def test_empty(self):
"""stats.rankdata([]) should return an empty array."""
a = np.array([], dtype=np.int)
r = rankdata(a)
assert_array_equal(r, np.array([], dtype=np.float64))
r = rankdata([])
assert_array_equal(r, np.array([], dtype=np.float64))
def test_empty(self):
"""stats.rankdata([]) should return an empty array."""
a = np.array([], dtype=np.int)
r = rankdata(a)
assert_array_equal(r, np.array([], dtype=np.float64))
r = rankdata([])
assert_array_equal(r, np.array([], dtype=np.float64))
def test_one(self):
"""Check stats.rankdata with an array of length 1."""
data = [100]
a = np.array(data, dtype=np.int)
r = rankdata(a)
assert_array_equal(r, np.array([1.0], dtype=np.float64))
r = rankdata(data)
assert_array_equal(r, np.array([1.0], dtype=np.float64))
def test_one(self):
"""Check stats.rankdata with an array of length 1."""
data = [100]
a = np.array(data, dtype=np.int)
r = rankdata(a)
assert_array_equal(r, np.array([1.0], dtype=np.float64))
r = rankdata(data)
assert_array_equal(r, np.array([1.0], dtype=np.float64))
def test_large_int(self):
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_large_int(self):
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(self):
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(self):
"""Basic tests of stats.rankdata."""
data = [100, 10, 50]
expected = np.array([3.0, 1.0, 2.0], dtype=np.float64)
a = np.array(data, dtype=np.int)
r = rankdata(a)
assert_array_equal(r, expected)
r = rankdata(data)
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=np.int)
r = rankdata(a)
assert_array_equal(r, expected)
r = rankdata(data)
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=np.int)
r = rankdata(a)
assert_array_equal(r, expected)
r = rankdata(data)
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 test_basic(self):
"""Basic tests of stats.rankdata."""
data = [100, 10, 50]
expected = np.array([3.0, 1.0, 2.0], dtype=np.float64)
a = np.array(data, dtype=np.int)
r = rankdata(a)
assert_array_equal(r, expected)
r = rankdata(data)
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=np.int)
r = rankdata(a)
assert_array_equal(r, expected)
r = rankdata(data)
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=np.int)
r = rankdata(a)
assert_array_equal(r, expected)
r = rankdata(data)
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_case(values, method, expected):
r = rankdata(values, method=method)
assert_array_equal(r, expected)
def check_case(values, method, expected):
r = rankdata(values, method=method)
assert_array_equal(r, expected)
def test_big_tie(self):
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_rankdata(self):
for n in self.get_n():
x, y, xm, ym = self.generate_xy_sample(n)
r = stats.rankdata(x)
rm = stats.mstats.rankdata(x)
assert_allclose(r, rm)
