def test_cross_coupling_xtalk(self):
# introduce a cross reflection at a single delay
outvis = sigchain.gen_cross_coupling_xtalk(self.freqs, self.Tsky, amp=1e-2, dly=300, phs=1)
ovfft = np.fft.fft(
outvis * windows.blackmanharris(len(self.freqs))[None, :], axis=1
)
# take covariance across time and assert delay 300 is highly covariant
# compared to neighbors
cov = np.cov(ovfft.T)
mcov = np.mean(np.abs(cov), axis=0)
select = np.argsort(np.abs(self.dlys - 300))[:10]
nt.assert_almost_equal(self.dlys[select][np.argmax(mcov[select])], 300.0)
# inspect for yourself: plt.matshow(np.log10(np.abs(cov)))
# conjugate it and assert it shows up at -300
outvis = sigchain.gen_cross_coupling_xtalk(self.freqs, self.Tsky, amp=1e-2, dly=300, phs=1, conj=True)
ovfft = np.fft.fft(
outvis * windows.blackmanharris(len(self.freqs))[None, :], axis=1
)
cov = np.cov(ovfft.T)
mcov = np.mean(np.abs(cov), axis=0)
select = np.argsort(np.abs(self.dlys - -300))[:10]
nt.assert_almost_equal(self.dlys[select][np.argmax(mcov[select])], -300.0)
# assert its phase stable across time
select = np.argmin(np.abs(self.dlys - -300))
nt.assert_true(
np.isclose(np.angle(ovfft[:, select]), -1, atol=1e-4, rtol=1e-4).all()
)
def test_basic(self):
assert_allclose(windows.blackmanharris(6, False),
[6.0e-05, 0.055645, 0.520575, 1.0, 0.520575, 0.055645])
assert_allclose(windows.blackmanharris(7, sym=False),
[6.0e-05, 0.03339172347815117, 0.332833504298565,
0.8893697722232837, 0.8893697722232838,
0.3328335042985652, 0.03339172347815122])
assert_allclose(windows.blackmanharris(6),
[6.0e-05, 0.1030114893456638, 0.7938335106543362,
0.7938335106543364, 0.1030114893456638, 6.0e-05])
assert_allclose(windows.blackmanharris(7, sym=True),
[6.0e-05, 0.055645, 0.520575, 1.0, 0.520575, 0.055645,
6.0e-05])
def test_basic(self):
assert_allclose(windows.blackmanharris(6, False),
[6.0e-05, 0.055645, 0.520575, 1.0, 0.520575, 0.055645])
assert_allclose(windows.blackmanharris(7, sym=False),
[6.0e-05, 0.03339172347815117, 0.332833504298565,
0.8893697722232837, 0.8893697722232838,
0.3328335042985652, 0.03339172347815122])
assert_allclose(windows.blackmanharris(6),
[6.0e-05, 0.1030114893456638, 0.7938335106543362,
0.7938335106543364, 0.1030114893456638, 6.0e-05])
assert_allclose(windows.blackmanharris(7, sym=True),
[6.0e-05, 0.055645, 0.520575, 1.0, 0.520575, 0.055645,
6.0e-05])
def spatial_to_wn(eta_grid, kmax):
"""Synthesize spatial grid to estimate wavenumber spectrum"""
win_len = eta_grid.shape[0]
# synthesize wave number field
win_2D = blackmanharris(win_len)[:,
None] * blackmanharris(win_len)[None, :]
s2 = np.sum(win_2D**2)
# transform and scale result
syn_kxky = np.abs(np.fft.rfft2(win_2D * eta_grid, axes=(1, 0)))**2
syn_kxky *= 2 / (kmax * s2)
# the fft shift makes thinking about this easier
syn_kxky = np.fft.fftshift(syn_kxky, axes=1)
return syn_kxky
def test_reflection_gains(self):
# introduce a cable reflection into the autocorrelation
gains = sigchain.gen_reflection_gains(self.freqs, [0], amp=[1e-1], dly=[300], phs=[1])
outvis = sigchain.apply_gains(self.vis, gains, [0, 0])
ovfft = np.fft.fft(
outvis * windows.blackmanharris(len(self.freqs))[None, :], axis=1
)
# assert reflection is at +300 ns and check its amplitude
select = self.dlys > 200
nt.assert_almost_equal(
self.dlys[select][np.argmax(np.mean(np.abs(ovfft), axis=0)[select])], 300
)
select = np.argmin(np.abs(self.dlys - 300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
# assert also reflection at -300 ns
select = self.dlys < -200
nt.assert_almost_equal(
self.dlys[select][np.argmax(np.mean(np.abs(ovfft), axis=0)[select])], -300
)
select = np.argmin(np.abs(self.dlys - -300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
def freq_from_fft(self, p, threshold=5000):
"""
Estimate frequency from peak of FFT
"""
# Debugging.
# start_time = time.time()
bits_per_sample = p.get_sample_size(self.FORMAT) * 8
dtype = 'int{0}'.format(bits_per_sample)
sig = np.frombuffer(b''.join(self.frames), dtype)
fs = self.RATE
# Compute Fourier transform of windowed signal
windowed = sig * blackmanharris(len(sig))
f = rfft(windowed)
# Find the peak and interpolate to get a more accurate peak
i = np.argmax(abs(f)) # Just use this for less-accurate, naive version
true_i = self.parabolic(np.log(abs(f)), i)[0]
# Convert to equivalent frequency
if (fs * true_i / len(windowed)) > threshold:
if self.scratching == False:
print("Sent message!")
arrow_down_event = pg.event.Event(pg.KEYDOWN, key=pg.K_RIGHT)
pg.event.post(arrow_down_event)
self.scratching = True
self.scratch_started_time = time.time()
# print(self.scratch_started_time) # Debugging
print('Scratch detected!')
print(fs * true_i / len(windowed))
def nuta(self, samplingFrequency, signalData):
windowed = signalData * blackmanharris(len(signalData))
f = rfft(windowed)
i = argmax(abs(f))
a = samplingFrequency * i / len(windowed)
print(a)
note = ("Nie mozna rozpoznać.")
if (a >= 390 and a < 403.5) or (a >= 762 and a < 790):
note = "Nuta - G"
elif a >= 403.5 and a < 427.5:
note = ("Nuta - G#")
elif a >= 427.5 and a < 451.5:
note = ("Nuta - A")
elif a >= 451.5 and a < 479.5:
note = ("Nuta - A#")
elif a >= 479.5 and a < 508:
note = ("Nuta - B")
elif a >= 508 and a < 538.5:
note = ("Nuta - C")
elif a >= 538.5 and a < 570.5:
note = ("Nuta - C#")
elif a >= 570.5 and a < 604.5:
note = ("Nuta - D")
elif a >= 604.5 and a < 640.5:
note = ("Nuta - D#")
elif a >= 640.5 and a < 678.5:
note = ("Nuta - E")
elif a >= 678.5 and a < 719:
note = ("Nuta - F")
elif a >= 719 and a < 762:
note = ("Nuta - F#")
tk.messagebox.showinfo("NUTA", note)
def __init__(self,
sample_file: str = DEFAULT_SOURCE,
output_dir='./output/'):
# read audio file
data, sample_rate = load_audio(sample_file)
assert sample_rate in (
8000,
16000,
), 'unsupported sample rate, valid: 8000, 16000)'
self._sample_rate: int = sample_rate
# force to signal channel, as ITU PESQ implementation doesn't support multi channels
self._sample_audio: np.ndarray = force_single_channel(data)
# generate signal wave form
_chirp_size = self._sample_rate
self._chirp = chirp(self._sample_rate,
_chirp_size) * blackmanharris(_chirp_size)
self._norm_chirp = normalize(self._chirp)
self._guard = np.zeros(self._sample_rate * 1)
self._sample_audio_with_guard = np.concatenate(
(self._guard, self._chirp, self._guard, self._sample_audio))
# others
self._output_dir = output_dir
self._file_no = 0
def t_wykres(self):
samplingFrequency, signalData = wavfile.read(self.filename)
if len(signalData.shape) == 2:
signalData = signalData[:, 0]
fs = samplingFrequency
fragment = signalData[:2048]
fragment = fragment / np.max(np.abs(fragment))
widmo = 20 * np.log10(
np.abs(np.fft.rfft(fragment * np.hamming(2048))) / 1024)
f = np.fft.rfftfreq(2048, 1 / fs)
f = f / 1000
self.wamplituda.clear()
self.wamplituda.grid(True)
self.wamplituda.set_xlim(xmin=0)
self.wamplituda.plot(f, widmo)
self.wamplituda.set_xlim(xmin=0, xmax=4)
self.wamplituda.set_ylim(ymin=-90, ymax=0)
self.waveform_canvas.draw()
windowed = signalData * blackmanharris(len(signalData))
f = rfft(windowed)
i = argmax(abs(f))
a = samplingFrequency * i / len(windowed)
print(a)
self.nuta(samplingFrequency, signalData)
def stochasticResidualAnal(x, N, H, sfreq, smag, sphase, fs, stocf):
"""
Subtract sinusoids from a sound and approximate the residual with an envelope
x: input sound, N: fft size, H: hop-size
sfreq, smag, sphase: sinusoidal frequencies, magnitudes and phases
fs: sampling rate; stocf: stochastic factor, used in the approximation
returns stocEnv: stochastic approximation of residual
"""
hN = N / 2 # half of fft size
x = np.append(
np.zeros(hN),
x) # add zeros at beginning to center first window at sample 0
x = np.append(x,
np.zeros(hN)) # add zeros at the end to analyze last sample
bh = blackmanharris(N) # synthesis window
w = bh / sum(bh) # normalize synthesis window
L = sfreq.shape[0] # number of frames, this works if no sines
pin = 0
for l in range(L):
xw = x[pin:pin + N] * w # window the input sound
X = fft(fftshift(xw)) # compute FFT
Yh = UF_C.genSpecSines(N * sfreq[l, :] / fs, smag[l, :], sphase[l, :],
N) # generate spec sines
Xr = X - Yh # subtract sines from original spectrum
mXr = 20 * np.log10(abs(Xr[:hN])) # magnitude spectrum of residual
mXrenv = resample(np.maximum(-200, mXr),
mXr.size * stocf) # decimate the mag spectrum
if l == 0: # if first frame
stocEnv = np.array([mXrenv])
else: # rest of frames
stocEnv = np.vstack((stocEnv, np.array([mXrenv])))
pin += H # advance sound pointer
return stocEnv
def harmonicModel(x, fs, w, N, t, nH, minf0, maxf0, f0et): """ Analysis/synthesis of a sound using the sinusoidal harmonic model x: input sound, fs: sampling rate, w: analysis window, N: FFT size (minimum 512), t: threshold in negative dB, nH: maximum number of harmonics, minf0: minimum f0 frequency in Hz, maxf0: maximim f0 frequency in Hz, f0et: error threshold in the f0 detection (ex: 5), returns y: output array sound """ hN = N/2 # size of positive spectrum hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding hM2 = int(math.floor(w.size/2)) # half analysis window size by floor x = np.append(np.zeros(hM2),x) # add zeros at beginning to center first window at sample 0 x = np.append(x,np.zeros(hM1)) # add zeros at the end to analyze last sample Ns = 512 # FFT size for synthesis (even) H = Ns/4 # Hop size used for analysis and synthesis hNs = Ns/2 pin = max(hNs, hM1) # init sound pointer in middle of anal window pend = x.size - max(hNs, hM1) # last sample to start a frame fftbuffer = np.zeros(N) # initialize buffer for FFT yh = np.zeros(Ns) # initialize output sound frame y = np.zeros(x.size) # initialize output array w = w / sum(w) # normalize analysis window sw = np.zeros(Ns) # initialize synthesis window ow = triang(2*H) # overlapping window sw[int(hNs-H):int(hNs+H)] = int(ow) bh = blackmanharris(Ns) # synthesis window bh = bh / sum(bh) # normalize synthesis window sw[int(hNs-H):int(hNs+H)] = sw[int(hNs-H):int(hNs+H)] / bh[int(hNs-H):int(hNs+H)] # window for overlap-add hfreqp = [] f0t = 0 f0stable = 0 while pin<pend: #-----analysis----- x1 = x[pin-hM1:pin+hM2] # select frame mX, pX = DFT.dftAnal(x1, w, N) # compute dft ploc = UF.peakDetection(mX, t) # detect peak locations iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values ipfreq = fs * iploc/N f0t = UF.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) # find f0 if ((f0stable==0)&(f0t>0)) \ or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)): f0stable = f0t # consider a stable f0 if it is close to the previous one else: f0stable = 0 hfreq, hmag, hphase = harmonicDetection(ipfreq, ipmag, ipphase, f0t, nH, hfreqp, fs) # find harmonics hfreqp = hfreq #-----synthesis----- Yh = UF.genSpecSines(hfreq, hmag, hphase, Ns, fs) # generate spec sines fftbuffer = np.real(ifft(Yh)) # inverse FFT yh[:int(hNs-1)] = fftbuffer[int(hNs+1):] # undo zero-phase window yh[int(hNs-1):] = fftbuffer[:int(hNs+1)] y[pin-hNs:pin+hNs] += sw*yh # overlap-add pin += H # advance sound pointer y = np.delete(y, range(hM2)) # delete half of first window which was added in stftAnal y = np.delete(y, range(y.size-hM1, y.size)) # add zeros at the end to analyze last sample return y
def update(self):
"""
Retrieve and process data from the queue to update the
PyQt plots
"""
try:
# get and pre-process data
data = self.queue.get()
data = self.decode(data, self.channels,
np.int16) * self.overall_amp_factor
# calculate waveform of data
y_wav = np.mean(data, axis=1)
y_wav = y_wav / self.max_freq # shifts y_wav to [-1, 1]
# smooths waveform values to be more easy on the eyes
if self.prev_y_wav is not None:
y_wav = (1 - self.wav_decay_speed
) * self.prev_y_wav + self.wav_decay_speed * y_wav
# apply blackman-harris window to data to normalize values a bit
window = blackmanharris(self.chunk_size)
data = window * np.mean(data, axis=1)
# calculate fourier transform of data
y_fft = np.abs(np.fft.rfft(data, n=self.fft_size * 2))
y_fft = np.delete(y_fft, len(y_fft) - 1)
y_fft = y_fft * 2 / (self.max_freq * 256) # shifts y_fft to [0, 1]
# gets highest bass frequency
bass = np.max(y_fft[0:self.bass_index])
# smooths bass values
if self.prev_bass is not None:
bass = (1 - self.bass_decay_speed
) * self.prev_bass + self.bass_decay_speed * bass
# smooths frequency spectrum values to be more easy on the eyes
if self.prev_y_fft is not None:
y_fft = (1 - self.fft_decay_speed
) * self.prev_y_fft + self.fft_decay_speed * y_fft
# draws data
self.draw_data(
np.sign(y_wav) * (np.abs(y_wav)**self.wav_amp_factor),
y_fft[self.low_index:self.high_index]**self.fft_amp_factor,
bass**self.bass_amp_factor)
# previous value updates
self.prev_y_wav = y_wav
self.prev_y_fft = y_fft
self.prev_bass = bass
self.frame_count += 1
except:
pass
def array_factor(number_of_elements, scan_angle, element_spacing, frequency,
theta, window_type, side_lobe_level):
"""
Calculate the array factor for a linear binomial excited array.
:param window_type: The string name of the window.
:param side_lobe_level: The sidelobe level for Tschebyscheff window (dB).
:param number_of_elements: The number of elements in the array.
:param scan_angle: The angle to which the main beam is scanned (rad).
:param element_spacing: The distance between elements.
:param frequency: The operating frequency (Hz).
:param theta: The angle at which to evaluate the array factor (rad).
:return: The array factor as a function of angle.
"""
# Calculate the wavenumber
k = 2.0 * pi * frequency / c
# Calculate the phase
psi = k * element_spacing * (cos(theta) - cos(scan_angle))
# Calculate the coefficients
if window_type == 'Uniform':
coefficients = ones(number_of_elements)
elif window_type == 'Binomial':
coefficients = binom(number_of_elements - 1,
range(0, number_of_elements))
elif window_type == 'Tschebyscheff':
warnings.simplefilter("ignore", UserWarning)
coefficients = chebwin(number_of_elements,
at=side_lobe_level,
sym=True)
elif window_type == 'Kaiser':
coefficients = kaiser(number_of_elements, 6, True)
elif window_type == 'Blackman-Harris':
coefficients = blackmanharris(number_of_elements, True)
elif window_type == 'Hanning':
coefficients = hanning(number_of_elements, True)
elif window_type == 'Hamming':
coefficients = hamming(number_of_elements, True)
# Calculate the offset for even/odd
offset = int(floor(number_of_elements / 2))
# Odd case
if number_of_elements & 1:
coefficients = roll(coefficients, offset + 1)
coefficients[0] *= 0.5
af = sum(coefficients[i] * cos(i * psi) for i in range(offset + 1))
return af / amax(abs(af))
# Even case
else:
coefficients = roll(coefficients, offset)
af = sum(coefficients[i] * cos((i + 0.5) * psi) for i in range(offset))
return af / amax(abs(af))
def get_peak(data):
"""
Peak frequency from signal
:param data: signal
:return: peak frequency
"""
data = data / max(data)
data = data * blackmanharris(len(data))
data = abs(rfft(data))
f_peak = 44100 * argmax(data) / len(data) / 2
return f_peak
def _get_peak_array(waveforms, peaks):
"""Return a binary array of contiguous peak sections."""
# Create empty array with length of waveform
peak_array = np.zeros(waveforms.shape[0], dtype=np.float32)
window = blackmanharris(21)
if peaks:
for peak_ids in peaks:
if peak_ids:
for peak_id in peak_ids:
if len(peak_array[peak_id - 10:peak_id + 11]) >= 21:
peak_array[peak_id - 10:peak_id + 11] += window
peak_array[peak_array <= 1] = 0
peak_array /= np.max(peak_array)
return peak_array
def sineModel(x, fs, w, N, t):
"""
Analysis/synthesis of a sound using the sinusoidal model, without sine tracking
x: input array sound, w: analysis window, N: size of complex spectrum, t: threshold in negative dB
returns y: output array sound
"""
hM1 = int(math.floor(
(w.size + 1) / 2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size / 2)) # half analysis window size by floor
Ns = 512 # FFT size for synthesis (even)
H = Ns / 4 # Hop size used for analysis and synthesis
hNs = Ns / 2 # half of synthesis FFT size
pin = max(hNs, hM1) # init sound pointer in middle of anal window
pend = x.size - max(hNs, hM1) # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
yw = np.zeros(Ns) # initialize output sound frame
y = np.zeros(x.size) # initialize output array
w = w / sum(w) # normalize analysis window
sw = np.zeros(Ns) # initialize synthesis window
ow = triang(2 * H) # triangular window
sw[hNs - H:hNs + H] = ow # add triangular window
bh = blackmanharris(Ns) # blackmanharris window
bh = bh / sum(bh) # normalized blackmanharris window
sw[hNs - H:hNs +
H] = sw[hNs - H:hNs + H] / bh[hNs - H:hNs +
H] # normalized synthesis window
while pin < pend: # while input sound pointer is within sound
#-----analysis-----
x1 = x[pin - hM1:pin + hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # detect locations of peaks
pmag = mX[ploc] # get the magnitude of the peaks
iploc, ipmag, ipphase = UF.peakInterp(
mX, pX, ploc) # refine peak values by interpolation
ipfreq = fs * iploc / float(N) # convert peak locations to Hertz
#-----synthesis-----
Y = UF.genSpecSines(ipfreq, ipmag, ipphase, Ns,
fs) # generate sines in the spectrum
fftbuffer = np.real(ifft(Y)) # compute inverse FFT
yw[:hNs - 1] = fftbuffer[hNs + 1:] # undo zero-phase window
yw[hNs - 1:] = fftbuffer[:hNs + 1]
y[pin - hNs:pin +
hNs] += sw * yw # overlap-add and apply a synthesis window
pin += H # advance sound pointer
return y
def blackmanharris(dataset, **kwargs):
"""
Calculate a minimum 4-term Blackman-Harris apodization.
For multidimensional NDDataset,
the apodization is by default performed on the last dimension.
The data in the last dimension MUST be time-domain or dimensionless,
otherwise an error is raised.
Parameters
----------
dataset : dataset
Input dataset.
Returns
-------
apodized
dataset.
apod_arr
The apodization array only if 'retapod' is True.
Other Parameters
----------------
dim : str or int, keyword parameter, optional, default='x'
Specify on which dimension to apply the apodization method. If `dim` is specified as an integer it is equivalent
to the usual `axis` numpy parameter.
inv : bool, keyword parameter, optional, default=False
True for inverse apodization.
rev : bool, keyword parameter, optional, default=False
True to reverse the apodization before applying it to the data.
inplace : bool, keyword parameter, optional, default=False
True if we make the transform inplace. If False, the function return a new datase
retapod : bool, keyword parameter, optional, default=False
True to return the apodization array along with the apodized object.
"""
x = dataset
return x * windows.blackmanharris(len(x), sym=True)
def sineModelSynth(tfreq, tmag, tphase, N, H, fs):
"""
Synthesis of a sound using the sinusoidal model
tfreq,tmag,tphase: frequencies, magnitudes and phases of sinusoids
N: synthesis FFT size, H: hop size, fs: sampling rate
returns y: output array sound
"""
hN = N // 2 # half of FFT size for synthesis
L = tfreq.shape[0] # number of frames
pout = 0 # initialize output sound pointer
ysize = H * (L + 3) # output sound size
y = np.zeros(ysize) # initialize output array
sw = np.zeros(N) # initialize synthesis window
ow = triang(2 * H) # triangular window
sw[hN - H:hN + H] = ow # add triangular window
bh = blackmanharris(N) # blackmanharris window
bh = bh / sum(bh) # normalized blackmanharris window
sw[hN - H:hN +
H] = sw[hN - H:hN + H] / bh[hN - H:hN +
H] # normalized synthesis window
lastytfreq = tfreq[0, :] # initialize synthesis frequencies
ytphase = 2 * np.pi * np.random.rand(
tfreq[0, :].size) # initialize synthesis phases
for l in range(L): # iterate over all frames
if (tphase.size > 0): # if no phases generate them
ytphase = tphase[l, :]
else:
ytphase += (np.pi * (lastytfreq + tfreq[l, :]) /
fs) * H # propagate phases
Y = UF.genSpecSines(tfreq[l, :], tmag[l, :], ytphase, N,
fs) # generate sines in the spectrum
lastytfreq = tfreq[l, :] # save frequency for phase propagation
ytphase = ytphase % (2 * np.pi) # make phase inside 2*pi
yw = np.real(fftshift(ifft(Y))) # compute inverse FFT
y[pout:pout + N] += sw * yw # overlap-add and apply a synthesis window
pout += H # advance sound pointer
y = np.delete(y, range(hN)) # delete half of first window
y = np.delete(y, range(y.size - hN,
y.size)) # delete half of the last window
return y
def get_fft_window(window_type, window_length):
# Generate the window with the right number of points
window = None
if window_type == "Bartlett":
window = windows.bartlett(window_length)
if window_type == "Blackman":
window = windows.blackman(window_length)
if window_type == "Blackman Harris":
window = windows.blackmanharris(window_length)
if window_type == "Flat Top":
window = windows.flattop(window_length)
if window_type == "Hamming":
window = windows.hamming(window_length)
if window_type == "Hanning":
window = windows.hann(window_length)
# If no window matched, use a rectangular window
if window is None:
window = np.ones(window_length)
# Return the window
return window
def sineSubtraction(x, N, H, sfreq, smag, sphase, fs):
"""
Subtract sinusoids from a sound
x: input sound, N: fft-size, H: hop-size
sfreq, smag, sphase: sinusoidal frequencies, magnitudes and phases
returns xr: residual sound
"""
hN = N / 2 # half of fft size
x = np.append(
np.zeros(hN),
x) # add zeros at beginning to center first window at sample 0
x = np.append(x,
np.zeros(hN)) # add zeros at the end to analyze last sample
bh = blackmanharris(N) # blackman harris window
w = bh / sum(bh) # normalize window
sw = np.zeros(N) # initialize synthesis window
sw[hN - H:hN + H] = triang(2 * H) / w[hN - H:hN + H] # synthesis window
L = sfreq.shape[0] # number of frames, this works if no sines
xr = np.zeros(x.size) # initialize output array
pin = 0
for l in range(L):
xw = x[pin:pin + N] * w # window the input sound
X = fft(fftshift(xw)) # compute FFT
Yh = UF_C.genSpecSines(N * sfreq[l, :] / fs, smag[l, :], sphase[l, :],
N) # generate spec sines
Xr = X - Yh # subtract sines from original spectrum
xrw = np.real(fftshift(ifft(Xr))) # inverse FFT
xr[pin:pin + N] += xrw * sw # overlap-add
pin += H # advance sound pointer
xr = np.delete(
xr,
range(hN)) # delete half of first window which was added in stftAnal
xr = np.delete(xr, range(
xr.size - hN,
xr.size)) # delete half of last window which was added in stftAnal
return xr
""" f = fft(data_x[0][0]) g = fftfreq(n=data_x[0][0].size, d=1/sampling_freq) print(f.shape) print(g.shape) """ #窓関数処理 N = extraction_freq #データ数 #ハニング窓 #hanning_window = windows.hann(N) hanning_window = np.hanning(N) #ハミング窓 hamming_window = windows.hamming(N) #ブラックマン窓 black_window = windows.blackmanharris(N) """ plt.plot(x,hanning_window) plt.plot(x,hamming_window) plt.plot(x,black_window) plt.show() """ a = 0 plt.plot(x,data_x[0][a]) plt.show() data_x *= hanning_window #data_x[:][:] /= np.amax(data_x[0][a])
from scipy.io import arff
import pandas as pd
import librosa as lib
from scipy.signal import windows as win
from collections import namedtuple
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
import os, glob
window = win.blackmanharris(2048)
folder_length = 89
root_folder = '../Musically-Motivated-CNN/data/'
steps_back = '../../'
LabeledChunk = namedtuple('Chunk', ['label', 'spectrogram'])
genres = [
'blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop',
'reggae', 'rock'
]
chunk_len = 80
def extract_labeled_spectrogram():
labeled_dataset = []
print('starting feature exctration')
for genre in genres:
os.chdir(root_folder + genre)
for file in glob.glob('*.au'):
def _update_canvas(self):
"""
Update the figure when the user changes an input value
:return:
"""
# Get the parameters from the form
number_of_steps = int(self.number_of_steps.text())
frequency_step = float(self.frequency_step.text())
prf = float(self.prf.text())
target_range = self.target_range.text().split(',')
target_rcs = self.target_rcs.text().split(',')
target_velocity = self.target_velocity.text().split(',')
t_range = [float(r) for r in target_range]
t_rcs = [float(r) for r in target_rcs]
t_velocity = [float(v) for v in target_velocity]
# Get the selected window from the form
window_type = self.window_type.currentText()
if window_type == 'Kaiser':
coefficients = kaiser(number_of_steps, 6, True)
elif window_type == 'Blackman-Harris':
coefficients = blackmanharris(number_of_steps, True)
elif window_type == 'Hanning':
coefficients = hanning(number_of_steps, True)
elif window_type == 'Hamming':
coefficients = hamming(number_of_steps, True)
elif window_type == 'Rectangular':
coefficients = ones(number_of_steps)
# Calculate the base band return signal
s = zeros(number_of_steps, dtype=complex)
for rng, rcs, v in zip(t_range, t_rcs, t_velocity):
s += [sqrt(rcs) * exp(-1j * 4.0 * pi / c * (i * frequency_step) * (rng - v * (i / prf)))
for i in range(number_of_steps)]
n = next_fast_len(10 * number_of_steps)
sf = ifft(s * coefficients, n) * float(n) / float(number_of_steps)
# range_resolution = c / (2.0 * number_of_steps * frequency_step)
range_unambiguous = c / (2.0 * frequency_step)
range_window = linspace(0, range_unambiguous, n)
# Clear the axes for the updated plot
self.axes1.clear()
# Create the line plot
self.axes1.plot(range_window, 20.0 * log10(abs(sf) + finfo(float).eps), '')
# Set the x and y axis labels
self.axes1.set_xlabel("Range (m)", size=12)
self.axes1.set_ylabel("Amplitude (dBsm)", size=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Set the plot title and labels
self.axes1.set_title('Stepped Frequency Range Profile', size=14)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Update the canvas
self.my_canvas.draw()
def _update_canvas(self):
"""
Update the figure when the user changes an input value
:return:
"""
# Get the parameters from the form
bandwidth = float(self.bandwidth.text())
pulsewidth = float(self.pulsewidth.text())
target_range = self.target_range.text().split(',')
target_rcs = self.target_rcs.text().split(',')
t_range = [float(r) for r in target_range]
t_rcs = [float(r) for r in target_rcs]
# Get the selected window from the form
window_type = self.window_type.currentText()
# Number of samples
N = int(2 * bandwidth * pulsewidth) * 8
if window_type == 'Kaiser':
coefficients = kaiser(N, 6, True)
elif window_type == 'Blackman-Harris':
coefficients = blackmanharris(N, True)
elif window_type == 'Hanning':
coefficients = hanning(N, True)
elif window_type == 'Hamming':
coefficients = hamming(N, True)
elif window_type == 'Rectangular':
coefficients = ones(N)
# Set up the time vector
t = linspace(-0.5 * pulsewidth, 0.5 * pulsewidth, N)
# Calculate the baseband return signal
s = zeros(N, dtype=complex)
# Chirp slope
alpha = 0.5 * bandwidth / pulsewidth
for r, rcs in zip(t_range, t_rcs):
s += sqrt(rcs) * exp(1j * 2.0 * pi * alpha * (t - 2.0 * r / c)**2)
# Transmit signal
st = exp(1j * 2 * pi * alpha * t**2)
# Impulse response and matched filtering
Hf = fft(conj(st * coefficients))
Si = fft(s)
so = fftshift(ifft(Si * Hf))
# Range window
range_window = linspace(-0.25 * c * pulsewidth, 0.25 * c * pulsewidth,
N)
# Clear the axes for the updated plot
self.axes1.clear()
# Create the line plot
self.axes1.plot(range_window,
20.0 * log10(abs(so) / N + finfo(float).eps), '')
self.axes1.set_xlim(0, max(t_range) + 100)
self.axes1.set_ylim(-60, max(20.0 * log10(abs(so) / N)) + 10)
# Set the x and y axis labels
self.axes1.set_xlabel("Range (m)", size=12)
self.axes1.set_ylabel("Amplitude (dBsm)", size=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Set the plot title and labels
self.axes1.set_title('Matched Filter Range Profile', size=14)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Update the canvas
self.my_canvas.draw()
def pre_processing(self):
"""
Complete various pre-processing steps for encoded protein sequences before
doing any of the DSP-related functions or transformations. Zero-pad
the sequences, remove any +/- infinity or NAN values, get the approximate
protein spectra and window function parameter names.
Parameters
----------
:self (PyDSP object):
instance of PyDSP class.
Returns
-------
None
"""
#zero-pad encoded sequences so they are all the same length
self.protein_seqs = zero_padding(self.protein_seqs)
#get shape parameters of proteins seqs
self.num_seqs = self.protein_seqs.shape[0]
self.signal_len = self.protein_seqs.shape[1]
#replace any positive or negative infinity or NAN values with 0
self.protein_seqs[self.protein_seqs == -np.inf] = 0
self.protein_seqs[self.protein_seqs == np.inf] = 0
self.protein_seqs[self.protein_seqs == np.nan] = 0
#replace any NAN's with 0's
#self.protein_seqs.fillna(0, inplace=True)
self.protein_seqs = np.nan_to_num(self.protein_seqs)
#initialise zeros array to store all protein spectra
self.fft_power = np.zeros((self.num_seqs, self.signal_len))
self.fft_real = np.zeros((self.num_seqs, self.signal_len))
self.fft_imag = np.zeros((self.num_seqs, self.signal_len))
self.fft_abs = np.zeros((self.num_seqs, self.signal_len))
#list of accepted spectra, window functions and filters
all_spectra = ['power', 'absolute', 'real', 'imaginary']
all_windows = [
'hamming', 'blackman', 'blackmanharris', 'gaussian', 'bartlett',
'kaiser', 'barthann', 'bohman', 'chebwin', 'cosine', 'exponential'
'flattop', 'hann', 'boxcar', 'hanning', 'nuttall', 'parzen',
'triang', 'tukey'
]
all_filters = [
'savgol', 'medfilt', 'symiirorder1', 'lfilter', 'hilbert'
]
#set required input parameters, raise error if spectrum is none
if self.spectrum == None:
raise ValueError(
'Invalid input Spectrum type ({}) not available in valid spectra: {}'
.format(self.spectrum, all_spectra))
else:
#get closest correct spectra from user input, if no close match then raise error
spectra_matches = (get_close_matches(self.spectrum,
all_spectra,
cutoff=0.4))
if spectra_matches == []:
raise ValueError(
'Invalid input Spectrum type ({}) not available in valid spectra: {}'
.format(self.spectrum, all_spectra))
else:
self.spectra = spectra_matches[0] #closest match in array
if self.window_type == None:
self.window = 1 #window = 1 is the same as applying no window
else:
#get closest correct window function from user input
window_matches = (get_close_matches(self.window,
all_windows,
cutoff=0.4))
#check if sym=True or sym=False
#get window function specified by window input parameter, if no match then window = 1
if window_matches != []:
if window_matches[0] == 'hamming':
self.window = hamming(self.signal_len, sym=True)
self.window_type = "hamming"
elif window_matches[0] == "blackman":
self.window = blackman(self.signal_len, sym=True)
self.window = "blackman"
elif window_matches[0] == "blackmanharris":
self.window = blackmanharris(self.signal_len,
sym=True) #**
self.window_type = "blackmanharris"
elif window_matches[0] == "bartlett":
self.window = bartlett(self.signal_len, sym=True)
self.window_type = "bartlett"
elif window_matches[0] == "gaussian":
self.window = gaussian(self.signal_len, std=7, sym=True)
self.window_type = "gaussian"
elif window_matches[0] == "kaiser":
self.window = kaiser(self.signal_len, beta=14, sym=True)
self.window_type = "kaiser"
elif window_matches[0] == "hanning":
self.window = hanning(self.signal_len, sym=True)
self.window_type = "hanning"
elif window_matches[0] == "barthann":
self.window = barthann(self.signal_len, sym=True)
self.window_type = "barthann"
elif window_matches[0] == "bohman":
self.window = bohman(self.signal_len, sym=True)
self.window_type = "bohman"
elif window_matches[0] == "chebwin":
self.window = chebwin(self.signal_len, sym=True)
self.window_type = "chebwin"
elif window_matches[0] == "cosine":
self.window = cosine(self.signal_len, sym=True)
self.window_type = "cosine"
elif window_matches[0] == "exponential":
self.window = exponential(self.signal_len, sym=True)
self.window_type = "exponential"
elif window_matches[0] == "flattop":
self.window = flattop(self.signal_len, sym=True)
self.window_type = "flattop"
elif window_matches[0] == "boxcar":
self.window = boxcar(self.signal_len, sym=True)
self.window_type = "boxcar"
elif window_matches[0] == "nuttall":
self.window = nuttall(self.signal_len, sym=True)
self.window_type = "nuttall"
elif window_matches[0] == "parzen":
self.window = parzen(self.signal_len, sym=True)
self.window_type = "parzen"
elif window_matches[0] == "triang":
self.window = triang(self.signal_len, sym=True)
self.window_type = "triang"
elif window_matches[0] == "tukey":
self.window = tukey(self.signal_len, sym=True)
self.window_type = "tukey"
else:
self.window = 1 #window = 1 is the same as applying no window
#calculate convolution from protein sequences
if self.convolution is not None:
if self.window is not None:
self.convoled_seqs = signal.convolve(
self.protein_seqs, self.window, mode='same') / sum(
self.window)
if self.filter != None:
#get closest correct filter from user input
filter_matches = (get_close_matches(self.filter,
all_filters,
cutoff=0.4))
#set filter attribute according to approximate user input
if filter_matches != []:
if filter_matches[0] == 'savgol':
self.filter = savgol_filter(self.signal_len,
self.signal_len)
elif filter_matches[0] == 'medfilt':
self.filter = medfilt(self.signal_len)
elif filter_matches[0] == 'symiirorder1':
self.filter = symiirorder1(self.signal_len, c0=1, z1=1)
elif filter_matches[0] == 'lfilter':
self.filter = lfilter(self.signal_len)
elif filter_matches[0] == 'hilbert':
self.filter = hilbert(self.signal_len)
else:
self.filter = "" #no filter
def test_reflection_gains(self):
# introduce a cable reflection into the autocorrelation
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[1e-1],
dly=[300],
phs=[1])
outvis = sigchain.apply_gains(self.vis, gains, [0, 0])
ovfft = np.fft.fft(outvis *
windows.blackmanharris(len(self.freqs))[None, :],
axis=1)
# assert reflection is at +300 ns and check its amplitude
select = self.dlys > 200
nt.assert_almost_equal(
self.dlys[select][np.argmax(
np.mean(np.abs(ovfft), axis=0)[select])], 300)
select = np.argmin(np.abs(self.dlys - 300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
# assert also reflection at -300 ns
select = self.dlys < -200
nt.assert_almost_equal(
self.dlys[select][np.argmax(
np.mean(np.abs(ovfft), axis=0)[select])], -300)
select = np.argmin(np.abs(self.dlys - -300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
# test reshaping into Ntimes
amp = np.linspace(1e-2, 1e-3, 3)
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
nt.assert_equal(gains[0].shape, (3, 100))
# test frequency evolution with one time
amp = np.linspace(1e-2, 1e-3, 100).reshape(1, -1)
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
nt.assert_equal(gains[0].shape, (1, 100))
# now test with multiple times
amp = np.repeat(np.linspace(1e-2, 1e-3, 100).reshape(1, -1),
10,
axis=0)
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
nt.assert_equal(gains[0].shape, (10, 100))
# exception
amp = np.linspace(1e-2, 1e-3, 2).reshape(1, -1)
nt.assert_raises(AssertionError,
sigchain.gen_reflection_gains,
self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
def _update_canvas(self):
"""
Update the figure when the user changes an input value
:return:
"""
# Get the parameters from the form
bandwidth = float(self.bandwidth.text())
pulsewidth = float(self.pulsewidth.text())
range_window_length = float(self.range_window_length.text())
target_range = self.target_range.text().split(',')
target_rcs = self.target_rcs.text().split(',')
t_range = [float(r) for r in target_range]
t_rcs = [float(r) for r in target_rcs]
# Get the selected window from the form
window_type = self.window_type.currentText()
# Number of samples
number_of_samples = int(ceil(4 * bandwidth * range_window_length / c))
if window_type == 'Kaiser':
coefficients = kaiser(number_of_samples, 6, True)
elif window_type == 'Blackman-Harris':
coefficients = blackmanharris(number_of_samples, True)
elif window_type == 'Hanning':
coefficients = hanning(number_of_samples, True)
elif window_type == 'Hamming':
coefficients = hamming(number_of_samples, True)
elif window_type == 'Rectangular':
coefficients = ones(number_of_samples)
# Time sampling
t, dt = linspace(-0.5 * pulsewidth,
0.5 * pulsewidth,
number_of_samples,
retstep=True)
# Sampled signal after mixing
so = zeros(number_of_samples, dtype=complex)
for r, rcs in zip(t_range, t_rcs):
so += sqrt(rcs) * exp(1j * 2.0 * pi * bandwidth / pulsewidth *
(2 * r / c) * t)
# Fourier transform
so = fftshift(fft(so * coefficients, 4 * number_of_samples))
# FFT frequencies
frequencies = fftshift(fftfreq(4 * number_of_samples, dt))
# Range window
range_window = 0.5 * frequencies * c * pulsewidth / bandwidth
# Clear the axes for the updated plot
self.axes1.clear()
# Create the line plot
self.axes1.plot(
range_window,
20.0 * log10(abs(so) / number_of_samples + finfo(float).eps), '')
self.axes1.set_xlim(min(t_range) - 5, max(t_range) + 5)
self.axes1.set_ylim(
-60,
max(20.0 * log10(abs(so) / number_of_samples)) + 10)
# Set the x and y axis labels
self.axes1.set_xlabel("Range (m)", size=12)
self.axes1.set_ylabel("Amplitude (dBsm)", size=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Set the plot title and labels
self.axes1.set_title('Stretch Processor Range Profile', size=14)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Update the canvas
self.my_canvas.draw()
