Python KDTree.all_search方法代碼示例

# 需要導入模塊: from Bio.KDTree import KDTree [as 別名]
# 或者: from Bio.KDTree.KDTree import all_search [as 別名]
def test_all_search(nr_points, dim, bucket_size, query_radius):
    """Test fixed neighbor search.

    Search all point pairs that are within radius.

    Arguments:
     - nr_points: number of points used in test
     - dim: dimension of coords
     - bucket_size: nr of points per tree node
     - query_radius: radius of search

    Returns true if the test passes.
    """
    kdt = KDTree(dim, bucket_size)
    coords = random.random((nr_points, dim))
    kdt.set_coords(coords)
    kdt.all_search(query_radius)
    indices = kdt.all_get_indices()
    if indices is None:
        l1 = 0
    else:
        l1 = len(indices)
    radii = kdt.all_get_radii()
    if radii is None:
        l2 = 0
    else:
        l2 = len(radii)
    if l1 == l2:
        return True
    else:
        return False

# 需要導入模塊: from Bio.KDTree import KDTree [as 別名]
# 或者: from Bio.KDTree.KDTree import all_search [as 別名]
class NeighborSearch(object):
    """Class for neighbor searching,

    This class can be used for two related purposes:

     1. To find all atoms/residues/chains/models/structures within radius
        of a given query position.
     2. To find all atoms/residues/chains/models/structures that are within
        a fixed radius of each other.

    NeighborSearch makes use of the Bio.KDTree C++ module, so it's fast.
    """

    def __init__(self, atom_list, bucket_size=10):
        """Create the object.

        Arguments:

         - atom_list - list of atoms. This list is used in the queries.
           It can contain atoms from different structures.
         - bucket_size - bucket size of KD tree. You can play around
           with this to optimize speed if you feel like it.
        """
        self.atom_list = atom_list
        # get the coordinates
        coord_list = [a.get_coord() for a in atom_list]
        # to Nx3 array of type float
        self.coords = numpy.array(coord_list).astype("f")
        assert bucket_size > 1
        assert self.coords.shape[1] == 3
        self.kdt = KDTree(3, bucket_size)
        self.kdt.set_coords(self.coords)

    # Private

    def _get_unique_parent_pairs(self, pair_list):
        # translate a list of (entity, entity) tuples to
        # a list of (parent entity, parent entity) tuples,
        # thereby removing duplicate (parent entity, parent entity)
        # pairs.
        # o pair_list - a list of (entity, entity) tuples
        parent_pair_list = []
        for (e1, e2) in pair_list:
            p1 = e1.get_parent()
            p2 = e2.get_parent()
            if p1 == p2:
                continue
            elif p1 < p2:
                parent_pair_list.append((p1, p2))
            else:
                parent_pair_list.append((p2, p1))
        return uniqueify(parent_pair_list)

    # Public

    def search(self, center, radius, level="A"):
        """Neighbor search.

        Return all atoms/residues/chains/models/structures
        that have at least one atom within radius of center.
        What entity level is returned (e.g. atoms or residues)
        is determined by level (A=atoms, R=residues, C=chains,
        M=models, S=structures).

        Arguments:

         - center - Numeric array
         - radius - float
         - level - char (A, R, C, M, S)
        """
        if level not in entity_levels:
            raise PDBException("%s: Unknown level" % level)
        self.kdt.search(center, radius)
        indices = self.kdt.get_indices()
        n_atom_list = []
        atom_list = self.atom_list
        for i in indices:
            a = atom_list[i]
            n_atom_list.append(a)
        if level == "A":
            return n_atom_list
        else:
            return unfold_entities(n_atom_list, level)

    def search_all(self, radius, level="A"):
        """All neighbor search.

        Search all entities that have atoms pairs within
        radius.

        Arguments:

         - radius - float
         - level - char (A, R, C, M, S)
        """
        if level not in entity_levels:
            raise PDBException("%s: Unknown level" % level)
        self.kdt.all_search(radius)
        indices = self.kdt.all_get_indices()
        atom_list = self.atom_list
#.........這裏部分代碼省略.........