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Fix or silence a number of failures reported against 0.11.0rc1 #286

Merged
merged 3 commits into from

2 participants

@rgommers
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rgommers added some commits
@rgommers rgommers TST: increase test tolerances for 2 scipy.special tests.
Reported by Yaroslav Halchenko to fail for 0.11.0rc1 on Debian, s390x
architecture, as well as by Derek Homeier for 0.8.0rc3 on PPC OS X.
f2b68b2
@rgommers rgommers TST: mark ndimage test as knownfail on all platforms except OS X.
This test has been reported to fail for a couple of releases on Windows at
least, and now also on Debian (s390x architecture).
883c74b
@rgommers rgommers TST: fix linalg.norm test failure. Thanks to Rudiger Kessel.
The intended output was obviously 0.5 (where numpy.linalg.norm gives 0.0); this
seems to be the case on most but not all systems.  The reason is that snrm
seems to internally use double precision for most BLAS versions, but not all.

Note that test_overflow() for this implementation of norm() passes on all
systems, so even where test_stable() fails the performance of norm() looks
better than for numpy.linalg.norm().
f5486bc
@rgommers
Owner

If someone can review this today that would be helpful. I really want to get 0.11.0rc2 out this weekend.

@pv
Owner
pv commented

Looks good to me.

@rgommers
Owner

Thanks. I'll merge this then.

@rgommers rgommers closed this
@rgommers rgommers reopened this
@rgommers rgommers merged commit 39b4776 into scipy:master
@jtravs jtravs referenced this pull request from a commit in jtravs/scipy
@jtravs jtravs BUG: use fitpack's regrid if possible, fixes #286
Also fixes parts of #1364 (strange warnings not transpose).
Also fixes parts of #1072 (strange behaviour, not lack of
bounds check)
b6bb799
@pv pv referenced this pull request from a commit
@jtravs jtravs ENH: interpolate: use fitpack's regrid if possible, fixes #286
Also works around knot selection warnings as seen in #1364 & #1072
7adfbbd
@ClemensFMN ClemensFMN referenced this pull request from a commit
Commit has since been removed from the repository and is no longer available.
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Commits on Aug 10, 2012
  1. @rgommers

    TST: increase test tolerances for 2 scipy.special tests.

    rgommers authored
    Reported by Yaroslav Halchenko to fail for 0.11.0rc1 on Debian, s390x
    architecture, as well as by Derek Homeier for 0.8.0rc3 on PPC OS X.
  2. @rgommers

    TST: mark ndimage test as knownfail on all platforms except OS X.

    rgommers authored
    This test has been reported to fail for a couple of releases on Windows at
    least, and now also on Debian (s390x architecture).
  3. @rgommers

    TST: fix linalg.norm test failure. Thanks to Rudiger Kessel.

    rgommers authored
    The intended output was obviously 0.5 (where numpy.linalg.norm gives 0.0); this
    seems to be the case on most but not all systems.  The reason is that snrm
    seems to internally use double precision for most BLAS versions, but not all.
    
    Note that test_overflow() for this implementation of norm() passes on all
    systems, so even where test_stable() fails the performance of norm() looks
    better than for numpy.linalg.norm().
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9 scipy/linalg/tests/test_basic.py
@@ -582,7 +582,14 @@ def test_overflow(self):
def test_stable(self):
# more stable than numpy's norm
a = array([1e4] + [1]*10000, dtype=float32)
- assert_almost_equal(norm(a) - 1e4, 0.5)
+ try:
+ # snrm in double precision; we obtain the same as for float64
+ assert_almost_equal(norm(a) - 1e4, 0.5)
+ except AssertionError:
+ # snrm implemented in single precision, == np.linalg.norm result
+ msg = ": Result should equal either 0.0 or 0.5 (depending on " \
+ "implementation of snrm2)."
+ assert_almost_equal(norm(a) - 1e4, 0.0, err_msg=msg)
def test_zero_norm(self):
assert_equal(norm([1,0,3], 0), 2)
View
15 scipy/ndimage/tests/test_datatypes.py
@@ -1,15 +1,15 @@
""" Testing data types for ndimage calls
"""
-import numpy as np
-
-from scipy import ndimage
+import sys
-from numpy.testing import (assert_array_almost_equal,
+import numpy as np
+from numpy.testing import (assert_array_almost_equal, dec,
assert_array_equal)
-
from nose.tools import assert_true, assert_equal, assert_raises
+from scipy import ndimage
+
def test_map_coordinates_dts():
# check that ndimage accepts different data types for interpolation
@@ -47,8 +47,11 @@ def test_map_coordinates_dts():
assert_array_almost_equal(these_data, out)
+@dec.knownfailureif(not sys.platform == 'darwin')
def test_uint64_max():
- # Test interpolation respects uint64 max
+ # Test interpolation respects uint64 max. Reported to fail at least on
+ # win32 (due to the 32 bit visual C compiler using signed int64 when
+ # converting between uint64 to double) and Debian on s390x.
big = 2**64-1
arr = np.array([big, big, big], dtype=np.uint64)
# Tests geometric transform (map_coordinates, affine_transform)
View
2  scipy/special/tests/test_basic.py
@@ -1656,7 +1656,7 @@ def test_iv_cephes_vs_amos_mass_test(self):
# Most error apparently comes from AMOS and not our implementation;
# there are some problems near integer orders there
- assert_(dc[k] < 1e-9, (v[k], x[k], special.iv(v[k], x[k]), special.iv(v[k], x[k]+0j)))
+ assert_(dc[k] < 2e-7, (v[k], x[k], special.iv(v[k], x[k]), special.iv(v[k], x[k]+0j)))
def test_kv_cephes_vs_amos(self):
#self.check_cephes_vs_amos(kv, kn, rtol=1e-9, atol=1e-305)
View
2  scipy/special/tests/test_data.py
@@ -96,7 +96,7 @@ def test_boost():
data(gamma, 'test_gamma_data_ipp-near_1', 0, 1),
data(gamma, 'test_gamma_data_ipp-near_2', 0, 1),
data(gamma, 'test_gamma_data_ipp-near_m10', 0, 1),
- data(gamma, 'test_gamma_data_ipp-near_m55', 0, 1),
+ data(gamma, 'test_gamma_data_ipp-near_m55', 0, 1, rtol=7e-12),
data(gamma, 'test_gamma_data_ipp-near_0', 0j, 1, rtol=2e-9),
data(gamma, 'test_gamma_data_ipp-near_1', 0j, 1, rtol=2e-9),
data(gamma, 'test_gamma_data_ipp-near_2', 0j, 1, rtol=2e-9),
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