code: def single(y):
      """
      Perform single/min/nearest linkage on the condensed distance matrix ``y``.
  
      Parameters
      ----------
      y : ndarray
          The upper triangular of the distance matrix. The result of
          ``pdist`` is returned in this form.
  
      Returns
      -------
      Z : ndarray
          The linkage matrix.
  
      See Also
      --------
      linkage : for advanced creation of hierarchical clusterings.
      scipy.spatial.distance.pdist : pairwise distance metrics
  
      Examples
      --------
      >>> from scipy.cluster.hierarchy import single, fcluster
      >>> from scipy.spatial.distance import pdist
  
      First, we need a toy dataset to play with::
  
          x x    x x
          x        x
  
          x        x
          x x    x x
  
      >>> X = [[0, 0], [0, 1], [1, 0],
      ...      [0, 4], [0, 3], [1, 4],
      ...      [4, 0], [3, 0], [4, 1],
      ...      [4, 4], [3, 4], [4, 3]]
  
      Then, we get a condensed distance matrix from this dataset:
  
      >>> y = pdist(X)
  
      Finally, we can perform the clustering:
  
      >>> Z = single(y)
      >>> Z
      array([[ 0.,  1.,  1.,  2.],
             [ 2., 12.,  1.,  3.],
             [ 3.,  4.,  1.,  2.],
             [ 5., 14.,  1.,  3.],
             [ 6.,  7.,  1.,  2.],
             [ 8., 16.,  1.,  3.],
             [ 9., 10.,  1.,  2.],
             [11., 18.,  1.,  3.],
             [13., 15.,  2.,  6.],
             [17., 20.,  2.,  9.],
             [19., 21.,  2., 12.]])
  
      The linkage matrix ``Z`` represents a dendrogram - see
      `scipy.cluster.hierarchy.linkage` for a detailed explanation of its
      contents.
  
      We can use `scipy.cluster.hierarchy.fcluster` to see to which cluster
      each initial point would belong given a distance threshold:
  
      >>> fcluster(Z, 0.9, criterion='distance')
      array([ 7,  8,  9, 10, 11, 12,  4,  5,  6,  1,  2,  3], dtype=int32)
      >>> fcluster(Z, 1, criterion='distance')
      array([3, 3, 3, 4, 4, 4, 2, 2, 2, 1, 1, 1], dtype=int32)
      >>> fcluster(Z, 2, criterion='distance')
      array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
  
      Also, `scipy.cluster.hierarchy.dendrogram` can be used to generate a
      plot of the dendrogram.
      """
      return linkage(y, method="single", metric="euclidean")
  
imports: import warnings
  import bisect
  from collections import deque
  import numpy
  from .None import _hierarchy, _optimal_leaf_ordering
  import scipy.spatial.distance
  from scipy._lib._disjoint_set import DisjointSet
label_desc: Use image node_id 5 for calling a global function with example usage: single(y) and returns: linkage