Python pyrsistent.pvector函数代码示例

    def start_cluster(self, reactor):
        # Destroy the box to begin, so that we are guaranteed
        # a clean build.
        yield run(reactor, ["vagrant", "destroy", "-f"], path=self.vagrant_path.path)

        if self.package_source.version:
            env = extend_environ(FLOCKER_BOX_VERSION=vagrant_version(self.package_source.version))
        else:
            env = os.environ
        # Boot the VMs
        yield run(reactor, ["vagrant", "up"], path=self.vagrant_path.path, env=env)

        for node in self.NODE_ADDRESSES:
            yield remove_known_host(reactor, node)

        nodes = pvector(ManagedNode(address=address, distribution=self.distribution) for address in self.NODE_ADDRESSES)

        certificates = Certificates(self.certificates_path)
        cluster = Cluster(
            all_nodes=pvector(nodes),
            control_node=nodes[0],
            agent_nodes=nodes,
            dataset_backend=self.dataset_backend,
            certificates=certificates,
        )

        returnValue(cluster)

def test_is_hashable(pvector):
    from collections import Hashable
    v = pvector([1, 2, 3])
    v2 = pvector([1, 2, 3])

    assert hash(v) == hash(v2)
    assert isinstance(pvector(), Hashable)

 def multiplier(l, cnt):
     if not isinstance(l, pvectorc.PVector):
         results = list(map(lambda x: wrapper_fn(map_fn)(x[1], x[0], cnt),
                            enumerate([l] * cnt)))
         return pvector(results)
     else:
         return pvector(list(map(lambda x: multiplier(x, cnt), l)))

def run_vector_random_access_performance():
    def random_access(o):
        result = 0
        for x in range(10000):
            for y in testdata:
                result = o[y]

        return result

    testdata = [0, 4, 55, 10000, 98763, -2, 30000, 42004, 37289, 100, 2, 999999]
    l = range(1000000)
    
    before = time.time()
    random_access(l)
    print("Random access large list: " + str(time.time() - before))

    v = pvector(l)
    before = time.time()
    random_access(v)
    print("Random access large vector: " + str(time.time() - before))

    testdata = [0, 4, 17, -2, 3, 7, 8, 11, 1, 13, 18, 10]
    l = range(20)
    
    before = time.time()
    random_access(l)
    print("Random access small list: " + str(time.time() - before))

    v = pvector(l)
    before = time.time()
    random_access(v)
    print("Random access small vector: " + str(time.time() - before))

def _create_diffs_for(current_path, subobj_a, subobj_b):
    """
    Computes a series of ``_IDiffChange`` s to turn ``subobj_a`` into
    ``subobj_b`` assuming that these subobjs are at ``current_path`` inside a
    nested pyrsistent object.

    :param current_path: An iterable of pyrsistent object describing the path
        inside the root pyrsistent object where the other arguments are
        located.  See ``PMap.transform`` for the format of this sort of path.

    :param subobj_a: The desired input sub object.

    :param subobj_b: The desired output sub object.

    :returns: An iterable of ``_IDiffChange`` s that will turn ``subobj_a``
        into ``subobj_b``.
    """
    if subobj_a == subobj_b:
        return pvector([])
    elif type(subobj_a) != type(subobj_b):
        return pvector([_Set(path=current_path, value=subobj_b)])
    elif isinstance(subobj_a, PClass) and isinstance(subobj_b, PClass):
        a_dict = subobj_a._to_dict()
        b_dict = subobj_b._to_dict()
        return _create_diffs_for_mappings(current_path, a_dict, b_dict)
    elif isinstance(subobj_a, PMap) and isinstance(subobj_b, PMap):
        return _create_diffs_for_mappings(
            current_path, subobj_a, subobj_b)
    elif isinstance(subobj_a, PSet) and isinstance(subobj_b, PSet):
        return _create_diffs_for_sets(
            current_path, subobj_a, subobj_b)
    # If the objects are not equal, and there is no intelligent way to recurse
    # inside the objects to make a smaller diff, simply set the current path
    # to the object in b.
    return pvector([_Set(path=current_path, value=subobj_b)])

def test_iterable(pvector):
    """
    PVectors can be created from iterables even though they can't be len()
    hinted.
    """

    assert pvector(iter("a")) == pvector(iter("a"))

def test_is_hashable(pvector):
    from pyrsistent._compat import Hashable
    v = pvector([1, 2, 3])
    v2 = pvector([1, 2, 3])

    assert hash(v) == hash(v2)
    assert isinstance(pvector(), Hashable)

    def __init__(self, node_addresses, package_source, distribution,
                 dataset_backend, dataset_backend_configuration):
        """
        :param list: A ``list`` of public IP addresses or
            ``[private_address, public_address]`` lists.

        See ``ManagedRunner`` and ``ManagedNode`` for other parameter
        documentation.
        """
        # Blow up if the list contains mixed types.
        [address_type] = set(type(address) for address in node_addresses)
        if address_type is list:
            # A list of 2 item lists
            self._nodes = pvector(
                ManagedNode(
                    address=address,
                    private_address=private_address,
                    distribution=distribution
                )
                for (private_address, address) in node_addresses
            )
        else:
            # A list of strings.
            self._nodes = pvector(
                ManagedNode(address=address, distribution=distribution)
                for address in node_addresses
            )
        self.package_source = package_source
        self.dataset_backend = dataset_backend
        self.dataset_backend_configuration = dataset_backend_configuration

def test_delete_slice(pvector):
    seq = pvector(range(5))
    assert seq.delete(1, 4) == pvector([0, 4])
    assert seq.delete(4, 1) == seq
    assert seq.delete(0, 1) == pvector([1, 2, 3, 4])
    assert seq.delete(6, 8) == seq
    assert seq.delete(-1, 1) == seq
    assert seq.delete(1, -1) == pvector([0, 4])

def test_evolver_set_followed_by_delete(pvector):
    evolver = pvector([1, 2]).evolver()
    evolver[1] = 3

    assert [evolver[i] for i in range(len(evolver))] == [1, 3]

    del evolver[0]

    assert evolver.persistent() == pvector([3])

 def finalize_cluster(cluster):
     # Make node lists immutable.
     return Cluster(
         all_nodes=pvector(cluster.all_nodes),
         control_node=cluster.node,
         agent_nodes=pvector(cluster.agent_nodes),
         dataset_backend=cluster.dataset_backend,
         default_volume_size=cluster.default_volume_size,
         certificates=cluster.certificates,
         dataset_backend_config_file=cluster.dataset_backend_config_file,
     )

 def __init__(self, mat, rows, cols):
     target_type = {numpy.dtype('int8'): numpy.dtype('int64'),
                    numpy.dtype('int16'): numpy.dtype('int64'),
                    numpy.dtype('int32'): numpy.dtype('int64'),
                    numpy.dtype('int64'): numpy.dtype('int64'),
                    numpy.dtype('float16'): numpy.dtype('float32'),
                    numpy.dtype('float32'): numpy.dtype('float32'),
                    numpy.dtype('float64'): numpy.dtype('float64')}
     self._matrix = mat.astype(target_type[mat.dtype])
     self._rows = pvector(rows)
     self._columns = pvector(cols)

def test_delete_of_non_existing_element(pvector):
    e = pvector([1, 2]).evolver()

    with pytest.raises(IndexError):
        del e[2]

    del e[0]
    del e[0]

    with pytest.raises(IndexError):
        del e[0]

    assert e.persistent() == pvector()

def test_repeat(pvector):
    v = pvector([1, 2])
    assert 5 * pvector() is pvector()
    assert v is 1 * v
    assert 0 * v is pvector()
    assert 2 * pvector([1, 2]) == pvector([1, 2, 1, 2])
    assert -3 * pvector([1, 2]) is pvector()

 def finalize_cluster(cluster):
     """
     :param Cluster cluster: Description of the cluster.
     :return: Cluster
     """
     # Make node lists immutable.
     return Cluster(
         all_nodes=pvector(cluster.all_nodes),
         control_node=cluster.node,
         agent_nodes=pvector(cluster.agent_nodes),
         dataset_backend=cluster.dataset_backend,
         default_volume_size=cluster.default_volume_size,
         certificates=cluster.certificates,
         dataset_backend_config_file=cluster.dataset_backend_config_file
     )