diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/fft/tests/test_fftpack.py | 14 | ||||
| -rw-r--r-- | numpy/fft/tests/test_helper.py | 34 |
2 files changed, 26 insertions, 22 deletions
diff --git a/numpy/fft/tests/test_fftpack.py b/numpy/fft/tests/test_fftpack.py index a2cbc0f63..7ac0488e4 100644 --- a/numpy/fft/tests/test_fftpack.py +++ b/numpy/fft/tests/test_fftpack.py @@ -2,8 +2,10 @@ from __future__ import division, absolute_import, print_function import numpy as np from numpy.random import random -from numpy.testing import TestCase, run_module_suite, assert_array_almost_equal -from numpy.testing import assert_array_equal +from numpy.testing import ( + run_module_suite, assert_array_almost_equal, assert_array_equal, + assert_raises, + ) import threading import sys if sys.version_info[0] >= 3: @@ -19,13 +21,13 @@ def fft1(x): return np.sum(x*np.exp(phase), axis=1) -class TestFFTShift(TestCase): +class TestFFTShift(object): def test_fft_n(self): - self.assertRaises(ValueError, np.fft.fft, [1, 2, 3], 0) + assert_raises(ValueError, np.fft.fft, [1, 2, 3], 0) -class TestFFT1D(TestCase): +class TestFFT1D(object): def test_fft(self): x = random(30) + 1j*random(30) @@ -145,7 +147,7 @@ class TestFFT1D(TestCase): assert_array_almost_equal(x_norm, np.linalg.norm(tmp)) -class TestFFTThreadSafe(TestCase): +class TestFFTThreadSafe(object): threads = 16 input_shape = (800, 200) diff --git a/numpy/fft/tests/test_helper.py b/numpy/fft/tests/test_helper.py index ff56ff63c..f02edf7cc 100644 --- a/numpy/fft/tests/test_helper.py +++ b/numpy/fft/tests/test_helper.py @@ -6,13 +6,15 @@ Copied from fftpack.helper by Pearu Peterson, October 2005 from __future__ import division, absolute_import, print_function import numpy as np -from numpy.testing import TestCase, run_module_suite, assert_array_almost_equal +from numpy.testing import ( + run_module_suite, assert_array_almost_equal, assert_equal, + ) from numpy import fft from numpy import pi from numpy.fft.helper import _FFTCache -class TestFFTShift(TestCase): +class TestFFTShift(object): def test_definition(self): x = [0, 1, 2, 3, 4, -4, -3, -2, -1] @@ -40,7 +42,7 @@ class TestFFTShift(TestCase): fft.ifftshift(shifted, axes=(0,))) -class TestFFTFreq(TestCase): +class TestFFTFreq(object): def test_definition(self): x = [0, 1, 2, 3, 4, -4, -3, -2, -1] @@ -51,7 +53,7 @@ class TestFFTFreq(TestCase): assert_array_almost_equal(10*pi*fft.fftfreq(10, pi), x) -class TestRFFTFreq(TestCase): +class TestRFFTFreq(object): def test_definition(self): x = [0, 1, 2, 3, 4] @@ -62,7 +64,7 @@ class TestRFFTFreq(TestCase): assert_array_almost_equal(10*pi*fft.rfftfreq(10, pi), x) -class TestIRFFTN(TestCase): +class TestIRFFTN(object): def test_not_last_axis_success(self): ar, ai = np.random.random((2, 16, 8, 32)) @@ -74,7 +76,7 @@ class TestIRFFTN(TestCase): fft.irfftn(a, axes=axes) -class TestFFTCache(TestCase): +class TestFFTCache(object): def test_basic_behaviour(self): c = _FFTCache(max_size_in_mb=1, max_item_count=4) @@ -90,7 +92,7 @@ class TestFFTCache(TestCase): np.zeros(2, dtype=np.float32)) # Nothing should be left. - self.assertEqual(len(c._dict), 0) + assert_equal(len(c._dict), 0) # Now put everything in twice so it can be retrieved once and each will # still have one item left. @@ -101,7 +103,7 @@ class TestFFTCache(TestCase): np.ones(2, dtype=np.float32)) assert_array_almost_equal(c.pop_twiddle_factors(2), np.zeros(2, dtype=np.float32)) - self.assertEqual(len(c._dict), 2) + assert_equal(len(c._dict), 2) def test_automatic_pruning(self): # That's around 2600 single precision samples. @@ -109,27 +111,27 @@ class TestFFTCache(TestCase): c.put_twiddle_factors(1, np.ones(200, dtype=np.float32)) c.put_twiddle_factors(2, np.ones(200, dtype=np.float32)) - self.assertEqual(list(c._dict.keys()), [1, 2]) + assert_equal(list(c._dict.keys()), [1, 2]) # This is larger than the limit but should still be kept. c.put_twiddle_factors(3, np.ones(3000, dtype=np.float32)) - self.assertEqual(list(c._dict.keys()), [1, 2, 3]) + assert_equal(list(c._dict.keys()), [1, 2, 3]) # Add one more. c.put_twiddle_factors(4, np.ones(3000, dtype=np.float32)) # The other three should no longer exist. - self.assertEqual(list(c._dict.keys()), [4]) + assert_equal(list(c._dict.keys()), [4]) # Now test the max item count pruning. c = _FFTCache(max_size_in_mb=0.01, max_item_count=2) c.put_twiddle_factors(2, np.empty(2)) c.put_twiddle_factors(1, np.empty(2)) # Can still be accessed. - self.assertEqual(list(c._dict.keys()), [2, 1]) + assert_equal(list(c._dict.keys()), [2, 1]) c.put_twiddle_factors(3, np.empty(2)) # 1 and 3 can still be accessed - c[2] has been touched least recently # and is thus evicted. - self.assertEqual(list(c._dict.keys()), [1, 3]) + assert_equal(list(c._dict.keys()), [1, 3]) # One last test. We will add a single large item that is slightly # bigger then the cache size. Some small items can still be added. @@ -138,18 +140,18 @@ class TestFFTCache(TestCase): c.put_twiddle_factors(2, np.ones(2, dtype=np.float32)) c.put_twiddle_factors(3, np.ones(2, dtype=np.float32)) c.put_twiddle_factors(4, np.ones(2, dtype=np.float32)) - self.assertEqual(list(c._dict.keys()), [1, 2, 3, 4]) + assert_equal(list(c._dict.keys()), [1, 2, 3, 4]) # One more big item. This time it is 6 smaller ones but they are # counted as one big item. for _ in range(6): c.put_twiddle_factors(5, np.ones(500, dtype=np.float32)) # '1' no longer in the cache. Rest still in the cache. - self.assertEqual(list(c._dict.keys()), [2, 3, 4, 5]) + assert_equal(list(c._dict.keys()), [2, 3, 4, 5]) # Another big item - should now be the only item in the cache. c.put_twiddle_factors(6, np.ones(4000, dtype=np.float32)) - self.assertEqual(list(c._dict.keys()), [6]) + assert_equal(list(c._dict.keys()), [6]) if __name__ == "__main__": |