def _model(self, slice_size, settings):
STFT = stft(resample_to=SR11025(), store_fft=True)
def pack(x):
arr = np.zeros((len(x), 16), dtype=np.uint64)
return ArrayWithUnits(arr, [x.dimensions[0], IdentityDimension()])
class Model(STFT, settings):
binary = ArrayWithUnitsFeature(
Binarize,
predicate=lambda data: data >= 0,
needs=STFT.fft,
store=False)
sliced = ArrayWithUnitsFeature(
Slice,
sl=slice(0, slice_size),
needs=binary,
store=True)
packed = ArrayWithUnitsFeature(
pack,
needs=sliced,
store=True)
return Model
def _model(self, slice_size, settings):
STFT = stft(resample_to=SR11025(), store_fft=True)
def pack(x):
arr = np.zeros((len(x), 16), dtype=np.uint64)
return ArrayWithUnits(arr, [x.dimensions[0], IdentityDimension()])
class Model(STFT, settings):
binary = ArrayWithUnitsFeature(
Binarize,
predicate=lambda data: data >= 0,
needs=STFT.fft,
store=False)
sliced = ArrayWithUnitsFeature(
Slice,
sl=slice(0, slice_size),
needs=binary,
store=True)
packed = ArrayWithUnitsFeature(
pack,
needs=sliced,
store=True)
return Model
def setUp(self):
self.samplerate = SR44100()
self.wscheme = HalfLapped()
self.STFT = stft(
store_fft=True,
resample_to=self.samplerate,
wscheme=self.wscheme)
def test_square_form_no_overlap_add(self):
samplerate = SR11025()
BaseModel = stft(resample_to=samplerate)
windowing_func = OggVorbisWindowingFunc()
scale = GeometricScale(20, 5000, 0.1, 25)
@simple_in_memory_settings
class Document(BaseModel):
long_windowed = ArrayWithUnitsFeature(
SlidingWindow,
wscheme=SampleRate(frequency=Milliseconds(500),
duration=Seconds(1)),
wfunc=windowing_func,
needs=BaseModel.resampled,
store=True)
dct = ArrayWithUnitsFeature(DCT,
scale_always_even=True,
needs=long_windowed,
store=True)
mdct = FrequencyAdaptiveFeature(FrequencyAdaptiveTransform,
transform=scipy.fftpack.idct,
scale=scale,
needs=dct,
store=True)
synth = TickSynthesizer(SR22050())
samples = synth.synthesize(Seconds(5), Milliseconds(200))
_id = Document.process(meta=samples.encode())
doc = Document(_id)
square = doc.mdct.square(30)
self.assertEqual(3, square.ndim)
self.assertEqual(30, square.shape[1])
self.assertEqual(25, square.shape[2])
def can_invert_fft(self, samplerate):
base_cls = stft(resample_to=samplerate,
store_fft=True,
store_windowed=True)
@simple_in_memory_settings
class Document(base_cls):
pass
synth = SineSynthesizer(samplerate)
audio = synth.synthesize(Seconds(2), freqs_in_hz=[440., 880.])
_id = Document.process(meta=audio.encode())
doc = Document(_id)
fft_synth = FFTSynthesizer()
recon = fft_synth.synthesize(doc.fft)
self.assertIsInstance(recon, ArrayWithUnits)
self.assertEqual(audio.dimensions, recon.dimensions)
def test_can_encode_and_decode_variable_rate_time_Series(self):
class TimestampEmitter(ff.Node):
def __init__(self, needs=None):
super(TimestampEmitter, self).__init__(needs=needs)
self.pos = Picoseconds(0)
def _process(self, data):
td = data.dimensions[0]
frequency = td.frequency
timestamps = [self.pos + (i * frequency)
for i, d in enumerate(data)
if random() > 0.9]
slices = TimeSlice.slices(timestamps)
yield VariableRateTimeSeries(
(ts, np.zeros(0)) for ts in slices)
self.pos += frequency * len(data)
graph = stft(store_fft=True)
@simple_in_memory_settings
class Document(graph):
slices = TimeSliceFeature(
TimestampEmitter,
needs=graph.fft,
store=True)
pooled = VariableRateTimeSeriesFeature(
Pooled,
op=np.max,
axis=0,
needs=(slices, graph.fft),
store=False)
signal = NoiseSynthesizer(SR44100())\
.synthesize(Seconds(10))\
.encode()
_id = Document.process(meta=signal)
doc = Document(_id)
self.assertIsInstance(doc.pooled, VariableRateTimeSeries)
self.assertEqual(doc.fft.shape[1], doc.pooled.slicedata.shape[1])
def test_can_encode_and_decode_variable_rate_time_Series(self):
class TimestampEmitter(ff.Node):
def __init__(self, needs=None):
super(TimestampEmitter, self).__init__(needs=needs)
self.pos = Picoseconds(0)
def _process(self, data):
td = data.dimensions[0]
frequency = td.frequency
timestamps = [self.pos + (i * frequency)
for i, d in enumerate(data)
if random() > 0.9]
slices = TimeSlice.slices(timestamps)
yield VariableRateTimeSeries(
(ts, np.zeros(0)) for ts in slices)
self.pos += frequency * len(data)
graph = stft(store_fft=True)
@simple_in_memory_settings
class Document(graph):
slices = TimeSliceFeature(
TimestampEmitter,
needs=graph.fft,
store=True)
pooled = VariableRateTimeSeriesFeature(
Pooled,
op=np.max,
axis=0,
needs=(slices, graph.fft),
store=False)
signal = NoiseSynthesizer(SR44100())\
.synthesize(Seconds(10))\
.encode()
_id = Document.process(meta=signal)
doc = Document(_id)
self.assertIsInstance(doc.pooled, VariableRateTimeSeries)
self.assertEqual(doc.fft.shape[1], doc.pooled.slicedata.shape[1])
def test_square_form_no_overlap_add(self):
samplerate = SR11025()
BaseModel = stft(resample_to=samplerate)
windowing_func = OggVorbisWindowingFunc()
scale = GeometricScale(20, 5000, 0.1, 25)
@simple_in_memory_settings
class Document(BaseModel):
long_windowed = ArrayWithUnitsFeature(
SlidingWindow,
wscheme=SampleRate(
frequency=Milliseconds(500),
duration=Seconds(1)),
wfunc=windowing_func,
needs=BaseModel.resampled,
store=True)
dct = ArrayWithUnitsFeature(
DCT,
scale_always_even=True,
needs=long_windowed,
store=True)
mdct = FrequencyAdaptiveFeature(
FrequencyAdaptiveTransform,
transform=scipy.fftpack.idct,
scale=scale,
needs=dct,
store=True)
synth = TickSynthesizer(SR22050())
samples = synth.synthesize(Seconds(5), Milliseconds(200))
_id = Document.process(meta=samples.encode())
doc = Document(_id)
square = doc.mdct.square(30)
self.assertEqual(3, square.ndim)
self.assertEqual(30, square.shape[1])
self.assertEqual(25, square.shape[2])
def setUp(self):
self.samplerate = SR44100()
self.wscheme = HalfLapped()
self.STFT = stft(store_fft=True,
resample_to=self.samplerate,
wscheme=self.wscheme)
class Document(stft(store_fft=True)):
pass