def source_exhausted_test(self):
"""
Testing when the source is exhausted:
# IN: | | | | | | | | | | | | x
# OUT: | | | x
"""
config.block_size = 2
def gen():
"""
[[0], [0.5], [1]] [[1.5], [2], [2.5]] ...
"""
for i in range(0, 4):
yield numpy.arange(i * 3 * 0.5, (i + 1) * 3 * 0.5, 0.5).reshape(3, 1)
resampler = stream.resample(gen())
ratio = 4
resampler.set_ratio(ratio)
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[0], [1 * 0.5 * ratio]], 8)
)
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[2 * 0.5 * ratio], [0]], 8)
)
self.assertRaises(StopIteration, next, resampler)
def sanity_check_test(self):
"""
Test that something's not fundamentally wrong.
"""
def zcr_f0(samples):
"""
Calculate frequency using zero-crossings method.
"""
frame_rate = config.frame_rate
frame_count = len(samples)
crossings = (numpy.diff(numpy.sign(samples)) != 0)
time = (numpy.ones(frame_count) / frame_rate).cumsum() - 1 / frame_rate
half_oscillation_times = numpy.diff(time[crossings])
self.assertTrue(half_oscillation_times.std() < 0.00005)
return 0.5 / half_oscillation_times.mean()
frame_count = 44100 * 20
config.block_size = frame_count
f0 = 440
ratio = 0.99999
time = numpy.arange(0, frame_count) / float(config.frame_rate)
samples = numpy.cos(2 * numpy.pi * f0 * time)
def gen():
yield samples.reshape(len(samples), 1)
resampler = stream.resample(gen())
resampler.set_ratio(ratio)
self.assertEqual(round(zcr_f0(samples), 3), round(f0, 3))
samples2 = next(resampler)[:,0]
self.assertEqual(round(zcr_f0(samples2), 3), round(f0 * ratio, 3))
def downsample2_test(self):
"""
Testing downsampling with the following configuration:
# IN: | | | | | | | | |
# OUT: | | |
"""
config.block_size = 3
def gen():
"""
[[0], [0.5], [1]] [[1.5], [2], [2.5]] ...
"""
for i in range(0, 10):
yield numpy.arange(i * 3 * 0.5, (i + 1) * 3 * 0.5, 0.5).reshape(3, 1)
resampler = stream.resample(gen())
ratio = 4
resampler.set_ratio(ratio)
# IN: 0 1 2 3 4 5 6 7 8
# OUT: 0 1 2
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[0], [1 * 0.5 * ratio], [2 * 0.5 * ratio]], 8)
)
# ...
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[3 * 0.5 * ratio], [4 * 0.5 * ratio], [5 * 0.5 * ratio]], 8)
)
def upsample1_test(self):
"""
Testing upsampling with the 2 following configurations :
IN: | | | .
OUT: | | | | | | | . . .
IN: | | | |
OUT: . | | | | | | | . .
"""
config.block_size = 7
def gen():
"""
[[0], [2], [4]] [[6], [8], [10]] ...
"""
for i in range(0, 4):
yield numpy.arange(i * 2 * 3, (i + 1) * 2 * 3, 2).reshape(3, 1)
resampler = stream.resample(gen())
resampler.set_ratio(1 / 3.0)
# IN: 0 1 2
# OUT: 0 1 2 3 4 5 6
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[0], [1 * 2.0/3], [2 * 2.0/3], [2], [4 * 2.0/3], [5 * 2.0/3], [4]], 8)
)
# IN: 2 3 4 5
# OUT: 7 8 9 a b c d
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[7 * 2.0/3], [8 * 2.0/3], [6], [10 * 2.0/3], [11 * 2.0/3], [8], [13 * 2.0/3]], 8)
)
# IN: 4 5 6 7
# OUT: e f g h i j k
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[14 * 2.0/3], [10], [16 * 2.0/3], [17 * 2.0/3], [12], [19 * 2.0/3], [20 * 2.0/3]], 8)
)
# IN: 7 8 9
# OUT: l m n o p q r
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[14], [22 * 2.0/3], [23 * 2.0/3], [16], [25 * 2.0/3], [26 * 2.0/3], [18]], 8)
)
def downsample1_test(self):
"""
Testing downsampling with the following configurations:
IN: | | | | . .
OUT: | | .
IN: | | | |
OUT: | |
"""
config.block_size = 2
def gen():
"""
[[0], [0.5], [1]] [[1.5], [2], [2.5]] ...
"""
for i in range(0, 10):
yield numpy.arange(i * 3 * 0.5, (i + 1) * 3 * 0.5, 0.5).reshape(3, 1)
resampler = stream.resample(gen())
ratio = 7/3.0
resampler.set_ratio(ratio)
# IN: 0 1 2 3 . .
# OUT: 0 1 .
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[0], [1 * 0.5 * ratio]], 8)
)
# IN: 3 4 5 6 7
# OUT: 2 3
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[2 * 0.5 * ratio], [3 * 0.5 * ratio]], 8)
)
# IN: 9 a b c
# OUT: 4 5
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[4 * 0.5 * ratio], [5 * 0.5 * ratio]], 8)
)
def ratio1_test(self):
"""
Ratio 1 test.
"""
config.block_size = 3
def gen():
"""
[[0], [0.5], [1]] [[1.5], [2], [2.5]] ...
"""
for i in range(0, 10):
yield numpy.arange(i * 3 * 0.5, (i + 1) * 3 * 0.5, 0.5).reshape(3, 1)
resampler = stream.resample(gen())
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[0], [1 * 0.5], [2 * 0.5]], 8)
)
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[3 * 0.5], [4 * 0.5], [5 * 0.5]], 8)
)
def upsample2_test(self):
"""
Here testing upsampling with the following configuration (+ testing stereo):
IN: | |
OUT: | | .
"""
config.block_size = 2
def gen():
"""
[[0, 0], [-2, 2], [-4, 4]] [[-6, 6], [-8, 8], [-10, 10]] ...
"""
for i in range(0, 4):
block_in = numpy.vstack([
numpy.arange(-i * 2 * 3, -(i + 1) * 2 * 3, -2),
numpy.arange(i * 2 * 3, (i + 1) * 2 * 3, 2)
]).transpose()
yield block_in
resampler = stream.resample(gen())
ratio = 2 / 3.0
resampler.set_ratio(ratio)
# IN: 0 1
# OUT: 0 1 .
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[0, 0], [1 * -2 * ratio, 1 * 2 * ratio]], 8)
)
# IN: 1 2
# OUT: 2 3
numpy.testing.assert_array_equal(
next(resampler).round(8),
numpy.round([[2 * -2 * ratio, 2 * 2 * ratio], [3 * -2 * ratio, 3 * 2 * ratio]], 8)
)
