Python scipy.signal 模块，sawtooth() 实例源码
我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用scipy.signal.sawtooth()。
def demoICA():
t = np.linspace(0.0, 30*np.pi, 1000)
s1 = spsig.sawtooth(t)
s2 = np.cos(5.0*t)
s3 = np.random.uniform(-1.0, 1.0, size=t.size)
s = np.vstack((s1,s2,s3)).T
m = np.random.random((3,3))
m /= m.sum(axis=0)
sMixed = s.dot(m)
icaFilt = ICA(sMixed, kurtosis='sub', verbose=True)
fig = plt.figure()
axOrig = fig.add_subplot(4,1, 1)
axOrig.plot(s+util.colsep(s))
axOrig.set_title('Unmixed Signal')
axOrig.autoscale(tight=True)
axMixed = fig.add_subplot(4, 2)
axMixed.plot(sMixed+util.colsep(sMixed))
axMixed.set_title('Mixed Signal (random transform)')
axMixed.autoscale(tight=True)
axUnmixed = fig.add_subplot(4, 3)
icaFilt.plottransform(sMixed, ax=axUnmixed)
axUnmixed.set_title('ICA Components')
axUnmixed.autoscale(tight=True)
axCleaned = fig.add_subplot(4, 4)
icaFilt.plotFilter(sMixed, comp=(0,), ax=axCleaned)
axCleaned.set_title('Cleaned Signal (First two components kept)')
axCleaned.autoscale(tight=True)
fig.tight_layout()
def demoMSF():
t = np.linspace(0.0, 1000)
s1 = spsig.sawtooth(t) #+ 3.0
s2 = np.cos(5.0*t)
s3 = np.random.uniform(-1.0,s3)).T
#m = np.array([ [0.5,0.5,0.0],[0.5,0.0,0.5],[0.0,0.5] ])
m = np.random.random((3,3))
m /= m.sum(axis=0)
sMixed = s.dot(m)
msfFilt = MSF(sMixed, lags=0)
fig = plt.figure()
axOrig = fig.add_subplot(4, 3)
msfFilt.plottransform(sMixed, ax=axUnmixed)
axUnmixed.set_title('MSF Components')
axUnmixed.autoscale(tight=True)
axCleaned = fig.add_subplot(4, 4)
msfFilt.plotFilter(sMixed, comp=(2, remove=True, ax=axCleaned)
axCleaned.set_title('Cleaned Signal (Last Component Removed)')
axCleaned.autoscale(tight=True)
fig.tight_layout()
def __init__(self, mgr, sampRate=128,
chans=[str(n)+'x' for n in np.power(2, np.arange(8))/2.0],
waveform='sinusoid', freq=1.0, mix='none', pollSize=2):
"""
Construct a new wave generator source.
Args:
sampRate: Floating point value of the initial sampling frequency.
chans: Tuple of strings containing the initial channel
configuration.
waveform: String describing the type of waveform to produce.
May be 'sinusoid' or 'sawtooth' or 'square'
freq: Base frequency. Each channel is a power-of-two
multiple of this frequency.
pollSize: Number of data samples collected during each poll.
Higher values result in better timing and marker
resolution but more cpu usage while higher values
typically use less cpu but worse timing results.
"""
self.waveform = mp.Value('I', 0)
self.freq = mp.Value('d', freq)
self.t0 = mp.Value('d', 0.0)
self.t0.value = 0.0
self.pollSize = pollSize
self.lock = mp.Lock()
Source.__init__(self, mgr=mgr, sampRate=sampRate, chans=chans,
configPanelClass=WaveGenConfigPanel)
self.setWaveform(waveform)
self.mixArr = mp.Array('d', self.getNChan()*self.getNChan())
self.mixMat = (np.frombuffer(self.mixArr.get_obj())
.reshape((-1,self.getNChan())))
self.setMix(mix)
def setWaveform(self, waveform):
"""Set the periodic waveform to generate.
Args:
waveform: String describing the type of waveform to produce.
May be 'sinusoid' or 'sawtooth' or 'square'
"""
waveform = waveform.lower()
with self.lock:
try:
# index into keys gives us an integer id
self.waveform.value = list(waveforms.keys()).index(waveform)
except ValueError:
raise ValueError('Invalid waveform %s.' % str(waveform))
def set_initial(self): # sets the phase history of the VCO with the frequency of the synchronized state under investigation
self.d_phi = self.sOmeg * self.dt
#+ 2.*pi*np.random.normal(loc=0.0,scale=np.sqrt(2.0*self.c)) * np.sqrt(*self.dt) this can be added only if the diffusion constant is normalized such that
# for changing tau (length of history) the diffusion of phases is the same - i.e. scale by sqrt(tau)
self.phi = self.phi + self.d_phi
#print('write history with noise')
return self.phi, self.d_phi
# y = 1 / n * sum h( x_delayed_neighbours - x_self )
# print('Phasedetector and Combiner: sawtooth')
def __init__(self,idx_self,idx_neighbours):
# print('Phasedetector and Combiner: sawtooth')
self.h = lambda x: sawtooth(x,width=0.5) # set the type of coupling function,here a sawtooth since we consider digital PLLs (rectangular signals)
self.idx_self = idx_self # assigns the index
self.idx_neighbours = idx_neighbours # assigns the neighbors according to the coupling topology
# print('Osci ',idx_self,',my neighbors are:',idx_neighbours)
def __init__(self,idx_neighbours):
# print('Phasedetector and Combiner: sawtooth')
self.part = 0.95 # this needs to come from the constructor! add/change... 1params.txt content?!
self.highHarm = 2.0
self.h = lambda x: sawtooth(x,width=0.5) + self.part * sawtooth(self.highHarm*x + 0.5*np.pi,width=0.5) # set the type of coupling function,idx_neighbours)
# y = 1 / n * sum h( x_delayed_neighbours - x_self )
def __call__(self, t):
return signal.sawtooth(2 * np.pi * self.freq * t, width=0.5)
def demoPCA():
n = 1000
t = np.linspace(0.0, n)
s1 = spsig.sawtooth(t)
s2 = np.cos(0.5*t)
#s3 = np.random.normal(scale=1.2,size=t.size)
s3 = np.random.uniform(-2.0, 2.0,s3)).T
theta1 = np.pi/6.0
rot1 = np.array([[np.cos(theta1), -np.sin(theta1), 0.0],
[np.sin(theta1), np.cos(theta1),
[0.0, 0.0, 1.0]])
theta2 = np.pi/4.0
rot2 = np.array([[ np.cos(theta2), 0.0, np.sin(theta2)],
[ 0.0, 1.0,
[-np.sin(theta2), np.cos(theta2)]])
theta3 = np.pi/5.0
rot3 = np.array([[1.0, 0.0], np.cos(theta3), -np.sin(theta3)], np.sin(theta3), np.cos(theta3)]])
sMixed = s.dot(rot1).dot(rot2).dot(rot3)
lags = 0
pcaFilt = PCA(sMixed, lags=lags)
##pcaFilt.plotMags()
fig = plt.figure()
axOrig = fig.add_subplot(4, 2)
axMixed.plot(sMixed+util.colsep(sMixed))
axMixed.set_title('Mixed Signal (3d rotation)')
axMixed.autoscale(tight=True)
axUnmixed = fig.add_subplot(4, 3)
pcaFilt.plottransform(sMixed, ax=axUnmixed)
axUnmixed.set_title('PCA Components')
axUnmixed.autoscale(tight=True)
axCleaned = fig.add_subplot(4, 4)
pcaFilt.plotFilter(sMixed, comp=(1,2, ax=axCleaned)
axCleaned.set_title('Cleaned Signal (First Component Removed)')
axCleaned.autoscale(tight=True)
fig.tight_layout()
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