def sum_streams(stream1, stream2):
'''Add two sample streams together
The time elements of each stream will not be aligned if they do not match.
Instead the time values from stream1 are used for the result. The iterator
terminates when either of the two streams ends.
This is a generator function.
stream1 (iterable of SampleChunk objects)
stream2 (iterable of SampleChunk objects)
The two sample streams to have their corresponding values added together.
Yields a sample stream.
'''
ex1 = ChunkExtractor(stream1)
ex2 = ChunkExtractor(stream2)
while True:
c1 = ex1.next_chunk()
c2 = ex2.next_chunk()
if c1 is None or c2 is None:
break
if len(c1.samples) != len(c2.samples):
size = min(len(c1.samples), len(c2.samples))
filt = c1.samples[:size] + c2.samples[:size]
else:
filt = c1.samples + c2.samples
yield SampleChunk(filt, c1.start_time, c1.sample_period)
def sum_streams(stream1, stream2):
"""Add two sample streams together
The time elements of each stream will not be aligned if they do not match.
Instead the time values from stream1 are used for the result. The iterator
terminates when either of the two streams ends.
This is a generator function.
stream1 (iterable of SampleChunk objects)
stream2 (iterable of SampleChunk objects)
The two sample streams to have their corresponding values added together.
Yields a sample stream.
"""
ex1 = ChunkExtractor(stream1)
ex2 = ChunkExtractor(stream2)
while True:
c1 = ex1.next_chunk()
c2 = ex2.next_chunk()
if c1 is None or c2 is None:
break
if len(c1.samples) != len(c2.samples):
size = min(len(c1.samples), len(c2.samples))
filt = c1.samples[:size] + c2.samples[:size]
else:
filt = c1.samples + c2.samples
yield SampleChunk(filt, c1.start_time, c1.sample_period)
def filter_waveform(samples,
sample_rate,
rise_time,
ripple_db=60.0,
chunk_size=10000):
'''Apply a bandwidth limiting low-pass filter to a sample stream
This is a generator function.
samples (iterable of SampleChunk objects)
An iterable sample stream to be filtered.
sample_rate (float)
The sample rate for converting the sample stream.
rise_time (float)
Rise (and fall) time for the filtered samples.
ripple_db (float)
Noise suppression in dB for the bandwidth filter stop band. This should
be a positive value.
chunk_size (int)
Internal FIR filter sample pool size. This can generally be ignored. To support
streaming of samples, the FIR filter operation is done piecewise so we don't have
to consume the entire input before producing filtered output. Larger values will
reduce the number of filter operations performed. Excessively small values will
waste time due to the reprocessing of overlapping samples between successive pools.
Yields a stream of SampleChunk objects.
'''
sample_period = 1.0 / sample_rate
nyquist = sample_rate / 2.0
edge_bw = approximate_bandwidth(rise_time)
transition_bw = edge_bw * 4.0 # This gives a nice smooth transition with no Gibbs effect
cutoff_hz = edge_bw
if cutoff_hz > nyquist:
min_rise = min_rise_time(sample_rate)
raise ValueError(
'Rise time is too fast for current sample rate (min: {0})'.format(
min_rise))
N, beta = signal.kaiserord(ripple_db, transition_bw / nyquist)
taps = signal.firwin(N, cutoff_hz / nyquist, window=('kaiser', beta))
# Filter delay
# delay = 0.5 * (N-1) / sample_rate
if chunk_size < 2 * N:
chunk_size = 2 * N
samp_ce = ChunkExtractor(samples)
# Get a pool of samples
spool = np.zeros((chunk_size + N - 1, ), dtype=np.float)
# Prime the initial portion of the pool with data that will be filtered out
prime_size = N - N // 2
sc = samp_ce.next_chunk(prime_size)
if sc is not None:
spool[0:N // 2 - 1] += sc.samples[
0] # Pad the first part of the pool with a copy of the first sample
spool[N // 2 - 1:N - 1] = sc.samples
while True:
# Fill the pool with samples
sc = samp_ce.next_chunk(chunk_size)
if sc is None:
break
spool[N - 1:len(sc.samples) + N - 1] = sc.samples
valid_samples = len(sc.samples) + N - 1
filt = signal.lfilter(
taps, 1.0, spool[:valid_samples]
) #NOTE: there may be an off-by-one error in the slice
# copy end samples to start of pool
spool[0:N - 1] = spool[chunk_size:chunk_size + N - 1]
#print('$$$ ce chunk', N, valid_samples, sc.start_time, sample_period)
yield SampleChunk(filt[N - 1:valid_samples], sc.start_time,
sample_period)
def filter_waveform(samples, sample_rate, rise_time, ripple_db=60.0, chunk_size=10000):
"""Apply a bandwidth limiting low-pass filter to a sample stream
This is a generator function.
samples (iterable of SampleChunk objects)
An iterable sample stream to be filtered.
sample_rate (float)
The sample rate for converting the sample stream.
rise_time (float)
Rise (and fall) time for the filtered samples.
ripple_db (float)
Noise suppression in dB for the bandwidth filter stop band. This should
be a positive value.
chunk_size (int)
Internal FIR filter sample pool size. This can generally be ignored. To support
streaming of samples, the FIR filter operation is done piecewise so we don't have
to consume the entire input before producing filtered output. Larger values will
reduce the number of filter operations performed. Excessively small values will
waste time due to the reprocessing of overlapping samples between successive pools.
Yields a stream of SampleChunk objects.
"""
sample_period = 1.0 / sample_rate
nyquist = sample_rate / 2.0
edge_bw = approximate_bandwidth(rise_time)
transition_bw = edge_bw * 4.0 # This gives a nice smooth transition with no Gibbs effect
cutoff_hz = edge_bw
if cutoff_hz > nyquist:
min_rise = min_rise_time(sample_rate)
raise ValueError("Rise time is too fast for current sample rate (min: {0})".format(min_rise))
N, beta = signal.kaiserord(ripple_db, transition_bw / nyquist)
taps = signal.firwin(N, cutoff_hz / nyquist, window=("kaiser", beta))
# Filter delay
# delay = 0.5 * (N-1) / sample_rate
if chunk_size < 2 * N:
chunk_size = 2 * N
samp_ce = ChunkExtractor(samples)
# Get a pool of samples
spool = np.zeros((chunk_size + N - 1,), dtype=np.float)
# Prime the initial portion of the pool with data that will be filtered out
prime_size = N - N // 2
sc = samp_ce.next_chunk(prime_size)
if sc is not None:
spool[0 : N // 2 - 1] += sc.samples[0] # Pad the first part of the pool with a copy of the first sample
spool[N // 2 - 1 : N - 1] = sc.samples
while True:
# Fill the pool with samples
sc = samp_ce.next_chunk(chunk_size)
if sc is None:
break
spool[N - 1 : len(sc.samples) + N - 1] = sc.samples
valid_samples = len(sc.samples) + N - 1
filt = signal.lfilter(
taps, 1.0, spool[:valid_samples]
) # NOTE: there may be an off-by-one error in the slice
# copy end samples to start of pool
spool[0 : N - 1] = spool[chunk_size : chunk_size + N - 1]
# print('$$$ ce chunk', N, valid_samples, sc.start_time, sample_period)
yield SampleChunk(filt[N - 1 : valid_samples], sc.start_time, sample_period)
