def sweep(duration, dt, f, method='linear', phi=0,
vertex_zero=True, autocorrelate=True, return_t=False):
"""
Generates a linear frequency modulated wavelet (sweep)
Does a wrapping of scipy.signal.chirp
:param duration: The length in seconds of the wavelet.
:param dt: is the sample interval in seconds (usually 0.001, 0.002, 0.004)
:param f: Tuple of (f1, f2), or a similar list. A list of lists
will create a wavelet bank.
:keyword method: {'linear','quadratic','logarithmic'}, optional
:keyword phi: float, phase offset in degrees
:keyword vertex_zero: bool, optional
This parameter is only used when method is 'quadratic'.
It determines whether the vertex of the parabola that
is the graph of the frequency is at t=0 or t=t1.
:returns: An LFM waveform.
"""
t0 = -duration/2.
t1 = duration/2.
t = np.arange(t0, t1, dt)
freq = np.array(f)
if freq.size == 2:
A = chirp(t, freq[0], t1, freq[1],
method, phi, vertex_zero)
if autocorrelate:
A = np.correlate(A, A, mode='same')
output = A / np.amax(A)
else:
output = np.zeros((t.size, freq.shape[1]))
for i in range(freq.shape[1]):
A = chirp(t, freq[0, i], t1, freq[1, i],
method, phi, vertex_zero)
if autocorrelate:
A = np.correlate(A, A, mode='same')
output[:, i] = A / np.max(A)
if return_t:
Sweep = namedtuple('Sweep', ['amplitude', 'time'])
return Sweep(output, t)
else:
return output
def tb_stimulus():
# pulse the reset
yield reset.pulse(100)
for ii in xrange(2):
yield clock.posedge
# chirp 1 (time response pictoral)
print(" chirp 1 ...")
samp_in = signal.chirp(np.arange(args.Nsamps/2)*1/args.Fs,
8, .64, 480,
method=u'logarithmic')*.94
samp_in = np.concatenate(
(samp_in,
np.array([ss for ss in reversed(samp_in[:-1])] )))
samp_out = []
fsamp_out=[]
# input samples, save the output
for ii in xrange(args.Nsamps-1):
sig_in.next = int(np.floor(samp_in[ii]*(sMax)))
yield clock.posedge
samp_out.append(sig_out//float(sMax))
fsamp_out.append(int(np.floor(samp_in[ii]*(sMax))))
samp_out = np.array(samp_out)
#fsamp_out = np.array(fsamp_out)
#fsamp_out.save('fsamp_out.dat')
fh=open('fsamp_out.dat','w')
cPickle.dump(fsamp_out,fh)
c = signal.lfilter(coef, 1, samp_in)
sdiff = np.abs(c[:-2] - samp_out[2:])
plt.figure(3); plt.plot(sdiff)
#print(np.max(sdiff), np.mean(sdiff**2))
#assert np.max(sdiff) < 1e-3, "error too large"
assert np.max(sdiff) > 1e-3, "error too large"
ia = np.concatenate((np.ones(args.Nflt/2)*.98, samp_in))
fig,ax = plt.subplots(1)
ax.plot(ia, 'b'); ax.plot(samp_out[1:], 'r'); ax.plot(c, 'y--')
fig.savefig('__plot2.png')
# chirp 2 (frequency response, more points)
print(" chrip 2 ...")
Nfft = 8*args.Nsamps
samp_in = signal.chirp(np.arange(Nfft)*1/args.Fs,
0.1, 1, 500)*.98
samp_out = []
for ii in xrange(Nfft):
sig_in.next = int(np.floor(samp_in[ii]*(sMax)))
yield clock.posedge
samp_out.append(sig_out//float(sMax))
samp_out = np.array(samp_out)
Pi,fi = mlab.psd(samp_in)
Po,fo = mlab.psd(samp_out)
ax1.plot(pi*fi, 10*log10(abs(Po/Pi)), 'r')
ax1.grid(True)
fig1.savefig('__plot1.png')
raise StopSimulation
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
pad = np.zeros(10e-3 * fs)
sound = np.concatenate((s, pad))
### Run model
anf = cochlea.run_zilany2014(
sound, fs, anf_num=(100, 0, 0), cf=(125, 20000, 100), seed=0, powerlaw="approximate", species="human"
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=["cf", "duration"])
anf_acc.sort("cf", ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1)
th.plot_signal(signal=sound, fs=fs, ax=ax[0])
th.plot_neurogram(anf_acc, fs, ax=ax[1])
plt.show()
def generate_chirp_spectrogram_plot():
fs = 10e3
N = 1e5
amp = 2 * np.sqrt(2)
noise_power = 0.001 * fs / 2
time = np.arange(N) / fs
freq = np.linspace(1e3, 2e3, N)
x = amp * chirp(time, 1e3, 2.0, 6e3, method='quadratic') + \
np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
f, t, Sxx = spectrogram(x, fs)
ax = plt.subplot(211)
ax.pcolormesh(t, f, Sxx)
ax.set_ylabel('Frequency [Hz]')
ax.set_xlabel('Time [sec]')
f, t, Sxx = spectrogram_lspopt(x, fs, c_parameter=20.0)
ax = plt.subplot(212)
ax.pcolormesh(t, f, Sxx)
ax.set_ylabel('Frequency [Hz]')
ax.set_xlabel('Time [sec]')
plt.show()
def sweep_cosine(n, fs, f0, f1, A=1., kwargs=None):
"""
Build a generator that yields a sweep sine between f0 and f1.
Parameters
----------
n : int
Number of samples generated.
fs : int
Sample rate in Hz.
f0 : float
Initial frequency in Hz.
f1 : float
Final frequency in Hz such that f1>f0
A : float, optional
Amplitude (the default is 1.0).
phi : float, optional
original phase in degrees, default is -90 (sin chirp).
Yields
------
s : float
Sequence of sweep values.
"""
if kwargs is None:
kwargs = {}
if 'phi' not in kwargs.keys():
kwargs.update({'phi': -90})
from numpy import linspace
from scipy.signal import chirp
T = float(n-1)/float(fs)
for t in linspace(0, T, n):
yield A*chirp(t, f0=f0, f1=f1, t1=T, **kwargs)
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
rates = cochlea.run_zilany2014_rate(
s,
fs,
anf_types=['msr'],
cf=(125, 20000, 100),
powerlaw='approximate',
species='human'
)
### Plot rates
fig, ax = plt.subplots()
img = ax.imshow(
rates.T,
aspect='auto'
)
plt.colorbar(img)
plt.show()
def sweepChirp(dur,f0,chirpLength,f1):
'''Generate a sweeping chirp from f0 to f1'''
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.chirp.html
t = range(1,dur+1)
t1 = chirpLength
sweep = chirp(t, f0, t1, f1, method='linear', phi=0, vertex_zero=True)
return sweep
def setUp(self):
samplerate = 16000 # LimsiSad require Fs = 16000 Hz
duration = 10
t = np.arange(0, duration, 1/samplerate)
samples = chirp(t=t, f0=20, t1=t[-1], f1=samplerate/2,
method='logarithmic')
decoder_cls = timeside.core.get_processor('array_decoder')
self.decoder = decoder_cls(samples, samplerate=samplerate)
def createChirp(rate,chirp_len,chunk,begin_freq,end_freq):
time_base=np.arange(0,chirp_len,1.0/rate)
chirp_data_Hex=10*'\x00\x00' # add some zeros before the signal, this must be counted for the time of flight
for t in np.nditer(time_base):
chirp_data_Hex=chirp_data_Hex+struct.pack('h',int(ss.chirp(t,begin_freq,chirp_len,end_freq)*(32767))) #32767 is the max value for format paInt16
chirp_data_Int=np.array(struct.unpack("%dh" % (len(chirp_data_Hex)/2), chirp_data_Hex))
norm_chirp=math.sqrt(((chirp_data_Int/10000.0)*(chirp_data_Int/10000.0)).sum())*10000
return chirp_data_Hex,chirp_data_Int,norm_chirp
def write_bin(self,rate,time):
t=arange(0,time,1.0/self._framerate)
wave_data=signal.chirp(t,rate,time,rate,method='linear')*self._volumn
wave_data=wave_data.astype(short)
if self.f:
self.f.writeframes(wave_data.tostring())
else:
self.data+=wave_data.tostring()
self._tall+=time
def create(self):
t = np.linspace(
0,
self.duration,
self.sample_rate * self.duration - 1
)
sweep = chirp(t, 20, self.duration, 20000, self.method) * 32767
return sweep.astype(np.int16)
def _render(self): times = self.t_axis() t1 = times[-1] return chirp( t=self.t_axis(), f0=self.f0, f1=self.f1, t1=t1, method=self.method )
def CreateSweep(framerate = 44100, duration = 3, freq_start = 20, freq_stop = 18000):
# 打开WAV文档
f = wave.open(r"sweep.wav", "wb")
# 配置声道数、量化位数和取样频率
f.setnchannels(1)
f.setsampwidth(2)
f.setframerate(framerate)
# 产生升序频率扫描波
t = np.arange(0, duration/2, 1.0/framerate)
wave_data = signal.chirp(t, freq_start, duration/2, freq_stop, method='linear') * 10000
wave_data = wave_data.astype(np.short)
# 将wav_data转换为二进制数据写入文件
f.writeframes(wave_data.tostring())
# 产生降序频率扫描波
wave_data = signal.chirp(t, freq_stop, duration/2, freq_start, method='linear') * 10000
wave_data = wave_data.astype(np.short)
# 将wav_data转换为二进制数据写入文件
f.writeframes(wave_data.tostring())
f.close()
def main(N=1e7):
N = int(N)
# Generate a chirp that sweeps all frequencies
c = ss.chirp(np.arange(N),0,N-1,0.49)
# Compute fft for plot
C = np.fft.fft(c, N*10)
# This is the groundtruth IF
f = 0.5*np.arange(N)/(N-1)
# Compute AM-FM DCA
ia,ip,ifeq = amfm_DCA(c)
print "%.2e"%np.linalg.norm(np.abs(ifeq-f)/N)
def GenerateData(freq_low, freq_high, length, sampling_freq):
'''
Simple chirp synthesis
'''
end_time_s = float(length) / sampling_freq
time = numpy.arange(0,
end_time_s,
1.0 / sampling_freq)
return signal.chirp(t = time,
f0 = freq_low,
t1 = end_time_s,
f1 = freq_high)
def test_strf_fit():
# stimulus features
lo_freq = 200 # Hz
hi_freq = 12000 # Hz
fs = hi_freq*2 # Hz
duration = 100 # seconds
tr_length = 1.0 # seconds
tr_sampling_rate = 1 # number of time-samples per TR to plot the STRF in
time_window = 0.5 # seconds
freq_window = 256 # this is 2x the number of freq bins we'll end up with in the spectrogram
scale_factor = 1.0 # how much to downsample the spectrotemporal space
num_timepoints = np.floor(duration / tr_length)
degrees = 0.0
# sample the time from 0-duration by the fs
time = np.linspace(0,duration,duration*fs)
# create a chirp stimulus
signal = chirp(time, lo_freq, duration, hi_freq)
# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, tr_length, freq_window, time_window, sampling_rate=fs,
tr_sampling_rate=tr_sampling_rate, scale_factor=scale_factor)
# set some parameters for the mock STRF
freq_center = 5678 # center frequency
freq_sigma = 234 # frequency dispersion
hrf_delay = 0.987 # seconds
# initialize the strf model
model = strf.SpectrotemporalModel(stimulus)
# generate the modeled BOLD response
data = strf.compute_model_ts(freq_center, freq_sigma, hrf_delay,
model.stimulus.time_coord,
model.stimulus.freq_coord,
model.stimulus.spectrogram.astype('double'),
tr_length, num_timepoints, norm_func=utils.zscore)
# set some searh parameters
search_bounds = ((lo_freq, hi_freq),(lo_freq, hi_freq/2),(-5,5),)
fit_bounds = ((lo_freq, hi_freq),(lo_freq, hi_freq/2),(-5,5),)
# fit the response
fit = strf.SpectrotemporalFit(data, model, search_bounds, fit_bounds, tr_length)
# assert
npt.assert_almost_equal(fit.estimate,[freq_center,freq_sigma,hrf_delay])
def signal(self, fs, atten, caldb, calv):
amp = self.amplitude(caldb, calv)
npts = self._duration*fs
t = np.arange(npts).astype(float)/fs
signal = chirp(t, f0=self._start_f, f1=self._stop_f, t1=self._duration)
amp_scale = signal_amplitude(signal, fs)
signal = ((signal/amp_scale)*amp)
if self._risefall > 0:
rf_npts = int(self._risefall * fs) / 2
wnd = hann(rf_npts*2) # cosine taper
signal[:rf_npts] = signal[:rf_npts] * wnd[:rf_npts]
signal[-rf_npts:] = signal[-rf_npts:] * wnd[rf_npts:]
return signal
def set_gram(self):
'''
Generate some raw data for testing.
Data is a chirp sweeping from 100Hz to 10kHz logarithmically over 10
seconds at a 44.1kHz sampling rate.
'''
fs = 44100
t_begin = 0
t_end = 100
t = np.linspace(t_begin, t_end, (t_end - t_begin)*fs)
signal = chirp(t, f0=100, t1=100, f1=10000, method='logarithmic')
self.gram = RawLTSA(signal, fs)
self.gram()
return self.gram
def generate_ess(fs, f0, f1, t_sw):
#compute Sweep time in samples
n_samp = int(fs * t_sw)
#generate time vector
t_vec = numpy.linspace(0, t_sw, n_samp)
#generate sine sweep
m_sig = chirp(t_vec, f0, t_sw , f1, method='logarithmic')
m_sig_list = m_sig.tolist()
return m_sig_list
def test_spectrogram_method():
"""Test the spectrogram method's functionality."""
fs = 10e3
N = 1e5
amp = 2 * np.sqrt(2)
noise_power = 0.001 * fs / 2
time = np.arange(N) / fs
freq = np.linspace(1e3, 2e3, N)
x = amp * chirp(time, 1e3, 2.0, 6e3, method='quadratic') + \
np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
f, t, Sxx = spectrogram_lspopt(x, fs, c_parameter=20.0)
f_sp, t_sp, Sxx_sp = spectrogram(x, fs)
assert True
def wave(duration, dt, f):
"""
Generates a monofrequency waveform
:param duration: The length in seconds of the wave.
:param dt: is the sample interval in seconds (usually 0.001, 0.002, 0.004)
:param f: frequency
:returns: A waveform.
"""
t = np.arange(0, duration , dt)
A = chirp(t, f0=f, f1=f, t1=duration, method="linear")
output = A / np.amax(A)
return output
def test_attenuation_curve():
fs = 5e5
duration = 0.2
npts = duration*fs
t = np.arange(npts).astype(float)/fs
# add linear drop to get log atten curve
desired_signal = signal.chirp(t, f0=5000, f1=1e5, t1=duration)
received_signal = desired_signal*np.linspace(10, 1, npts)
atten = tools.attenuation_curve(desired_signal, received_signal, fs, 5000, smooth_pts=99)
atten_range = np.amax(atten[100:20000]) - np.amin(atten[100:20000])
assert np.around(atten_range) == 20
assert np.around(atten[20000]) == 20
assert atten[1000] == 0
def _create_chirp(args, imax=8):
"""generate a chirp signal, DUT input
"""
tarray = np.arange(args.Nsamps/2)*(1./args.Fs)
# chirp(tarray, time zero freq, time end freq, end freq)
xin = signal.chirp(tarray, 2, tarray[-1], 230,
method=u'logarithmic') * .94
# chirp down and up
xin = np.concatenate(
(xin,
np.array([-1*ss for ss in reversed(xin[:-1])]),
-1*xin[:30],
))
xin = map(int, [round(xx*imax) for xx in xin])
return xin
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
anf = cochlea.run_zilany2009(
s,
fs,
anf_num=(100,0,0),
cf=(80, 20000, 100),
seed=0,
powerlaw='approximate'
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
cfs = anf.cf.unique()
fig, ax = plt.subplots(2,1, sharex=True)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[0]
)
th.plot_raster(
anf[anf.cf==cfs[30]],
ax=ax[1]
)
ax[1].set_title("CF = {}".format(cfs[30]))
plt.show()
def sweep(duration, dt, f, method = 'linear'):
"""
Generates a sweep
Does a wrapping of scipy.signal.chirp
:param duration: The length in seconds of the wavelet.
:param dt: is the sample interval in seconds (usually 0.001, 0.002, 0.004)
:param f: Tuple of (f1, f2) with starting and end frequencies
:keyword method: {'linear','quadratic','logarithmic'}, optional
:returns: An LFM waveform.
"""
t = np.arange(0, duration , dt)
freq = np.array( f )
A = chirp(t, f0=freq[0], f1=freq[1], t1=duration, method=method)
output = A / np.amax(A)
return output
def main():
fs = 48e3
### Make sound
tmax = 0.03
t = np.arange(0, tmax, 1/fs)
s = dsp.chirp(t, 80, t[-1], 16000)
sound = cochlea.set_dbspl(s, 50)
### Run model
anf = run_matlab_auditory_periphery(
sound,
fs,
anf_num=(100,50,20),
cf=(125, 16000, 80),
seed=0,
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1, sharex=True)
th.plot_signal(
signal=sound,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
def test_sweep(self):
amp = db_to_ratio(-6)
te = 8
t = numpy.linspace(0, 8, self.from_rate*8)
x = signal.chirp(t, f0=0, f1=self.from_rate, t1=te, method='quadratic') * amp
print("done chirp")
y = self.resample(x)
print("done filtering")
nfft = 64
win = scipy.hamming(nfft)
plot.subplot(211)
plot.specgram(x, NFFT=nfft, Fs=self.from_rate, noverlap=nfft/2, window=win)
plot.subplot(212)
plot.specgram(y, NFFT=nfft, Fs=self.to_rate, noverlap=nfft/2, window=win)
plot.show()
def xtest_calibrate_signal():
fs = 5e5
duration = 0.2
npts = duration*fs
t = np.arange(npts).astype(float)/fs
frange = [5000, 100000]
# add linear drop to get log atten curve
desired_signal = signal.chirp(t, f0=5000, f1=1e5, t1=duration)
received_signal = desired_signal*np.linspace(1, 0.1, npts)
calibrated_signal = tools.calibrate_signal(desired_signal, received_signal, fs, frange)
plt.plot(desired_signal)
plt.figure()
plt.plot(calibrated_signal)
plt.show()
assert calibrated_signal.shape == desired_signal.shape
print 'signal max', np.amax(calibrated_signal)
assert np.amax(calibrated_signal) == 10
def demo(args):
"""
Demo of AM-FM.
Parameters
----------
args: argparse args
"""
logger.info("Running a demo of AM-FM")
# number of time points
N = args.N
logger.info("Generating a chirp that sweeps all frequencies")
c = ss.chirp(range(N), 0 , N - 1, 0.49)
logger.info("Computing fft for plot")
C = np.fft.fft(c, N * 10)
# This is the groundtruth IF
f = 0.5 * np.arange(N) / (N - 1)
logger.info("Computing AM-FM DCA")
ia, ip, ifeq = amfm_DCA(c)
# plot results
logger.info("Plotting")
plt.subplot(311)
plt.plot(c)
plt.title("Time series")
plt.subplot(312)
plt.plot(np.fft.fftfreq(N * 10), np.abs(C), ".")
plt.title("Frequency spectrum")
plt.subplot(313)
plt.plot(f)
plt.plot(ifeq)
plt.legend(["Ideal", "Estimated"], loc = "best")
plt.title("Frequency vs time")
if args.out_file:
plt.savefig(args.out_file)
else:
plt.show()
def main():
fs = 48e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
anf_trains = cochlea.run_holmberg2007(
s,
fs,
anf_num=(100,0,0),
seed=0,
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf_trains, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
fig, ax = plt.subplots(2,1)
th.plot_signal(
signal=s,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
############ Mixed sine
print("chirp")
NSAMP = 500
F0 = 200 + (50 * np.random.rand(NSAMP, ))
F1 = 900 + (100 * np.random.rand(NSAMP, ))
### amps in 0.5, 3.0
amps = 2.5 * np.random.rand(NSAMP, )
amps += 0.5
###
meth = ['linear', 'quadratic', 'logarithmic', 'hyperbolic']
imeth = np.random.randint(0, 4, size=(NSAMP, ))
### output array
MS = np.zeros((NSAMP, tsize))
### workloop
for i in range(NSAMP):
MS[i] = amps[i] * ssg.chirp(
t, f0=F0[i], t1=T, f1=F1[i], method=meth[imeth[i]])
MS[i] += np.random.randn(tsize, )
### end
### spectogram
spdict = {'fs': fs, 'nperseg': fs // 9, 'noverlap': fs // 10, 'nfft': fs // 9}
###### size from ipy shell
MSP = np.zeros((NSAMP, 114, 80))
for i in range(NSAMP):
sf, st, sxx = ssg.spectrogram(MS[i], **spdict)
MSP[i] = sxx
### end
if True:
np.save("chirp_timeseries_noise.npy", MS)
np.save("chirp_sxx_noise.npy", MSP)
print("Serial connected")
mode = str(sys.argv[1])
# Torque bandwidth: 0.1, 40, 60, 400, 6
# Hysteresis: .1, .1, 180, 400, 6
freq_start = 2 #0.1#.25 #Hz
freq_end = 2 #40#.25 #Hz
time_end = 3 #400 #seconds
eval_freq = 400 #300#200 #evaluations per second
max_torque = 15 #10 #Nm
file_name = str(sys.argv[2]) + '_' + str(max_torque) + 'Nm' + '.csv'
t = np.linspace(0, time_end, time_end * eval_freq)
chrp = signal.chirp(t, freq_start, time_end, freq_end)
def p():
pub = rospy.Publisher("/blue_controllers/torque_controller/command",
Float64MultiArray,
queue_size=1)
rospy.init_node('p')
rate = rospy.Rate(
500) #Make sure this is fast enough to handle our frequency
start_time = time.time() + 1 # in seconds
pitch = 0
roll = 0
count = 0
cmd = Float64MultiArray()
while not rospy.is_shutdown():
# declare general signal variables: dur = 3 # duration in seconds sr = 44100 # sampling rate t = np.linspace(0, dur, dur * sr, endpoint=False) # time vector # Q: What's the dimensionality of the time vector? why? # A: 1x132300, there are 132300 timestamps refering to each sample # Q: What is the Nyquist limit of this signal? # A:sr/2 -> 22050Hz # Define the nyquist limit using the relevant variables defined so far nyq = int(sr / 2) # your code here # generate a sine sweep lasting dur and going from 20Hz to 20kHz. Hint: use the scipy chirp function y = chirp(t, 20, 3, 20000) # your code here # we can listen to the resulting signal using these functions from IPython.display import Audio Audio(data=y, rate=sr) librosa.display.waveplot(y, sr) # compute the FFT of the signal using scipy's fft Y = fft(y) # your code here # the FFT is complex-valued, so get its magnitude using np.abs Y = np.abs(Y) # your code here # compute the frequency bins for the fft using np.linspace f = np.linspace(0, Y.shape[0], Y.shape[0]) * sr / Y.shape[0] # your code here
def chirp(b, tp, oversamplerate, f):
fs = oversamplerate * b
n = int(fs * tp)
t = np.arange(n) / fs
return signal.chirp(t, f[0], tp, f[1])
def generate_sweep(f1, f2, fs, T, volume):
assert volume > 0 and volume <= 1, "The volume must be between 0 and 1"
t = np.linspace(0, T, T*fs)
w = (chirp(t, f1, T, f2, method='linear', phi=90)*volume).astype(np.float32)
return w
def genSyncPulse(f0 = 440.0, f1 = 4200.0, fs = 44100.0, t1 = 0.05, method='quadratic'):
t = np.r_[0:t1:1.0/fs]
return sp.chirp(t,f0,t1,f1, method=method)
def demoBandpassFilterFIR():
order = 20
sampRate = 256.0
nyquist = sampRate / 2.0
lowFreq = 1.5
highFreq = 40.0
sincBla = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='sinc-blackman')
sincHan = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='sinc-hann')
sincHam = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='sinc-hamming')
lanczos = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='lanczos')
fig = plt.figure(figsize=(18, 10))
fig.canvas.set_window_title('FIR Bandpass Filter Demo')
axLn = fig.add_subplot(2, 2, 1)
axDb = fig.add_subplot(2, 2, 2)
for ax, scale in zip((axLn, axDb), ('linear', 'db')):
sincBla.plotFreqResponse(showCorners=True,
scale=scale,
label='Sinc-Blackman',
ax=ax,
linewidth=2)
sincHan.plotFreqResponse(showCorners=False,
scale=scale,
label='Sinc-Hann',
ax=ax,
linewidth=2)
sincHam.plotFreqResponse(showCorners=False,
scale=scale,
label='Sinc-Hamming',
ax=ax,
linewidth=2)
lanczos.plotFreqResponse(showCorners=False,
scale=scale,
label='Lanczos',
ax=ax,
linewidth=2)
#ax.grid()
axLn.autoscale(tight=True)
axLn.set_title('Amplitude Response')
axLn.legend(loc='upper right')
axDb.set_xlim((0.0, nyquist))
axDb.set_ylim((-100.0, 0.0))
axDb.set_title('Power Response')
axPh = fig.add_subplot(2, 2, 3)
scale = 'radians'
sincBla.plotPhaseResponse(showCorners=False,
scale=scale,
label='Sinc-Blackman',
ax=axPh,
linewidth=2)
sincHan.plotPhaseResponse(showCorners=False,
scale=scale,
label='Sinc-Hann',
ax=axPh,
linewidth=2)
sincHam.plotPhaseResponse(showCorners=False,
scale=scale,
label='Sinc-Hamming',
ax=axPh,
linewidth=2)
lanczos.plotPhaseResponse(showCorners=True,
scale=scale,
label='Lanczos',
ax=axPh,
linewidth=2)
axPh.autoscale(tight=True)
axPh.set_title('Phase Response')
t = np.linspace(0.0, 2.0, 2.0 * sampRate, endpoint=False)
f = np.linspace(0.0, nyquist, 2.0 * sampRate, endpoint=False)
chirp = spsig.chirp(t, 0.0, 2.0, nyquist)
chirpSincBla = sincBla.filter(chirp)
chirpSincHan = sincHan.filter(chirp)
chirpSincHam = sincHam.filter(chirp)
chirpLanczos = lanczos.filter(chirp)
sep = -np.arange(0, 5) * 2.0
chirpAll = np.vstack((chirpSincBla, chirpSincHan, chirpSincHam,
chirpLanczos, chirp)).T + sep
axCh = fig.add_subplot(2, 2, 4)
axCh.plot(f, chirpAll)
axCh.vlines(lowFreq, 1, -9, color='violet', linestyle='--')
axCh.vlines(highFreq, 1, -9, color='violet', linestyle='--')
axCh.set_yticks([])
axCh.set_xlabel('Frequency (Hz)')
axCh.set_ylabel('Chirp')
axCh.autoscale(tight=True)
axChTwiny = axCh.twiny()
axChTwiny.hlines(sep, 0.0, t[-1], linestyle='--', color='black')
axChTwiny.set_xlabel('Time (s)')
fig.tight_layout()
Fs = 96000 # Sampling frequency
Fnyq = Fs / 2 # Nyquist frequency
dt = 1.0 / Fs # Sampling interval
T = 1.0 # Acquisition time
N = Fs * T # Total number of samples
f_range = np.arange(1, Fnyq) * np.pi / Fnyq # Digital frequencies range
ramp = np.arange(0, N) # Linear ramp
t = ramp * dt # Time vector
Fmin = 1 # Chirp min. frequency
Fmax = 3000 # Chirp max. frequency
sel = np.arange(Fmin, Fmax) # Plot range selection
Win_len = T # Length of Hanning window
#
# Chirp signal with passed with an hp filter
#
x = chirp(t, Fmin, T, Fmax, method='linear', phi=0)
#
f_hp = 10
# bessel(N, Wn, btype='low', analog=False, output='ba', fs=None)
B_hp, A_hp = bessel(1,
f_hp / Fnyq,
'hp',
analog=False,
output='ba',
norm='mag')
#
# freqz(b, a=1, worN=None, whole=0, plot=None)
w_norm_hp, h_hp = freqz(b=B_hp,
a=A_hp,
worN=f_range,
whole=False,
H = 256
if from_file:
x = np.fromfile('/tmp/me.f64', dtype='float64')
N = 0
while N < (len(x) - H):
N += H
x = x[:N]
n = np.arange(N)
x += np.random.standard_normal(N) * 1e-8
else:
N = 500 * H
attack_times = np.array([100 * H + 10, 200 * H - 10, 300 * H - 20])
n = np.arange(N)
# chirp frequency
f0 = 0.01
x = signal.chirp(n, f0, N, f0)
x[attack_times] = 1
av = attack_avoider(attack_times, -H, H + W, H)
# stretch factor
S = 1.
# shift factors
min_P = 1.5
max_P = .5
P_osc_freq = 0.00001
p = signal.chirp(n, P_osc_freq, N, P_osc_freq)
p = 0.5 * (p + 1)
p *= (max_P - min_P)
p += min_P
def noise_gen(f0, len=21):
t = np.arange(0, len, 1.0 / 128)
w = chirp(t, f0, f1=0, t1=21, method="quadratic")
# pl.plot(t,w)
# pl.show()
return w
def gen_sweep(start, end, duration):
t = np.linspace(0, duration, fs*duration)
w = chirp(t, f0=start, f1=end, t1=duration, method='linear')
return w
# nodes = [node.path for node in wp.get_level(2, 'freq')]
# np_lst = []
# for node in nodes:
# np_lst.append(wp[node].data)
# viz = np.stack(np_lst)
# print(res)
# print(viz)
# print(np.round(res))
# print(np.round(viz))
# print('err', np.mean(np.abs(res - viz)))
# print('stop')
t = np.linspace(0, 10, 5001)
wavelet = pywt.Wavelet('db4')
w = signal.chirp(t, f0=.00001, f1=20, t1=10, method='linear')
plt.plot(t, w)
plt.title("Linear Chirp, f(0)=6, f(10)=1")
plt.xlabel('t (sec)')
plt.show()
wp = WaveletPacket(data=np.expand_dims(np.expand_dims(w, 0), 0),
wavelet=wavelet,
mode='reflect')
nodes = wp.get_level(7)
np_lst = []
for node in nodes:
np_lst.append(np.squeeze(wp[node]))
viz = np.stack(np_lst)
plt.imshow(viz[:20, :])
'''
# -*- coding: utf-8 -*-
'''
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as signal
a = np.array([1.0, -1.947463016918843, 0.9555873701383931])
b = np.array([0.9833716591860479, -1.947463016918843, 0.9722157109523452])
# 44.1kHz， 1秒的频率扫描波
t = np.arange(0, 0.5, 1 / 44100.0)
x = signal.chirp(t, f0=10, t1=0.5, f1=1000.0)
# 直接一次计算滤波器的输出
y = signal.lfilter(b, a, x)
# 将输入信号分为50个数据一组
x2 = x.reshape((-1, 50))
# 滤波器的初始状态为0， 长度是滤波器系数长度-1
z = np.zeros(max(len(a), len(b)) - 1, dtype=np.float)
y2 = [] # 保存输出的列表
for tx in x2:
# 对每段信号进行滤波，并更新滤波器的状态z
ty, z = signal.lfilter(b, a, tx, zi=z)
# 将输出添加到输出列表中
y2.append(ty)
# 将输出y2转换为一维数组
import soundfile as sf
from scipy.signal import chirp
import numpy as np
import matplotlib.pyplot as plt
# 数値をminus→逆位相にする関数
def calc_minus(n):
return n * (-1)
# サンプル波形を生成（チャープ信号）
samplerate = 44100 # サンプリングレート
ts = 0 # 信号の開始時間
tf = 3 # 信号の終了時間
t = np.linspace(ts, tf, tf * samplerate) # 時間軸を作成
L = chirp(t, f0=100, f1=300, t1=tf,
method='linear') # 100Hz - 300Hz linear に tf秒で出力する
R = list(map(calc_minus, L)) #逆位相のリストを作成する
LR_minus = []
LR_plus = []
for (L_data, R_data) in zip(L, R):
LR_minus.append([L_data, R_data])
LR_plus.append([L_data, L_data])
#[[L,R],[L,R],[L,R] ・・・] というリストを soundfile はwav に変換することができる
sf.write('stereo_minus.wav', LR_minus, samplerate)
sf.write('stereo_plus.wav', LR_plus, samplerate)
# -*- coding: utf-8 -*-
import scipy.signal as signal
import pylab as pl
import numpy as np
from numpy.lib.stride_tricks import as_strided
sampling_rate = 8000.0
fft_size = 1024
step = fft_size/16
time = 2
t = np.arange(0, time, 1/sampling_rate)
sweep = signal.chirp(t, f0=100, t1 = time, f1=0.8*sampling_rate/2, method="logarithmic")
number = (len(sweep)-fft_size)/step
data = as_strided(sweep, shape=(number, fft_size), strides=(step*8, 8))
window = signal.hann(fft_size)
data = data * window
spectrogram = np.abs(np.fft.rfft(data, axis=1))
spectrogram /= fft_size/2
np.log10(spectrogram, spectrogram)
spectrogram *= 20.0
pl.figure(figsize=(8,4))
im = pl.imshow(spectrogram.T, origin = "lower",
extent=[0, 2, 0, sampling_rate/2], aspect='auto')
bar = pl.colorbar(im, fraction=0.05)
bar.set_label("能量(dB)")
pl.xlabel("时间(秒)")
pl.ylabel("频率(Hz)")
def sweep(freqMin=None,
freqMax=None,
samplingRate=None,
fftDegree=None,
startMargin=None,
stopMargin=None,
method='logarithmic',
windowing='hann'):
"""
Generates a chirp signal defined by the "method" input, windowed, with
silence interval at the beggining and end of the signal, plus a hanning
fade in and fade out.
>>> x = pytta.generate.sweep()
>>> x.plot_time()
Return a signalObj containing a logarithmic chirp signal from 17.8 Hz
to 22050 Hz, with a fade in beginning at 17.8 Hz time instant and ending at
the 20 Hz time instant; plus a fade out beginning at 20000 Hz time instant
and ending at 22050 Hz time instant.
The fade in and the fade out are made with half hanning window. First half
for the fade in and last half for the fade out. Different number of points
are used for each fade, so the number of time samples during each frequency
is respected.
Input arguments (default), (type):
------------------------
* freqMin (20), (float)
* freqMax (20), (float)
* samplingRate (44100), (int)
* fftDegree (18), (float)
* startMargin (0.3), (float)
* stopMargin (0.7), (float)
* method (logarithmic'), (string)
* windowing ('hann'), (string)
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames == ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
# It was done like this because a function default argument is a value
# assigned at import time, and PyTTa have a default object that handles
# default values for all functions and all classes across all submodules.
# In order to it work as expected, the values should be reassigned at
# every function call to get updated default values. Otherwise, despite
# how the default has it's properties values changed, it won't change
# for the function calls.
if freqMin is None:
freqMin = default.freqMin
if freqMax is None:
freqMax = default.freqMax
if samplingRate is None:
samplingRate = default.samplingRate
if fftDegree is None:
fftDegree = default.fftDegree
if startMargin is None:
startMargin = default.startMargin
if stopMargin is None:
stopMargin = default.stopMargin
# frequency limits [Hz]
freqLimits = {
'freqMin': freqMin / (2**(1 / 6)),
'freqMax': min(freqMax * (2**(1 / 6)), samplingRate / 2)
}
samplingTime = 1 / samplingRate # [s] sampling period
stopSamples = stopMargin * samplingRate
# [samples] initial silence number of samples
startSamples = startMargin * samplingRate
# [samples] ending silence number of samples
marginSamples = startSamples + stopSamples
# [samples] total silence number of samples
numSamples = 2**fftDegree # [samples] full signal number of samples
sweepSamples = numSamples - marginSamples + 1
# [samples] actual sweep number of samples
if sweepSamples < samplingRate / 10:
raise Exception('Too small resultant sweep. For such big margins you' +
' must increase your fftDegree.')
sweepTime = sweepSamples / samplingRate # [s] sweep's time length
timeVecSweep = np.arange(0, sweepTime, samplingTime) # [s] time vector
if timeVecSweep.size > sweepSamples:
timeVecSweep = timeVecSweep[0:int(sweepSamples)] # adjust length
sweep = 0.95 * ss.chirp(timeVecSweep,
freqLimits['freqMin'],
sweepTime,
freqLimits['freqMax'],
'logarithmic',
phi=-90) # sweep, time domain
sweep = __do_sweep_windowing(sweep, timeVecSweep, freqLimits, freqMin,
freqMax, windowing) # fade in and fade out
# add initial and ending slices
timeSignal = np.concatenate(
(np.zeros(int(startSamples)), sweep, np.zeros(int(stopSamples))))
if timeSignal.size != numSamples:
timeSignal = timeSignal[0:int(numSamples)] # adjust length
# transforms into a pytta signalObj and sets the correct name
sweepSignal = SignalObj(signalArray=timeSignal,
domain='time',
samplingRate=samplingRate,
**freqLimits)
sweepSignal.creation_name = creation_name
return sweepSignal
w, h = signal.freqz(b, a)
# 计算增益
power = 20 * np.log10(np.clip(np.abs(h), 1e-8, 1e100))
# 绘制增益
pl.subplot(211)
pl.plot(w / np.pi * sampling_rate / 2, power)
pl.title("freqz计算的滤波器频谱")
pl.ylim(-100, 20)
pl.ylabel("增益(dB)")
# 产生2秒钟的取样频率为sampling_rate Hz的频率扫描信号
# 开始频率为0， 结束频率为sampling_rate/2
t = np.arange(0, 2, 1 / sampling_rate)
sweep = signal.chirp(t, f0=0, t1=2, f1=sampling_rate / 2)
# 将频率扫描信号进行滤波
out = signal.lfilter(b, a, sweep)
# 将波形转换为能量
out = 20 * np.log10(np.abs(out))
# 找到所有局部最大值的下标
index = np.where(np.logical_and(out[1:-1] > out[:-2],
out[1:-1] > out[2:]))[0] + 1
# 绘制滤波之后的波形的增益
pl.subplot(212)
pl.plot(t[index] / 2.0 * 4000, out[index])
pl.title("频率扫描波测量的滤波器频谱")
from __future__ import division
import numpy as np
import scipy.signal as sp
from helpers import *
#import matplotlib.pyplot as plt
f0 = 440.0
f1 = 4200.0
fs = 44100.0
t1 = 0.05
t = np.r_[0:t1:1.0/fs]
SYNC_PULSE = sp.chirp(t,f0,t1,f1, method='quadratic')
def genPreamble(data = None, mod_func = None, sync_pulse = SYNC_PULSE):
if data:
mod_data = mod_func(data)
return np.append(sync_pulse, mod_data)
else:
return sync_pulse
def genSyncPulse(f0 = 440.0, f1 = 4200.0, fs = 44100.0, t1 = 0.05, method='quadratic'):
t = np.r_[0:t1:1.0/fs]
return sp.chirp(t,f0,t1,f1, method=method)
def genSyncPulse2(fc= 1800, fd = 600, fs = 44100.0, t = .05):
num_samples = int(t * fs)
pulse = np.ones(num_samples)
def demoBandpassFilterIIR():
order = 5
sampRate = 256.0
nyquist = sampRate / 2.0
lowFreq = 1.5
highFreq = 45.0
zeroPhase = True
butter = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='butter',
zeroPhase=zeroPhase)
#cheby1 = BandpassFilter(0.0, highFreq, sampRate, order,
# filtType='cheby1', rp=1.0, zeroPhase=zeroPhase)
cheby2 = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='cheby2',
rs=20.0,
zeroPhase=zeroPhase)
ellip = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='ellip',
rp=1.0,
rs=20.0,
zeroPhase=zeroPhase)
bessel = BandpassFilter(lowFreq,
highFreq,
sampRate,
order,
filtType='bessel',
zeroPhase=zeroPhase)
fig = plt.figure(figsize=(18, 10))
fig.canvas.set_window_title('IIR Bandpass Filter Demo')
axLn = fig.add_subplot(2, 2, 1)
axDb = fig.add_subplot(2, 2, 2)
for ax, scale in zip((axLn, axDb), ('linear', 'db')):
butter.plotFreqResponse(showCorners=False,
scale=scale,
label='Butterworth',
ax=ax,
linewidth=2)
#cheby1.plotFreqResponse(showCorners=False, scale=scale, label='Cbebyshev-I', ax=ax, linewidth=2)
cheby2.plotFreqResponse(showCorners=False,
scale=scale,
label='Cbebyshev-II',
ax=ax,
linewidth=2)
ellip.plotFreqResponse(showCorners=False,
scale=scale,
label='Elliptical',
ax=ax,
linewidth=2)
bessel.plotFreqResponse(showCorners=True,
scale=scale,
label='Bessel',
ax=ax,
linewidth=2)
#ax.grid()
axLn.autoscale(tight=True)
axLn.set_title('Amplitude Response')
axLn.legend(loc='upper right')
axDb.set_xlim((0.0, nyquist))
axDb.set_ylim((-100.0, 0.0))
axDb.set_title('Power Response')
axPh = fig.add_subplot(2, 2, 3)
scale = 'radians'
butter.plotPhaseResponse(showCorners=False,
scale=scale,
label='Butterworth',
ax=axPh,
linewidth=2)
#cheby1.plotPhaseResponse(showCorners=False, scale=scale, label='Chebyshev-I', ax=axPh, linewidth=2)
cheby2.plotPhaseResponse(showCorners=False,
scale=scale,
label='Chebyshev-II',
ax=axPh,
linewidth=2)
ellip.plotPhaseResponse(showCorners=False,
scale=scale,
label='Elliptical',
ax=axPh,
linewidth=2)
bessel.plotPhaseResponse(showCorners=True,
scale=scale,
label='Bessel',
ax=axPh,
linewidth=2)
axPh.autoscale(tight=True)
axPh.set_title('Phase Response')
t = np.linspace(0.0, 2.0, 2.0 * sampRate, endpoint=False)
f = np.linspace(0.0, nyquist, 2.0 * sampRate, endpoint=False)
chirp = spsig.chirp(t, 0.0, 2.0, nyquist)
chirpButter = butter.filter(chirp)
#chirpCheby1 = cheby1.filter(chirp)
chirpCheby2 = cheby2.filter(chirp)
chirpEllip = ellip.filter(chirp)
chirpBessel = bessel.filter(chirp)
sep = -np.arange(0, 5) * 2.0
chirpAll = np.vstack(
(chirpButter, chirpCheby2, chirpEllip, chirpBessel, chirp)).T + sep
axCh = fig.add_subplot(2, 2, 4)
axCh.plot(f, chirpAll)
axCh.vlines(lowFreq, 1, -9, color='violet', linestyle='--')
axCh.vlines(highFreq, 1, -9, color='violet', linestyle='--')
axCh.set_yticks([])
axCh.set_xlabel('Frequency (Hz)')
axCh.set_ylabel('Chirp')
axCh.autoscale(tight=True)
axChTwiny = axCh.twiny()
axChTwiny.hlines(sep, 0.0, t[-1], linestyle='--', color='black')
axChTwiny.set_xlabel('Time (s)')
fig.tight_layout()
#!/usr/bin/env python from matplotlib import pyplot as plt from scipy.io import wavfile import numpy as np from scipy.signal import decimate,chirp from scipy.fftpack import fft, ifft,fftfreq input_dir='/is/ei/naji/Dropbox/Winter Semster 2014/Master Thesis/Programming/Echo Test/' #s=wavfile.read(input_dir+'Linchirp.wav') #x=s[1][:200000] rate=10000 t=np.linspace(0,5,rate*5) x=chirp(t, f0=2000, t1=5, f1=5000, method='linear') print x.shape #x = s1 + s2 + nse # the signal NFFT = 1000 # the length of the windowing segments Fs = 1. # the sampling frequency # Pxx is the segments x freqs array of instantaneous power, freqs is # the frequency vector, bins are the centers of the time bins in which # the power is computed, and im is the matplotlib.image.AxesImage # instance wavfile.write(input_dir+'chirp_test.wav',rate,x) ax1 = plt.subplot(211) plt.plot(t, x) plt.subplot(212, sharex=ax1) Pxx, freqs, bins, im = plt.specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900,cmap=plt.cm.gist_heat) plt.figure() #print fftfreq(x.shape[0]) plt.plot(fftfreq(x.shape[0]),abs(fft(x))) plt.show()
show_plot = True SR = 16000 T = 16 N = SR * T n = np.arange(N) t = n / SR x = np.zeros(N) T_start = 6 N_start = int(np.round(T_start * SR)) T_end = 12 N_end = int(np.round(T_end * SR)) L_tone = N_end - N_start n_tone = np.arange(L_tone) f0 = 1000 x[N_start:N_end] = signal.chirp(n_tone, f0 / SR, L_tone, f0 / SR) x += np.random.standard_normal(N) * 1e-6 attack_times = [N_start, N_end] # first time-stretch factor ts_0 = 3 / 2 # first pitch-shift factor ps_0 = 0.75 # prepare control signals W = 1024 H = 128 T_out = T N_out = int(np.ceil(SR * T_out)) y = np.zeros(N_out)
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy import signal, misc
from scipy import fftpack
f0 = 165
f1 = 295
f2 = 575
fs = 8000
t = np.linspace(0, 2, 2 * fs, endpoint=False)
x0 = np.sin(f0 * 2 * np.pi * t)
x1 = np.sin(f1 * 2 * np.pi * t)
x2 = np.sin(f2 * 2 * np.pi * t)
x3 = signal.chirp(t, 700, 2, 900, method='linear')
x4 = x0 + x1 + x2 + x3
X0 = fftpack.fft(x0)
X1 = fftpack.fft(x1)
X2 = fftpack.fft(x2)
X3 = fftpack.fft(x3)
X4 = fftpack.fft(x4)
fig, ax = plt.subplots(5)
freqs = fftpack.fftfreq(len(t)) * fs
ax[0].stem(freqs, np.abs(X0), use_line_collection=True)
ax[0].set_xlim(-fs/8, fs/8)
ax[0].set_ylabel('(A)', rotation=0, fontsize=15)
ax[1].stem(freqs, np.abs(X1), use_line_collection=True)
from scipy import io, signal # we will also import the signal module, from s
def plot_spectrogram(spg, t, f, freq_lims=[0, 100], plot_db=False):
plt.figure(figsize=(15, 4))
if plot_db:
plt.imshow(10 * np.log10(spg),
aspect='auto',
extent=[t[0], t[-1], f[-1], f[0]])
else:
plt.imshow(spg, aspect='auto', extent=[t[0], t[-1], f[-1], f[0]])
plt.xlabel('Time')
plt.ylabel('Frequency(Hz)')
plt.ylim(freq_lims)
plt.colorbar()
plt.tight_layout()
T, fs = 20, 1000
t = np.arange(0, T, 1 / float(fs))
# simulate a signal
# refer to the function documentation for f0, t1, f1
sig = signal.chirp(t, f0=10, t1=20, f1=30)
# plot it
plt.figure(figsize=(15, 3))
plt.plot(t, sig)
plt.xlim([0, 5])
plt.xlabel('Time (s)')
plt.ylabel('Voltage (V)')
freq, H = signal.freqz(b)
filterFig, filterAxList = plt.subplots(2,1,sharex=True)
filterAxList[0].plot(1e-6*fs*freq/2/np.pi,20*np.log10(np.abs(H)),'b')
filterAxList[0].set_ylabel('Amplitude Response [dB]')
filterAxList[0].axis((0, 1e-6*fs/2, -150, 0))
filterAxList[0].grid()
filterAxList[1].plot(1e-6*fs*freq/2/np.pi,np.unwrap(np.angle(H)),'b')
filterAxList[1].set_xlabel('Frequency [MHz]')
filterAxList[1].set_ylabel('Phase Response [radians]')
filterAxList[1].grid()
chirpAmplitude = 0.3
chirpLength = 256
chirpSignal = chirpAmplitude*signal.hilbert(signal.chirp(np.arange(chirpLength)/fs, f0=150e6, f1=180e6, t1=(chirpLength-1)/fs, phi=90, method='linear')).astype(np.complex64)
intAmplitude = 1
intLength = 512
intSignal = intAmplitude*signal.hilbert(np.cos(2*np.pi*50e6*np.arange(intLength)/fs)).astype(np.complex64)
noiseVar = 0.01
group_delay = 16
rxSig_raw = np.concatenate((np.zeros(10), intSignal, np.zeros(64), chirpSignal, np.zeros(128)))
rxSig_noisy = rxSig_raw + np.sqrt(noiseVar/2)*(np.random.randn(len(rxSig_raw))+1j*np.random.randn(len(rxSig_raw)))
# rxSig_filtered = signal.lfilter(b,1,rxSig_noisy)[group_delay:]
rxSig_filtered = signal.filtfilt(b,1,rxSig_noisy)
import pdb; pdb.set_trace()
sigFig = plt.figure()
plt.plot(np.real(rxSig_raw),'r',label='Original')
# -*- coding: utf-8 -*- import wave import numpy as np import scipy.signal as signal framerate = 44100 time = 10 # 产生10秒44.1kHz的100Hz - 1kHz的频率扫描波 t = np.arange(0, time, 1.0/framerate) wave_data = signal.chirp(t, 100, time, 1000, method='linear') * 10000 wave_data = wave_data.astype(np.short) # 打开WAV文档 f = wave.open(r"sweep.wav", "wb") # 配置声道数、量化位数和取样频率 f.setnchannels(1) f.setsampwidth(2) f.setframerate(framerate) # 将wav_data转换为二进制数据写入文件 f.writeframes(wave_data.tostring()) f.close()
### ------------------- Test that it works properly ---------------------------
if __name__ == '__main__':
import numpy.random
import scipy.signal as ssi
import matplotlib.pyplot as plt
# Send a chirp with some noise, and try to recover its frequency
# Input signal
f0 = 50
f1 = 1000
t = np.arange(0, 10, 1 / 44100)
ft = f0 * (f1 / f0)**(t / max(t))
chirp = ssi.chirp(t, f0, max(t), f1, method='log')
noise_lv = 0.1
chirp += numpy.random.randn(len(chirp)) * noise_lv
# Slicing the signal
N = 256 # slice size
sliced = [chirp[N * k:N * (k + 1)] for k in range(int(len(chirp) / N))]
f_correct = [ft[N * k] for k in range(int(len(chirp) / N))]
# Getting the estimation
pf = PeriodFinder()
f_estimates = []
for slice_ in sliced:
f_estimates.append(pf(slice_))
# Comparison
from pylab import *
import scipy.signal as signal
import stockwell.smt as smt
signal.chirp
#?signal.chirp
t = linspace(0, 10, num=1000)
ch = signal.chirp(t, 1.0, 6.0, 20.0)
#figure()
#plot(t,ch)
K = 4
N = len(t)
tapers = smt.calc_tapers(K, N)
chs = smt.mtst(K, tapers, ch, 0, len(ch) / 2)
chtapers = smt.calc_tapers(K, len(ch))
chs = smt.mtst(K, chtapers, ch, 0, len(ch) / 2)
# these don't quite look right
#figure(); imshow(chs); axis('auto')
#figure(); subplot(211); plot(ch); subplot(212); imshow(chs); axis('auto')
## now try a longer signal
import stockwell.plots as stplot
print("powerstack of multitaper version of signal")
stplot.stpowerstack(ch, chs)
from time_map_tstretch import attack_avoider
W = 1024
H = 256
N = 500 * H
n = np.arange(N)
# chirp frequency
f0 = 0.01
# stretch factor
S = 1.
# shift factor
P = 0.5
attack_times = np.array([1513, 3013, 4513])
x = signal.chirp(n, f0, N, f0)
x[attack_times] = 1
av = attack_avoider(attack_times, -H, H + W, H)
output_times = np.round(np.arange(0, N, S * H)).astype('int')
y = np.zeros(len(output_times) * H, dtype=x.dtype)
wl = window_tools.windowed_lookup(x, W)
pv = pvoc_synth(signal.get_window('hann', W), signal.get_window('hann', W), W,
H, lambda t: wl.access(t))
h_y = 0
for h in output_times:
lookup_times = np.arange(0, H * P, P)
n_lookups = int(np.ceil((H + 2) / H * P))
y_tmp = np.zeros(H * n_lookups)
def sweep(freqMin = None,
freqMax = None,
samplingRate = None,
fftDegree = None,
startMargin = None,
stopMargin = None,
method = 'logarithmic',
windowing = 'hann'):
"""
Generates a chirp signal defined by the "method" input, windowed, with
silence interval at the beggining and end of the signal, plus a hanning
fade in and fade out.
>>> x = pytta.generate.sweep()
>>> x.plot_time()
Return a signalObj containing a logarithmic chirp signal from 17.8 Hz
to 22050 Hz, with a fade in beginning at 17.8 Hz time instant and ending at
the 20 Hz time instant; plus a fade out beginning at 20000 Hz time instant
and ending at 22050 Hz time instant.
The fade in and the fade out are made with half hanning window. First half
for the fade in and last half for the fade out. Different number of points
are used for each fade, so the number of time samples during each frequency
is respected.
"""
if freqMin is None: freqMin = default.freqMin
if freqMax is None: freqMax = default.freqMax
if samplingRate is None: samplingRate = default.samplingRate
if fftDegree is None: fftDegree = default.fftDegree
if startMargin is None: startMargin = default.startMargin
if stopMargin is None: stopMargin = default.stopMargin
freqLimits = np.array( [ freqMin / ( 2**(1/6) ), \
min( freqMax*( 2**(1/6) ), \
samplingRate/2 )
] ) # frequency limits [Hz]
samplingTime = 1/samplingRate # [s] sampling period
stopSamples = stopMargin*samplingRate
# [samples] initial silence number of samples
startSamples = startMargin*samplingRate
# [samples] ending silence number of samples
marginSamples = startSamples + stopSamples
# [samples] total silence number of samples
numSamples = 2**fftDegree # [samples] full signal number of samples
sweepSamples = numSamples - marginSamples +1
# [samples] actual sweep number of samples
sweepTime = sweepSamples/samplingRate # [s] sweep's time length
timeVecSweep = np.arange(0, sweepTime, samplingTime) # [s] sweep time vector
if timeVecSweep.size > sweepSamples:
timeVecSweep = timeVecSweep[0:int(sweepSamples)] # adjust length
sweep = 0.8*signal.chirp(timeVecSweep, \
freqLimits[0],\
sweepTime, \
freqLimits[1],\
'logarithmic', \
phi=-90) # sweep, time domain
sweep = __do_sweep_windowing(sweep, \
timeVecSweep, \
freqLimits, \
freqMin, \
freqMax, \
windowing) # fade in and fade out
timeSignal = np.concatenate( ( np.zeros( \
int(startSamples) ), \
sweep, \
np.zeros( \
int(stopSamples)
) ) ) # add initial and ending sileces
if timeSignal.size != numSamples:
timeSignal = timeSignal[0:int(numSamples)] # adjust length
sweepSignal = SignalObj(timeSignal,'time',samplingRate)
# transforms into a pytta signalObj
sweepSignal._freqMin, sweepSignal._freqMax \
= freqLimits[0], freqLimits[1]
# pass on the frequency limits considering the fade in and fade out
return sweepSignal
plt.subplot(1,2,1) #6
f, tt, Sxx =sg.spectrogram(signalAndNoise,fs,wind,len(wind),overl) #7
plt.pcolormesh(tt,f,Sxx,cmap='hot') #!4
plt.ylabel('Frequency (Hz)');plt.xlabel('Time (sec)') #15 label axes
plt.ylim([0,20]) #16
wind = np.hanning(windLength);#8
plt.subplot(1,2,2) #9
f, tt, Sxx =sg.spectrogram(signalAndNoise,fs,wind,len(wind),overl) #7
plt.pcolormesh(tt,f,Sxx,cmap='hot') #14
plt.ylabel('Frequency (Hz)');plt.xlabel('Time (sec)')#15 label axes
plt.ylim([0,20]) #16
#%% Chirp
time = np.linspace(0,2,fs*2)#1
y=sg.chirp(time,100,1,200,'quadratic'); #2
#3 playing sounds is beyond "1" in Python
fig=plt.figure(figsize=(10,10))#4
ax = plt.subplot(4,1,1)#5
ax.plot(time,y) #6
ax = plt.subplot(4,1,2) #7
windLength = 128; #8
overl = windLength-1; #9
freqBins = 250; #10
wind=np.kaiser(windLength,0)
f, tt, Sxx =sg.spectrogram(y,fs,wind,len(wind),overl); #7
plt.pcolormesh(tt,f,Sxx);
ax = plt.subplot(4,1,3) #12
wind=np.hanning(windLength);
f, tt, Sxx =sg.spectrogram(y,fs,wind,len(wind),overl); #7
plt.pcolormesh(tt,f,Sxx)
