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-rw-r--r--numpy/lib/tests/data/python3.npybin0 -> 96 bytes
-rw-r--r--numpy/lib/tests/data/win64python2.npybin0 -> 96 bytes
-rw-r--r--numpy/lib/tests/test_format.py10
-rw-r--r--numpy/lib/tests/test_function_base.py38
-rw-r--r--numpy/lib/tests/test_io.py11
-rw-r--r--numpy/lib/tests/test_nanfunctions.py16
6 files changed, 70 insertions, 5 deletions
diff --git a/numpy/lib/tests/data/python3.npy b/numpy/lib/tests/data/python3.npy
new file mode 100644
index 000000000..7c6997dd6
--- /dev/null
+++ b/numpy/lib/tests/data/python3.npy
Binary files differ
diff --git a/numpy/lib/tests/data/win64python2.npy b/numpy/lib/tests/data/win64python2.npy
new file mode 100644
index 000000000..d9bc36af7
--- /dev/null
+++ b/numpy/lib/tests/data/win64python2.npy
Binary files differ
diff --git a/numpy/lib/tests/test_format.py b/numpy/lib/tests/test_format.py
index c09386789..ee77386bc 100644
--- a/numpy/lib/tests/test_format.py
+++ b/numpy/lib/tests/test_format.py
@@ -524,6 +524,16 @@ def test_compressed_roundtrip():
assert_array_equal(arr, arr1)
+def test_python2_python3_interoperability():
+ if sys.version_info[0] >= 3:
+ fname = 'win64python2.npy'
+ else:
+ fname = 'python3.npy'
+ path = os.path.join(os.path.dirname(__file__), 'data', fname)
+ data = np.load(path)
+ assert_array_equal(data, np.ones(2))
+
+
def test_version_2_0():
f = BytesIO()
# requires more than 2 byte for header
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index c800f8347..80faf85a6 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -124,6 +124,11 @@ class TestAverage(TestCase):
assert_array_equal(average(y1, weights=w2, axis=1), desired)
assert_equal(average(y1, weights=w2), 5.)
+ y3 = rand(5).astype(np.float32)
+ w3 = rand(5).astype(np.float64)
+
+ assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3))
+
def test_returned(self):
y = np.array([[1, 2, 3], [4, 5, 6]])
@@ -526,8 +531,18 @@ class TestGradient(TestCase):
def test_masked(self):
# Make sure that gradient supports subclasses like masked arrays
- x = np.ma.array([[1, 1], [3, 4]])
- assert_equal(type(gradient(x)[0]), type(x))
+ x = np.ma.array([[1, 1], [3, 4]],
+ mask=[[False, False], [False, False]])
+ out = gradient(x)[0]
+ assert_equal(type(out), type(x))
+ # And make sure that the output and input don't have aliased mask
+ # arrays
+ assert_(x.mask is not out.mask)
+ # Also check that edge_order=2 doesn't alter the original mask
+ x2 = np.ma.arange(5)
+ x2[2] = np.ma.masked
+ np.gradient(x2, edge_order=2)
+ assert_array_equal(x2.mask, [False, False, True, False, False])
def test_datetime64(self):
# Make sure gradient() can handle special types like datetime64
@@ -536,7 +551,7 @@ class TestGradient(TestCase):
'1910-10-12', '1910-12-12', '1912-12-12'],
dtype='datetime64[D]')
dx = np.array(
- [-7, -3, 0, 31, 61, 396, 1066],
+ [-5, -3, 0, 31, 61, 396, 731],
dtype='timedelta64[D]')
assert_array_equal(gradient(x), dx)
assert_(dx.dtype == np.dtype('timedelta64[D]'))
@@ -547,7 +562,7 @@ class TestGradient(TestCase):
[-5, -3, 10, 12, 61, 321, 300],
dtype='timedelta64[D]')
dx = np.array(
- [-3, 7, 7, 25, 154, 119, -161],
+ [2, 7, 7, 25, 154, 119, -21],
dtype='timedelta64[D]')
assert_array_equal(gradient(x), dx)
assert_(dx.dtype == np.dtype('timedelta64[D]'))
@@ -561,7 +576,7 @@ class TestGradient(TestCase):
dx = x[1] - x[0]
y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
analytical = 6 * x ** 2 + 8 * x + 2
- num_error = np.abs((np.gradient(y, dx) / analytical) - 1)
+ num_error = np.abs((np.gradient(y, dx, edge_order=2) / analytical) - 1)
assert_(np.all(num_error < 0.03) == True)
@@ -1604,6 +1619,9 @@ class TestInterp(TestCase):
def test_exceptions(self):
assert_raises(ValueError, interp, 0, [], [])
assert_raises(ValueError, interp, 0, [0], [1, 2])
+ assert_raises(ValueError, interp, 0, [0, 1], [1, 2], period=0)
+ assert_raises(ValueError, interp, 0, [], [], period=360)
+ assert_raises(ValueError, interp, 0, [0], [1, 2], period=360)
def test_basic(self):
x = np.linspace(0, 1, 5)
@@ -1644,6 +1662,16 @@ class TestInterp(TestCase):
fp = np.sin(xp)
assert_almost_equal(np.interp(np.pi, xp, fp), 0.0)
+ def test_period(self):
+ x = [-180, -170, -185, 185, -10, -5, 0, 365]
+ xp = [190, -190, 350, -350]
+ fp = [5, 10, 3, 4]
+ y = [7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75]
+ assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+ x = np.array(x, order='F').reshape(2, -1)
+ y = np.array(y, order='C').reshape(2, -1)
+ assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+
def compare_results(res, desired):
for i in range(len(desired)):
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index 4038d6a7f..68b2018cd 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -216,6 +216,17 @@ class TestSavezLoad(RoundtripTest, TestCase):
l = np.load(c)
assert_equal(a, l['file_a'])
assert_equal(b, l['file_b'])
+
+ def test_BagObj(self):
+ a = np.array([[1, 2], [3, 4]], float)
+ b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex)
+ c = BytesIO()
+ np.savez(c, file_a=a, file_b=b)
+ c.seek(0)
+ l = np.load(c)
+ assert_equal(sorted(dir(l.f)), ['file_a','file_b'])
+ assert_equal(a, l.f.file_a)
+ assert_equal(b, l.f.file_b)
def test_savez_filename_clashes(self):
# Test that issue #852 is fixed
diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py
index 3da6b5149..35ae86c20 100644
--- a/numpy/lib/tests/test_nanfunctions.py
+++ b/numpy/lib/tests/test_nanfunctions.py
@@ -645,6 +645,22 @@ class TestNanFunctions_Median(TestCase):
assert_raises(IndexError, np.nanmedian, d, axis=(0, 4))
assert_raises(ValueError, np.nanmedian, d, axis=(1, 1))
+ def test_float_special(self):
+ with warnings.catch_warnings(record=True):
+ warnings.simplefilter('ignore', RuntimeWarning)
+ a = np.array([[np.inf, np.nan], [np.nan, np.nan]])
+ assert_equal(np.nanmedian(a, axis=0), [np.inf, np.nan])
+ assert_equal(np.nanmedian(a, axis=1), [np.inf, np.nan])
+ assert_equal(np.nanmedian(a), np.inf)
+
+ # minimum fill value check
+ a = np.array([[np.nan, np.nan, np.inf], [np.nan, np.nan, np.inf]])
+ assert_equal(np.nanmedian(a, axis=1), np.inf)
+
+ # no mask path
+ a = np.array([[np.inf, np.inf], [np.inf, np.inf]])
+ assert_equal(np.nanmedian(a, axis=1), np.inf)
+
class TestNanFunctions_Percentile(TestCase):
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