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-rw-r--r--numpy/lib/tests/test_arraysetops.py39
-rw-r--r--numpy/lib/tests/test_format.py3
-rw-r--r--numpy/lib/tests/test_function_base.py187
-rw-r--r--numpy/lib/tests/test_io.py64
-rw-r--r--numpy/lib/tests/test_loadtxt.py37
-rw-r--r--numpy/lib/tests/test_nanfunctions.py2
-rw-r--r--numpy/lib/tests/test_shape_base.py4
-rw-r--r--numpy/lib/tests/test_twodim_base.py11
-rw-r--r--numpy/lib/tests/test_type_check.py2
-rw-r--r--numpy/lib/tests/test_ufunclike.py6
10 files changed, 308 insertions, 47 deletions
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index bb07e25a9..a180accbe 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -414,13 +414,48 @@ class TestSetOps:
with pytest.raises(ValueError):
in1d(a, b, kind="table")
+ @pytest.mark.parametrize(
+ "dtype1,dtype2",
+ [
+ (np.int8, np.int16),
+ (np.int16, np.int8),
+ (np.uint8, np.uint16),
+ (np.uint16, np.uint8),
+ (np.uint8, np.int16),
+ (np.int16, np.uint8),
+ ]
+ )
+ @pytest.mark.parametrize("kind", [None, "sort", "table"])
+ def test_in1d_mixed_dtype(self, dtype1, dtype2, kind):
+ """Test that in1d works as expected for mixed dtype input."""
+ is_dtype2_signed = np.issubdtype(dtype2, np.signedinteger)
+ ar1 = np.array([0, 0, 1, 1], dtype=dtype1)
+
+ if is_dtype2_signed:
+ ar2 = np.array([-128, 0, 127], dtype=dtype2)
+ else:
+ ar2 = np.array([127, 0, 255], dtype=dtype2)
+
+ expected = np.array([True, True, False, False])
+
+ expect_failure = kind == "table" and any((
+ dtype1 == np.int8 and dtype2 == np.int16,
+ dtype1 == np.int16 and dtype2 == np.int8
+ ))
+
+ if expect_failure:
+ with pytest.raises(RuntimeError, match="exceed the maximum"):
+ in1d(ar1, ar2, kind=kind)
+ else:
+ assert_array_equal(in1d(ar1, ar2, kind=kind), expected)
+
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_in1d_mixed_boolean(self, kind):
"""Test that in1d works as expected for bool/int input."""
for dtype in np.typecodes["AllInteger"]:
a = np.array([True, False, False], dtype=bool)
- b = np.array([1, 1, 1, 1], dtype=dtype)
- expected = np.array([True, False, False], dtype=bool)
+ b = np.array([0, 0, 0, 0], dtype=dtype)
+ expected = np.array([False, True, True], dtype=bool)
assert_array_equal(in1d(a, b, kind=kind), expected)
a, b = b, a
diff --git a/numpy/lib/tests/test_format.py b/numpy/lib/tests/test_format.py
index 6f6406cf8..58d08f1e5 100644
--- a/numpy/lib/tests/test_format.py
+++ b/numpy/lib/tests/test_format.py
@@ -531,7 +531,8 @@ def test_load_padded_dtype(tmpdir, dt):
def test_python2_python3_interoperability():
fname = 'win64python2.npy'
path = os.path.join(os.path.dirname(__file__), 'data', fname)
- data = np.load(path)
+ with pytest.warns(UserWarning, match="Reading.*this warning\\."):
+ data = np.load(path)
assert_array_equal(data, np.ones(2))
def test_pickle_python2_python3():
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index e38a187d8..b0944ec85 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -8,7 +8,7 @@ import pytest
import hypothesis
from hypothesis.extra.numpy import arrays
import hypothesis.strategies as st
-
+from functools import partial
import numpy as np
from numpy import ma
@@ -229,8 +229,8 @@ class TestAny:
def test_nd(self):
y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
assert_(np.any(y1))
- assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0])
- assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1])
+ assert_array_equal(np.any(y1, axis=0), [1, 1, 0])
+ assert_array_equal(np.any(y1, axis=1), [0, 1, 1])
class TestAll:
@@ -247,8 +247,8 @@ class TestAll:
def test_nd(self):
y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
assert_(not np.all(y1))
- assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1])
- assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1])
+ assert_array_equal(np.all(y1, axis=0), [0, 0, 1])
+ assert_array_equal(np.all(y1, axis=1), [0, 0, 1])
class TestCopy:
@@ -1217,6 +1217,13 @@ class TestGradient:
dfdx = gradient(f, x)
assert_array_equal(dfdx, [0.5, 0.5])
+ def test_return_type(self):
+ res = np.gradient(([1, 2], [2, 3]))
+ if np._using_numpy2_behavior():
+ assert type(res) is tuple
+ else:
+ assert type(res) is list
+
class TestAngle:
@@ -1780,6 +1787,70 @@ class TestVectorize:
assert_equal(type(r), subclass)
assert_equal(r, m * v)
+ def test_name(self):
+ #See gh-23021
+ @np.vectorize
+ def f2(a, b):
+ return a + b
+
+ assert f2.__name__ == 'f2'
+
+ def test_decorator(self):
+ @vectorize
+ def addsubtract(a, b):
+ if a > b:
+ return a - b
+ else:
+ return a + b
+
+ r = addsubtract([0, 3, 6, 9], [1, 3, 5, 7])
+ assert_array_equal(r, [1, 6, 1, 2])
+
+ def test_docstring(self):
+ @vectorize
+ def f(x):
+ """Docstring"""
+ return x
+
+ if sys.flags.optimize < 2:
+ assert f.__doc__ == "Docstring"
+
+ def test_partial(self):
+ def foo(x, y):
+ return x + y
+
+ bar = partial(foo, 3)
+ vbar = np.vectorize(bar)
+ assert vbar(1) == 4
+
+ def test_signature_otypes_decorator(self):
+ @vectorize(signature='(n)->(n)', otypes=['float64'])
+ def f(x):
+ return x
+
+ r = f([1, 2, 3])
+ assert_equal(r.dtype, np.dtype('float64'))
+ assert_array_equal(r, [1, 2, 3])
+ assert f.__name__ == 'f'
+
+ def test_bad_input(self):
+ with assert_raises(TypeError):
+ A = np.vectorize(pyfunc = 3)
+
+ def test_no_keywords(self):
+ with assert_raises(TypeError):
+ @np.vectorize("string")
+ def foo():
+ return "bar"
+
+ def test_positional_regression_9477(self):
+ # This supplies the first keyword argument as a positional,
+ # to ensure that they are still properly forwarded after the
+ # enhancement for #9477
+ f = vectorize((lambda x: x), ['float64'])
+ r = f([2])
+ assert_equal(r.dtype, np.dtype('float64'))
+
class TestLeaks:
class A:
@@ -3467,9 +3538,20 @@ class TestPercentile:
np.percentile([1, 2, 3, 4.0], q)
+quantile_methods = [
+ 'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+ 'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear',
+ 'median_unbiased', 'normal_unbiased', 'nearest', 'lower', 'higher',
+ 'midpoint']
+
+
class TestQuantile:
# most of this is already tested by TestPercentile
+ def V(self, x, y, alpha):
+ # Identification function used in several tests.
+ return (x >= y) - alpha
+
def test_max_ulp(self):
x = [0.0, 0.2, 0.4]
a = np.quantile(x, 0.45)
@@ -3484,7 +3566,6 @@ class TestQuantile:
assert_equal(np.quantile(x, 1), 3.5)
assert_equal(np.quantile(x, 0.5), 1.75)
- @pytest.mark.xfail(reason="See gh-19154")
def test_correct_quantile_value(self):
a = np.array([True])
tf_quant = np.quantile(True, False)
@@ -3549,11 +3630,7 @@ class TestQuantile:
method="nearest")
assert res.dtype == dtype
- @pytest.mark.parametrize("method",
- ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
- 'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear',
- 'median_unbiased', 'normal_unbiased',
- 'nearest', 'lower', 'higher', 'midpoint'])
+ @pytest.mark.parametrize("method", quantile_methods)
def test_quantile_monotonic(self, method):
# GH 14685
# test that the return value of quantile is monotonic if p0 is ordered
@@ -3584,6 +3661,94 @@ class TestQuantile:
assert np.isscalar(actual)
assert_equal(np.quantile(a, 0.5), np.nan)
+ @pytest.mark.parametrize("method", quantile_methods)
+ @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+ def test_quantile_identification_equation(self, method, alpha):
+ # Test that the identification equation holds for the empirical
+ # CDF:
+ # E[V(x, Y)] = 0 <=> x is quantile
+ # with Y the random variable for which we have observed values and
+ # V(x, y) the canonical identification function for the quantile (at
+ # level alpha), see
+ # https://doi.org/10.48550/arXiv.0912.0902
+ rng = np.random.default_rng(4321)
+ # We choose n and alpha such that we cover 3 cases:
+ # - n * alpha is an integer
+ # - n * alpha is a float that gets rounded down
+ # - n * alpha is a float that gest rounded up
+ n = 102 # n * alpha = 20.4, 51. , 91.8
+ y = rng.random(n)
+ x = np.quantile(y, alpha, method=method)
+ if method in ("higher",):
+ # These methods do not fulfill the identification equation.
+ assert np.abs(np.mean(self.V(x, y, alpha))) > 0.1 / n
+ elif int(n * alpha) == n * alpha:
+ # We can expect exact results, up to machine precision.
+ assert_allclose(np.mean(self.V(x, y, alpha)), 0, atol=1e-14)
+ else:
+ # V = (x >= y) - alpha cannot sum to zero exactly but within
+ # "sample precision".
+ assert_allclose(np.mean(self.V(x, y, alpha)), 0,
+ atol=1 / n / np.amin([alpha, 1 - alpha]))
+
+ @pytest.mark.parametrize("method", quantile_methods)
+ @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+ def test_quantile_add_and_multiply_constant(self, method, alpha):
+ # Test that
+ # 1. quantile(c + x) = c + quantile(x)
+ # 2. quantile(c * x) = c * quantile(x)
+ # 3. quantile(-x) = -quantile(x, 1 - alpha)
+ # On empirical quantiles, this equation does not hold exactly.
+ # Koenker (2005) "Quantile Regression" Chapter 2.2.3 calls these
+ # properties equivariance.
+ rng = np.random.default_rng(4321)
+ # We choose n and alpha such that we have cases for
+ # - n * alpha is an integer
+ # - n * alpha is a float that gets rounded down
+ # - n * alpha is a float that gest rounded up
+ n = 102 # n * alpha = 20.4, 51. , 91.8
+ y = rng.random(n)
+ q = np.quantile(y, alpha, method=method)
+ c = 13.5
+
+ # 1
+ assert_allclose(np.quantile(c + y, alpha, method=method), c + q)
+ # 2
+ assert_allclose(np.quantile(c * y, alpha, method=method), c * q)
+ # 3
+ q = -np.quantile(-y, 1 - alpha, method=method)
+ if method == "inverted_cdf":
+ if (
+ n * alpha == int(n * alpha)
+ or np.round(n * alpha) == int(n * alpha) + 1
+ ):
+ assert_allclose(q, np.quantile(y, alpha, method="higher"))
+ else:
+ assert_allclose(q, np.quantile(y, alpha, method="lower"))
+ elif method == "closest_observation":
+ if n * alpha == int(n * alpha):
+ assert_allclose(q, np.quantile(y, alpha, method="higher"))
+ elif np.round(n * alpha) == int(n * alpha) + 1:
+ assert_allclose(
+ q, np.quantile(y, alpha + 1/n, method="higher"))
+ else:
+ assert_allclose(q, np.quantile(y, alpha, method="lower"))
+ elif method == "interpolated_inverted_cdf":
+ assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+ elif method == "nearest":
+ if n * alpha == int(n * alpha):
+ assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+ else:
+ assert_allclose(q, np.quantile(y, alpha, method=method))
+ elif method == "lower":
+ assert_allclose(q, np.quantile(y, alpha, method="higher"))
+ elif method == "higher":
+ assert_allclose(q, np.quantile(y, alpha, method="lower"))
+ else:
+ # "averaged_inverted_cdf", "hazen", "weibull", "linear",
+ # "median_unbiased", "normal_unbiased", "midpoint"
+ assert_allclose(q, np.quantile(y, alpha, method=method))
+
class TestLerp:
@hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False,
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index 4699935ca..c1032df8e 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -232,6 +232,17 @@ class TestSavezLoad(RoundtripTest):
assert_equal(a, l['file_a'])
assert_equal(b, l['file_b'])
+
+ def test_tuple_getitem_raises(self):
+ # gh-23748
+ a = np.array([1, 2, 3])
+ f = BytesIO()
+ np.savez(f, a=a)
+ f.seek(0)
+ l = np.load(f)
+ with pytest.raises(KeyError, match="(1, 2)"):
+ l[1, 2]
+
def test_BagObj(self):
a = np.array([[1, 2], [3, 4]], float)
b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex)
@@ -321,6 +332,21 @@ class TestSavezLoad(RoundtripTest):
data.close()
assert_(fp.closed)
+ @pytest.mark.parametrize("count, expected_repr", [
+ (1, "NpzFile {fname!r} with keys: arr_0"),
+ (5, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4"),
+ # _MAX_REPR_ARRAY_COUNT is 5, so files with more than 5 keys are
+ # expected to end in '...'
+ (6, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4..."),
+ ])
+ def test_repr_lists_keys(self, count, expected_repr):
+ a = np.array([[1, 2], [3, 4]], float)
+ with temppath(suffix='.npz') as tmp:
+ np.savez(tmp, *[a]*count)
+ l = np.load(tmp)
+ assert repr(l) == expected_repr.format(fname=tmp)
+ l.close()
+
class TestSaveTxt:
def test_array(self):
@@ -522,7 +548,7 @@ class TestSaveTxt:
def test_unicode(self):
utf8 = b'\xcf\x96'.decode('UTF-8')
- a = np.array([utf8], dtype=np.unicode_)
+ a = np.array([utf8], dtype=np.str_)
with tempdir() as tmpdir:
# set encoding as on windows it may not be unicode even on py3
np.savetxt(os.path.join(tmpdir, 'test.csv'), a, fmt=['%s'],
@@ -530,7 +556,7 @@ class TestSaveTxt:
def test_unicode_roundtrip(self):
utf8 = b'\xcf\x96'.decode('UTF-8')
- a = np.array([utf8], dtype=np.unicode_)
+ a = np.array([utf8], dtype=np.str_)
# our gz wrapper support encoding
suffixes = ['', '.gz']
if HAS_BZ2:
@@ -542,12 +568,12 @@ class TestSaveTxt:
np.savetxt(os.path.join(tmpdir, 'test.csv' + suffix), a,
fmt=['%s'], encoding='UTF-16-LE')
b = np.loadtxt(os.path.join(tmpdir, 'test.csv' + suffix),
- encoding='UTF-16-LE', dtype=np.unicode_)
+ encoding='UTF-16-LE', dtype=np.str_)
assert_array_equal(a, b)
def test_unicode_bytestream(self):
utf8 = b'\xcf\x96'.decode('UTF-8')
- a = np.array([utf8], dtype=np.unicode_)
+ a = np.array([utf8], dtype=np.str_)
s = BytesIO()
np.savetxt(s, a, fmt=['%s'], encoding='UTF-8')
s.seek(0)
@@ -555,7 +581,7 @@ class TestSaveTxt:
def test_unicode_stringstream(self):
utf8 = b'\xcf\x96'.decode('UTF-8')
- a = np.array([utf8], dtype=np.unicode_)
+ a = np.array([utf8], dtype=np.str_)
s = StringIO()
np.savetxt(s, a, fmt=['%s'], encoding='UTF-8')
s.seek(0)
@@ -597,8 +623,8 @@ class TestSaveTxt:
# in our process if needed, see gh-16889
memoryerror_raised = Value(c_bool)
- # Since Python 3.8, the default start method for multiprocessing has
- # been changed from 'fork' to 'spawn' on macOS, causing inconsistency
+ # Since Python 3.8, the default start method for multiprocessing has
+ # been changed from 'fork' to 'spawn' on macOS, causing inconsistency
# on memory sharing model, lead to failed test for check_large_zip
ctx = get_context('fork')
p = ctx.Process(target=check_large_zip, args=(memoryerror_raised,))
@@ -652,12 +678,12 @@ class LoadTxtBase:
with temppath() as path:
with open(path, "wb") as f:
f.write(nonascii.encode("UTF-16"))
- x = self.loadfunc(path, encoding="UTF-16", dtype=np.unicode_)
+ x = self.loadfunc(path, encoding="UTF-16", dtype=np.str_)
assert_array_equal(x, nonascii)
def test_binary_decode(self):
utf16 = b'\xff\xfeh\x04 \x00i\x04 \x00j\x04'
- v = self.loadfunc(BytesIO(utf16), dtype=np.unicode_, encoding='UTF-16')
+ v = self.loadfunc(BytesIO(utf16), dtype=np.str_, encoding='UTF-16')
assert_array_equal(v, np.array(utf16.decode('UTF-16').split()))
def test_converters_decode(self):
@@ -665,7 +691,7 @@ class LoadTxtBase:
c = TextIO()
c.write(b'\xcf\x96')
c.seek(0)
- x = self.loadfunc(c, dtype=np.unicode_,
+ x = self.loadfunc(c, dtype=np.str_,
converters={0: lambda x: x.decode('UTF-8')})
a = np.array([b'\xcf\x96'.decode('UTF-8')])
assert_array_equal(x, a)
@@ -676,7 +702,7 @@ class LoadTxtBase:
with temppath() as path:
with io.open(path, 'wt', encoding='UTF-8') as f:
f.write(utf8)
- x = self.loadfunc(path, dtype=np.unicode_,
+ x = self.loadfunc(path, dtype=np.str_,
converters={0: lambda x: x + 't'},
encoding='UTF-8')
a = np.array([utf8 + 't'])
@@ -1161,7 +1187,7 @@ class TestLoadTxt(LoadTxtBase):
with open(path, "wb") as f:
f.write(butf8)
with open(path, "rb") as f:
- x = np.loadtxt(f, encoding="UTF-8", dtype=np.unicode_)
+ x = np.loadtxt(f, encoding="UTF-8", dtype=np.str_)
assert_array_equal(x, sutf8)
# test broken latin1 conversion people now rely on
with open(path, "rb") as f:
@@ -2219,7 +2245,7 @@ M 33 21.99
ctl = np.array([
["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"],
["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"]],
- dtype=np.unicode_)
+ dtype=np.str_)
assert_array_equal(test, ctl)
# test a mixed dtype
@@ -2262,7 +2288,7 @@ M 33 21.99
["norm1", "norm2", "norm3"],
["norm1", latin1, "norm3"],
["test1", "testNonethe" + utf8, "test3"]],
- dtype=np.unicode_)
+ dtype=np.str_)
assert_array_equal(test, ctl)
def test_recfromtxt(self):
@@ -2737,3 +2763,13 @@ def test_load_refcount():
with assert_no_gc_cycles():
x = np.loadtxt(TextIO("0 1 2 3"), dtype=dt)
assert_equal(x, np.array([((0, 1), (2, 3))], dtype=dt))
+
+def test_load_multiple_arrays_until_eof():
+ f = BytesIO()
+ np.save(f, 1)
+ np.save(f, 2)
+ f.seek(0)
+ assert np.load(f) == 1
+ assert np.load(f) == 2
+ with pytest.raises(EOFError):
+ np.load(f)
diff --git a/numpy/lib/tests/test_loadtxt.py b/numpy/lib/tests/test_loadtxt.py
index 0b8fe3c47..2d805e434 100644
--- a/numpy/lib/tests/test_loadtxt.py
+++ b/numpy/lib/tests/test_loadtxt.py
@@ -244,6 +244,14 @@ def test_converters_negative_indices_with_usecols():
usecols=[0, -1], converters={-1: (lambda x: -1)})
assert_array_equal(res, [[0, -1], [0, -1]])
+
+def test_ragged_error():
+ rows = ["1,2,3", "1,2,3", "4,3,2,1"]
+ with pytest.raises(ValueError,
+ match="the number of columns changed from 3 to 4 at row 3"):
+ np.loadtxt(rows, delimiter=",")
+
+
def test_ragged_usecols():
# usecols, and negative ones, work even with varying number of columns.
txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n")
@@ -534,12 +542,27 @@ def test_quoted_field(q):
assert_array_equal(res, expected)
+@pytest.mark.parametrize("q", ('"', "'", "`"))
+def test_quoted_field_with_whitepace_delimiter(q):
+ txt = StringIO(
+ f"{q}alpha, x{q} 2.5\n{q}beta, y{q} 4.5\n{q}gamma, z{q} 5.0\n"
+ )
+ dtype = np.dtype([('f0', 'U8'), ('f1', np.float64)])
+ expected = np.array(
+ [("alpha, x", 2.5), ("beta, y", 4.5), ("gamma, z", 5.0)], dtype=dtype
+ )
+
+ res = np.loadtxt(txt, dtype=dtype, delimiter=None, quotechar=q)
+ assert_array_equal(res, expected)
+
+
def test_quote_support_default():
"""Support for quoted fields is disabled by default."""
txt = StringIO('"lat,long", 45, 30\n')
dtype = np.dtype([('f0', 'U24'), ('f1', np.float64), ('f2', np.float64)])
- with pytest.raises(ValueError, match="the number of columns changed"):
+ with pytest.raises(ValueError,
+ match="the dtype passed requires 3 columns but 4 were"):
np.loadtxt(txt, dtype=dtype, delimiter=",")
# Enable quoting support with non-None value for quotechar param
@@ -1011,3 +1034,15 @@ def test_control_characters_as_bytes():
"""Byte control characters (comments, delimiter) are supported."""
a = np.loadtxt(StringIO("#header\n1,2,3"), comments=b"#", delimiter=b",")
assert_equal(a, [1, 2, 3])
+
+
+@pytest.mark.filterwarnings('ignore::UserWarning')
+def test_field_growing_cases():
+ # Test empty field appending/growing (each field still takes 1 character)
+ # to see if the final field appending does not create issues.
+ res = np.loadtxt([""], delimiter=",", dtype=bytes)
+ assert len(res) == 0
+
+ for i in range(1, 1024):
+ res = np.loadtxt(["," * i], delimiter=",", dtype=bytes)
+ assert len(res) == i+1
diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py
index 45cacb792..257de381b 100644
--- a/numpy/lib/tests/test_nanfunctions.py
+++ b/numpy/lib/tests/test_nanfunctions.py
@@ -824,6 +824,7 @@ class TestNanFunctions_Median:
(-3, -1),
]
)
+ @pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning")
def test_keepdims_out(self, axis):
d = np.ones((3, 5, 7, 11))
# Randomly set some elements to NaN:
@@ -1027,6 +1028,7 @@ class TestNanFunctions_Percentile:
(-3, -1),
]
)
+ @pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning")
def test_keepdims_out(self, q, axis):
d = np.ones((3, 5, 7, 11))
# Randomly set some elements to NaN:
diff --git a/numpy/lib/tests/test_shape_base.py b/numpy/lib/tests/test_shape_base.py
index 76058cf20..eb6628904 100644
--- a/numpy/lib/tests/test_shape_base.py
+++ b/numpy/lib/tests/test_shape_base.py
@@ -492,7 +492,7 @@ class TestColumnStack:
assert_equal(actual, expected)
def test_generator(self):
- with assert_warns(FutureWarning):
+ with pytest.raises(TypeError, match="arrays to stack must be"):
column_stack((np.arange(3) for _ in range(2)))
@@ -529,7 +529,7 @@ class TestDstack:
assert_array_equal(res, desired)
def test_generator(self):
- with assert_warns(FutureWarning):
+ with pytest.raises(TypeError, match="arrays to stack must be"):
dstack((np.arange(3) for _ in range(2)))
diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py
index 141f508fd..eb008c600 100644
--- a/numpy/lib/tests/test_twodim_base.py
+++ b/numpy/lib/tests/test_twodim_base.py
@@ -4,20 +4,14 @@
from numpy.testing import (
assert_equal, assert_array_equal, assert_array_max_ulp,
assert_array_almost_equal, assert_raises, assert_
- )
-
+)
from numpy import (
arange, add, fliplr, flipud, zeros, ones, eye, array, diag, histogram2d,
tri, mask_indices, triu_indices, triu_indices_from, tril_indices,
tril_indices_from, vander,
- )
-
+)
import numpy as np
-
-from numpy.core.tests.test_overrides import requires_array_function
-
-
import pytest
@@ -283,7 +277,6 @@ class TestHistogram2d:
assert_array_equal(H, answer)
assert_array_equal(xe, array([0., 0.25, 0.5, 0.75, 1]))
- @requires_array_function
def test_dispatch(self):
class ShouldDispatch:
def __array_function__(self, function, types, args, kwargs):
diff --git a/numpy/lib/tests/test_type_check.py b/numpy/lib/tests/test_type_check.py
index 3f4ca6309..ea0326139 100644
--- a/numpy/lib/tests/test_type_check.py
+++ b/numpy/lib/tests/test_type_check.py
@@ -155,7 +155,7 @@ class TestIscomplex:
def test_fail(self):
z = np.array([-1, 0, 1])
res = iscomplex(z)
- assert_(not np.sometrue(res, axis=0))
+ assert_(not np.any(res, axis=0))
def test_pass(self):
z = np.array([-1j, 1, 0])
diff --git a/numpy/lib/tests/test_ufunclike.py b/numpy/lib/tests/test_ufunclike.py
index c280b6969..fac4f41d0 100644
--- a/numpy/lib/tests/test_ufunclike.py
+++ b/numpy/lib/tests/test_ufunclike.py
@@ -80,12 +80,6 @@ class TestUfunclike:
assert_(isinstance(f0d, MyArray))
assert_equal(f0d.metadata, 'bar')
- def test_deprecated(self):
- # NumPy 1.13.0, 2017-04-26
- assert_warns(DeprecationWarning, ufl.fix, [1, 2], y=nx.empty(2))
- assert_warns(DeprecationWarning, ufl.isposinf, [1, 2], y=nx.empty(2))
- assert_warns(DeprecationWarning, ufl.isneginf, [1, 2], y=nx.empty(2))
-
def test_scalar(self):
x = np.inf
actual = np.isposinf(x)
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