from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import assert_equal, assert_ from pytest import raises as assert_raises from scipy._lib._util import _aligned_zeros, check_random_state 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')