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import random
from copy import copy
import pytest
import networkx as nx
from networkx.utils import (
PythonRandomInterface,
arbitrary_element,
create_py_random_state,
create_random_state,
dict_to_numpy_array,
discrete_sequence,
flatten,
groups,
make_list_of_ints,
pairwise,
powerlaw_sequence,
)
from networkx.utils.misc import _dict_to_numpy_array1, _dict_to_numpy_array2
nested_depth = (
1,
2,
(3, 4, ((5, 6, (7,), (8, (9, 10), 11), (12, 13, (14, 15)), 16), 17), 18, 19),
20,
)
nested_set = {
(1, 2, 3, 4),
(5, 6, 7, 8, 9),
(10, 11, (12, 13, 14), (15, 16, 17, 18)),
19,
20,
}
nested_mixed = [
1,
(2, 3, {4, (5, 6), 7}, [8, 9]),
{10: "foo", 11: "bar", (12, 13): "baz"},
{(14, 15): "qwe", 16: "asd"},
(17, (18, "19"), 20),
]
@pytest.mark.parametrize("result", [None, [], ["existing"], ["existing1", "existing2"]])
@pytest.mark.parametrize("nested", [nested_depth, nested_mixed, nested_set])
def test_flatten(nested, result):
if result is None:
val = flatten(nested, result)
assert len(val) == 20
else:
_result = copy(result) # because pytest passes parameters as is
nexisting = len(_result)
val = flatten(nested, _result)
assert len(val) == len(_result) == 20 + nexisting
assert issubclass(type(val), tuple)
def test_make_list_of_ints():
mylist = [1, 2, 3.0, 42, -2]
assert make_list_of_ints(mylist) is mylist
assert make_list_of_ints(mylist) == mylist
assert type(make_list_of_ints(mylist)[2]) is int
pytest.raises(nx.NetworkXError, make_list_of_ints, [1, 2, 3, "kermit"])
pytest.raises(nx.NetworkXError, make_list_of_ints, [1, 2, 3.1])
def test_random_number_distribution():
# smoke test only
z = powerlaw_sequence(20, exponent=2.5)
z = discrete_sequence(20, distribution=[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 3])
class TestNumpyArray:
@classmethod
def setup_class(cls):
global np
np = pytest.importorskip("numpy")
def test_numpy_to_list_of_ints(self):
a = np.array([1, 2, 3], dtype=np.int64)
b = np.array([1.0, 2, 3])
c = np.array([1.1, 2, 3])
assert type(make_list_of_ints(a)) == list
assert make_list_of_ints(b) == list(b)
B = make_list_of_ints(b)
assert type(B[0]) == int
pytest.raises(nx.NetworkXError, make_list_of_ints, c)
def test__dict_to_numpy_array1(self):
d = {"a": 1, "b": 2}
a = _dict_to_numpy_array1(d, mapping={"a": 0, "b": 1})
np.testing.assert_allclose(a, np.array([1, 2]))
a = _dict_to_numpy_array1(d, mapping={"b": 0, "a": 1})
np.testing.assert_allclose(a, np.array([2, 1]))
a = _dict_to_numpy_array1(d)
np.testing.assert_allclose(a.sum(), 3)
def test__dict_to_numpy_array2(self):
d = {"a": {"a": 1, "b": 2}, "b": {"a": 10, "b": 20}}
mapping = {"a": 1, "b": 0}
a = _dict_to_numpy_array2(d, mapping=mapping)
np.testing.assert_allclose(a, np.array([[20, 10], [2, 1]]))
a = _dict_to_numpy_array2(d)
np.testing.assert_allclose(a.sum(), 33)
def test_dict_to_numpy_array_a(self):
d = {"a": {"a": 1, "b": 2}, "b": {"a": 10, "b": 20}}
mapping = {"a": 0, "b": 1}
a = dict_to_numpy_array(d, mapping=mapping)
np.testing.assert_allclose(a, np.array([[1, 2], [10, 20]]))
mapping = {"a": 1, "b": 0}
a = dict_to_numpy_array(d, mapping=mapping)
np.testing.assert_allclose(a, np.array([[20, 10], [2, 1]]))
a = _dict_to_numpy_array2(d)
np.testing.assert_allclose(a.sum(), 33)
def test_dict_to_numpy_array_b(self):
d = {"a": 1, "b": 2}
mapping = {"a": 0, "b": 1}
a = dict_to_numpy_array(d, mapping=mapping)
np.testing.assert_allclose(a, np.array([1, 2]))
a = _dict_to_numpy_array1(d)
np.testing.assert_allclose(a.sum(), 3)
def test_pairwise():
nodes = range(4)
node_pairs = [(0, 1), (1, 2), (2, 3)]
node_pairs_cycle = node_pairs + [(3, 0)]
assert list(pairwise(nodes)) == node_pairs
assert list(pairwise(iter(nodes))) == node_pairs
assert list(pairwise(nodes, cyclic=True)) == node_pairs_cycle
empty_iter = iter(())
assert list(pairwise(empty_iter)) == []
empty_iter = iter(())
assert list(pairwise(empty_iter, cyclic=True)) == []
def test_groups():
many_to_one = dict(zip("abcde", [0, 0, 1, 1, 2]))
actual = groups(many_to_one)
expected = {0: {"a", "b"}, 1: {"c", "d"}, 2: {"e"}}
assert actual == expected
assert {} == groups({})
def test_create_random_state():
np = pytest.importorskip("numpy")
rs = np.random.RandomState
assert isinstance(create_random_state(1), rs)
assert isinstance(create_random_state(None), rs)
assert isinstance(create_random_state(np.random), rs)
assert isinstance(create_random_state(rs(1)), rs)
# Support for numpy.random.Generator
rng = np.random.default_rng()
assert isinstance(create_random_state(rng), np.random.Generator)
pytest.raises(ValueError, create_random_state, "a")
assert np.all(rs(1).rand(10) == create_random_state(1).rand(10))
def test_create_py_random_state():
pyrs = random.Random
assert isinstance(create_py_random_state(1), pyrs)
assert isinstance(create_py_random_state(None), pyrs)
assert isinstance(create_py_random_state(pyrs(1)), pyrs)
pytest.raises(ValueError, create_py_random_state, "a")
np = pytest.importorskip("numpy")
rs = np.random.RandomState
rng = np.random.default_rng(1000)
rng_explicit = np.random.Generator(np.random.SFC64())
nprs = PythonRandomInterface
assert isinstance(create_py_random_state(np.random), nprs)
assert isinstance(create_py_random_state(rs(1)), nprs)
assert isinstance(create_py_random_state(rng), nprs)
assert isinstance(create_py_random_state(rng_explicit), nprs)
# test default rng input
assert isinstance(PythonRandomInterface(), nprs)
def test_PythonRandomInterface_RandomState():
np = pytest.importorskip("numpy")
rs = np.random.RandomState
rng = PythonRandomInterface(rs(42))
rs42 = rs(42)
# make sure these functions are same as expected outcome
assert rng.randrange(3, 5) == rs42.randint(3, 5)
assert rng.choice([1, 2, 3]) == rs42.choice([1, 2, 3])
assert rng.gauss(0, 1) == rs42.normal(0, 1)
assert rng.expovariate(1.5) == rs42.exponential(1 / 1.5)
assert np.all(rng.shuffle([1, 2, 3]) == rs42.shuffle([1, 2, 3]))
assert np.all(
rng.sample([1, 2, 3], 2) == rs42.choice([1, 2, 3], (2,), replace=False)
)
assert np.all(
[rng.randint(3, 5) for _ in range(100)]
== [rs42.randint(3, 6) for _ in range(100)]
)
assert rng.random() == rs42.random_sample()
def test_PythonRandomInterface_Generator():
np = pytest.importorskip("numpy")
rng = np.random.default_rng(42)
pri = PythonRandomInterface(np.random.default_rng(42))
# make sure these functions are same as expected outcome
assert pri.randrange(3, 5) == rng.integers(3, 5)
assert pri.choice([1, 2, 3]) == rng.choice([1, 2, 3])
assert pri.gauss(0, 1) == rng.normal(0, 1)
assert pri.expovariate(1.5) == rng.exponential(1 / 1.5)
assert np.all(pri.shuffle([1, 2, 3]) == rng.shuffle([1, 2, 3]))
assert np.all(
pri.sample([1, 2, 3], 2) == rng.choice([1, 2, 3], (2,), replace=False)
)
assert np.all(
[pri.randint(3, 5) for _ in range(100)]
== [rng.integers(3, 6) for _ in range(100)]
)
assert pri.random() == rng.random()
@pytest.mark.parametrize(
("iterable_type", "expected"), ((list, 1), (tuple, 1), (str, "["), (set, 1))
)
def test_arbitrary_element(iterable_type, expected):
iterable = iterable_type([1, 2, 3])
assert arbitrary_element(iterable) == expected
@pytest.mark.parametrize(
"iterator", ((i for i in range(3)), iter([1, 2, 3])) # generator
)
def test_arbitrary_element_raises(iterator):
"""Value error is raised when input is an iterator."""
with pytest.raises(ValueError, match="from an iterator"):
arbitrary_element(iterator)
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