Mini Shell
import warnings
import sys
import os
import itertools
import pytest
import weakref
import numpy as np
from numpy.testing import (
assert_equal, assert_array_equal, assert_almost_equal,
assert_array_almost_equal, assert_array_less, build_err_msg,
assert_raises, assert_warns, assert_no_warnings, assert_allclose,
assert_approx_equal, assert_array_almost_equal_nulp, assert_array_max_ulp,
clear_and_catch_warnings, suppress_warnings, assert_string_equal, assert_,
tempdir, temppath, assert_no_gc_cycles, HAS_REFCOUNT
)
class _GenericTest:
def _test_equal(self, a, b):
self._assert_func(a, b)
def _test_not_equal(self, a, b):
with assert_raises(AssertionError):
self._assert_func(a, b)
def test_array_rank1_eq(self):
"""Test two equal array of rank 1 are found equal."""
a = np.array([1, 2])
b = np.array([1, 2])
self._test_equal(a, b)
def test_array_rank1_noteq(self):
"""Test two different array of rank 1 are found not equal."""
a = np.array([1, 2])
b = np.array([2, 2])
self._test_not_equal(a, b)
def test_array_rank2_eq(self):
"""Test two equal array of rank 2 are found equal."""
a = np.array([[1, 2], [3, 4]])
b = np.array([[1, 2], [3, 4]])
self._test_equal(a, b)
def test_array_diffshape(self):
"""Test two arrays with different shapes are found not equal."""
a = np.array([1, 2])
b = np.array([[1, 2], [1, 2]])
self._test_not_equal(a, b)
def test_objarray(self):
"""Test object arrays."""
a = np.array([1, 1], dtype=object)
self._test_equal(a, 1)
def test_array_likes(self):
self._test_equal([1, 2, 3], (1, 2, 3))
class TestArrayEqual(_GenericTest):
def setup_method(self):
self._assert_func = assert_array_equal
def test_generic_rank1(self):
"""Test rank 1 array for all dtypes."""
def foo(t):
a = np.empty(2, t)
a.fill(1)
b = a.copy()
c = a.copy()
c.fill(0)
self._test_equal(a, b)
self._test_not_equal(c, b)
# Test numeric types and object
for t in '?bhilqpBHILQPfdgFDG':
foo(t)
# Test strings
for t in ['S1', 'U1']:
foo(t)
def test_0_ndim_array(self):
x = np.array(473963742225900817127911193656584771)
y = np.array(18535119325151578301457182298393896)
assert_raises(AssertionError, self._assert_func, x, y)
y = x
self._assert_func(x, y)
x = np.array(43)
y = np.array(10)
assert_raises(AssertionError, self._assert_func, x, y)
y = x
self._assert_func(x, y)
def test_generic_rank3(self):
"""Test rank 3 array for all dtypes."""
def foo(t):
a = np.empty((4, 2, 3), t)
a.fill(1)
b = a.copy()
c = a.copy()
c.fill(0)
self._test_equal(a, b)
self._test_not_equal(c, b)
# Test numeric types and object
for t in '?bhilqpBHILQPfdgFDG':
foo(t)
# Test strings
for t in ['S1', 'U1']:
foo(t)
def test_nan_array(self):
"""Test arrays with nan values in them."""
a = np.array([1, 2, np.nan])
b = np.array([1, 2, np.nan])
self._test_equal(a, b)
c = np.array([1, 2, 3])
self._test_not_equal(c, b)
def test_string_arrays(self):
"""Test two arrays with different shapes are found not equal."""
a = np.array(['floupi', 'floupa'])
b = np.array(['floupi', 'floupa'])
self._test_equal(a, b)
c = np.array(['floupipi', 'floupa'])
self._test_not_equal(c, b)
def test_recarrays(self):
"""Test record arrays."""
a = np.empty(2, [('floupi', float), ('floupa', float)])
a['floupi'] = [1, 2]
a['floupa'] = [1, 2]
b = a.copy()
self._test_equal(a, b)
c = np.empty(2, [('floupipi', float),
('floupi', float), ('floupa', float)])
c['floupipi'] = a['floupi'].copy()
c['floupa'] = a['floupa'].copy()
with pytest.raises(TypeError):
self._test_not_equal(c, b)
def test_masked_nan_inf(self):
# Regression test for gh-11121
a = np.ma.MaskedArray([3., 4., 6.5], mask=[False, True, False])
b = np.array([3., np.nan, 6.5])
self._test_equal(a, b)
self._test_equal(b, a)
a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, False, False])
b = np.array([np.inf, 4., 6.5])
self._test_equal(a, b)
self._test_equal(b, a)
def test_subclass_that_overrides_eq(self):
# While we cannot guarantee testing functions will always work for
# subclasses, the tests should ideally rely only on subclasses having
# comparison operators, not on them being able to store booleans
# (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
class MyArray(np.ndarray):
def __eq__(self, other):
return bool(np.equal(self, other).all())
def __ne__(self, other):
return not self == other
a = np.array([1., 2.]).view(MyArray)
b = np.array([2., 3.]).view(MyArray)
assert_(type(a == a), bool)
assert_(a == a)
assert_(a != b)
self._test_equal(a, a)
self._test_not_equal(a, b)
self._test_not_equal(b, a)
def test_subclass_that_does_not_implement_npall(self):
class MyArray(np.ndarray):
def __array_function__(self, *args, **kwargs):
return NotImplemented
a = np.array([1., 2.]).view(MyArray)
b = np.array([2., 3.]).view(MyArray)
with assert_raises(TypeError):
np.all(a)
self._test_equal(a, a)
self._test_not_equal(a, b)
self._test_not_equal(b, a)
def test_suppress_overflow_warnings(self):
# Based on issue #18992
with pytest.raises(AssertionError):
with np.errstate(all="raise"):
np.testing.assert_array_equal(
np.array([1, 2, 3], np.float32),
np.array([1, 1e-40, 3], np.float32))
def test_array_vs_scalar_is_equal(self):
"""Test comparing an array with a scalar when all values are equal."""
a = np.array([1., 1., 1.])
b = 1.
self._test_equal(a, b)
def test_array_vs_scalar_not_equal(self):
"""Test comparing an array with a scalar when not all values equal."""
a = np.array([1., 2., 3.])
b = 1.
self._test_not_equal(a, b)
def test_array_vs_scalar_strict(self):
"""Test comparing an array with a scalar with strict option."""
a = np.array([1., 1., 1.])
b = 1.
with pytest.raises(AssertionError):
assert_array_equal(a, b, strict=True)
def test_array_vs_array_strict(self):
"""Test comparing two arrays with strict option."""
a = np.array([1., 1., 1.])
b = np.array([1., 1., 1.])
assert_array_equal(a, b, strict=True)
def test_array_vs_float_array_strict(self):
"""Test comparing two arrays with strict option."""
a = np.array([1, 1, 1])
b = np.array([1., 1., 1.])
with pytest.raises(AssertionError):
assert_array_equal(a, b, strict=True)
class TestBuildErrorMessage:
def test_build_err_msg_defaults(self):
x = np.array([1.00001, 2.00002, 3.00003])
y = np.array([1.00002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg)
b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
'1.00001, 2.00002, 3.00003])\n DESIRED: array([1.00002, '
'2.00003, 3.00004])')
assert_equal(a, b)
def test_build_err_msg_no_verbose(self):
x = np.array([1.00001, 2.00002, 3.00003])
y = np.array([1.00002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg, verbose=False)
b = '\nItems are not equal: There is a mismatch'
assert_equal(a, b)
def test_build_err_msg_custom_names(self):
x = np.array([1.00001, 2.00002, 3.00003])
y = np.array([1.00002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg, names=('FOO', 'BAR'))
b = ('\nItems are not equal: There is a mismatch\n FOO: array(['
'1.00001, 2.00002, 3.00003])\n BAR: array([1.00002, 2.00003, '
'3.00004])')
assert_equal(a, b)
def test_build_err_msg_custom_precision(self):
x = np.array([1.000000001, 2.00002, 3.00003])
y = np.array([1.000000002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg, precision=10)
b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
'1.000000001, 2.00002 , 3.00003 ])\n DESIRED: array(['
'1.000000002, 2.00003 , 3.00004 ])')
assert_equal(a, b)
class TestEqual(TestArrayEqual):
def setup_method(self):
self._assert_func = assert_equal
def test_nan_items(self):
self._assert_func(np.nan, np.nan)
self._assert_func([np.nan], [np.nan])
self._test_not_equal(np.nan, [np.nan])
self._test_not_equal(np.nan, 1)
def test_inf_items(self):
self._assert_func(np.inf, np.inf)
self._assert_func([np.inf], [np.inf])
self._test_not_equal(np.inf, [np.inf])
def test_datetime(self):
self._test_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-01", "s")
)
self._test_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-01", "m")
)
# gh-10081
self._test_not_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-02", "s")
)
self._test_not_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-02", "m")
)
def test_nat_items(self):
# not a datetime
nadt_no_unit = np.datetime64("NaT")
nadt_s = np.datetime64("NaT", "s")
nadt_d = np.datetime64("NaT", "ns")
# not a timedelta
natd_no_unit = np.timedelta64("NaT")
natd_s = np.timedelta64("NaT", "s")
natd_d = np.timedelta64("NaT", "ns")
dts = [nadt_no_unit, nadt_s, nadt_d]
tds = [natd_no_unit, natd_s, natd_d]
for a, b in itertools.product(dts, dts):
self._assert_func(a, b)
self._assert_func([a], [b])
self._test_not_equal([a], b)
for a, b in itertools.product(tds, tds):
self._assert_func(a, b)
self._assert_func([a], [b])
self._test_not_equal([a], b)
for a, b in itertools.product(tds, dts):
self._test_not_equal(a, b)
self._test_not_equal(a, [b])
self._test_not_equal([a], [b])
self._test_not_equal([a], np.datetime64("2017-01-01", "s"))
self._test_not_equal([b], np.datetime64("2017-01-01", "s"))
self._test_not_equal([a], np.timedelta64(123, "s"))
self._test_not_equal([b], np.timedelta64(123, "s"))
def test_non_numeric(self):
self._assert_func('ab', 'ab')
self._test_not_equal('ab', 'abb')
def test_complex_item(self):
self._assert_func(complex(1, 2), complex(1, 2))
self._assert_func(complex(1, np.nan), complex(1, np.nan))
self._test_not_equal(complex(1, np.nan), complex(1, 2))
self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
def test_negative_zero(self):
self._test_not_equal(np.PZERO, np.NZERO)
def test_complex(self):
x = np.array([complex(1, 2), complex(1, np.nan)])
y = np.array([complex(1, 2), complex(1, 2)])
self._assert_func(x, x)
self._test_not_equal(x, y)
def test_object(self):
#gh-12942
import datetime
a = np.array([datetime.datetime(2000, 1, 1),
datetime.datetime(2000, 1, 2)])
self._test_not_equal(a, a[::-1])
class TestArrayAlmostEqual(_GenericTest):
def setup_method(self):
self._assert_func = assert_array_almost_equal
def test_closeness(self):
# Note that in the course of time we ended up with
# `abs(x - y) < 1.5 * 10**(-decimal)`
# instead of the previously documented
# `abs(x - y) < 0.5 * 10**(-decimal)`
# so this check serves to preserve the wrongness.
# test scalars
self._assert_func(1.499999, 0.0, decimal=0)
assert_raises(AssertionError,
lambda: self._assert_func(1.5, 0.0, decimal=0))
# test arrays
self._assert_func([1.499999], [0.0], decimal=0)
assert_raises(AssertionError,
lambda: self._assert_func([1.5], [0.0], decimal=0))
def test_simple(self):
x = np.array([1234.2222])
y = np.array([1234.2223])
self._assert_func(x, y, decimal=3)
self._assert_func(x, y, decimal=4)
assert_raises(AssertionError,
lambda: self._assert_func(x, y, decimal=5))
def test_nan(self):
anan = np.array([np.nan])
aone = np.array([1])
ainf = np.array([np.inf])
self._assert_func(anan, anan)
assert_raises(AssertionError,
lambda: self._assert_func(anan, aone))
assert_raises(AssertionError,
lambda: self._assert_func(anan, ainf))
assert_raises(AssertionError,
lambda: self._assert_func(ainf, anan))
def test_inf(self):
a = np.array([[1., 2.], [3., 4.]])
b = a.copy()
a[0, 0] = np.inf
assert_raises(AssertionError,
lambda: self._assert_func(a, b))
b[0, 0] = -np.inf
assert_raises(AssertionError,
lambda: self._assert_func(a, b))
def test_subclass(self):
a = np.array([[1., 2.], [3., 4.]])
b = np.ma.masked_array([[1., 2.], [0., 4.]],
[[False, False], [True, False]])
self._assert_func(a, b)
self._assert_func(b, a)
self._assert_func(b, b)
# Test fully masked as well (see gh-11123).
a = np.ma.MaskedArray(3.5, mask=True)
b = np.array([3., 4., 6.5])
self._test_equal(a, b)
self._test_equal(b, a)
a = np.ma.masked
b = np.array([3., 4., 6.5])
self._test_equal(a, b)
self._test_equal(b, a)
a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])
b = np.array([1., 2., 3.])
self._test_equal(a, b)
self._test_equal(b, a)
a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])
b = np.array(1.)
self._test_equal(a, b)
self._test_equal(b, a)
def test_subclass_that_cannot_be_bool(self):
# While we cannot guarantee testing functions will always work for
# subclasses, the tests should ideally rely only on subclasses having
# comparison operators, not on them being able to store booleans
# (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
class MyArray(np.ndarray):
def __eq__(self, other):
return super().__eq__(other).view(np.ndarray)
def __lt__(self, other):
return super().__lt__(other).view(np.ndarray)
def all(self, *args, **kwargs):
raise NotImplementedError
a = np.array([1., 2.]).view(MyArray)
self._assert_func(a, a)
class TestAlmostEqual(_GenericTest):
def setup_method(self):
self._assert_func = assert_almost_equal
def test_closeness(self):
# Note that in the course of time we ended up with
# `abs(x - y) < 1.5 * 10**(-decimal)`
# instead of the previously documented
# `abs(x - y) < 0.5 * 10**(-decimal)`
# so this check serves to preserve the wrongness.
# test scalars
self._assert_func(1.499999, 0.0, decimal=0)
assert_raises(AssertionError,
lambda: self._assert_func(1.5, 0.0, decimal=0))
# test arrays
self._assert_func([1.499999], [0.0], decimal=0)
assert_raises(AssertionError,
lambda: self._assert_func([1.5], [0.0], decimal=0))
def test_nan_item(self):
self._assert_func(np.nan, np.nan)
assert_raises(AssertionError,
lambda: self._assert_func(np.nan, 1))
assert_raises(AssertionError,
lambda: self._assert_func(np.nan, np.inf))
assert_raises(AssertionError,
lambda: self._assert_func(np.inf, np.nan))
def test_inf_item(self):
self._assert_func(np.inf, np.inf)
self._assert_func(-np.inf, -np.inf)
assert_raises(AssertionError,
lambda: self._assert_func(np.inf, 1))
assert_raises(AssertionError,
lambda: self._assert_func(-np.inf, np.inf))
def test_simple_item(self):
self._test_not_equal(1, 2)
def test_complex_item(self):
self._assert_func(complex(1, 2), complex(1, 2))
self._assert_func(complex(1, np.nan), complex(1, np.nan))
self._assert_func(complex(np.inf, np.nan), complex(np.inf, np.nan))
self._test_not_equal(complex(1, np.nan), complex(1, 2))
self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
def test_complex(self):
x = np.array([complex(1, 2), complex(1, np.nan)])
z = np.array([complex(1, 2), complex(np.nan, 1)])
y = np.array([complex(1, 2), complex(1, 2)])
self._assert_func(x, x)
self._test_not_equal(x, y)
self._test_not_equal(x, z)
def test_error_message(self):
"""Check the message is formatted correctly for the decimal value.
Also check the message when input includes inf or nan (gh12200)"""
x = np.array([1.00000000001, 2.00000000002, 3.00003])
y = np.array([1.00000000002, 2.00000000003, 3.00004])
# Test with a different amount of decimal digits
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y, decimal=12)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[3], 'Mismatched elements: 3 / 3 (100%)')
assert_equal(msgs[4], 'Max absolute difference: 1.e-05')
assert_equal(msgs[5], 'Max relative difference: 3.33328889e-06')
assert_equal(
msgs[6],
' x: array([1.00000000001, 2.00000000002, 3.00003 ])')
assert_equal(
msgs[7],
' y: array([1.00000000002, 2.00000000003, 3.00004 ])')
# With the default value of decimal digits, only the 3rd element
# differs. Note that we only check for the formatting of the arrays
# themselves.
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[3], 'Mismatched elements: 1 / 3 (33.3%)')
assert_equal(msgs[4], 'Max absolute difference: 1.e-05')
assert_equal(msgs[5], 'Max relative difference: 3.33328889e-06')
assert_equal(msgs[6], ' x: array([1. , 2. , 3.00003])')
assert_equal(msgs[7], ' y: array([1. , 2. , 3.00004])')
# Check the error message when input includes inf
x = np.array([np.inf, 0])
y = np.array([np.inf, 1])
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[3], 'Mismatched elements: 1 / 2 (50%)')
assert_equal(msgs[4], 'Max absolute difference: 1.')
assert_equal(msgs[5], 'Max relative difference: 1.')
assert_equal(msgs[6], ' x: array([inf, 0.])')
assert_equal(msgs[7], ' y: array([inf, 1.])')
# Check the error message when dividing by zero
x = np.array([1, 2])
y = np.array([0, 0])
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[3], 'Mismatched elements: 2 / 2 (100%)')
assert_equal(msgs[4], 'Max absolute difference: 2')
assert_equal(msgs[5], 'Max relative difference: inf')
def test_error_message_2(self):
"""Check the message is formatted correctly when either x or y is a scalar."""
x = 2
y = np.ones(20)
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[3], 'Mismatched elements: 20 / 20 (100%)')
assert_equal(msgs[4], 'Max absolute difference: 1.')
assert_equal(msgs[5], 'Max relative difference: 1.')
y = 2
x = np.ones(20)
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[3], 'Mismatched elements: 20 / 20 (100%)')
assert_equal(msgs[4], 'Max absolute difference: 1.')
assert_equal(msgs[5], 'Max relative difference: 0.5')
def test_subclass_that_cannot_be_bool(self):
# While we cannot guarantee testing functions will always work for
# subclasses, the tests should ideally rely only on subclasses having
# comparison operators, not on them being able to store booleans
# (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
class MyArray(np.ndarray):
def __eq__(self, other):
return super().__eq__(other).view(np.ndarray)
def __lt__(self, other):
return super().__lt__(other).view(np.ndarray)
def all(self, *args, **kwargs):
raise NotImplementedError
a = np.array([1., 2.]).view(MyArray)
self._assert_func(a, a)
class TestApproxEqual:
def setup_method(self):
self._assert_func = assert_approx_equal
def test_simple_0d_arrays(self):
x = np.array(1234.22)
y = np.array(1234.23)
self._assert_func(x, y, significant=5)
self._assert_func(x, y, significant=6)
assert_raises(AssertionError,
lambda: self._assert_func(x, y, significant=7))
def test_simple_items(self):
x = 1234.22
y = 1234.23
self._assert_func(x, y, significant=4)
self._assert_func(x, y, significant=5)
self._assert_func(x, y, significant=6)
assert_raises(AssertionError,
lambda: self._assert_func(x, y, significant=7))
def test_nan_array(self):
anan = np.array(np.nan)
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(anan, anan)
assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
def test_nan_items(self):
anan = np.array(np.nan)
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(anan, anan)
assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
class TestArrayAssertLess:
def setup_method(self):
self._assert_func = assert_array_less
def test_simple_arrays(self):
x = np.array([1.1, 2.2])
y = np.array([1.2, 2.3])
self._assert_func(x, y)
assert_raises(AssertionError, lambda: self._assert_func(y, x))
y = np.array([1.0, 2.3])
assert_raises(AssertionError, lambda: self._assert_func(x, y))
assert_raises(AssertionError, lambda: self._assert_func(y, x))
def test_rank2(self):
x = np.array([[1.1, 2.2], [3.3, 4.4]])
y = np.array([[1.2, 2.3], [3.4, 4.5]])
self._assert_func(x, y)
assert_raises(AssertionError, lambda: self._assert_func(y, x))
y = np.array([[1.0, 2.3], [3.4, 4.5]])
assert_raises(AssertionError, lambda: self._assert_func(x, y))
assert_raises(AssertionError, lambda: self._assert_func(y, x))
def test_rank3(self):
x = np.ones(shape=(2, 2, 2))
y = np.ones(shape=(2, 2, 2))+1
self._assert_func(x, y)
assert_raises(AssertionError, lambda: self._assert_func(y, x))
y[0, 0, 0] = 0
assert_raises(AssertionError, lambda: self._assert_func(x, y))
assert_raises(AssertionError, lambda: self._assert_func(y, x))
def test_simple_items(self):
x = 1.1
y = 2.2
self._assert_func(x, y)
assert_raises(AssertionError, lambda: self._assert_func(y, x))
y = np.array([2.2, 3.3])
self._assert_func(x, y)
assert_raises(AssertionError, lambda: self._assert_func(y, x))
y = np.array([1.0, 3.3])
assert_raises(AssertionError, lambda: self._assert_func(x, y))
def test_nan_noncompare(self):
anan = np.array(np.nan)
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(anan, anan)
assert_raises(AssertionError, lambda: self._assert_func(aone, anan))
assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
def test_nan_noncompare_array(self):
x = np.array([1.1, 2.2, 3.3])
anan = np.array(np.nan)
assert_raises(AssertionError, lambda: self._assert_func(x, anan))
assert_raises(AssertionError, lambda: self._assert_func(anan, x))
x = np.array([1.1, 2.2, np.nan])
assert_raises(AssertionError, lambda: self._assert_func(x, anan))
assert_raises(AssertionError, lambda: self._assert_func(anan, x))
y = np.array([1.0, 2.0, np.nan])
self._assert_func(y, x)
assert_raises(AssertionError, lambda: self._assert_func(x, y))
def test_inf_compare(self):
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(aone, ainf)
self._assert_func(-ainf, aone)
self._assert_func(-ainf, ainf)
assert_raises(AssertionError, lambda: self._assert_func(ainf, aone))
assert_raises(AssertionError, lambda: self._assert_func(aone, -ainf))
assert_raises(AssertionError, lambda: self._assert_func(ainf, ainf))
assert_raises(AssertionError, lambda: self._assert_func(ainf, -ainf))
assert_raises(AssertionError, lambda: self._assert_func(-ainf, -ainf))
def test_inf_compare_array(self):
x = np.array([1.1, 2.2, np.inf])
ainf = np.array(np.inf)
assert_raises(AssertionError, lambda: self._assert_func(x, ainf))
assert_raises(AssertionError, lambda: self._assert_func(ainf, x))
assert_raises(AssertionError, lambda: self._assert_func(x, -ainf))
assert_raises(AssertionError, lambda: self._assert_func(-x, -ainf))
assert_raises(AssertionError, lambda: self._assert_func(-ainf, -x))
self._assert_func(-ainf, x)
class TestWarns:
def test_warn(self):
def f():
warnings.warn("yo")
return 3
before_filters = sys.modules['warnings'].filters[:]
assert_equal(assert_warns(UserWarning, f), 3)
after_filters = sys.modules['warnings'].filters
assert_raises(AssertionError, assert_no_warnings, f)
assert_equal(assert_no_warnings(lambda x: x, 1), 1)
# Check that the warnings state is unchanged
assert_equal(before_filters, after_filters,
"assert_warns does not preserver warnings state")
def test_context_manager(self):
before_filters = sys.modules['warnings'].filters[:]
with assert_warns(UserWarning):
warnings.warn("yo")
after_filters = sys.modules['warnings'].filters
def no_warnings():
with assert_no_warnings():
warnings.warn("yo")
assert_raises(AssertionError, no_warnings)
assert_equal(before_filters, after_filters,
"assert_warns does not preserver warnings state")
def test_warn_wrong_warning(self):
def f():
warnings.warn("yo", DeprecationWarning)
failed = False
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
try:
# Should raise a DeprecationWarning
assert_warns(UserWarning, f)
failed = True
except DeprecationWarning:
pass
if failed:
raise AssertionError("wrong warning caught by assert_warn")
class TestAssertAllclose:
def test_simple(self):
x = 1e-3
y = 1e-9
assert_allclose(x, y, atol=1)
assert_raises(AssertionError, assert_allclose, x, y)
a = np.array([x, y, x, y])
b = np.array([x, y, x, x])
assert_allclose(a, b, atol=1)
assert_raises(AssertionError, assert_allclose, a, b)
b[-1] = y * (1 + 1e-8)
assert_allclose(a, b)
assert_raises(AssertionError, assert_allclose, a, b, rtol=1e-9)
assert_allclose(6, 10, rtol=0.5)
assert_raises(AssertionError, assert_allclose, 10, 6, rtol=0.5)
def test_min_int(self):
a = np.array([np.iinfo(np.int_).min], dtype=np.int_)
# Should not raise:
assert_allclose(a, a)
def test_report_fail_percentage(self):
a = np.array([1, 1, 1, 1])
b = np.array([1, 1, 1, 2])
with pytest.raises(AssertionError) as exc_info:
assert_allclose(a, b)
msg = str(exc_info.value)
assert_('Mismatched elements: 1 / 4 (25%)\n'
'Max absolute difference: 1\n'
'Max relative difference: 0.5' in msg)
def test_equal_nan(self):
a = np.array([np.nan])
b = np.array([np.nan])
# Should not raise:
assert_allclose(a, b, equal_nan=True)
def test_not_equal_nan(self):
a = np.array([np.nan])
b = np.array([np.nan])
assert_raises(AssertionError, assert_allclose, a, b, equal_nan=False)
def test_equal_nan_default(self):
# Make sure equal_nan default behavior remains unchanged. (All
# of these functions use assert_array_compare under the hood.)
# None of these should raise.
a = np.array([np.nan])
b = np.array([np.nan])
assert_array_equal(a, b)
assert_array_almost_equal(a, b)
assert_array_less(a, b)
assert_allclose(a, b)
def test_report_max_relative_error(self):
a = np.array([0, 1])
b = np.array([0, 2])
with pytest.raises(AssertionError) as exc_info:
assert_allclose(a, b)
msg = str(exc_info.value)
assert_('Max relative difference: 0.5' in msg)
def test_timedelta(self):
# see gh-18286
a = np.array([[1, 2, 3, "NaT"]], dtype="m8[ns]")
assert_allclose(a, a)
def test_error_message_unsigned(self):
"""Check the the message is formatted correctly when overflow can occur
(gh21768)"""
# Ensure to test for potential overflow in the case of:
# x - y
# and
# y - x
x = np.asarray([0, 1, 8], dtype='uint8')
y = np.asarray([4, 4, 4], dtype='uint8')
with pytest.raises(AssertionError) as exc_info:
assert_allclose(x, y, atol=3)
msgs = str(exc_info.value).split('\n')
assert_equal(msgs[4], 'Max absolute difference: 4')
class TestArrayAlmostEqualNulp:
def test_float64_pass(self):
# The number of units of least precision
# In this case, use a few places above the lowest level (ie nulp=1)
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
# Addition
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
# Subtraction
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
def test_float64_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
def test_float64_ignore_nan(self):
# Ignore ULP differences between various NAN's
# Note that MIPS may reverse quiet and signaling nans
# so we use the builtin version as a base.
offset = np.uint64(0xffffffff)
nan1_i64 = np.array(np.nan, dtype=np.float64).view(np.uint64)
nan2_i64 = nan1_i64 ^ offset # nan payload on MIPS is all ones.
nan1_f64 = nan1_i64.view(np.float64)
nan2_f64 = nan2_i64.view(np.float64)
assert_array_max_ulp(nan1_f64, nan2_f64, 0)
def test_float32_pass(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
def test_float32_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
def test_float32_ignore_nan(self):
# Ignore ULP differences between various NAN's
# Note that MIPS may reverse quiet and signaling nans
# so we use the builtin version as a base.
offset = np.uint32(0xffff)
nan1_i32 = np.array(np.nan, dtype=np.float32).view(np.uint32)
nan2_i32 = nan1_i32 ^ offset # nan payload on MIPS is all ones.
nan1_f32 = nan1_i32.view(np.float32)
nan2_f32 = nan2_i32.view(np.float32)
assert_array_max_ulp(nan1_f32, nan2_f32, 0)
def test_float16_pass(self):
nulp = 5
x = np.linspace(-4, 4, 10, dtype=np.float16)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
def test_float16_fail(self):
nulp = 5
x = np.linspace(-4, 4, 10, dtype=np.float16)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
def test_float16_ignore_nan(self):
# Ignore ULP differences between various NAN's
# Note that MIPS may reverse quiet and signaling nans
# so we use the builtin version as a base.
offset = np.uint16(0xff)
nan1_i16 = np.array(np.nan, dtype=np.float16).view(np.uint16)
nan2_i16 = nan1_i16 ^ offset # nan payload on MIPS is all ones.
nan1_f16 = nan1_i16.view(np.float16)
nan2_f16 = nan2_i16.view(np.float16)
assert_array_max_ulp(nan1_f16, nan2_f16, 0)
def test_complex128_pass(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
# The test condition needs to be at least a factor of sqrt(2) smaller
# because the real and imaginary parts both change
y = x + x*eps*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
y = x - x*epsneg*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
def test_complex128_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
# The test condition needs to be at least a factor of sqrt(2) smaller
# because the real and imaginary parts both change
y = x + x*eps*nulp
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
y = x - x*epsneg*nulp
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
def test_complex64_pass(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
y = x + x*eps*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
y = x - x*epsneg*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
def test_complex64_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
y = x + x*eps*nulp
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
y = x - x*epsneg*nulp
assert_raises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
class TestULP:
def test_equal(self):
x = np.random.randn(10)
assert_array_max_ulp(x, x, maxulp=0)
def test_single(self):
# Generate 1 + small deviation, check that adding eps gives a few UNL
x = np.ones(10).astype(np.float32)
x += 0.01 * np.random.randn(10).astype(np.float32)
eps = np.finfo(np.float32).eps
assert_array_max_ulp(x, x+eps, maxulp=20)
def test_double(self):
# Generate 1 + small deviation, check that adding eps gives a few UNL
x = np.ones(10).astype(np.float64)
x += 0.01 * np.random.randn(10).astype(np.float64)
eps = np.finfo(np.float64).eps
assert_array_max_ulp(x, x+eps, maxulp=200)
def test_inf(self):
for dt in [np.float32, np.float64]:
inf = np.array([np.inf]).astype(dt)
big = np.array([np.finfo(dt).max])
assert_array_max_ulp(inf, big, maxulp=200)
def test_nan(self):
# Test that nan is 'far' from small, tiny, inf, max and min
for dt in [np.float32, np.float64]:
if dt == np.float32:
maxulp = 1e6
else:
maxulp = 1e12
inf = np.array([np.inf]).astype(dt)
nan = np.array([np.nan]).astype(dt)
big = np.array([np.finfo(dt).max])
tiny = np.array([np.finfo(dt).tiny])
zero = np.array([np.PZERO]).astype(dt)
nzero = np.array([np.NZERO]).astype(dt)
assert_raises(AssertionError,
lambda: assert_array_max_ulp(nan, inf,
maxulp=maxulp))
assert_raises(AssertionError,
lambda: assert_array_max_ulp(nan, big,
maxulp=maxulp))
assert_raises(AssertionError,
lambda: assert_array_max_ulp(nan, tiny,
maxulp=maxulp))
assert_raises(AssertionError,
lambda: assert_array_max_ulp(nan, zero,
maxulp=maxulp))
assert_raises(AssertionError,
lambda: assert_array_max_ulp(nan, nzero,
maxulp=maxulp))
class TestStringEqual:
def test_simple(self):
assert_string_equal("hello", "hello")
assert_string_equal("hello\nmultiline", "hello\nmultiline")
with pytest.raises(AssertionError) as exc_info:
assert_string_equal("foo\nbar", "hello\nbar")
msg = str(exc_info.value)
assert_equal(msg, "Differences in strings:\n- foo\n+ hello")
assert_raises(AssertionError,
lambda: assert_string_equal("foo", "hello"))
def test_regex(self):
assert_string_equal("a+*b", "a+*b")
assert_raises(AssertionError,
lambda: assert_string_equal("aaa", "a+b"))
def assert_warn_len_equal(mod, n_in_context):
try:
mod_warns = mod.__warningregistry__
except AttributeError:
# the lack of a __warningregistry__
# attribute means that no warning has
# occurred; this can be triggered in
# a parallel test scenario, while in
# a serial test scenario an initial
# warning (and therefore the attribute)
# are always created first
mod_warns = {}
num_warns = len(mod_warns)
if 'version' in mod_warns:
# Python 3 adds a 'version' entry to the registry,
# do not count it.
num_warns -= 1
assert_equal(num_warns, n_in_context)
def test_warn_len_equal_call_scenarios():
# assert_warn_len_equal is called under
# varying circumstances depending on serial
# vs. parallel test scenarios; this test
# simply aims to probe both code paths and
# check that no assertion is uncaught
# parallel scenario -- no warning issued yet
class mod:
pass
mod_inst = mod()
assert_warn_len_equal(mod=mod_inst,
n_in_context=0)
# serial test scenario -- the __warningregistry__
# attribute should be present
class mod:
def __init__(self):
self.__warningregistry__ = {'warning1':1,
'warning2':2}
mod_inst = mod()
assert_warn_len_equal(mod=mod_inst,
n_in_context=2)
def _get_fresh_mod():
# Get this module, with warning registry empty
my_mod = sys.modules[__name__]
try:
my_mod.__warningregistry__.clear()
except AttributeError:
# will not have a __warningregistry__ unless warning has been
# raised in the module at some point
pass
return my_mod
def test_clear_and_catch_warnings():
# Initial state of module, no warnings
my_mod = _get_fresh_mod()
assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
with clear_and_catch_warnings(modules=[my_mod]):
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_equal(my_mod.__warningregistry__, {})
# Without specified modules, don't clear warnings during context.
# catch_warnings doesn't make an entry for 'ignore'.
with clear_and_catch_warnings():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# Manually adding two warnings to the registry:
my_mod.__warningregistry__ = {'warning1': 1,
'warning2': 2}
# Confirm that specifying module keeps old warning, does not add new
with clear_and_catch_warnings(modules=[my_mod]):
warnings.simplefilter('ignore')
warnings.warn('Another warning')
assert_warn_len_equal(my_mod, 2)
# Another warning, no module spec it clears up registry
with clear_and_catch_warnings():
warnings.simplefilter('ignore')
warnings.warn('Another warning')
assert_warn_len_equal(my_mod, 0)
def test_suppress_warnings_module():
# Initial state of module, no warnings
my_mod = _get_fresh_mod()
assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
def warn_other_module():
# Apply along axis is implemented in python; stacklevel=2 means
# we end up inside its module, not ours.
def warn(arr):
warnings.warn("Some warning 2", stacklevel=2)
return arr
np.apply_along_axis(warn, 0, [0])
# Test module based warning suppression:
assert_warn_len_equal(my_mod, 0)
with suppress_warnings() as sup:
sup.record(UserWarning)
# suppress warning from other module (may have .pyc ending),
# if apply_along_axis is moved, had to be changed.
sup.filter(module=np.lib.shape_base)
warnings.warn("Some warning")
warn_other_module()
# Check that the suppression did test the file correctly (this module
# got filtered)
assert_equal(len(sup.log), 1)
assert_equal(sup.log[0].message.args[0], "Some warning")
assert_warn_len_equal(my_mod, 0)
sup = suppress_warnings()
# Will have to be changed if apply_along_axis is moved:
sup.filter(module=my_mod)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# And test repeat works:
sup.filter(module=my_mod)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# Without specified modules
with suppress_warnings():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
def test_suppress_warnings_type():
# Initial state of module, no warnings
my_mod = _get_fresh_mod()
assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
# Test module based warning suppression:
with suppress_warnings() as sup:
sup.filter(UserWarning)
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
sup = suppress_warnings()
sup.filter(UserWarning)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# And test repeat works:
sup.filter(module=my_mod)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# Without specified modules
with suppress_warnings():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
def test_suppress_warnings_decorate_no_record():
sup = suppress_warnings()
sup.filter(UserWarning)
@sup
def warn(category):
warnings.warn('Some warning', category)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
warn(UserWarning) # should be supppressed
warn(RuntimeWarning)
assert_equal(len(w), 1)
def test_suppress_warnings_record():
sup = suppress_warnings()
log1 = sup.record()
with sup:
log2 = sup.record(message='Some other warning 2')
sup.filter(message='Some warning')
warnings.warn('Some warning')
warnings.warn('Some other warning')
warnings.warn('Some other warning 2')
assert_equal(len(sup.log), 2)
assert_equal(len(log1), 1)
assert_equal(len(log2),1)
assert_equal(log2[0].message.args[0], 'Some other warning 2')
# Do it again, with the same context to see if some warnings survived:
with sup:
log2 = sup.record(message='Some other warning 2')
sup.filter(message='Some warning')
warnings.warn('Some warning')
warnings.warn('Some other warning')
warnings.warn('Some other warning 2')
assert_equal(len(sup.log), 2)
assert_equal(len(log1), 1)
assert_equal(len(log2), 1)
assert_equal(log2[0].message.args[0], 'Some other warning 2')
# Test nested:
with suppress_warnings() as sup:
sup.record()
with suppress_warnings() as sup2:
sup2.record(message='Some warning')
warnings.warn('Some warning')
warnings.warn('Some other warning')
assert_equal(len(sup2.log), 1)
assert_equal(len(sup.log), 1)
def test_suppress_warnings_forwarding():
def warn_other_module():
# Apply along axis is implemented in python; stacklevel=2 means
# we end up inside its module, not ours.
def warn(arr):
warnings.warn("Some warning", stacklevel=2)
return arr
np.apply_along_axis(warn, 0, [0])
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("always"):
for i in range(2):
warnings.warn("Some warning")
assert_equal(len(sup.log), 2)
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("location"):
for i in range(2):
warnings.warn("Some warning")
warnings.warn("Some warning")
assert_equal(len(sup.log), 2)
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("module"):
for i in range(2):
warnings.warn("Some warning")
warnings.warn("Some warning")
warn_other_module()
assert_equal(len(sup.log), 2)
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("once"):
for i in range(2):
warnings.warn("Some warning")
warnings.warn("Some other warning")
warn_other_module()
assert_equal(len(sup.log), 2)
def test_tempdir():
with tempdir() as tdir:
fpath = os.path.join(tdir, 'tmp')
with open(fpath, 'w'):
pass
assert_(not os.path.isdir(tdir))
raised = False
try:
with tempdir() as tdir:
raise ValueError()
except ValueError:
raised = True
assert_(raised)
assert_(not os.path.isdir(tdir))
def test_temppath():
with temppath() as fpath:
with open(fpath, 'w'):
pass
assert_(not os.path.isfile(fpath))
raised = False
try:
with temppath() as fpath:
raise ValueError()
except ValueError:
raised = True
assert_(raised)
assert_(not os.path.isfile(fpath))
class my_cacw(clear_and_catch_warnings):
class_modules = (sys.modules[__name__],)
def test_clear_and_catch_warnings_inherit():
# Test can subclass and add default modules
my_mod = _get_fresh_mod()
with my_cacw():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_equal(my_mod.__warningregistry__, {})
@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
class TestAssertNoGcCycles:
""" Test assert_no_gc_cycles """
def test_passes(self):
def no_cycle():
b = []
b.append([])
return b
with assert_no_gc_cycles():
no_cycle()
assert_no_gc_cycles(no_cycle)
def test_asserts(self):
def make_cycle():
a = []
a.append(a)
a.append(a)
return a
with assert_raises(AssertionError):
with assert_no_gc_cycles():
make_cycle()
with assert_raises(AssertionError):
assert_no_gc_cycles(make_cycle)
@pytest.mark.slow
def test_fails(self):
"""
Test that in cases where the garbage cannot be collected, we raise an
error, instead of hanging forever trying to clear it.
"""
class ReferenceCycleInDel:
"""
An object that not only contains a reference cycle, but creates new
cycles whenever it's garbage-collected and its __del__ runs
"""
make_cycle = True
def __init__(self):
self.cycle = self
def __del__(self):
# break the current cycle so that `self` can be freed
self.cycle = None
if ReferenceCycleInDel.make_cycle:
# but create a new one so that the garbage collector has more
# work to do.
ReferenceCycleInDel()
try:
w = weakref.ref(ReferenceCycleInDel())
try:
with assert_raises(RuntimeError):
# this will be unable to get a baseline empty garbage
assert_no_gc_cycles(lambda: None)
except AssertionError:
# the above test is only necessary if the GC actually tried to free
# our object anyway, which python 2.7 does not.
if w() is not None:
pytest.skip("GC does not call __del__ on cyclic objects")
raise
finally:
# make sure that we stop creating reference cycles
ReferenceCycleInDel.make_cycle = False
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