381 lines
13 KiB
Python
381 lines
13 KiB
Python
from multiprocessing import Pool
|
|
from multiprocessing.pool import Pool as PWL
|
|
import os
|
|
import re
|
|
import math
|
|
from fractions import Fraction
|
|
|
|
import numpy as np
|
|
from numpy.testing import assert_equal, assert_
|
|
import pytest
|
|
from pytest import raises as assert_raises, deprecated_call
|
|
|
|
import scipy
|
|
from scipy._lib._util import (_aligned_zeros, check_random_state, MapWrapper,
|
|
getfullargspec_no_self, FullArgSpec,
|
|
rng_integers, _validate_int, _rename_parameter,
|
|
_contains_nan)
|
|
|
|
|
|
def test__aligned_zeros():
|
|
niter = 10
|
|
|
|
def check(shape, dtype, order, align):
|
|
err_msg = repr((shape, dtype, order, align))
|
|
x = _aligned_zeros(shape, dtype, order, align=align)
|
|
if align is None:
|
|
align = np.dtype(dtype).alignment
|
|
assert_equal(x.__array_interface__['data'][0] % align, 0)
|
|
if hasattr(shape, '__len__'):
|
|
assert_equal(x.shape, shape, err_msg)
|
|
else:
|
|
assert_equal(x.shape, (shape,), err_msg)
|
|
assert_equal(x.dtype, dtype)
|
|
if order == "C":
|
|
assert_(x.flags.c_contiguous, err_msg)
|
|
elif order == "F":
|
|
if x.size > 0:
|
|
# Size-0 arrays get invalid flags on NumPy 1.5
|
|
assert_(x.flags.f_contiguous, err_msg)
|
|
elif order is None:
|
|
assert_(x.flags.c_contiguous, err_msg)
|
|
else:
|
|
raise ValueError()
|
|
|
|
# try various alignments
|
|
for align in [1, 2, 3, 4, 8, 16, 32, 64, None]:
|
|
for n in [0, 1, 3, 11]:
|
|
for order in ["C", "F", None]:
|
|
for dtype in [np.uint8, np.float64]:
|
|
for shape in [n, (1, 2, 3, n)]:
|
|
for j in range(niter):
|
|
check(shape, dtype, order, align)
|
|
|
|
|
|
def test_check_random_state():
|
|
# If seed is None, return the RandomState singleton used by np.random.
|
|
# If seed is an int, return a new RandomState instance seeded with seed.
|
|
# If seed is already a RandomState instance, return it.
|
|
# Otherwise raise ValueError.
|
|
rsi = check_random_state(1)
|
|
assert_equal(type(rsi), np.random.RandomState)
|
|
rsi = check_random_state(rsi)
|
|
assert_equal(type(rsi), np.random.RandomState)
|
|
rsi = check_random_state(None)
|
|
assert_equal(type(rsi), np.random.RandomState)
|
|
assert_raises(ValueError, check_random_state, 'a')
|
|
if hasattr(np.random, 'Generator'):
|
|
# np.random.Generator is only available in NumPy >= 1.17
|
|
rg = np.random.Generator(np.random.PCG64())
|
|
rsi = check_random_state(rg)
|
|
assert_equal(type(rsi), np.random.Generator)
|
|
|
|
|
|
def test_getfullargspec_no_self():
|
|
p = MapWrapper(1)
|
|
argspec = getfullargspec_no_self(p.__init__)
|
|
assert_equal(argspec, FullArgSpec(['pool'], None, None, (1,), [],
|
|
None, {}))
|
|
argspec = getfullargspec_no_self(p.__call__)
|
|
assert_equal(argspec, FullArgSpec(['func', 'iterable'], None, None, None,
|
|
[], None, {}))
|
|
|
|
class _rv_generic:
|
|
def _rvs(self, a, b=2, c=3, *args, size=None, **kwargs):
|
|
return None
|
|
|
|
rv_obj = _rv_generic()
|
|
argspec = getfullargspec_no_self(rv_obj._rvs)
|
|
assert_equal(argspec, FullArgSpec(['a', 'b', 'c'], 'args', 'kwargs',
|
|
(2, 3), ['size'], {'size': None}, {}))
|
|
|
|
|
|
def test_mapwrapper_serial():
|
|
in_arg = np.arange(10.)
|
|
out_arg = np.sin(in_arg)
|
|
|
|
p = MapWrapper(1)
|
|
assert_(p._mapfunc is map)
|
|
assert_(p.pool is None)
|
|
assert_(p._own_pool is False)
|
|
out = list(p(np.sin, in_arg))
|
|
assert_equal(out, out_arg)
|
|
|
|
with assert_raises(RuntimeError):
|
|
p = MapWrapper(0)
|
|
|
|
|
|
def test_pool():
|
|
with Pool(2) as p:
|
|
p.map(math.sin, [1, 2, 3, 4])
|
|
|
|
|
|
def test_mapwrapper_parallel():
|
|
in_arg = np.arange(10.)
|
|
out_arg = np.sin(in_arg)
|
|
|
|
with MapWrapper(2) as p:
|
|
out = p(np.sin, in_arg)
|
|
assert_equal(list(out), out_arg)
|
|
|
|
assert_(p._own_pool is True)
|
|
assert_(isinstance(p.pool, PWL))
|
|
assert_(p._mapfunc is not None)
|
|
|
|
# the context manager should've closed the internal pool
|
|
# check that it has by asking it to calculate again.
|
|
with assert_raises(Exception) as excinfo:
|
|
p(np.sin, in_arg)
|
|
|
|
assert_(excinfo.type is ValueError)
|
|
|
|
# can also set a PoolWrapper up with a map-like callable instance
|
|
with Pool(2) as p:
|
|
q = MapWrapper(p.map)
|
|
|
|
assert_(q._own_pool is False)
|
|
q.close()
|
|
|
|
# closing the PoolWrapper shouldn't close the internal pool
|
|
# because it didn't create it
|
|
out = p.map(np.sin, in_arg)
|
|
assert_equal(list(out), out_arg)
|
|
|
|
|
|
# get our custom ones and a few from the "import *" cases
|
|
@pytest.mark.parametrize(
|
|
'key', ('ifft', 'diag', 'arccos', 'randn', 'rand', 'array'))
|
|
def test_numpy_deprecation(key):
|
|
"""Test that 'from numpy import *' functions are deprecated."""
|
|
if key in ('ifft', 'diag', 'arccos'):
|
|
arg = [1.0, 0.]
|
|
elif key == 'finfo':
|
|
arg = float
|
|
else:
|
|
arg = 2
|
|
func = getattr(scipy, key)
|
|
match = r'scipy\.%s is deprecated.*2\.0\.0' % key
|
|
with deprecated_call(match=match) as dep:
|
|
func(arg) # deprecated
|
|
# in case we catch more than one dep warning
|
|
fnames = [os.path.splitext(d.filename)[0] for d in dep.list]
|
|
basenames = [os.path.basename(fname) for fname in fnames]
|
|
assert 'test__util' in basenames
|
|
if key in ('rand', 'randn'):
|
|
root = np.random
|
|
elif key == 'ifft':
|
|
root = np.fft
|
|
else:
|
|
root = np
|
|
func_np = getattr(root, key)
|
|
func_np(arg) # not deprecated
|
|
assert func_np is not func
|
|
# classes should remain classes
|
|
if isinstance(func_np, type):
|
|
assert isinstance(func, type)
|
|
|
|
|
|
def test_numpy_deprecation_functionality():
|
|
# Check that the deprecation wrappers don't break basic NumPy
|
|
# functionality
|
|
with deprecated_call():
|
|
x = scipy.array([1, 2, 3], dtype=scipy.float64)
|
|
assert x.dtype == scipy.float64
|
|
assert x.dtype == np.float64
|
|
|
|
x = scipy.finfo(scipy.float32)
|
|
assert x.eps == np.finfo(np.float32).eps
|
|
|
|
assert scipy.float64 == np.float64
|
|
assert issubclass(np.float64, scipy.float64)
|
|
|
|
|
|
def test_rng_integers():
|
|
rng = np.random.RandomState()
|
|
|
|
# test that numbers are inclusive of high point
|
|
arr = rng_integers(rng, low=2, high=5, size=100, endpoint=True)
|
|
assert np.max(arr) == 5
|
|
assert np.min(arr) == 2
|
|
assert arr.shape == (100, )
|
|
|
|
# test that numbers are inclusive of high point
|
|
arr = rng_integers(rng, low=5, size=100, endpoint=True)
|
|
assert np.max(arr) == 5
|
|
assert np.min(arr) == 0
|
|
assert arr.shape == (100, )
|
|
|
|
# test that numbers are exclusive of high point
|
|
arr = rng_integers(rng, low=2, high=5, size=100, endpoint=False)
|
|
assert np.max(arr) == 4
|
|
assert np.min(arr) == 2
|
|
assert arr.shape == (100, )
|
|
|
|
# test that numbers are exclusive of high point
|
|
arr = rng_integers(rng, low=5, size=100, endpoint=False)
|
|
assert np.max(arr) == 4
|
|
assert np.min(arr) == 0
|
|
assert arr.shape == (100, )
|
|
|
|
# now try with np.random.Generator
|
|
try:
|
|
rng = np.random.default_rng()
|
|
except AttributeError:
|
|
return
|
|
|
|
# test that numbers are inclusive of high point
|
|
arr = rng_integers(rng, low=2, high=5, size=100, endpoint=True)
|
|
assert np.max(arr) == 5
|
|
assert np.min(arr) == 2
|
|
assert arr.shape == (100, )
|
|
|
|
# test that numbers are inclusive of high point
|
|
arr = rng_integers(rng, low=5, size=100, endpoint=True)
|
|
assert np.max(arr) == 5
|
|
assert np.min(arr) == 0
|
|
assert arr.shape == (100, )
|
|
|
|
# test that numbers are exclusive of high point
|
|
arr = rng_integers(rng, low=2, high=5, size=100, endpoint=False)
|
|
assert np.max(arr) == 4
|
|
assert np.min(arr) == 2
|
|
assert arr.shape == (100, )
|
|
|
|
# test that numbers are exclusive of high point
|
|
arr = rng_integers(rng, low=5, size=100, endpoint=False)
|
|
assert np.max(arr) == 4
|
|
assert np.min(arr) == 0
|
|
assert arr.shape == (100, )
|
|
|
|
|
|
class TestValidateInt:
|
|
|
|
@pytest.mark.parametrize('n', [4, np.uint8(4), np.int16(4), np.array(4)])
|
|
def test_validate_int(self, n):
|
|
n = _validate_int(n, 'n')
|
|
assert n == 4
|
|
|
|
@pytest.mark.parametrize('n', [4.0, np.array([4]), Fraction(4, 1)])
|
|
def test_validate_int_bad(self, n):
|
|
with pytest.raises(TypeError, match='n must be an integer'):
|
|
_validate_int(n, 'n')
|
|
|
|
def test_validate_int_below_min(self):
|
|
with pytest.raises(ValueError, match='n must be an integer not '
|
|
'less than 0'):
|
|
_validate_int(-1, 'n', 0)
|
|
|
|
|
|
class TestRenameParameter:
|
|
# check that wrapper `_rename_parameter` for backward-compatible
|
|
# keyword renaming works correctly
|
|
|
|
# Example method/function that still accepts keyword `old`
|
|
@_rename_parameter("old", "new")
|
|
def old_keyword_still_accepted(self, new):
|
|
return new
|
|
|
|
# Example method/function for which keyword `old` is deprecated
|
|
@_rename_parameter("old", "new", dep_version="1.9.0")
|
|
def old_keyword_deprecated(self, new):
|
|
return new
|
|
|
|
def test_old_keyword_still_accepted(self):
|
|
# positional argument and both keyword work identically
|
|
res1 = self.old_keyword_still_accepted(10)
|
|
res2 = self.old_keyword_still_accepted(new=10)
|
|
res3 = self.old_keyword_still_accepted(old=10)
|
|
assert res1 == res2 == res3 == 10
|
|
|
|
# unexpected keyword raises an error
|
|
message = re.escape("old_keyword_still_accepted() got an unexpected")
|
|
with pytest.raises(TypeError, match=message):
|
|
self.old_keyword_still_accepted(unexpected=10)
|
|
|
|
# multiple values for the same parameter raises an error
|
|
message = re.escape("old_keyword_still_accepted() got multiple")
|
|
with pytest.raises(TypeError, match=message):
|
|
self.old_keyword_still_accepted(10, new=10)
|
|
with pytest.raises(TypeError, match=message):
|
|
self.old_keyword_still_accepted(10, old=10)
|
|
with pytest.raises(TypeError, match=message):
|
|
self.old_keyword_still_accepted(new=10, old=10)
|
|
|
|
def test_old_keyword_deprecated(self):
|
|
# positional argument and both keyword work identically,
|
|
# but use of old keyword results in DeprecationWarning
|
|
dep_msg = "Use of keyword argument `old` is deprecated"
|
|
res1 = self.old_keyword_deprecated(10)
|
|
res2 = self.old_keyword_deprecated(new=10)
|
|
with pytest.warns(DeprecationWarning, match=dep_msg):
|
|
res3 = self.old_keyword_deprecated(old=10)
|
|
assert res1 == res2 == res3 == 10
|
|
|
|
# unexpected keyword raises an error
|
|
message = re.escape("old_keyword_deprecated() got an unexpected")
|
|
with pytest.raises(TypeError, match=message):
|
|
self.old_keyword_deprecated(unexpected=10)
|
|
|
|
# multiple values for the same parameter raises an error and,
|
|
# if old keyword is used, results in DeprecationWarning
|
|
message = re.escape("old_keyword_deprecated() got multiple")
|
|
with pytest.raises(TypeError, match=message):
|
|
self.old_keyword_deprecated(10, new=10)
|
|
with pytest.raises(TypeError, match=message), \
|
|
pytest.warns(DeprecationWarning, match=dep_msg):
|
|
self.old_keyword_deprecated(10, old=10)
|
|
with pytest.raises(TypeError, match=message), \
|
|
pytest.warns(DeprecationWarning, match=dep_msg):
|
|
self.old_keyword_deprecated(new=10, old=10)
|
|
|
|
|
|
class TestContainsNaNTest:
|
|
|
|
def test_policy(self):
|
|
data = np.array([1, 2, 3, np.nan])
|
|
|
|
contains_nan, nan_policy = _contains_nan(data, nan_policy="propagate")
|
|
assert contains_nan
|
|
assert nan_policy == "propagate"
|
|
|
|
contains_nan, nan_policy = _contains_nan(data, nan_policy="omit")
|
|
assert contains_nan
|
|
assert nan_policy == "omit"
|
|
|
|
msg = "The input contains nan values"
|
|
with pytest.raises(ValueError, match=msg):
|
|
_contains_nan(data, nan_policy="raise")
|
|
|
|
msg = "nan_policy must be one of"
|
|
with pytest.raises(ValueError, match=msg):
|
|
_contains_nan(data, nan_policy="nan")
|
|
|
|
def test_contains_nan_1d(self):
|
|
data1 = np.array([1, 2, 3])
|
|
assert not _contains_nan(data1)[0]
|
|
|
|
data2 = np.array([1, 2, 3, np.nan])
|
|
assert _contains_nan(data2)[0]
|
|
|
|
data3 = np.array([np.nan, 2, 3, np.nan])
|
|
assert _contains_nan(data3)[0]
|
|
|
|
data4 = np.array([1, 2, "3", np.nan]) # converted to string "nan"
|
|
assert not _contains_nan(data4)[0]
|
|
|
|
data5 = np.array([1, 2, "3", np.nan], dtype='object')
|
|
assert _contains_nan(data5)[0]
|
|
|
|
def test_contains_nan_2d(self):
|
|
data1 = np.array([[1, 2], [3, 4]])
|
|
assert not _contains_nan(data1)[0]
|
|
|
|
data2 = np.array([[1, 2], [3, np.nan]])
|
|
assert _contains_nan(data2)[0]
|
|
|
|
data3 = np.array([["1", 2], [3, np.nan]]) # converted to string "nan"
|
|
assert not _contains_nan(data3)[0]
|
|
|
|
data4 = np.array([["1", 2], [3, np.nan]], dtype='object')
|
|
assert _contains_nan(data4)[0]
|