def test_basic_behaviour(self):
c = _FFTCache(max_size_in_mb=1, max_item_count=4)
# Put
c.put_twiddle_factors(1, np.ones(2, dtype=np.float32))
c.put_twiddle_factors(2, np.zeros(2, dtype=np.float32))
# Get
assert_array_almost_equal(c.pop_twiddle_factors(1),
np.ones(2, dtype=np.float32))
assert_array_almost_equal(c.pop_twiddle_factors(2),
np.zeros(2, dtype=np.float32))
# Nothing should be left.
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.
for _ in range(2):
c.put_twiddle_factors(1, np.ones(2, dtype=np.float32))
c.put_twiddle_factors(2, np.zeros(2, dtype=np.float32))
assert_array_almost_equal(c.pop_twiddle_factors(1),
np.ones(2, dtype=np.float32))
assert_array_almost_equal(c.pop_twiddle_factors(2),
np.zeros(2, dtype=np.float32))
assert_equal(len(c._dict), 2)
def test_basic_behaviour(self):
c = _FFTCache(max_size_in_mb=1, max_item_count=4)
# Put
c.put_twiddle_factors(1, np.ones(2, dtype=np.float32))
c.put_twiddle_factors(2, np.zeros(2, dtype=np.float32))
# Get
assert_array_almost_equal(c.pop_twiddle_factors(1),
np.ones(2, dtype=np.float32))
assert_array_almost_equal(c.pop_twiddle_factors(2),
np.zeros(2, dtype=np.float32))
# Nothing should be left.
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.
for _ in range(2):
c.put_twiddle_factors(1, np.ones(2, dtype=np.float32))
c.put_twiddle_factors(2, np.zeros(2, dtype=np.float32))
assert_array_almost_equal(c.pop_twiddle_factors(1),
np.ones(2, dtype=np.float32))
assert_array_almost_equal(c.pop_twiddle_factors(2),
np.zeros(2, dtype=np.float32))
assert_equal(len(c._dict), 2)
def test_automatic_pruning(self):
# That's around 2600 single precision samples.
c = _FFTCache(max_size_in_mb=0.01, max_item_count=4)
c.put_twiddle_factors(1, np.ones(200, dtype=np.float32))
c.put_twiddle_factors(2, np.ones(200, dtype=np.float32))
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))
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.
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.
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.
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.
c = _FFTCache(max_size_in_mb=0.01, max_item_count=5)
c.put_twiddle_factors(1, np.ones(3000, dtype=np.float32))
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))
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.
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))
assert_equal(list(c._dict.keys()), [6])
def test_automatic_pruning(self):
# That's around 2600 single precision samples.
c = _FFTCache(max_size_in_mb=0.01, max_item_count=4)
c.put_twiddle_factors(1, np.ones(200, dtype=np.float32))
c.put_twiddle_factors(2, np.ones(200, dtype=np.float32))
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))
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.
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.
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.
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.
c = _FFTCache(max_size_in_mb=0.01, max_item_count=5)
c.put_twiddle_factors(1, np.ones(3000, dtype=np.float32))
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))
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.
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))
assert_equal(list(c._dict.keys()), [6])
