TST: Use new warnings context manager in all tests

In some places, just remove aparently unnecessary filters.

After this, all cases of ignore filters should be removed from
the tests, making testing (even multiple runs) normally fully
predictable.

1 files changed, 14 insertions, 19 deletions

diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 0d7b11c44..4535c1e7f 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -8,7 +8,7 @@ from numpy.testing import (
     run_module_suite, TestCase, assert_, assert_equal, assert_array_equal,
     assert_almost_equal, assert_array_almost_equal, assert_raises,
     assert_allclose, assert_array_max_ulp, assert_warns,
-    assert_raises_regex, dec, clear_and_catch_warnings
+    assert_raises_regex, dec, suppress_warnings
 )
 from numpy.testing.utils import HAS_REFCOUNT
 import numpy.lib.function_base as nfb
@@ -320,7 +320,11 @@ class TestAverage(TestCase):
         a = np.array([[1,2],[3,4]]).view(subclass)
         w = np.array([[1,2],[3,4]]).view(subclass)
 
-        assert_equal(type(np.average(a, weights=w)), subclass)
+        with suppress_warnings() as sup:
+            # Note that the warning is spurious, because the test checks
+            # for weights while a is ignored.
+            sup.filter(FutureWarning, "np.average currently does not preserve")
+            assert_equal(type(np.average(a, weights=w)), subclass)
 
         # also test matrices
         a = np.matrix([[1,2],[3,4]])
@@ -1457,12 +1461,12 @@ class TestHistogram(TestCase):
         a, b = histogram([], bins=([0, 1]))
         assert_array_equal(a, np.array([0]))
         assert_array_equal(b, np.array([0, 1]))
-        
+
     def test_error_binnum_type (self):
         # Tests if right Error is raised if bins argument is float
         vals = np.linspace(0.0, 1.0, num=100)
         histogram(vals, 5)
-        assert_raises(TypeError, histogram, vals, 2.4)   
+        assert_raises(TypeError, histogram, vals, 2.4)
 
     def test_finite_range(self):
         # Normal ranges should be fine
@@ -1792,15 +1796,6 @@ class TestCheckFinite(TestCase):
         assert_(a.dtype == np.float64)
 
 
-class catch_warn_nfb(clear_and_catch_warnings):
-
-    """
-    Context manager to catch, reset warnings in function_base module
-
-    """
-    class_modules = (nfb,)
-
-
 class TestCorrCoef(TestCase):
     A = np.array(
         [[0.15391142, 0.18045767, 0.14197213],
@@ -1837,10 +1832,10 @@ class TestCorrCoef(TestCase):
 
     def test_ddof(self):
         # ddof raises DeprecationWarning
-        with catch_warn_nfb():
+        with suppress_warnings() as sup:
             warnings.simplefilter("always")
             assert_warns(DeprecationWarning, corrcoef, self.A, ddof=-1)
-            warnings.simplefilter("ignore")
+            sup.filter(DeprecationWarning)
             # ddof has no or negligible effect on the function
             assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
             assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)
@@ -1849,11 +1844,11 @@ class TestCorrCoef(TestCase):
 
     def test_bias(self):
         # bias raises DeprecationWarning
-        with catch_warn_nfb():
+        with suppress_warnings() as sup:
             warnings.simplefilter("always")
             assert_warns(DeprecationWarning, corrcoef, self.A, self.B, 1, 0)
             assert_warns(DeprecationWarning, corrcoef, self.A, bias=0)
-            warnings.simplefilter("ignore")
+            sup.filter(DeprecationWarning)
             # bias has no or negligible effect on the function
             assert_almost_equal(corrcoef(self.A, bias=1), self.res1)
 
@@ -2316,7 +2311,7 @@ class TestInterp(TestCase):
         assert_almost_equal(np.interp(x0, x, y), x0)
         x0 = np.nan
         assert_almost_equal(np.interp(x0, x, y), x0)
-        
+
     def test_complex_interp(self):
         # test complex interpolation
         x = np.linspace(0, 1, 5)
@@ -2335,7 +2330,7 @@ class TestInterp(TestCase):
         x = [-180, -170, -185, 185, -10, -5, 0, 365]
         xp = [190, -190, 350, -350]
         fp = [5+1.0j, 10+2j, 3+3j, 4+4j]
-        y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j, 
+        y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j,
              3.5+3.5j, 3.75+3.75j]
         assert_almost_equal(np.interp(x, xp, fp, period=360), y)