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243 lines
7.8 KiB
Python
243 lines
7.8 KiB
Python
from multiprocessing import Pool
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from multiprocessing.pool import Pool as PWL
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import os
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import math
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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, deprecated_call
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import scipy
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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)
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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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if hasattr(np.random, 'Generator'):
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# np.random.Generator is only available in NumPy >= 1.17
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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,), [], None, {}))
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argspec = getfullargspec_no_self(p.__call__)
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assert_equal(argspec, FullArgSpec(['func', 'iterable'], None, None, None, [], None, {}))
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class _rv_generic(object):
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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', (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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# get our custom ones and a few from the "import *" cases
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@pytest.mark.parametrize(
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'key', ('ifft', 'diag', 'arccos', 'randn', 'rand', 'array'))
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def test_numpy_deprecation(key):
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"""Test that 'from numpy import *' functions are deprecated."""
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if key in ('ifft', 'diag', 'arccos'):
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arg = [1.0, 0.]
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elif key == 'finfo':
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arg = float
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else:
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arg = 2
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func = getattr(scipy, key)
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match = r'scipy\.%s is deprecated.*2\.0\.0' % key
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with deprecated_call(match=match) as dep:
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func(arg) # deprecated
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# in case we catch more than one dep warning
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fnames = [os.path.splitext(d.filename)[0] for d in dep.list]
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basenames = [os.path.basename(fname) for fname in fnames]
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assert 'test__util' in basenames
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if key in ('rand', 'randn'):
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root = np.random
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elif key == 'ifft':
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root = np.fft
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else:
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root = np
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func_np = getattr(root, key)
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func_np(arg) # not deprecated
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assert func_np is not func
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# classes should remain classes
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if isinstance(func_np, type):
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assert isinstance(func, type)
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def test_numpy_deprecation_functionality():
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# Check that the deprecation wrappers don't break basic NumPy
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# functionality
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with deprecated_call():
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x = scipy.array([1, 2, 3], dtype=scipy.float64)
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assert x.dtype == scipy.float64
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assert x.dtype == np.float64
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x = scipy.finfo(scipy.float32)
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assert x.eps == np.finfo(np.float32).eps
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assert scipy.float64 == np.float64
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assert issubclass(np.float64, scipy.float64)
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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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