdist(self.path_1, self.path_2),
+                               axis=1))
+        assert_almost_equal(actual, expected, decimal=9)
+
+    def test_brute_force_comparison_reverse(self):
+        # Ensure that the algorithm for directed_hausdorff gives the
+        # same result as the simple / brute force approach in the
+        # reverse direction.
+        actual = directed_hausdorff(self.path_2, self.path_1)[0]
+        # brute force over columns:
+        expected = max(np.amin(distance.cdist(self.path_1, self.path_2), 
+                               axis=0))
+        assert_almost_equal(actual, expected, decimal=9)
+
+    def test_degenerate_case(self):
+        # The directed Hausdorff distance must be zero if both input
+        # data arrays match.
+        actual = directed_hausdorff(self.path_1, self.path_1)[0]
+        assert_almost_equal(actual, 0.0, decimal=9)
+
+    def test_2d_data_forward(self):
+        # Ensure that 2D data is handled properly for a simple case
+        # relative to brute force approach.
+        actual = directed_hausdorff(self.path_1[..., :2],
+                                    self.path_2[..., :2])[0]
+        expected = max(np.amin(distance.cdist(self.path_1[..., :2],
+                                              self.path_2[..., :2]),
+                               axis=1))
+        assert_almost_equal(actual, expected, decimal=9)
+
+    def test_4d_data_reverse(self):
+        # Ensure that 4D data is handled properly for a simple case
+        # relative to brute force approach.
+        actual = directed_hausdorff(self.path_2_4d, self.path_1_4d)[0]
+        # brute force over columns:
+        expected = max(np.amin(distance.cdist(self.path_1_4d, self.path_2_4d), 
+                               axis=0))
+        assert_almost_equal(actual, expected, decimal=9)
+
+    def test_indices(self):
+        # Ensure that correct point indices are returned -- they should
+        # correspond to the Hausdorff pair
+        path_simple_1 = np.array([[-1,-12],[0,0], [1,1], [3,7], [1,2]])
+        path_simple_2 = np.array([[0,0], [1,1], [4,100], [10,9]])
+        actual = directed_hausdorff(path_simple_2, path_simple_1)[1:]
+        expected = (2, 3)
+        assert_array_equal(actual, expected)
+
+    def test_random_state(self):
+        # ensure that the global random state is not modified because
+        # the directed Hausdorff algorithm uses randomization
+        rs = check_random_state(None)
+        old_global_state = rs.get_state()
+        directed_hausdorff(self.path_1, self.path_2)
+        rs2 = check_random_state(None)
+        new_global_state = rs2.get_state()
+        assert_equal(new_global_state, old_global_state)
+
+    def test_random_state_None_int(self):
+        # check that seed values of None or int do not alter global
+        # random state
+        for seed in [None, 27870671]:
+            rs = check_random_state(None)
+            old_global_state = rs.get_state()
+            directed_hausdorff(self.path_1, self.path_2, seed)
+            rs2 = check_random_state(None)
+            new_global_state = rs2.get_state()
+            assert_equal(new_global_state, old_global_state)

utils/venv/lib/python3.6/site-packages/scipy/spatial/tests/test_spherical_voronoi.py

@@ -0,0 +1,166 @@
+from __future__ import print_function
+import numpy as np
+import itertools
+from numpy.testing import (assert_equal,
+                           assert_almost_equal,
+                           assert_array_equal,
+                           assert_array_almost_equal)
+from pytest import raises as assert_raises
+from scipy.spatial import SphericalVoronoi, distance
+from scipy.spatial import _spherical_voronoi as spherical_voronoi
+
+
+class TestCircumcenters(object):
+
+    def test_circumcenters(self):
+        tetrahedrons = np.array([
+            [[1, 2, 3],
+             [-1.1, -2.1, -3.1],
+             [-1.2, 2.2, 3.2],
+             [-1.3, -2.3, 3.3]],
+            [[10, 20, 30],
+             [-10.1, -20.1, -30.1],
+             [-10.2, 20.2, 30.2],
+             [-10.3, -20.3, 30.3]]
+        ])
+
+        result = spherical_voronoi.calc_circumcenters(tetrahedrons)
+
+        expected = [
+            [-0.5680861153262529, -0.133279590288315, 0.1843323216995444],
+            [-0.5965330784014926, -0.1480377040397778, 0.1981967854886021]
+        ]
+
+        assert_array_almost_equal(result, expected)
+
+
+class TestProjectToSphere(object):
+
+    def test_unit_sphere(self):
+        points = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        center = np.array([0, 0, 0])
+        radius = 1
+        projected = spherical_voronoi.project_to_sphere(points, center, radius)
+        assert_array_almost_equal(points, projected)
+
+    def test_scaled_points(self):
+        points = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        center = np.array([0, 0, 0])
+        radius = 1
+        scaled = points * 2
+        projected = spherical_voronoi.project_to_sphere(scaled, center, radius)
+        assert_array_almost_equal(points, projected)
+
+    def test_translated_sphere(self):
+        points = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        center = np.array([1, 2, 3])
+        translated = points + center
+        radius = 1
+        projected = spherical_voronoi.project_to_sphere(translated, center,
+                                                        radius)
+        assert_array_almost_equal(translated, projected)
+
+
+class TestSphericalVoronoi(object):
+
+    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, None, 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)
+
+    def test_vertices_regions_translation_invariance(self):
+        sv_origin = SphericalVoronoi(self.points)
+        center = np.array([1, 1, 1])
+        sv_translated = SphericalVoronoi(self.points + center, None, center)
+        assert_array_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_array_equal(sv_unit.regions, sv_scaled.regions)
+        assert_array_almost_equal(sv_unit.vertices * 2,
+                                  sv_scaled.vertices)
+
+    def test_sort_vertices_of_regions(self):
+        sv = SphericalVoronoi(self.points)
+        unsorted_regions = sv.regions
+        sv.sort_vertices_of_regions()
+        assert_array_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)))
+        sv = SphericalVoronoi(self.points)
+        sv.sort_vertices_of_regions()
+        actual = list(itertools.chain(*sorted(sv.regions)))
+        assert_array_equal(actual, expected)
+
+    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 = spherical_voronoi.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):
+            sv = spherical_voronoi.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):
+            sv = spherical_voronoi.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):
+            sv = spherical_voronoi.SphericalVoronoi(self.points,
+                                                    center=[0.1,0,0])

utils/venv/lib/python3.6/site-packages/scipy/spatial/transform/__init__.py

@@ -0,0 +1,28 @@
+"""
+========================================================
+Spatial Transformations (:mod:`scipy.spatial.transform`)
+========================================================
+
+.. currentmodule:: scipy.spatial.transform
+
+This package implements various spatial transformations. For now,
+only rotations are supported.
+
+Rotations in 3 dimensions
+=========================
+.. autosummary::
+   :toctree: generated/
+
+   Rotation
+   Slerp
+
+"""
+from __future__ import division, print_function, absolute_import
+
+from .rotation import Rotation, Slerp
+
+__all__ = ['Rotation', 'Slerp']
+
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester

utils/venv/lib/python3.6/site-packages/scipy/spatial/transform/setup.py

@@ -0,0 +1,11 @@
+from __future__ import division, print_function, absolute_import
+
+
+def configuration(parent_package='', top_path=None):
+    from numpy.distutils.misc_util import Configuration
+
+    config = Configuration('transform', parent_package, top_path)
+
+    config.add_data_dir('tests')
+
+    return config

utils/venv/lib/python3.6/site-packages/scipy/spatial/transform/tests/test_rotation.py

@@ -0,0 +1,945 @@
+from __future__ import division, print_function, absolute_import
+
+import pytest
+
+import numpy as np
+from numpy.testing import assert_equal, assert_array_almost_equal
+from numpy.testing import assert_allclose
+from scipy.spatial.transform import Rotation, Slerp
+from scipy.stats import special_ortho_group
+from itertools import permutations
+
+
+def test_generic_quat_matrix():
+    x = np.array([[3, 4, 0, 0], [5, 12, 0, 0]])
+    r = Rotation.from_quat(x)
+    expected_quat = x / np.array([[5], [13]])
+    assert_array_almost_equal(r.as_quat(), expected_quat)
+
+
+def test_from_single_1d_quaternion():
+    x = np.array([3, 4, 0, 0])
+    r = Rotation.from_quat(x)
+    expected_quat = x / 5
+    assert_array_almost_equal(r.as_quat(), expected_quat)
+
+
+def test_from_single_2d_quaternion():
+    x = np.array([[3, 4, 0, 0]])
+    r = Rotation.from_quat(x)
+    expected_quat = x / 5
+    assert_array_almost_equal(r.as_quat(), expected_quat)
+
+
+def test_from_square_quat_matrix():
+    # Ensure proper norm array broadcasting
+    x = np.array([
+        [3, 0, 0, 4],
+        [5, 0, 12, 0],
+        [0, 0, 0, 1],
+        [0, 0, 0, -1]
+        ])
+    r = Rotation.from_quat(x)
+    expected_quat = x / np.array([[5], [13], [1], [1]])
+    assert_array_almost_equal(r.as_quat(), expected_quat)
+
+
+def test_malformed_1d_from_quat():
+    with pytest.raises(ValueError):
+        Rotation.from_quat(np.array([1, 2, 3]))
+
+
+def test_malformed_2d_from_quat():
+    with pytest.raises(ValueError):
+        Rotation.from_quat(np.array([
+            [1, 2, 3, 4, 5],
+            [4, 5, 6, 7, 8]
+            ]))
+
+
+def test_zero_norms_from_quat():
+    x = np.array([
+            [3, 4, 0, 0],
+            [0, 0, 0, 0],
+            [5, 0, 12, 0]
+            ])
+    with pytest.raises(ValueError):
+        Rotation.from_quat(x)
+
+
+def test_as_dcm_single_1d_quaternion():
+    quat = [0, 0, 0, 1]
+    mat = Rotation.from_quat(quat).as_dcm()
+    # mat.shape == (3,3) due to 1d input
+    assert_array_almost_equal(mat, np.eye(3))
+
+
+def test_as_dcm_single_2d_quaternion():
+    quat = [[0, 0, 1, 1]]
+    mat = Rotation.from_quat(quat).as_dcm()
+    assert_equal(mat.shape, (1, 3, 3))
+    expected_mat = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+        ])
+    assert_array_almost_equal(mat[0], expected_mat)
+
+
+def test_as_dcm_from_square_input():
+    quats = [
+            [0, 0, 1, 1],
+            [0, 1, 0, 1],
+            [0, 0, 0, 1],
+            [0, 0, 0, -1]
+            ]
+    mat = Rotation.from_quat(quats).as_dcm()
+    assert_equal(mat.shape, (4, 3, 3))
+
+    expected0 = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+        ])
+    assert_array_almost_equal(mat[0], expected0)
+
+    expected1 = np.array([
+        [0, 0, 1],
+        [0, 1, 0],
+        [-1, 0, 0]
+        ])
+    assert_array_almost_equal(mat[1], expected1)
+
+    assert_array_almost_equal(mat[2], np.eye(3))
+    assert_array_almost_equal(mat[3], np.eye(3))
+
+
+def test_as_dcm_from_generic_input():
+    quats = [
+            [0, 0, 1, 1],
+            [0, 1, 0, 1],
+            [1, 2, 3, 4]
+            ]
+    mat = Rotation.from_quat(quats).as_dcm()
+    assert_equal(mat.shape, (3, 3, 3))
+
+    expected0 = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+        ])
+    assert_array_almost_equal(mat[0], expected0)
+
+    expected1 = np.array([
+        [0, 0, 1],
+        [0, 1, 0],
+        [-1, 0, 0]
+        ])
+    assert_array_almost_equal(mat[1], expected1)
+
+    expected2 = np.array([
+        [0.4, -2, 2.2],
+        [2.8, 1, 0.4],
+        [-1, 2, 2]
+        ]) / 3
+    assert_array_almost_equal(mat[2], expected2)
+
+
+def test_from_single_2d_dcm():
+    dcm = [
+            [0, 0, 1],
+            [1, 0, 0],
+            [0, 1, 0]
+            ]
+    expected_quat = [0.5, 0.5, 0.5, 0.5]
+    assert_array_almost_equal(
+            Rotation.from_dcm(dcm).as_quat(),
+            expected_quat)
+
+
+def test_from_single_3d_dcm():
+    dcm = np.array([
+        [0, 0, 1],
+        [1, 0, 0],
+        [0, 1, 0]
+        ]).reshape((1, 3, 3))
+    expected_quat = np.array([0.5, 0.5, 0.5, 0.5]).reshape((1, 4))
+    assert_array_almost_equal(
+            Rotation.from_dcm(dcm).as_quat(),
+            expected_quat)
+
+
+def test_from_dcm_calculation():
+    expected_quat = np.array([1, 1, 6, 1]) / np.sqrt(39)
+    dcm = np.array([
+            [-0.8974359, -0.2564103, 0.3589744],
+            [0.3589744, -0.8974359, 0.2564103],
+            [0.2564103, 0.3589744, 0.8974359]
+            ])
+    assert_array_almost_equal(
+            Rotation.from_dcm(dcm).as_quat(),
+            expected_quat)
+    assert_array_almost_equal(
+            Rotation.from_dcm(dcm.reshape((1, 3, 3))).as_quat(),
+            expected_quat.reshape((1, 4)))
+
+
+def test_dcm_calculation_pipeline():
+    dcm = special_ortho_group.rvs(3, size=10, random_state=0)
+    assert_array_almost_equal(Rotation.from_dcm(dcm).as_dcm(), dcm)
+
+
+def test_from_dcm_ortho_output():
+    np.random.seed(0)
+    dcm = np.random.random((100, 3, 3))
+    ortho_dcm = Rotation.from_dcm(dcm).as_dcm()
+
+    mult_result = np.einsum('...ij,...jk->...ik', ortho_dcm,
+                            ortho_dcm.transpose((0, 2, 1)))
+
+    eye3d = np.zeros((100, 3, 3))
+    for i in range(3):
+        eye3d[:, i, i] = 1.0
+
+    assert_array_almost_equal(mult_result, eye3d)
+
+
+def test_from_1d_single_rotvec():
+    rotvec = [1, 0, 0]
+    expected_quat = np.array([0.4794255, 0, 0, 0.8775826])
+    result = Rotation.from_rotvec(rotvec)
+    assert_array_almost_equal(result.as_quat(), expected_quat)
+
+
+def test_from_2d_single_rotvec():
+    rotvec = [[1, 0, 0]]
+    expected_quat = np.array([[0.4794255, 0, 0, 0.8775826]])
+    result = Rotation.from_rotvec(rotvec)
+    assert_array_almost_equal(result.as_quat(), expected_quat)
+
+
+def test_from_generic_rotvec():
+    rotvec = [
+            [1, 2, 2],
+            [1, -1, 0.5],
+            [0, 0, 0]
+            ]
+    expected_quat = np.array([
+        [0.3324983, 0.6649967, 0.6649967, 0.0707372],
+        [0.4544258, -0.4544258, 0.2272129, 0.7316889],
+        [0, 0, 0, 1]
+        ])
+    assert_array_almost_equal(
+            Rotation.from_rotvec(rotvec).as_quat(),
+            expected_quat)
+
+
+def test_from_rotvec_small_angle():
+    rotvec = np.array([
+        [5e-4 / np.sqrt(3), -5e-4 / np.sqrt(3), 5e-4 / np.sqrt(3)],
+        [0.2, 0.3, 0.4],
+        [0, 0, 0]
+        ])
+
+    quat = Rotation.from_rotvec(rotvec).as_quat()
+    # cos(theta/2) ~~ 1 for small theta
+    assert_allclose(quat[0, 3], 1)
+    # sin(theta/2) / theta ~~ 0.5 for small theta
+    assert_allclose(quat[0, :3], rotvec[0] * 0.5)
+
+    assert_allclose(quat[1, 3], 0.9639685)
+    assert_allclose(
+            quat[1, :3],
+            np.array([
+                0.09879603932153465,
+                0.14819405898230198,
+                0.19759207864306931
+                ]))
+
+    assert_equal(quat[2], np.array([0, 0, 0, 1]))
+
+
+def test_malformed_1d_from_rotvec():
+    with pytest.raises(ValueError, match='Expected `rot_vec` to have shape'):
+        Rotation.from_rotvec([1, 2])
+
+
+def test_malformed_2d_from_rotvec():
+    with pytest.raises(ValueError, match='Expected `rot_vec` to have shape'):
+        Rotation.from_rotvec([
+            [1, 2, 3, 4],
+            [5, 6, 7, 8]
+            ])
+
+
+def test_as_generic_rotvec():
+    quat = np.array([
+            [1, 2, -1, 0.5],
+            [1, -1, 1, 0.0003],
+            [0, 0, 0, 1]
+            ])
+    quat /= np.linalg.norm(quat, axis=1)[:, None]
+
+    rotvec = Rotation.from_quat(quat).as_rotvec()
+    angle = np.linalg.norm(rotvec, axis=1)
+
+    assert_allclose(quat[:, 3], np.cos(angle/2))
+    assert_allclose(np.cross(rotvec, quat[:, :3]), np.zeros((3, 3)))
+
+
+def test_as_rotvec_single_1d_input():
+    quat = np.array([1, 2, -3, 2])
+    expected_rotvec = np.array([0.5772381, 1.1544763, -1.7317144])
+
+    actual_rotvec = Rotation.from_quat(quat).as_rotvec()
+
+    assert_equal(actual_rotvec.shape, (3,))
+    assert_allclose(actual_rotvec, expected_rotvec)
+
+
+def test_as_rotvec_single_2d_input():
+    quat = np.array([[1, 2, -3, 2]])
+    expected_rotvec = np.array([[0.5772381, 1.1544763, -1.7317144]])
+
+    actual_rotvec = Rotation.from_quat(quat).as_rotvec()
+
+    assert_equal(actual_rotvec.shape, (1, 3))
+    assert_allclose(actual_rotvec, expected_rotvec)
+
+
+def test_rotvec_calc_pipeline():
+    # Include small angles
+    rotvec = np.array([
+        [0, 0, 0],
+        [1, -1, 2],
+        [-3e-4, 3.5e-4, 7.5e-5]
+        ])
+    assert_allclose(Rotation.from_rotvec(rotvec).as_rotvec(), rotvec)
+
+
+def test_from_euler_single_rotation():
+    quat = Rotation.from_euler('z', 90, degrees=True).as_quat()
+    expected_quat = np.array([0, 0, 1, 1]) / np.sqrt(2)
+    assert_allclose(quat, expected_quat)
+
+
+def test_single_intrinsic_extrinsic_rotation():
+    extrinsic = Rotation.from_euler('z', 90, degrees=True).as_dcm()
+    intrinsic = Rotation.from_euler('Z', 90, degrees=True).as_dcm()
+    assert_allclose(extrinsic, intrinsic)
+
+
+def test_from_euler_rotation_order():
+    # Intrinsic rotation is same as extrinsic with order reversed
+    np.random.seed(0)
+    a = np.random.randint(low=0, high=180, size=(6, 3))
+    b = a[:, ::-1]
+    x = Rotation.from_euler('xyz', a, degrees=True).as_quat()
+    y = Rotation.from_euler('ZYX', b, degrees=True).as_quat()
+    assert_allclose(x, y)
+
+
+def test_from_euler_elementary_extrinsic_rotation():
+    # Simple test to check if extrinsic rotations are implemented correctly
+    dcm = Rotation.from_euler('zx', [90, 90], degrees=True).as_dcm()
+    expected_dcm = np.array([
+        [0, -1, 0],
+        [0, 0, -1],
+        [1, 0, 0]
+    ])
+    assert_array_almost_equal(dcm, expected_dcm)
+
+
+def test_from_euler_intrinsic_rotation_312():
+    angles = [
+        [30, 60, 45],
+        [30, 60, 30],
+        [45, 30, 60]
+        ]
+    dcm = Rotation.from_euler('ZXY', angles, degrees=True).as_dcm()
+
+    assert_array_almost_equal(dcm[0], np.array([
+        [0.3061862, -0.2500000, 0.9185587],
+        [0.8838835, 0.4330127, -0.1767767],
+        [-0.3535534, 0.8660254, 0.3535534]
+    ]))
+
+    assert_array_almost_equal(dcm[1], np.array([
+        [0.5334936, -0.2500000, 0.8080127],
+        [0.8080127, 0.4330127, -0.3995191],
+        [-0.2500000, 0.8660254, 0.4330127]
+    ]))
+
+    assert_array_almost_equal(dcm[2], np.array([
+        [0.0473672, -0.6123725, 0.7891491],
+        [0.6597396, 0.6123725, 0.4355958],
+        [-0.7500000, 0.5000000, 0.4330127]
+    ]))
+
+
+def test_from_euler_intrinsic_rotation_313():
+    angles = [
+        [30, 60, 45],
+        [30, 60, 30],
+        [45, 30, 60]
+        ]
+    dcm = Rotation.from_euler('ZXZ', angles, degrees=True).as_dcm()
+
+    assert_array_almost_equal(dcm[0], np.array([
+        [0.43559574, -0.78914913, 0.4330127],
+        [0.65973961, -0.04736717, -0.750000],
+        [0.61237244, 0.61237244, 0.500000]
+    ]))
+
+    assert_array_almost_equal(dcm[1], np.array([
+        [0.6250000, -0.64951905, 0.4330127],
+        [0.64951905, 0.1250000, -0.750000],
+        [0.4330127, 0.750000, 0.500000]
+    ]))
+
+    assert_array_almost_equal(dcm[2], np.array([
+        [-0.1767767, -0.91855865, 0.35355339],
+        [0.88388348, -0.30618622, -0.35355339],
+        [0.4330127, 0.25000000, 0.8660254]
+    ]))
+
+
+def test_from_euler_extrinsic_rotation_312():
+    angles = [
+        [30, 60, 45],
+        [30, 60, 30],
+        [45, 30, 60]
+        ]
+    dcm = Rotation.from_euler('zxy', angles, degrees=True).as_dcm()
+
+    assert_array_almost_equal(dcm[0], np.array([
+        [0.91855865, 0.1767767, 0.35355339],
+        [0.25000000, 0.4330127, -0.8660254],
+        [-0.30618622, 0.88388348, 0.35355339]
+    ]))
+
+    assert_array_almost_equal(dcm[1], np.array([
+        [0.96650635, -0.0580127, 0.2500000],
+        [0.25000000, 0.4330127, -0.8660254],
+        [-0.0580127, 0.89951905, 0.4330127]
+    ]))
+
+    assert_array_almost_equal(dcm[2], np.array([
+        [0.65973961, -0.04736717, 0.7500000],
+        [0.61237244, 0.61237244, -0.5000000],
+        [-0.43559574, 0.78914913, 0.4330127]
+    ]))
+
+
+def test_from_euler_extrinsic_rotation_313():
+    angles = [
+        [30, 60, 45],
+        [30, 60, 30],
+        [45, 30, 60]
+        ]
+    dcm = Rotation.from_euler('zxz', angles, degrees=True).as_dcm()
+
+    assert_array_almost_equal(dcm[0], np.array([
+        [0.43559574, -0.65973961, 0.61237244],
+        [0.78914913, -0.04736717, -0.61237244],
+        [0.4330127, 0.75000000, 0.500000]
+    ]))
+
+    assert_array_almost_equal(dcm[1], np.array([
+        [0.62500000, -0.64951905, 0.4330127],
+        [0.64951905, 0.12500000, -0.750000],
+        [0.4330127, 0.75000000, 0.500000]
+    ]))
+
+    assert_array_almost_equal(dcm[2], np.array([
+        [-0.1767767, -0.88388348, 0.4330127],
+        [0.91855865, -0.30618622, -0.250000],
+        [0.35355339, 0.35355339, 0.8660254]
+    ]))
+
+
+def test_as_euler_asymmetric_axes():
+    np.random.seed(0)
+    n = 10
+    angles = np.empty((n, 3))
+    angles[:, 0] = np.random.uniform(low=-np.pi, high=np.pi, size=(n,))
+    angles[:, 1] = np.random.uniform(low=-np.pi / 2, high=np.pi / 2, size=(n,))
+    angles[:, 2] = np.random.uniform(low=-np.pi, high=np.pi, size=(n,))
+
+    for seq_tuple in permutations('xyz'):
+        # Extrinsic rotations
+        seq = ''.join(seq_tuple)
+        assert_allclose(angles, Rotation.from_euler(seq, angles).as_euler(seq))
+        # Intrinsic rotations
+        seq = seq.upper()
+        assert_allclose(angles, Rotation.from_euler(seq, angles).as_euler(seq))
+
+
+def test_as_euler_symmetric_axes():
+    np.random.seed(0)
+    n = 10
+    angles = np.empty((n, 3))
+    angles[:, 0] = np.random.uniform(low=-np.pi, high=np.pi, size=(n,))
+    angles[:, 1] = np.random.uniform(low=0, high=np.pi, size=(n,))
+    angles[:, 2] = np.random.uniform(low=-np.pi, high=np.pi, size=(n,))
+
+    for axis1 in ['x', 'y', 'z']:
+        for axis2 in ['x', 'y', 'z']:
+            if axis1 == axis2:
+                continue
+            # Extrinsic rotations
+            seq = axis1 + axis2 + axis1
+            assert_allclose(
+                angles, Rotation.from_euler(seq, angles).as_euler(seq))
+            # Intrinsic rotations
+            seq = seq.upper()
+            assert_allclose(
+                angles, Rotation.from_euler(seq, angles).as_euler(seq))
+
+
+def test_as_euler_degenerate_asymmetric_axes():
+    # Since we cannot check for angle equality, we check for dcm equality
+    angles = np.array([
+        [45, 90, 35],
+        [35, -90, 20],
+        [35, 90, 25],
+        [25, -90, 15]
+        ])
+
+    with pytest.warns(UserWarning, match="Gimbal lock"):
+        for seq_tuple in permutations('xyz'):
+            # Extrinsic rotations
+            seq = ''.join(seq_tuple)
+            rotation = Rotation.from_euler(seq, angles, degrees=True)
+            dcm_expected = rotation.as_dcm()
+
+            angle_estimates = rotation.as_euler(seq, degrees=True)
+            dcm_estimated = Rotation.from_euler(
+                seq, angle_estimates, degrees=True
+                ).as_dcm()
+
+            assert_array_almost_equal(dcm_expected, dcm_estimated)
+
+            # Intrinsic rotations
+            seq = seq.upper()
+            rotation = Rotation.from_euler(seq, angles, degrees=True)
+            dcm_expected = rotation.as_dcm()
+
+            angle_estimates = rotation.as_euler(seq, degrees=True)
+            dcm_estimated = Rotation.from_euler(
+                seq, angle_estimates, degrees=True
+                ).as_dcm()
+
+            assert_array_almost_equal(dcm_expected, dcm_estimated)
+
+
+def test_as_euler_degenerate_symmetric_axes():
+    # Since we cannot check for angle equality, we check for dcm equality
+    angles = np.array([
+        [15, 0, 60],
+        [35, 0, 75],
+        [60, 180, 35],
+        [15, -180, 25],
+        ])
+
+    with pytest.warns(UserWarning, match="Gimbal lock"):
+        for axis1 in ['x', 'y', 'z']:
+            for axis2 in ['x', 'y', 'z']:
+                if axis1 == axis2:
+                    continue
+
+                # Extrinsic rotations
+                seq = axis1 + axis2 + axis1
+                rotation = Rotation.from_euler(seq, angles, degrees=True)
+                dcm_expected = rotation.as_dcm()
+
+                angle_estimates = rotation.as_euler(seq, degrees=True)
+                dcm_estimated = Rotation.from_euler(
+                    seq, angle_estimates, degrees=True
+                    ).as_dcm()
+
+                assert_array_almost_equal(dcm_expected, dcm_estimated)
+
+                # Intrinsic rotations
+                seq = seq.upper()
+                rotation = Rotation.from_euler(seq, angles, degrees=True)
+                dcm_expected = rotation.as_dcm()
+
+                angle_estimates = rotation.as_euler(seq, degrees=True)
+                dcm_estimated = Rotation.from_euler(
+                    seq, angle_estimates, degrees=True
+                    ).as_dcm()
+
+                assert_array_almost_equal(dcm_expected, dcm_estimated)
+
+
+def test_inv():
+    np.random.seed(0)
+    n = 10
+    p = Rotation.from_quat(np.random.normal(size=(n, 4)))
+    q = p.inv()
+
+    p_dcm = p.as_dcm()
+    q_dcm = q.as_dcm()
+    result1 = np.einsum('...ij,...jk->...ik', p_dcm, q_dcm)
+    result2 = np.einsum('...ij,...jk->...ik', q_dcm, p_dcm)
+
+    eye3d = np.empty((n, 3, 3))
+    eye3d[:] = np.eye(3)
+
+    assert_array_almost_equal(result1, eye3d)
+    assert_array_almost_equal(result2, eye3d)
+
+
+def test_inv_single_rotation():
+    np.random.seed(0)
+    p = Rotation.from_quat(np.random.normal(size=(4,)))
+    q = p.inv()
+
+    p_dcm = p.as_dcm()
+    q_dcm = q.as_dcm()
+    res1 = np.dot(p_dcm, q_dcm)
+    res2 = np.dot(q_dcm, p_dcm)
+
+    eye = np.eye(3)
+
+    assert_array_almost_equal(res1, eye)
+    assert_array_almost_equal(res2, eye)
+
+    x = Rotation.from_quat(np.random.normal(size=(1, 4)))
+    y = x.inv()
+
+    x_dcm = x.as_dcm()
+    y_dcm = y.as_dcm()
+    result1 = np.einsum('...ij,...jk->...ik', x_dcm, y_dcm)
+    result2 = np.einsum('...ij,...jk->...ik', y_dcm, x_dcm)
+
+    eye3d = np.empty((1, 3, 3))
+    eye3d[:] = np.eye(3)
+
+    assert_array_almost_equal(result1, eye3d)
+    assert_array_almost_equal(result2, eye3d)
+
+
+def test_apply_single_rotation_single_point():
+    dcm = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+    ])
+    r_1d = Rotation.from_dcm(dcm)
+    r_2d = Rotation.from_dcm(np.expand_dims(dcm, axis=0))
+
+    v_1d = np.array([1, 2, 3])
+    v_2d = np.expand_dims(v_1d, axis=0)
+    v1d_rotated = np.array([-2, 1, 3])
+    v2d_rotated = np.expand_dims(v1d_rotated, axis=0)
+
+    assert_allclose(r_1d.apply(v_1d), v1d_rotated)
+    assert_allclose(r_1d.apply(v_2d), v2d_rotated)
+    assert_allclose(r_2d.apply(v_1d), v2d_rotated)
+    assert_allclose(r_2d.apply(v_2d), v2d_rotated)
+
+    v1d_inverse = np.array([2, -1, 3])
+    v2d_inverse = np.expand_dims(v1d_inverse, axis=0)
+
+    assert_allclose(r_1d.apply(v_1d, inverse=True), v1d_inverse)
+    assert_allclose(r_1d.apply(v_2d, inverse=True), v2d_inverse)
+    assert_allclose(r_2d.apply(v_1d, inverse=True), v2d_inverse)
+    assert_allclose(r_2d.apply(v_2d, inverse=True), v2d_inverse)
+
+
+def test_apply_single_rotation_multiple_points():
+    dcm = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+    ])
+    r1 = Rotation.from_dcm(dcm)
+    r2 = Rotation.from_dcm(np.expand_dims(dcm, axis=0))
+
+    v = np.array([[1, 2, 3], [4, 5, 6]])
+    v_rotated = np.array([[-2, 1, 3], [-5, 4, 6]])
+
+    assert_allclose(r1.apply(v), v_rotated)
+    assert_allclose(r2.apply(v), v_rotated)
+
+    v_inverse = np.array([[2, -1, 3], [5, -4, 6]])
+
+    assert_allclose(r1.apply(v, inverse=True), v_inverse)
+    assert_allclose(r2.apply(v, inverse=True), v_inverse)
+
+
+def test_apply_multiple_rotations_single_point():
+    dcm = np.empty((2, 3, 3))
+    dcm[0] = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+    ])
+    dcm[1] = np.array([
+        [1, 0, 0],
+        [0, 0, -1],
+        [0, 1, 0]
+    ])
+    r = Rotation.from_dcm(dcm)
+
+    v1 = np.array([1, 2, 3])
+    v2 = np.expand_dims(v1, axis=0)
+
+    v_rotated = np.array([[-2, 1, 3], [1, -3, 2]])
+
+    assert_allclose(r.apply(v1), v_rotated)
+    assert_allclose(r.apply(v2), v_rotated)
+
+    v_inverse = np.array([[2, -1, 3], [1, 3, -2]])
+
+    assert_allclose(r.apply(v1, inverse=True), v_inverse)
+    assert_allclose(r.apply(v2, inverse=True), v_inverse)
+
+
+def test_apply_multiple_rotations_multiple_points():
+    dcm = np.empty((2, 3, 3))
+    dcm[0] = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+    ])
+    dcm[1] = np.array([
+        [1, 0, 0],
+        [0, 0, -1],
+        [0, 1, 0]
+    ])
+    r = Rotation.from_dcm(dcm)
+
+    v = np.array([[1, 2, 3], [4, 5, 6]])
+    v_rotated = np.array([[-2, 1, 3], [4, -6, 5]])
+    assert_allclose(r.apply(v), v_rotated)
+
+    v_inverse = np.array([[2, -1, 3], [4, 6, -5]])
+    assert_allclose(r.apply(v, inverse=True), v_inverse)
+
+
+def test_getitem():
+    dcm = np.empty((2, 3, 3))
+    dcm[0] = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+    ])
+    dcm[1] = np.array([
+        [1, 0, 0],
+        [0, 0, -1],
+        [0, 1, 0]
+    ])
+    r = Rotation.from_dcm(dcm)
+
+    assert_allclose(r[0].as_dcm(), dcm[0])
+    assert_allclose(r[1].as_dcm(), dcm[1])
+    assert_allclose(r[:-1].as_dcm(), np.expand_dims(dcm[0], axis=0))
+
+
+def test_n_rotations():
+    dcm = np.empty((2, 3, 3))
+    dcm[0] = np.array([
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, 0, 1]
+    ])
+    dcm[1] = np.array([
+        [1, 0, 0],
+        [0, 0, -1],
+        [0, 1, 0]
+    ])
+    r = Rotation.from_dcm(dcm)
+
+    assert_equal(len(r), 2)
+    assert_equal(len(r[0]), 1)
+    assert_equal(len(r[1]), 1)
+    assert_equal(len(r[:-1]), 1)
+
+
+def test_quat_ownership():
+    # Ensure that users cannot accidentally corrupt object
+    quat = np.array([
+        [1, 0, 0, 0],
+        [0, 1, 0, 0],
+        [0, 0, 1, 0]
+    ])
+    r = Rotation.from_quat(quat, normalized=True)
+    s = r[0:2]
+
+    r._quat[0] = np.array([0, -1, 0, 0])
+    assert_allclose(s._quat[0], np.array([1, 0, 0, 0]))
+
+
+def test_match_vectors_no_rotation():
+    x = np.array([[1, 2, 3], [4, 5, 6]])
+    y = x.copy()
+
+    r, p = Rotation.match_vectors(x, y)
+    assert_array_almost_equal(r.as_dcm(), np.eye(3))
+
+
+def test_match_vectors_no_noise():
+    np.random.seed(0)
+    c = Rotation.from_quat(np.random.normal(size=4))
+    b = np.random.normal(size=(5, 3))
+    a = c.apply(b)
+
+    est, cov = Rotation.match_vectors(a, b)
+    assert_allclose(c.as_quat(), est.as_quat())
+
+
+def test_match_vectors_noise():
+    np.random.seed(0)
+    n_vectors = 100
+    rot = Rotation.from_euler('xyz', np.random.normal(size=3))
+    vectors = np.random.normal(size=(n_vectors, 3))
+    result = rot.apply(vectors)
+
+    # The paper adds noise as indepedently distributed angular errors
+    sigma = np.deg2rad(1)
+    tolerance = 1.5 * sigma
+    noise = Rotation.from_rotvec(
+        np.random.normal(
+            size=(n_vectors, 3),
+            scale=sigma
+        )
+    )
+
+    # Attitude errors must preserve norm. Hence apply individual random
+    # rotations to each vector.
+    noisy_result = noise.apply(result)
+
+    est, cov = Rotation.match_vectors(noisy_result, vectors)
+
+    # Use rotation compositions to find out closeness
+    error_vector = (rot * est.inv()).as_rotvec()
+    assert_allclose(error_vector[0], 0, atol=tolerance)
+    assert_allclose(error_vector[1], 0, atol=tolerance)
+    assert_allclose(error_vector[2], 0, atol=tolerance)
+
+    # Check error bounds using covariance matrix
+    cov *= sigma
+    assert_allclose(cov[0, 0], 0, atol=tolerance)
+    assert_allclose(cov[1, 1], 0, atol=tolerance)
+    assert_allclose(cov[2, 2], 0, atol=tolerance)
+
+
+def test_random_rotation_shape():
+    assert_equal(Rotation.random().as_quat().shape, (4,))
+    assert_equal(Rotation.random(None).as_quat().shape, (4,))
+
+    assert_equal(Rotation.random(1).as_quat().shape, (1, 4))
+    assert_equal(Rotation.random(5).as_quat().shape, (5, 4))
+
+
+def test_slerp():
+    np.random.seed(0)
+
+    key_rots = Rotation.from_quat(np.random.uniform(size=(5, 4)))
+    key_quats = key_rots.as_quat()
+
+    key_times = [0, 1, 2, 3, 4]
+    interpolator = Slerp(key_times, key_rots)
+
+    times = [0, 0.5, 0.25, 1, 1.5, 2, 2.75, 3, 3.25, 3.60, 4]
+    interp_rots = interpolator(times)
+    interp_quats = interp_rots.as_quat()
+
+    # Dot products are affected by sign of quaternions
+    interp_quats[interp_quats[:, -1] < 0] *= -1
+    # Checking for quaternion equality, perform same operation
+    key_quats[key_quats[:, -1] < 0] *= -1
+
+    # Equality at keyframes, including both endpoints
+    assert_allclose(interp_quats[0], key_quats[0])
+    assert_allclose(interp_quats[3], key_quats[1])
+    assert_allclose(interp_quats[5], key_quats[2])
+    assert_allclose(interp_quats[7], key_quats[3])
+    assert_allclose(interp_quats[10], key_quats[4])
+
+    # Constant angular velocity between keyframes. Check by equating
+    # cos(theta) between quaternion pairs with equal time difference.
+    cos_theta1 = np.sum(interp_quats[0] * interp_quats[2])
+    cos_theta2 = np.sum(interp_quats[2] * interp_quats[1])
+    assert_allclose(cos_theta1, cos_theta2)
+
+    cos_theta4 = np.sum(interp_quats[3] * interp_quats[4])
+    cos_theta5 = np.sum(interp_quats[4] * interp_quats[5])
+    assert_allclose(cos_theta4, cos_theta5)
+
+    # theta1: 0 -> 0.25, theta3 : 0.5 -> 1
+    # Use double angle formula for double the time difference
+    cos_theta3 = np.sum(interp_quats[1] * interp_quats[3])
+    assert_allclose(cos_theta3, 2 * (cos_theta1**2) - 1)
+
+    # Miscellaneous checks
+    assert_equal(len(interp_rots), len(times))
+
+
+def test_slerp_single_rot():
+    with pytest.raises(ValueError, match="at least 2 rotations"):
+        r = Rotation.from_quat([1, 2, 3, 4])
+        Slerp([1], r)
+
+
+def test_slerp_time_dim_mismatch():
+    with pytest.raises(ValueError,
+                       match="times to be specified in a 1 dimensional array"):
+        np.random.seed(0)
+        r = Rotation.from_quat(np.random.uniform(size=(2, 4)))
+        t = np.array([[1],
+                      [2]])
+        Slerp(t, r)
+
+
+def test_slerp_num_rotations_mismatch():
+    with pytest.raises(ValueError, match="number of rotations to be equal to "
+                                         "number of timestamps"):
+        np.random.seed(0)
+        r = Rotation.from_quat(np.random.uniform(size=(5, 4)))
+        t = np.arange(7)
+        Slerp(t, r)
+
+
+def test_slerp_equal_times():
+    with pytest.raises(ValueError, match="strictly increasing order"):
+        np.random.seed(0)
+        r = Rotation.from_quat(np.random.uniform(size=(5, 4)))
+        t = [0, 1, 2, 2, 4]
+        Slerp(t, r)
+
+
+def test_slerp_decreasing_times():
+    with pytest.raises(ValueError, match="strictly increasing order"):
+        np.random.seed(0)
+        r = Rotation.from_quat(np.random.uniform(size=(5, 4)))
+        t = [0, 1, 3, 2, 4]
+        Slerp(t, r)
+
+
+def test_slerp_call_time_dim_mismatch():
+    np.random.seed(0)
+    r = Rotation.from_quat(np.random.uniform(size=(5, 4)))
+    t = np.arange(5)
+    s = Slerp(t, r)
+
+    with pytest.raises(ValueError,
+                       match="times to be specified in a 1 dimensional array"):
+        interp_times = np.array([[3.5],
+                                 [4.2]])
+        s(interp_times)
+
+
+def test_slerp_call_time_out_of_range():
+    np.random.seed(0)
+    r = Rotation.from_quat(np.random.uniform(size=(5, 4)))
+    t = np.arange(5) + 1
+    s = Slerp(t, r)
+
+    with pytest.raises(ValueError, match="times must be within the range"):
+        s([0, 1, 2])
+    with pytest.raises(ValueError, match="times must be within the range"):
+        s([1, 2, 6])