def noise(nblock=None, state=None, color='white', ntaps=None):
"""Generate white noise with standard Gaussian distribution.
:param nblock: Amount of samples per block.
:param state: State of PRNG.
:type state: :class:`np.random.RandomState`
:returns: When `nblock=None`, individual samples are generated and a :class:`streaming.Stream` is
returned. When integer, a :class:`streaming.BlockStream` is returned.
"""
if state is None:
state = np.random.RandomState()
# Generate white noise
if nblock is None:
# Generate individual samples.
stream = Stream((state.randn(1)[0] for i in itertools.count()))
else:
# Generate blocks.
stream = BlockStream((state.randn(nblock) for i in itertools.count()),
nblock=nblock,
noverlap=0)
# Apply filter for specific color
if color is not 'white':
if ntaps is None:
raise ValueError("Amount of taps has not been specified.")
ir = noisy.COLORS[color](ntaps)
if nblock is None:
nhop = ntaps
else:
nhop = max(nblock, ntaps)
stream = convolve_overlap_add(stream, constant(ir), nhop,
ntaps).samples().drop(ntaps // 2)
# Output as desired
if nblock is None:
return stream.samples()
else:
return stream.blocks(nblock)
def noise(nblock=None, state=None, color='white', ntaps=None):
"""Generate white noise with standard Gaussian distribution.
:param nblock: Amount of samples per block.
:param state: State of PRNG.
:type state: :class:`np.random.RandomState`
:returns: When `nblock=None`, individual samples are generated and a :class:`streaming.Stream` is
returned. When integer, a :class:`streaming.BlockStream` is returned.
"""
if state is None:
state = np.random.RandomState()
# Generate white noise
if nblock is None:
# Generate individual samples.
stream = Stream((state.randn(1)[0] for i in itertools.count()))
else:
# Generate blocks.
stream = BlockStream((state.randn(nblock) for i in itertools.count()), nblock=nblock, noverlap=0)
# Apply filter for specific color
if color is not 'white':
if ntaps is None:
raise ValueError("Amount of taps has not been specified.")
ir = noisy.COLORS[color](ntaps)
if nblock is None:
nhop = ntaps
else:
nhop = max(nblock, ntaps)
stream = convolve_overlap_add(stream, constant(ir), nhop, ntaps).samples().drop(ntaps//2)
# Output as desired
if nblock is None:
return stream.samples()
else:
return stream.blocks(nblock)
def stream(sequence, blocked, nblock):
stream = Stream(sequence)
if blocked:
stream = stream.blocks(nblock)
return stream
