def test_large_int():
data = np.array([2 ** 60, 2 ** 60 + 1], dtype=np.uint64)
r = rankdata(data)
assert_array_equal(r, [1.0, 2.0])
data = np.array([2 ** 60, 2 ** 60 + 1], dtype=np.int64)
r = rankdata(data)
assert_array_equal(r, [1.0, 2.0])
data = np.array([2 ** 60, -(2 ** 60) + 1], dtype=np.int64)
r = rankdata(data)
assert_array_equal(r, [2.0, 1.0])
def test_big_tie():
for n in [10000, 100000, 1000000]:
data = np.ones(n, dtype=int)
r = rankdata(data)
expected_rank = 0.5 * (n + 1)
assert_array_equal(r, expected_rank * data,
"test failed with n=%d" % n)
def test_basic():
"""Basic tests of rankdata."""
data = [100, 10, 50]
expected = np.array([3.0, 1.0, 2.0], dtype=np.float64)
a = np.array(data, dtype=int)
r = rankdata(a)
assert_array_equal(r, expected)
data = [40, 10, 30, 10, 50]
expected = np.array([4.0, 1.5, 3.0, 1.5, 5.0], dtype=np.float64)
a = np.array(data, dtype=int)
r = rankdata(a)
assert_array_equal(r, expected)
data = [20, 20, 20, 10, 10, 10]
expected = np.array([5.0, 5.0, 5.0, 2.0, 2.0, 2.0], dtype=np.float64)
a = np.array(data, dtype=int)
r = rankdata(a)
assert_array_equal(r, expected)
# The docstring states explicitly that the argument is flattened.
a2d = a.reshape(2, 3)
r = rankdata(a2d)
assert_array_equal(r, expected)
def check_ranks(a):
for method in "min", "max", "dense", "ordinal", "average":
out = rankdata(a, method=method)
assert_array_equal(out, rankf[method](a))
def test_empty():
"""rankdata([]) should return an empty array."""
a = np.array([], dtype=int)
r = rankdata(a)
assert_array_equal(r, np.array([], dtype=np.float64))
def test_cases():
for values, method, expected in _cases:
r = rankdata(values, method=method)
assert_array_equal(r, expected)
def test_one():
"""Check rankdata with an array of length 1."""
data = [100]
a = np.array(data, dtype=int)
r = rankdata(a)
assert_array_equal(r, np.array([1.0], dtype=np.float64))
