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TST: fix stats.iqr test to not emit warnings, and fix line lengths.
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@rgommers
rgommers committed Jun 29, 2016
1 parent 1c8385d commit 3d9021b
Showing 1 changed file with 50 additions and 38 deletions.
88 changes: 50 additions & 38 deletions scipy/stats/tests/test_stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1443,7 +1443,7 @@ class _numpy_version_warn_context_mgr(object):
versions raise warnings.
This manager does not apply for cases where the old code returns
different values.
different values.
"""
def __init__(self, min_numpy_version, warning_type, num_warnings):
if NumpyVersion(np.__version__) < min_numpy_version:
Expand All @@ -1463,14 +1463,14 @@ def __enter__(self):
def __exit__(self, exc_type, exc_value, traceback):
if self.numpy_is_old:
self.delegate.__exit__(exc_type, exc_value, traceback)
_check_warnings(self.warn_list, self.warning_type, self.num_warnings)
_check_warnings(self.warn_list, self.warning_type, self.num_warnings)


def _check_warnings(warn_list, expected_type, expected_len):
"""
Checks that all of the warnings from a list returned by
`warnings.catch_all(record=True)` are of the required type and that the list
contains expected number of warnings.
contains expected number of warnings.
"""
assert_equal(len(warn_list), expected_len, "number of warnings")
for warn_ in warn_list:
Expand Down Expand Up @@ -1515,7 +1515,7 @@ def test_constant(self):
assert_equal(stats.iqr(x, interpolation='lower'), 0.0)
assert_equal(stats.iqr(x, interpolation='higher'), 0.0)

# 0 only along constant dimensions
# 0 only along constant dimensions
# This also tests much of `axis`
y = np.ones((4, 5, 6)) * np.arange(6)
assert_array_equal(stats.iqr(y, axis=0), np.zeros((5, 6)))
Expand Down Expand Up @@ -1706,9 +1706,9 @@ def test_nanpolicy(self):

# Yes NaNs
x[1, 2] = np.nan
if numpy_version < '1.10.0a':
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
if numpy_version < '1.10.0a':
# Fails over to mishmash of omit/propagate, but mostly omit
# The first case showcases the "incorrect" behavior of np.percentile
assert_equal(stats.iqr(x, nan_policy='propagate'), 8)
Expand All @@ -1719,23 +1719,23 @@ def test_nanpolicy(self):
# some fixes to percentile nan handling in 1.9
assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'), [2, np.nan, 2])
_check_warnings(w, RuntimeWarning, 3)
else:
assert_equal(stats.iqr(x, nan_policy='propagate'), np.nan)
assert_equal(stats.iqr(x, axis=0, nan_policy='propagate'), [5, 5, np.nan, 5, 5])
assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'), [2, np.nan, 2])
else:
assert_equal(stats.iqr(x, nan_policy='propagate'), np.nan)
assert_equal(stats.iqr(x, axis=0, nan_policy='propagate'), [5, 5, np.nan, 5, 5])
assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'), [2, np.nan, 2])

if numpy_version < '1.9.0a':
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
if numpy_version < '1.9.0a':
# Fails over to mishmash of omit/propagate, but mostly omit
assert_equal(stats.iqr(x, nan_policy='omit'), 8)
assert_equal(stats.iqr(x, axis=0, nan_policy='omit'), [5, 5, np.nan, 5, 5])
assert_equal(stats.iqr(x, axis=1, nan_policy='omit'), [2, 3, 2])
_check_warnings(w, RuntimeWarning, 3)
else:
assert_equal(stats.iqr(x, nan_policy='omit'), 7.5)
assert_equal(stats.iqr(x, axis=0, nan_policy='omit'), 5 * np.ones(5))
assert_equal(stats.iqr(x, axis=1, nan_policy='omit'), [2, 2.5, 2])
else:
assert_equal(stats.iqr(x, nan_policy='omit'), 7.5)
assert_equal(stats.iqr(x, axis=0, nan_policy='omit'), 5 * np.ones(5))
assert_equal(stats.iqr(x, axis=1, nan_policy='omit'), [2, 2.5, 2])

assert_raises(ValueError, stats.iqr, x, nan_policy='raise')
assert_raises(ValueError, stats.iqr, x, axis=0, nan_policy='raise')
Expand All @@ -1755,47 +1755,59 @@ def test_scale(self):

# Yes NaNs
x[1, 2] = np.nan
if numpy_version < '1.10.0a':
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
if numpy_version < '1.10.0a':
# Fails over to mishmash of omit/propagate, but mostly omit
assert_equal(stats.iqr(x, scale='raw', nan_policy='propagate'), 8)
assert_almost_equal(stats.iqr(x, scale='normal', nan_policy='propagate'), 8 / 1.3489795)
assert_almost_equal(stats.iqr(x, scale='normal',
nan_policy='propagate'),
8 / 1.3489795)
assert_equal(stats.iqr(x, scale=2.0, nan_policy='propagate'), 4)
# axis=1 chosen to show behavior with both nans and without
if numpy_version < '1.9.0a':
assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'), [2, 3, 2])
assert_almost_equal(stats.iqr(x, axis=1, scale='normal', nan_policy='propagate'), np.array([2, 3, 2]) / 1.3489795)
assert_equal(stats.iqr(x, axis=1, scale=2.0, nan_policy='propagate'), [1, 1.5, 1])
assert_almost_equal(stats.iqr(x, axis=1, scale='normal',
nan_policy='propagate'),
np.array([2, 3, 2]) / 1.3489795)
assert_equal(stats.iqr(x, axis=1, scale=2.0,
nan_policy='propagate'), [1, 1.5, 1])
else:
# some fixes to percentile nan handling in 1.9
assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'), [2, np.nan, 2])
assert_almost_equal(stats.iqr(x, axis=1, scale='normal', nan_policy='propagate'), np.array([2, np.nan, 2]) / 1.3489795)
assert_equal(stats.iqr(x, axis=1, scale=2.0, nan_policy='propagate'), [1, np.nan, 1])
assert_almost_equal(stats.iqr(x, axis=1, scale='normal',
nan_policy='propagate'),
np.array([2, np.nan, 2]) / 1.3489795)
assert_equal(stats.iqr(x, axis=1, scale=2.0,
nan_policy='propagate'), [1, np.nan, 1])
_check_warnings(w, RuntimeWarning, 6)
else:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
else:
assert_equal(stats.iqr(x, scale='raw', nan_policy='propagate'), np.nan)
assert_equal(stats.iqr(x, scale='normal', nan_policy='propagate'), np.nan)
assert_equal(stats.iqr(x, scale=2.0, nan_policy='propagate'), np.nan)
# axis=1 chosen to show behavior with both nans and without
assert_equal(stats.iqr(x, axis=1, scale='raw', nan_policy='propagate'), [2, np.nan, 2])
assert_almost_equal(stats.iqr(x, axis=1, scale='normal', nan_policy='propagate'), np.array([2, np.nan, 2]) / 1.3489795)
assert_equal(stats.iqr(x, axis=1, scale=2.0, nan_policy='propagate'), [1, np.nan, 1])
assert_equal(stats.iqr(x, axis=1, scale='raw',
nan_policy='propagate'), [2, np.nan, 2])
assert_almost_equal(stats.iqr(x, axis=1, scale='normal',
nan_policy='propagate'),
np.array([2, np.nan, 2]) / 1.3489795)
assert_equal(stats.iqr(x, axis=1, scale=2.0, nan_policy='propagate'),
[1, np.nan, 1])
_check_warnings(w, RuntimeWarning, 6)

if numpy_version < '1.9.0a':
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
# Fails over to mishmash of omit/propagate, but mostly omit
assert_equal(stats.iqr(x, scale='raw', nan_policy='omit'), 8)
assert_almost_equal(stats.iqr(x, scale='normal', nan_policy='omit'), 8 / 1.3489795)
assert_almost_equal(stats.iqr(x, scale='normal', nan_policy='omit'),
8 / 1.3489795)
assert_equal(stats.iqr(x, scale=2.0, nan_policy='omit'), 4)
_check_warnings(w, RuntimeWarning, 3)
else:
assert_equal(stats.iqr(x, scale='raw', nan_policy='omit'), 7.5)
assert_almost_equal(stats.iqr(x, scale='normal', nan_policy='omit'), 7.5 / 1.3489795)
assert_almost_equal(stats.iqr(x, scale='normal', nan_policy='omit'),
7.5 / 1.3489795)
assert_equal(stats.iqr(x, scale=2.0, nan_policy='omit'), 3.75)

# Bad scale
Expand Down Expand Up @@ -2683,9 +2695,9 @@ def test_ttest_rel_nan_2nd_arg():

# NB: arguments are paired when NaNs are dropped
r3 = stats.ttest_rel(y[1:], x[1:])
assert_allclose(r2, r3, atol=1e-15)
assert_allclose(r2, r3, atol=1e-15)

# .. and this is consistent with R. R code:
# .. and this is consistent with R. R code:
# x = c(NA, 2.0, 3.0, 4.0)
# y = c(1.0, 2.0, 1.0, 2.0)
# t.test(x, y, paired=TRUE)
Expand Down Expand Up @@ -2902,9 +2914,9 @@ def test_ttest_ind_nan_2nd_arg():

# NB: arguments are not paired when NaNs are dropped
r3 = stats.ttest_ind(y, x[1:])
assert_allclose(r2, r3, atol=1e-15)
assert_allclose(r2, r3, atol=1e-15)

# .. and this is consistent with R. R code:
# .. and this is consistent with R. R code:
# x = c(NA, 2.0, 3.0, 4.0)
# y = c(1.0, 2.0, 1.0, 2.0)
# t.test(x, y, var.equal=TRUE)
Expand Down

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