409 lines
14 KiB
Python
409 lines
14 KiB
Python
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from multiprocessing import Pool
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from multiprocessing.pool import Pool as PWL
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import re
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import math
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from fractions import Fraction
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import numpy as np
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from numpy.testing import assert_equal, assert_
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import pytest
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from pytest import raises as assert_raises
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import hypothesis.extra.numpy as npst
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from hypothesis import given, strategies, reproduce_failure # noqa: F401
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from scipy.conftest import array_api_compatible
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from scipy._lib._array_api import xp_assert_equal
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from scipy._lib._util import (_aligned_zeros, check_random_state, MapWrapper,
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getfullargspec_no_self, FullArgSpec,
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rng_integers, _validate_int, _rename_parameter,
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_contains_nan, _rng_html_rewrite, _lazywhere)
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def test__aligned_zeros():
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niter = 10
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def check(shape, dtype, order, align):
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err_msg = repr((shape, dtype, order, align))
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x = _aligned_zeros(shape, dtype, order, align=align)
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if align is None:
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align = np.dtype(dtype).alignment
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assert_equal(x.__array_interface__['data'][0] % align, 0)
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if hasattr(shape, '__len__'):
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assert_equal(x.shape, shape, err_msg)
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else:
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assert_equal(x.shape, (shape,), err_msg)
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assert_equal(x.dtype, dtype)
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if order == "C":
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assert_(x.flags.c_contiguous, err_msg)
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elif order == "F":
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if x.size > 0:
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# Size-0 arrays get invalid flags on NumPy 1.5
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assert_(x.flags.f_contiguous, err_msg)
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elif order is None:
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assert_(x.flags.c_contiguous, err_msg)
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else:
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raise ValueError()
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# try various alignments
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for align in [1, 2, 3, 4, 8, 16, 32, 64, None]:
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for n in [0, 1, 3, 11]:
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for order in ["C", "F", None]:
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for dtype in [np.uint8, np.float64]:
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for shape in [n, (1, 2, 3, n)]:
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for j in range(niter):
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check(shape, dtype, order, align)
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def test_check_random_state():
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# If seed is None, return the RandomState singleton used by np.random.
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# If seed is an int, return a new RandomState instance seeded with seed.
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# If seed is already a RandomState instance, return it.
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# Otherwise raise ValueError.
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rsi = check_random_state(1)
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assert_equal(type(rsi), np.random.RandomState)
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rsi = check_random_state(rsi)
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assert_equal(type(rsi), np.random.RandomState)
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rsi = check_random_state(None)
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assert_equal(type(rsi), np.random.RandomState)
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assert_raises(ValueError, check_random_state, 'a')
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rg = np.random.Generator(np.random.PCG64())
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rsi = check_random_state(rg)
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assert_equal(type(rsi), np.random.Generator)
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def test_getfullargspec_no_self():
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p = MapWrapper(1)
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argspec = getfullargspec_no_self(p.__init__)
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assert_equal(argspec, FullArgSpec(['pool'], None, None, (1,), [],
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None, {}))
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argspec = getfullargspec_no_self(p.__call__)
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assert_equal(argspec, FullArgSpec(['func', 'iterable'], None, None, None,
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[], None, {}))
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class _rv_generic:
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def _rvs(self, a, b=2, c=3, *args, size=None, **kwargs):
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return None
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rv_obj = _rv_generic()
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argspec = getfullargspec_no_self(rv_obj._rvs)
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assert_equal(argspec, FullArgSpec(['a', 'b', 'c'], 'args', 'kwargs',
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(2, 3), ['size'], {'size': None}, {}))
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def test_mapwrapper_serial():
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in_arg = np.arange(10.)
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out_arg = np.sin(in_arg)
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p = MapWrapper(1)
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assert_(p._mapfunc is map)
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assert_(p.pool is None)
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assert_(p._own_pool is False)
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out = list(p(np.sin, in_arg))
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assert_equal(out, out_arg)
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with assert_raises(RuntimeError):
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p = MapWrapper(0)
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def test_pool():
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with Pool(2) as p:
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p.map(math.sin, [1, 2, 3, 4])
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def test_mapwrapper_parallel():
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in_arg = np.arange(10.)
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out_arg = np.sin(in_arg)
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with MapWrapper(2) as p:
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out = p(np.sin, in_arg)
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assert_equal(list(out), out_arg)
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assert_(p._own_pool is True)
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assert_(isinstance(p.pool, PWL))
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assert_(p._mapfunc is not None)
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# the context manager should've closed the internal pool
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# check that it has by asking it to calculate again.
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with assert_raises(Exception) as excinfo:
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p(np.sin, in_arg)
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assert_(excinfo.type is ValueError)
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# can also set a PoolWrapper up with a map-like callable instance
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with Pool(2) as p:
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q = MapWrapper(p.map)
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assert_(q._own_pool is False)
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q.close()
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# closing the PoolWrapper shouldn't close the internal pool
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# because it didn't create it
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out = p.map(np.sin, in_arg)
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assert_equal(list(out), out_arg)
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def test_rng_integers():
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rng = np.random.RandomState()
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# test that numbers are inclusive of high point
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arr = rng_integers(rng, low=2, high=5, size=100, endpoint=True)
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assert np.max(arr) == 5
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assert np.min(arr) == 2
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assert arr.shape == (100, )
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# test that numbers are inclusive of high point
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arr = rng_integers(rng, low=5, size=100, endpoint=True)
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assert np.max(arr) == 5
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assert np.min(arr) == 0
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assert arr.shape == (100, )
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# test that numbers are exclusive of high point
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arr = rng_integers(rng, low=2, high=5, size=100, endpoint=False)
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assert np.max(arr) == 4
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assert np.min(arr) == 2
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assert arr.shape == (100, )
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# test that numbers are exclusive of high point
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arr = rng_integers(rng, low=5, size=100, endpoint=False)
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assert np.max(arr) == 4
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assert np.min(arr) == 0
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assert arr.shape == (100, )
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# now try with np.random.Generator
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try:
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rng = np.random.default_rng()
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except AttributeError:
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return
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# test that numbers are inclusive of high point
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arr = rng_integers(rng, low=2, high=5, size=100, endpoint=True)
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assert np.max(arr) == 5
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assert np.min(arr) == 2
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assert arr.shape == (100, )
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# test that numbers are inclusive of high point
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arr = rng_integers(rng, low=5, size=100, endpoint=True)
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assert np.max(arr) == 5
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assert np.min(arr) == 0
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assert arr.shape == (100, )
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# test that numbers are exclusive of high point
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arr = rng_integers(rng, low=2, high=5, size=100, endpoint=False)
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assert np.max(arr) == 4
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assert np.min(arr) == 2
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assert arr.shape == (100, )
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# test that numbers are exclusive of high point
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arr = rng_integers(rng, low=5, size=100, endpoint=False)
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assert np.max(arr) == 4
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assert np.min(arr) == 0
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assert arr.shape == (100, )
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class TestValidateInt:
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@pytest.mark.parametrize('n', [4, np.uint8(4), np.int16(4), np.array(4)])
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def test_validate_int(self, n):
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n = _validate_int(n, 'n')
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assert n == 4
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@pytest.mark.parametrize('n', [4.0, np.array([4]), Fraction(4, 1)])
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def test_validate_int_bad(self, n):
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with pytest.raises(TypeError, match='n must be an integer'):
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_validate_int(n, 'n')
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def test_validate_int_below_min(self):
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with pytest.raises(ValueError, match='n must be an integer not '
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'less than 0'):
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_validate_int(-1, 'n', 0)
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class TestRenameParameter:
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# check that wrapper `_rename_parameter` for backward-compatible
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# keyword renaming works correctly
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# Example method/function that still accepts keyword `old`
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@_rename_parameter("old", "new")
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def old_keyword_still_accepted(self, new):
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return new
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# Example method/function for which keyword `old` is deprecated
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@_rename_parameter("old", "new", dep_version="1.9.0")
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def old_keyword_deprecated(self, new):
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return new
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def test_old_keyword_still_accepted(self):
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# positional argument and both keyword work identically
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res1 = self.old_keyword_still_accepted(10)
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res2 = self.old_keyword_still_accepted(new=10)
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res3 = self.old_keyword_still_accepted(old=10)
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assert res1 == res2 == res3 == 10
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# unexpected keyword raises an error
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message = re.escape("old_keyword_still_accepted() got an unexpected")
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with pytest.raises(TypeError, match=message):
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self.old_keyword_still_accepted(unexpected=10)
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# multiple values for the same parameter raises an error
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message = re.escape("old_keyword_still_accepted() got multiple")
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with pytest.raises(TypeError, match=message):
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self.old_keyword_still_accepted(10, new=10)
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with pytest.raises(TypeError, match=message):
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self.old_keyword_still_accepted(10, old=10)
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with pytest.raises(TypeError, match=message):
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self.old_keyword_still_accepted(new=10, old=10)
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def test_old_keyword_deprecated(self):
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# positional argument and both keyword work identically,
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# but use of old keyword results in DeprecationWarning
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dep_msg = "Use of keyword argument `old` is deprecated"
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res1 = self.old_keyword_deprecated(10)
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res2 = self.old_keyword_deprecated(new=10)
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with pytest.warns(DeprecationWarning, match=dep_msg):
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res3 = self.old_keyword_deprecated(old=10)
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assert res1 == res2 == res3 == 10
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# unexpected keyword raises an error
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message = re.escape("old_keyword_deprecated() got an unexpected")
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with pytest.raises(TypeError, match=message):
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self.old_keyword_deprecated(unexpected=10)
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# multiple values for the same parameter raises an error and,
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# if old keyword is used, results in DeprecationWarning
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message = re.escape("old_keyword_deprecated() got multiple")
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with pytest.raises(TypeError, match=message):
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self.old_keyword_deprecated(10, new=10)
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with pytest.raises(TypeError, match=message), \
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pytest.warns(DeprecationWarning, match=dep_msg):
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self.old_keyword_deprecated(10, old=10)
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with pytest.raises(TypeError, match=message), \
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pytest.warns(DeprecationWarning, match=dep_msg):
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self.old_keyword_deprecated(new=10, old=10)
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class TestContainsNaNTest:
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def test_policy(self):
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data = np.array([1, 2, 3, np.nan])
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contains_nan, nan_policy = _contains_nan(data, nan_policy="propagate")
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assert contains_nan
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assert nan_policy == "propagate"
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contains_nan, nan_policy = _contains_nan(data, nan_policy="omit")
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assert contains_nan
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assert nan_policy == "omit"
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msg = "The input contains nan values"
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with pytest.raises(ValueError, match=msg):
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_contains_nan(data, nan_policy="raise")
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msg = "nan_policy must be one of"
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with pytest.raises(ValueError, match=msg):
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_contains_nan(data, nan_policy="nan")
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def test_contains_nan_1d(self):
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data1 = np.array([1, 2, 3])
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assert not _contains_nan(data1)[0]
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data2 = np.array([1, 2, 3, np.nan])
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assert _contains_nan(data2)[0]
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data3 = np.array([np.nan, 2, 3, np.nan])
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assert _contains_nan(data3)[0]
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data4 = np.array([1, 2, "3", np.nan]) # converted to string "nan"
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assert not _contains_nan(data4)[0]
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data5 = np.array([1, 2, "3", np.nan], dtype='object')
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assert _contains_nan(data5)[0]
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def test_contains_nan_2d(self):
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data1 = np.array([[1, 2], [3, 4]])
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assert not _contains_nan(data1)[0]
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data2 = np.array([[1, 2], [3, np.nan]])
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assert _contains_nan(data2)[0]
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data3 = np.array([["1", 2], [3, np.nan]]) # converted to string "nan"
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assert not _contains_nan(data3)[0]
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data4 = np.array([["1", 2], [3, np.nan]], dtype='object')
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assert _contains_nan(data4)[0]
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def test__rng_html_rewrite():
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def mock_str():
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lines = [
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'np.random.default_rng(8989843)',
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'np.random.default_rng(seed)',
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'np.random.default_rng(0x9a71b21474694f919882289dc1559ca)',
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' bob ',
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]
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return lines
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res = _rng_html_rewrite(mock_str)()
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ref = [
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'np.random.default_rng()',
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'np.random.default_rng(seed)',
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'np.random.default_rng()',
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' bob ',
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]
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assert res == ref
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class TestLazywhere:
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n_arrays = strategies.integers(min_value=1, max_value=3)
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rng_seed = strategies.integers(min_value=1000000000, max_value=9999999999)
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|
dtype = strategies.sampled_from((np.float32, np.float64))
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|
p = strategies.floats(min_value=0, max_value=1)
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|
|
data = strategies.data()
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||
|
|
|
||
|
|
@pytest.mark.filterwarnings('ignore::RuntimeWarning') # overflows, etc.
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||
|
|
@array_api_compatible
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|
|
@given(n_arrays=n_arrays, rng_seed=rng_seed, dtype=dtype, p=p, data=data)
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||
|
|
def test_basic(self, n_arrays, rng_seed, dtype, p, data, xp):
|
||
|
|
mbs = npst.mutually_broadcastable_shapes(num_shapes=n_arrays+1,
|
||
|
|
min_side=0)
|
||
|
|
input_shapes, result_shape = data.draw(mbs)
|
||
|
|
cond_shape, *shapes = input_shapes
|
||
|
|
fillvalue = xp.asarray(data.draw(npst.arrays(dtype=dtype, shape=tuple())))
|
||
|
|
arrays = [xp.asarray(data.draw(npst.arrays(dtype=dtype, shape=shape)))
|
||
|
|
for shape in shapes]
|
||
|
|
|
||
|
|
def f(*args):
|
||
|
|
return sum(arg for arg in args)
|
||
|
|
|
||
|
|
def f2(*args):
|
||
|
|
return sum(arg for arg in args) / 2
|
||
|
|
|
||
|
|
rng = np.random.default_rng(rng_seed)
|
||
|
|
cond = xp.asarray(rng.random(size=cond_shape) > p)
|
||
|
|
|
||
|
|
res1 = _lazywhere(cond, arrays, f, fillvalue)
|
||
|
|
res2 = _lazywhere(cond, arrays, f, f2=f2)
|
||
|
|
|
||
|
|
# Ensure arrays are at least 1d to follow sane type promotion rules.
|
||
|
|
if xp == np:
|
||
|
|
cond, fillvalue, *arrays = np.atleast_1d(cond, fillvalue, *arrays)
|
||
|
|
|
||
|
|
ref1 = xp.where(cond, f(*arrays), fillvalue)
|
||
|
|
ref2 = xp.where(cond, f(*arrays), f2(*arrays))
|
||
|
|
|
||
|
|
if xp == np:
|
||
|
|
ref1 = ref1.reshape(result_shape)
|
||
|
|
ref2 = ref2.reshape(result_shape)
|
||
|
|
res1 = xp.asarray(res1)[()]
|
||
|
|
res2 = xp.asarray(res2)[()]
|
||
|
|
|
||
|
|
isinstance(res1, type(xp.asarray([])))
|
||
|
|
xp_assert_equal(res1, ref1)
|
||
|
|
assert_equal(res1.shape, ref1.shape)
|
||
|
|
assert_equal(res1.dtype, ref1.dtype)
|
||
|
|
|
||
|
|
isinstance(res2, type(xp.asarray([])))
|
||
|
|
xp_assert_equal(res2, ref2)
|
||
|
|
assert_equal(res2.shape, ref2.shape)
|
||
|
|
assert_equal(res2.dtype, ref2.dtype)
|