class Filter:
def __init__(self, B, A):
"""Create an IIR filter, given the B and A coefficient vectors"""
self.B = B
self.A = A
if len(A) > 2:
self.prev_outputs = Ringbuffer(len(A) - 1)
else:
self.prev_outputs = Ringbuffer(3)
self.prev_inputs = Ringbuffer(len(B))
def filter(self, x):
#take one sample, and filter it. Return the output
y = 0
self.prev_inputs.new_sample(x)
k = 0
for b in self.B:
y = y + b * self.prev_inputs.reverse_index(k)
k = k + 1
k = 0
for a in self.A[1:]:
y = y - a * self.prev_outputs.reverse_index(k)
k = k + 1
y = y / self.A[0]
self.prev_outputs.new_sample(y)
return y
def new_sample(self, x):
return self.filter(x)
class Filter:
def __init__(self, B, A):
"""Create an IIR filter, given the B and A coefficient vectors"""
self.B = B
self.A = A
if len(A)>2:
self.prev_outputs = Ringbuffer(len(A)-1)
else:
self.prev_outputs = Ringbuffer(3)
self.prev_inputs = Ringbuffer(len(B))
def filter(self, x):
#take one sample, and filter it. Return the output
y = 0
self.prev_inputs.new_sample(x)
k =0
for b in self.B:
y = y + b * self.prev_inputs.reverse_index(k)
k = k + 1
k = 0
for a in self.A[1:]:
y = y - a * self.prev_outputs.reverse_index(k)
k = k + 1
y = y / self.A[0]
self.prev_outputs.new_sample(y)
return y
def new_sample(self,x):
return self.filter(x)
class WindowedAverage:
def __init__(self, window):
if have_numpy:
self.window = numpy.array(window)
else:
self.window = list(window)
self.buffer = Ringbuffer(len(window))
def new_sample(self, x):
self.buffer.new_sample(x)
if have_numpy:
avg = numpy.mean(self.buffer.get_samples() * self.window)
else:
i = 0
sum = 0
for s in self.buffer.get_samples():
sum += self.window[i]*s
i = i + 1
avg = sum/len(self.window)
return avg
class Erosion:
def __init__(self, window):
self.window = window
self.buffer = Ringbuffer(window)
def new_sample(self, x):
self.buffer.new_sample(x)
return min(self.buffer.get_samples())
class MovingAverage:
def __init__(self, window_len):
self.window_len = window_len
self.buffer = Ringbuffer(window_len)
def new_sample(self, x):
self.buffer.new_sample(x)
return self.buffer.get_sum() / float(self.window_len)
class Erosion:
def __init__(self, window):
self.window = window
self.buffer = Ringbuffer(window)
def new_sample(self, x):
self.buffer.new_sample(x)
return min(self.buffer.get_samples())
class EnergyBuffer:
def __init__(self, len):
self.buffer = Ringbuffer(len)
self.differentiator = Differentiator()
def new_sample(self, x):
d = self.differentiator.new_sample(x)
self.buffer.new_sample(x*x)
return math.sqrt(self.buffer.get_mean())
class MovingAverage:
def __init__(self, window_len):
self.window_len = window_len
self.buffer = Ringbuffer(window_len)
def new_sample(self, x):
self.buffer.new_sample(x)
return self.buffer.get_sum()/float(self.window_len)
class RandomDelay:
def __init__(self, window):
self.window = window
self.buffer = Ringbuffer(window)
def new_sample(self, x):
self.buffer.new_sample(x)
s = self.buffer.get_samples
return random.choice(s)
class RandomDelay:
def __init__(self, window):
self.window = window
self.buffer = Ringbuffer(window)
def new_sample(self, x):
self.buffer.new_sample(x)
s = self.buffer.get_samples
return random.choice(s)
class EnergyBuffer:
def __init__(self, len):
self.buffer = Ringbuffer(len)
self.differentiator = Differentiator()
def new_sample(self, x):
d = self.differentiator.new_sample(x)
self.buffer.new_sample(x * x)
return math.sqrt(self.buffer.get_mean())
class PLL:
#phase locked loop
def __init__(self, bp_filter, lp_filter, centre_freq, sr, loop_gain=1.0, in_gain=1.0):
self.sr = sr
self.base_rate = (centre_freq*2*math.pi) / sr
self.int = 0
self.bp_filter = bp_filter
self.lp_filter = lp_filter
self.phase_buffer = Ringbuffer(int(sr/centre_freq))
self.loop_gain = loop_gain
self.in_gain = in_gain
def set_gain(self, gain):
self.gain = gain
def set_base_freq(self, freq):
self.base_rate = (freq*2*math.pi) / self.sr
def get_sync(self):
return abs(self.phase_buffer.get_sum())
def new_sample(self, x):
(osc,int,y) = self.run(x)
return int
def run(self, x):
#band pass the input
y = self.bp_filter.new_sample(x*self.in_gain)
# drive the oscillator at natural frequency
self.int = self.int + self.base_rate
osc = math.sin(self.int)
#compute phase difference
phase_com = osc * y
#store phase comparison
self.phase_buffer.new_sample(phase_com)
# filter the phase comparator
ph_adj = self.lp_filter.new_sample(phase_com)
# shift the integrator
self.int = self.int + ph_adj * self.loop_gain
#return value of oscillator, the phase value (unwrapped), and the bp filtered input signal
return (osc, self.int, y)
class PLL:
#phase locked loop
def __init__(self,
bp_filter,
lp_filter,
centre_freq,
sr,
loop_gain=1.0,
in_gain=1.0):
self.sr = sr
self.base_rate = (centre_freq * 2 * math.pi) / sr
self.int = 0
self.bp_filter = bp_filter
self.lp_filter = lp_filter
self.phase_buffer = Ringbuffer(int(sr / centre_freq))
self.loop_gain = loop_gain
self.in_gain = in_gain
def set_gain(self, gain):
self.gain = gain
def set_base_freq(self, freq):
self.base_rate = (freq * 2 * math.pi) / self.sr
def get_sync(self):
return abs(self.phase_buffer.get_sum())
def new_sample(self, x):
(osc, int, y) = self.run(x)
return int
def run(self, x):
#band pass the input
y = self.bp_filter.new_sample(x * self.in_gain)
# drive the oscillator at natural frequency
self.int = self.int + self.base_rate
osc = math.sin(self.int)
#compute phase difference
phase_com = osc * y
#store phase comparison
self.phase_buffer.new_sample(phase_com)
# filter the phase comparator
ph_adj = self.lp_filter.new_sample(phase_com)
# shift the integrator
self.int = self.int + ph_adj * self.loop_gain
#return value of oscillator, the phase value (unwrapped), and the bp filtered input signal
return (osc, self.int, y)
class WindowedReduceFilter:
def __init__(self, binop, window):
self.state = None
self.window = window
self.buffer = Ringbuffer(window)
self.binop = binop
def new_sample(self, x):
self.buffer.new_sample(x)
samples = self.buffer.get_samples()
state = reduce(self.binop, samples)
return state
class WindowedReduceFilter:
def __init__(self, binop, window):
self.state = None
self.window = window
self.buffer = Ringbuffer(window)
self.binop = binop
def new_sample(self, x):
self.buffer.new_sample(x)
samples = self.buffer.get_samples()
state = reduce(self.binop, samples)
return state
class Delay:
def __init__(self, delay):
self.delay = delay
self.buffer = Ringbuffer(delay)
def new_sample(self, x):
return self.buffer.new_sample(x)
class Delay:
def __init__(self, delay):
self.delay = delay
self.buffer = Ringbuffer(delay)
def new_sample(self, x):
return self.buffer.new_sample(x)
class Median:
def __init__(self, window):
self.window = window
self.buffer = Ringbuffer(window)
def new_sample(self, x):
self.buffer.new_sample(x)
b = list(self.buffer.get_samples())
b.sort()
lb = len(b)
# even case
if lb % 2 == 0:
return b[len(b) / 2] + b[len(b) / 2 + 1]
else:
# odd case
return b[len(b + 1) / 2]
class Median:
def __init__(self, window):
self.window = window
self.buffer = Ringbuffer(window)
def new_sample(self, x):
self.buffer.new_sample(x)
b = list(self.buffer.get_samples())
b.sort()
lb = len(b)
# even case
if lb%2==0:
return b[len(b)/2]+b[len(b)/2+1]
else:
# odd case
return b[len(b+1)/2]
class WindowedAverage:
def __init__(self, window):
if have_numpy:
self.window = numpy.array(window)
else:
self.window = list(window)
self.buffer = Ringbuffer(len(window))
def new_sample(self, x):
self.buffer.new_sample(x)
if have_numpy:
avg = numpy.mean(self.buffer.get_samples() * self.window)
else:
i = 0
sum = 0
for s in self.buffer.get_samples():
sum += self.window[i] * s
i = i + 1
avg = sum / len(self.window)
return avg
