def get_response(sr, audio, f, bw=0, damping=0.0001):
""" Get DetectorBank output
Parameters
----------
sr : int
Sample rate
audio : array
Input samples
f : one-element array
Frequency to test, in an array
bw : float
Bandwidth. Default is minimum bandwidth
damping : float
Damping factor. Default is 0.0001
"""
method = DetectorBank.runge_kutta
f_norm = DetectorBank.freq_unnormalized
a_norm = DetectorBank.amp_unnormalized
gain = 1
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
z = np.zeros((len(f),len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det = DetectorBank(sr, audio, 4, det_char, method|f_norm|a_norm, damping,
gain)
det.getZ(z)
det.absZ(r, z)
return r[0]
def plot_complex(f0, a_norm, do_plots):
sr = 48000
dur = 3
t = np.linspace(0, 2 * np.pi * f0 * dur, sr * dur)
audio = np.sin(t)
audio = np.append(audio, np.zeros(sr))
method = DetectorBank.runge_kutta
f_norm = DetectorBank.freq_unnormalized
d = 0.0001
gain = 5
f = np.array([f0])
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d, gain)
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
det.getZ(z)
r = np.zeros(z.shape)
det.absZ(r, z)
# number of oscillations to plot
nOsc = 5
# signal may have been modulated by DetectorBank
# detFreq is the frequency the detector is actually operating at, therefore
# the frequency of the orbits
detFreq = det.getW(0) / (2 * np.pi)
# number of samples in nOsc
sOsc = int(nOsc / detFreq * sr)
t0 = (dur * sr) - sOsc # int(0.0 * sr)
t1 = dur * sr # int(nOsc/f[0] * sr)
x = z[0].real[t0:t1]
y = z[0].imag[t0:t1]
c = ['darkmagenta']
a, b = getEllipseParams(x, y)
e = np.sqrt(a**2 - b**2) / a
if do_plots:
plt.plot(x, y, c[0])
plt.grid()
plt.title('{}Hz'.format(f0))
plt.xlabel('real')
plt.ylabel('imag')
plt.show()
plt.close()
return e
def _get_maxima(self, progress):
# make DetectorBank, get responses and return max value in response
size = len(self.f)
maxima = np.zeros(size)
# don't need all responses at once - just get max for each frequency
# run in blocks of numPerRun channels to try and speed it up a bit
numPerRun = 100
numBlocks = int(np.ceil(size / numPerRun))
count = 0
i0, i1 = 0, 0
more = True
while more:
i0 = i1
i1 += numPerRun
if i1 >= size:
i1 = size
more = False
if progress:
print('Running block {} of {}'.format(count, numBlocks),
end='\r')
# print('{:.0f}%'.format(100*(i0/size)), end='\r')
freq = self.f[i0:i1]
bandwidth = np.zeros(len(freq))
det_char = np.column_stack((freq, bandwidth))
det = DetectorBank(self.sr, self.audio.astype(np.float32), 4,
det_char, *self.dbp)
# get output
z = np.zeros((len(freq), len(self.audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z)
det.absZ(r, z)
for n in range(len(freq)):
if np.isinf(np.max(r[n])):
file = 'debug/{:.0f}Hz.csv'.format(freq[n])
np.savetxt(file, r[n], delimiter='\t')
maxima[i0 + n] = np.max(r[n])
count += 1
# maxima[n] = m
return maxima
def absZ_test(sr):
"""Feed a signal at given sample rate
to the detectorbank and take the absolute value
of the response.
Return the result, and max error comparing
the abzZ method results and numpy.abs() results."""
f = np.array([200])
t = np.linspace(0, f[0]*2*np.pi, sr)
audio = np.sin(t)
#audio = np.append(audio, np.zeros(sr))
z = np.zeros((len(f),len(audio)), dtype=np.complex128)
method = DetectorBank.runge_kutta
f_norm = DetectorBank.freq_unnormalized
a_norm = DetectorBank.amp_normalized
d = 0.0001
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
gain = 25
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method|f_norm|a_norm, d, gain)
det.getZ(z)
r = np.zeros(z.shape)
_ = det.absZ(r, z)
return r, np.amax(np.abs(r-np.abs(z)))
def _get_abs_z(self, freq, bandwidth, dbp):
# make a DetectorBank with the given parameters and return |z|
size = len(freq)
bw = np.zeros(size)
bw.fill(bandwidth)
det_char = np.array(list(zip(freq, bw)))
det = DetectorBank(self.sr, self.audio.astype(np.float32), 0, det_char,
*dbp)
# get output
z = np.zeros((size, len(self.audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z, size)
det.absZ(r, z)
return r
def get_responses(sr, audio, f):
method = DetectorBank.runge_kutta
f_norm = DetectorBank.freq_unnormalized
a_norm = DetectorBank.amp_normalized
gain = 25
d = 0.0001
bandwidth = np.zeros(len(f))
#bandwidth.fill(5) # uncomment for degerate detectors
det_char = np.array(list(zip(f, bandwidth)))
z = np.zeros((len(f),len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det = DetectorBank(sr, audio, 4, det_char, method|f_norm|a_norm, d, gain)
det.getZ(z)
det.absZ(r, z)
return r
def getResponses(sr, mode, method, f_norm):
""" Get DetectorBank responses
Parameters
----------
sr : int
sample rate
mode : {'a', 'low', 'high'}
frequency range
method : DetectorBank Feature
DetectorBank numerical method
f_norm : DetectorBank Feature
DetectorBank frequency normalisaion
Returns
-------
2D array of |z| values
"""
f = getFrequencies(mode)
audio = make_audio(f, np.ones(len(f)), sr)
d = 0.0001
gain = 5
a_norm = DetectorBank.amp_unnormalized
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d, gain)
det.getZ(z)
det.absZ(r, z)
return r
def _get_abs_z(self, freq, flc):
# make a DetectorBank with the given parameters and return |z|
size = len(freq)
bw = np.zeros(size)
bw.fill(flc)
det_char = np.array(list(zip(freq, bw)))
# NB have changed DetectorBank code to regard det_char as freq,fLc
# pairs instead of freq,bw pairs
# all that is required to do this is change AbstractDetector::getLyapunov
# to simply return the value it is passed
# i.e. we pretend that 'bandwidth' values are Lyapunov coeff
det = DetectorBank(self.sr, self.audio.astype(np.float32), 0, det_char,
*self.dbp)
# get output
z = np.zeros((size, len(self.audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z, size)
det.absZ(r, z)
return r
def getMax(f0, sr, method, f_norm):
""" Get maximum abs value in response. Also returns internal detector
frequency (which will be different from f0 if search normalisation is
used)
Parameters
----------
fo : float
Centre frequency
sr : int
Sample rate
method : DetectorBank Feature
Numerical method
f_norm : DetectorBank Feature
Frequency normalisation
Returns
-------
z value at point which is max value in |z|, max value in |z|,
frequency used by detector
"""
f = np.array([f0])
b = np.zeros(len(f))
det_char = np.array(list(zip(f, b)))
d = 0.0001
amp = 5
a_norm = DetectorBank.amp_unnormalized
audio = make_audio(f0, 1, 4, sr)
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d, amp)
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z)
m = det.absZ(r, z)
i = np.where(r[0] == m)[0][0]
mz = z[0][i]
f_adjusted = det.getW(0) / (2 * np.pi)
return mz, m, f_adjusted
def _get_max(self, f, dbp):
# make DetectorBank, get responses and return max value in response
f = np.array([f])
size = len(f)
bandwidth = np.zeros(size)
det_char = np.array(list(zip(f, bandwidth)))
det = DetectorBank(self.sr, self.audio.astype(np.float32), 0, det_char,
*dbp)
# get output
z = np.zeros((size, len(self.audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z, size)
m = det.absZ(r, z)
return m
def getLyapunov(fd, f0, sr, plot=True, b=None):
freq = np.array([f0 - fd, f0, f0 + fd])
if b is None:
b = get_b(2 * fd)
print('Frequency difference: {}Hz; Bandwidth: {}Hz'.format(fd, 2 * fd))
print('First Lyapunov coefficient: {:.3f}'.format(b))
method = DetectorBank.runge_kutta
f_norm = DetectorBank.freq_unnormalized
a_norm = DetectorBank.amp_unnormalized
gain = 1
damping = 0.0001
dbp = (method | f_norm | a_norm, damping, gain)
audio = make_input(f0, sr, 3)
audio *= 25
size = len(freq)
bw = np.zeros(size)
bw.fill(b)
det_char = np.array(list(zip(freq, bw)))
# NB have changed DetectorBank code to regard det_char as freq,fLc
# pairs instead of freq,bw pairs
# all that is required to do this is change AbstractDetector::getLyapunov
# to simply return the value it is passed
# i.e. we pretend that 'bandwidth' values are Lyapunov coeff
det = DetectorBank(sr, audio.astype(np.float32), 0, det_char, *dbp)
# get output
z = np.zeros((size, len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z, size)
det.absZ(r, z)
t = np.linspace(0, r.shape[1] / sr, r.shape[1])
c = ['blue', 'darkmagenta', 'red']
mx_centre = np.max(r[np.where(freq == f0)[0][0]])
maxima = np.array([np.max(k) for k in r])
ratio = maxima / mx_centre
ratio_db = 20 * np.log10(ratio)
for k in range(r.shape[0]):
print('{:.0f}Hz; max = {:.4f}; ratio = {:.4f}; {:.4f}dB'.format(
freq[k], maxima[k], ratio[k], ratio_db[k]))
if plot:
line, = plt.plot(t, r[k], c[k])
mean = (ratio_db[0] + ratio_db[2]) / 2
diff = abs(-3 - mean)
print('Mean ampltiude: {:.4f}dB'.format(mean))
print('Difference from -3dB: {:.4f}dB'.format(diff))
if plot:
plt.show()
plt.close()
def get_responses(file,
damping,
sp,
tp=None,
fp=None,
fn=None,
sl=None,
t0=None,
t1=None,
spa=None):
sns.set_style('whitegrid')
head, tail = os.path.split(file)
base, ext = os.path.splitext(tail)
# `base` is file name with data separated by dots
# go backwards through each item and see if it is a note
baselst = base.split('.')
i = -1
while True:
# if we've gone as far back as we can without finding a note, exit
if i <= -len(baselst):
msg = 'Cannot parse note in filename {}'.format(tail)
raise ValueError(msg)
# check if the file is a (xylophone) glissandi
if baselst[i].lower() == 'gliss':
# assuming same xylophone used for all recordings
if baselst[i + 1].lower() == 'up':
k = get_note_num('F4')
break
elif baselst[i + 1].lower() == 'down':
k = get_note_num('C8')
break
# note_n = list(range(k0, k1+1))
# try this item
try:
# get note number relative to A4 (for frequency calculation)
k = get_note_num(baselst[i])
break
# if this item isn't a note, go backwards and try again
except ValueError:
i -= 1
edo = 12
method = DetectorBank.runge_kutta
f_norm = DetectorBank.freq_unnormalized
a_norm = DetectorBank.amp_unnormalized
gain = 50
bw = {1e-4: 0.922, 2e-4: 1.832, 3e-4: 2.752, 4e-4: 3.606, 5e-4: 4.86}
f0 = 440 * 2**(k / edo)
f = get_f(f0, b=bw[damping], edo=edo)
print('Band size: {}'.format(len(f)))
audio, sr = sf.read(file)
if spa is not None:
plot_audio(sr, audio, spa, t0, t1, tp=tp, fp=fp)
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, damping, gain)
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z)
det.absZ(r, z)
c = [
'black', 'blue', 'chocolate', 'cyan', 'darkmagenta', 'khaki',
'deeppink', 'aquamarine', 'darkorange', 'firebrick', 'green',
'lightslategrey', 'dodgerblue', 'magenta', 'mediumvioletred', 'orange',
'pink', 'red', 'skyblue', 'lightgrey', 'yellow'
]
centre_idx = c.index('green')
centre_f = np.where(f == f0)[0][0]
color_offset = centre_idx - centre_f
print('Centre freq at index: {}'.format(centre_f))
print('Colour offset: {}'.format(color_offset))
t = np.linspace(0, r.shape[1] / sr, r.shape[1])
if t0 is None:
t0 = 0
else:
t0 = int(sr * t0)
if t1 is None:
t1 = len(audio)
else:
t1 = int(sr * t1)
for k in range(r.shape[0]):
plt.plot(t[t0:t1],
r[k][t0:t1],
color=c[(k + color_offset) % len(c)],
label='{:.3f}Hz'.format(f[k]))
if tp is not None:
for onset in tp:
plt.axvline(onset, color='lime')
if fp is not None:
for onset in fp:
plt.axvline(onset, color='mediumorchid', linestyle='--')
if fn is not None:
for onset in fn:
plt.axvline(onset, color='indigo', linestyle='--')
plt.xlabel('Time (s)')
plt.ylabel('|z|', rotation='horizontal', labelpad=10)
# ax = plt.gca()
# ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0))
if sl is not None:
colours = [c[k + color_offset] for k in range(r.shape[0])]
labels = ['{:.3f} Hz'.format(f[k]) for k in range(r.shape[0])]
sl.plot(labels=labels,
colours=colours,
ncol=1,
title='Detector frequencies')
# else:
# plt.legend()
plt.grid(True)
sp.plot(plt)
audio = mix(f, amplitudes)
r = np.zeros((len(d), len(audio)))
for n in range(len(d)):
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d[n], gain)
det.getZ(z)
r0 = np.zeros(z.shape)
m = det.absZ(r0, z)
r[n] = r0
for k in range(len(r) - 1):
rxamp = max(r[k]) / np.e
mxtm = np.where(r[k] == max(r[k]))[0][0]
rxtm = np.where(r[k][mxtm:] <= rxamp)[0][0]
print('Damping: {:.0e}; Relaxation Time: {:.4f}'.format(d[k], rxtm / sr))
c = ['blue', 'darkmagenta', 'red', 'green']
style = ['-', '-', '-', '-']
majorLocator = MultipleLocator(1)
minorLocator = MultipleLocator(0.25)
f_norm = DetectorBank.freq_unnormalized
a_norm = DetectorBank.amp_unnormalized
d = 0.0001
gain = 25
f = np.array([f0])
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
# get response
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d, gain)
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.getZ(z)
det.absZ(r, z)
# put in list
responses.append(r[0])
# plot 5Hz response
if f0 == 5:
t = np.linspace(0, len(audio) / sr, len(audio))
plt.plot(t, r[0], color='darkmagenta')
plt.ylabel('|z|', rotation='horizontal')
plt.xlabel('Time (s)')
plt.grid(True)
ax = plt.gca()
ax.yaxis.labelpad = 10
def plot_complex(f0, i, nOsc, end):
d = 0.0001
sr = 48000
dur = 5
t = np.linspace(0, 2 * np.pi * f0 * dur, sr * dur)
audio = np.sin(t)
audio = np.append(audio, np.zeros(sr))
gain = 5
method = DetectorBank.runge_kutta #central_difference #
f_norm = DetectorBank.freq_unnormalized
a_norm = DetectorBank.amp_unnormalized
d = 0.0001
gain = 5
f = np.array([f0])
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d, gain)
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
det.getZ(z)
r = np.zeros(z.shape)
det.absZ(r, z)
# number of oscillations to plot
# signal may have been modulated by DetectorBank
# detFreq is the frequency the detector is actually operating at, therefore
# the frequency of the orbits
detFreq = det.getW(0) / (2 * np.pi)
# number of samples in nOsc
sOsc = int(nOsc / detFreq * sr)
if end == 'first':
t0 = 0
t1 = sOsc
elif end == 'last':
t0 = (dur * sr) - sOsc
t1 = dur * sr
x = z[0].real[t0:t1]
y = z[0].imag[t0:t1]
c = ['darkmagenta']
a, b = getEllipseParams(x, y)
e = np.sqrt(a**2 - b**2) / a
plt.plot(x, y, c[0])
plt.grid(True)
plt.xlabel('real')
plt.ylabel('imag')
fig = plt.gcf()
fig.set_size_inches(8, 8)
plt.show()
plt.close()
return e
def plotMean(audio, sr, freq, onset, found, sp):
sns.set_style('whitegrid')
if audio.ndim > 1:
audio = np.mean(audio, axis=1)
offset = 1 / 4
pad = np.zeros(int(sr * offset))
# pad.fill(audio[0])
audio = np.append(pad, audio)
params = getParams()
method = params['method']
f_norm = params['f_norm']
a_norm = params['a_norm']
d = params['damping']
gain = params['gain']
bw = params['real_bandwidth']
edo = params['edo']
f = makeBand(freq, bw, edo)
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d, gain)
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
det.getZ(z)
r = np.zeros(z.shape)
det.absZ(r, z)
meanlog = np.zeros(len(audio))
for n in range(len(meanlog)):
mean = 0
for k in range(det.getChans()):
mean += zeroLog(r[k, n])
meanlog[n] = mean
t = np.linspace(0, len(audio) / sr, len(audio))
t0 = 0.25
t1 = 0.5
i0 = int(sr * t0)
i1 = int(sr * t1)
plt.figure()
plt.plot(t[i0:i1], meanlog[i0:i1])
plt.axvline(onset + offset, color='lime') # linestyle='--', )
for t in found:
plt.axvline(t + offset, linestyle='--', color='red')
ax = plt.gca()
xtx = ax.get_xticks()
xtxlab = ['{:.0f}'.format(1000 * (item - offset)) for item in xtx]
ax.set_xticklabels(xtxlab)
# plt.title('{:.3f}Hz'.format(freq))
plt.xlabel('Time (ms)')
# plt.ylabel('Log(|z|)')
plt.ylabel('Mean log')
plt.grid(True)
sp.plot(plt)
damping = 0.0001
# minimum bandwidth detectors
bandwidth = np.zeros(len(f))
#bandwidth[1] = 5
#bandwidth[2] = 7
#bandwidth.fill(5)
det_char = np.array(list(zip(f, bandwidth)))
gain = 5
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method|f_norm|a_norm, damping, gain)
z = np.zeros((len(f),len(audio)), dtype=np.complex128)
det.getZ(z)
r = np.zeros(z.shape)
m = det.absZ(r, z)
c = ['darkmagenta', 'red', 'blue', 'green']
t = np.linspace(0, r.shape[1]/sr, r.shape[1])
for k in range(r.shape[0]):
line, = plt.plot(t, r[k], 'darkmagenta')
ax = plt.gca()
plt.xlabel('Time (s)')
plt.ylabel('|z|', rotation='horizontal')
#ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0))
ax.yaxis.labelpad = 10
ax.grid(True)
plt.show()
plt.close()
def get_rise_relax_times(d,
f0,
sr,
method,
f_norm,
a_norm,
gain,
plot_times,
sp=None):
""" Get rise and relax times for a DetectorBank at five damping factors
Parameters
----------
d : list
List of damping factors to test
f0 : float
Centre frequency
sr : int
Sample rate
method : { DetectorBank.runge_kutta, DetectorBank.central_difference }
DetectorBank method
f_norm : { DetectorBank.freq_unnormalized, DetectorBank.search_normalized}
DetectorBank frequency normalisation
a_norm : { DetectorBank.amp_unnormalized, DetectorBank.amp_normalized}
DetectorBank amplitude normalisation
gain : float
Input gain
plot_times : bool
Whether or not to plot the responses
sp : list of SavePlot object
If plot=True, please also provide a SavePlot object for rise and
relax
Returns
-------
Two lists of tuples: rise times and relax times.
In each case, the tuples are the boundary times (10% time and 90% time
or max time and 1/e time). The rise and relaxation times are the
difference between these values.
"""
# make input
# 'dur' is tone duration
# 2 seconds of silence will automatically be appended
dur = 3
audio = make_audio([f0], sr, dur)
# make frequency/bandwidth pairs
f = np.array([f0])
b = np.zeros(len(f))
det_char = np.array(list(zip(f, b)))
# relaxation times
rxtms = []
# rise times
rstms = []
# rise time is time from 10% to 90% of final value
r_min, r_max = 0.1, 0.9
# if we're plotting, we'll have to store all the responses
if plot_times:
all_r = np.zeros((len(d), len(audio)))
# also check there is a SavePlot object
if sp is None:
raise ValueError('Please provide a SavePlot object is you wish to '
'plot the responses')
# get response for each damping factor
for n in range(len(d)):
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d[n], gain)
det.getZ(z)
mx = det.absZ(r, z)
if plot_times:
all_r[n] = r
# r is a 2D array. As there's only one channel, r[0] contains the output
# rise time
rise0 = np.where(r[0] >= r_min * mx)[0][0]
rise1 = np.where(r[0] >= r_max * mx)[0][0]
rstms.append((rise0, rise1))
# relaxation time is time for amplitude to fall to 1/e of max
rxamp = max(r[0]) / np.e
# max is end of tone
# NB can't say np.where(r[k]==mx), as this may return values before the
# end of the tone
mxtm = sr * dur
# samples from max time to 1/e
rxtm = np.where(r[0][mxtm:] <= rxamp)[0][0]
rxtms.append((mxtm, mxtm + rxtm))
if plot_times:
plot(all_r, rstms, sr, len(audio), sp[0], t0=0, t1=1.1)
plot(all_r, rxtms, sr, len(audio), sp[1], t0=2.95, t1=3.65)
return rstms, rxtms