def mfreqz(self, b,a=1):
'''
plotting freqz of filter , like matlab representation.
:param b: nominator
:param a: denominator
default: a = 1
'''
from matplotlib import pyplot as plt
from pylab import unwrap, arctan2, imag, real, log10
w, h = signal.freqz(b,a)
h_dB = 20 * log10(abs(h))
plt.subplot(211)
plt.plot(w/max(w), h_dB)
plt.grid()
plt.ylim(-150, 5)
plt.ylabel('Magnitude (db)')
plt.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
plt.title(r'Frequency response')
plt.subplot(212)
h_Phase = unwrap(arctan2(imag(h),real(h)))
plt.plot(w/max(w),h_Phase)
plt.grid()
plt.ylabel('Phase (radians)')
plt.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
plt.title(r'Phase response')
plt.subplots_adjust(hspace=0.5)
plt.show(block=False)
def update_design(self):
ax = self.ax
ax.cla()
ax2 = self.ax2
ax2.cla()
wp = self.wp
ws = self.ws
gpass = self.gpass
gstop = self.gstop
b, a = ss.iirdesign(wp, ws, gpass, gstop, ftype=self.ftype, output='ba')
self.a = a
self.b = b
#b = [1,2]; a = [1,2]
#Print this on command line so we can use it in our programs
print 'b = ', pylab.array_repr(b)
print 'a = ', pylab.array_repr(a)
my_w = pylab.logspace(pylab.log10(.1*self.ws[0]), 0.0, num=512)
#import pdb;pdb.set_trace()
w, h = freqz(b, a, worN=my_w*pylab.pi)
gp = 10**(-gpass/20.)#Go from db to regular
gs = 10**(-gstop/20.)
self.design_line, = ax.plot([.1*self.ws[0], self.ws[0], wp[0], wp[1], ws[1], 1.0], [gs, gs, gp, gp, gs, gs], 'ko:', lw=2, picker=5)
ax.semilogx(w/pylab.pi, pylab.absolute(h),lw=2)
ax.text(.5,1.0, '{:d}/{:d}'.format(len(b), len(a)))
pylab.setp(ax, 'xlim', [.1*self.ws[0], 1.2], 'ylim', [-.1, max(1.1,1.1*pylab.absolute(h).max())], 'xticklabels', [])
ax2.semilogx(w/pylab.pi, pylab.unwrap(pylab.angle(h)),lw=2)
pylab.setp(ax2, 'xlim', [.1*self.ws[0], 1.2])
ax2.set_xlabel('Normalized frequency')
pylab.draw()
def plot_phase_response(b,a=1):
w,h = scipy.signal.freqz(b,a)
h_Phase = pylab.unwrap(numpy.arctan2(numpy.imag(h),numpy.real(h)))
pylab.plot(w/max(w),h_Phase)
pylab.ylabel('Phase (radians)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Phase response')
pylab.subplots_adjust(hspace=0.5)
def plot_phase_response(b, a=1):
w, h = scipy.signal.freqz(b, a)
h_Phase = pylab.unwrap(numpy.arctan2(numpy.imag(h), numpy.real(h)))
pylab.plot(w / max(w), h_Phase)
pylab.ylabel('Phase (radians)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Phase response')
pylab.subplots_adjust(hspace=0.5)
def plot_upd_mfreqz(self, fig, taps, a=1):
if not signal:
return
self.plot_init_mfreqz(fig)
ax1, ax2 = fig.get_axes()
w, h = signal.freqz(taps, a)
if sum(abs(h)) == 0:
return
h_dB = 20 * pylab.log10(abs(h))
ax1.plot(w / max(w), h_dB)
h_Phase = pylab.unwrap(pylab.arctan2(pylab.imag(h), pylab.real(h)))
ax2.plot(w / max(w), h_Phase)
def mfreqz(b,a=1):
'''
#Plot frequency and phase response
from http://mpastell.com/2010/01/18/fir-with-scipy/
'''
w,h = signal.freqz(b,a)
h_dB = 20 * log10 (abs(h))
pylab.subplot(211)
pylab.plot(w/max(w),h_dB)
pylab.ylim(-150, 5)
pylab.ylabel('Magnitude (db)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Frequency response')
pylab.subplot(212)
h_Phase = pylab.unwrap(arctan2(imag(h),real(h)))
pylab.plot(w/max(w),h_Phase)
pylab.ylabel('Phase (radians)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Phase response')
pylab.subplots_adjust(hspace=0.5)
def mfreqz(b, a=1):
'''
#Plot frequency and phase response
from http://mpastell.com/2010/01/18/fir-with-scipy/
'''
w, h = signal.freqz(b, a)
h_dB = 20 * log10(abs(h))
pylab.subplot(211)
pylab.plot(w / max(w), h_dB)
pylab.ylim(-150, 5)
pylab.ylabel('Magnitude (db)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Frequency response')
pylab.subplot(212)
h_Phase = pylab.unwrap(arctan2(imag(h), real(h)))
pylab.plot(w / max(w), h_Phase)
pylab.ylabel('Phase (radians)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Phase response')
pylab.subplots_adjust(hspace=0.5)
def view_filter(h, fp=None, fs=None):
'''view filter'''
w, H = signal.freqz(h, 1)
H_phase = pl.unwrap([pl.degrees(cmath.phase(H[i])) for i in range(len(H))],
180)
H = 20 * pl.log10(abs(H[:]))
x = range(0, len(h))
step = pl.cumsum(h)
pl.figure(figsize=(16, 6.6), dpi=80)
pl.subplot(221)
pl.stem(x, h)
pl.ylabel('Amplitude')
pl.xlabel(r'n (samples)')
pl.title(r'Impulse response')
pl.text(0.2, 0.7, 'N_taps = {0}'.format(len(h)))
pl.subplot(222)
pl.stem(x, step)
pl.ylabel('Amplitude')
pl.xlabel(r'n (samples)')
pl.title(r'Step response')
pl.subplot(223)
pl.plot(w / (2.0 * pl.pi), H)
pl.ylabel('Magnitude (db)')
pl.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pl.title(r'Frequency response')
if fp != None:
pl.axvline(fp, linewidth=1, color='k', ls='-')
if fs != None:
pl.axvline(fs, linewidth=1, color='k', ls='-')
pl.subplot(224)
pl.plot(w / (2.0 * pl.pi), H_phase)
pl.ylabel('Phase (radians)')
pl.xlabel(r'Normalized Frequency (Hz)')
pl.title(r'Phase response')
def view_filter(h, fp=None, fs=None):
'''view filter'''
w, H = signal.freqz(h,1)
H_phase = pl.unwrap([pl.degrees(cmath.phase(H[i])) for i in range(len(H))], 180)
H = 20 * pl.log10 (abs(H[:]))
x = range(0,len(h))
step = pl.cumsum(h)
pl.figure(figsize=(16, 6.6), dpi=80)
pl.subplot(221)
pl.stem(x, h)
pl.ylabel('Amplitude')
pl.xlabel(r'n (samples)')
pl.title(r'Impulse response')
pl.text(0.2, 0.7, 'N_taps = {0}'.format(len(h)))
pl.subplot(222)
pl.stem(x, step)
pl.ylabel('Amplitude')
pl.xlabel(r'n (samples)')
pl.title(r'Step response')
pl.subplot(223)
pl.plot(w/(2.0*pl.pi), H)
pl.ylabel('Magnitude (db)')
pl.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pl.title(r'Frequency response')
if fp != None:
pl.axvline(fp, linewidth=1, color='k', ls='-')
if fs != None:
pl.axvline(fs, linewidth=1, color='k', ls='-')
pl.subplot(224)
pl.plot(w/(2.0*pl.pi), H_phase)
pl.ylabel('Phase (radians)')
pl.xlabel(r'Normalized Frequency (Hz)')
pl.title(r'Phase response')
def _calculatefdData(self,tdData):
#no need to copy it before (access member variable is ugly but shorter)
#calculate the fft of the X channel
fd=py.fft(tdData.getEX())
#calculate absolute and phase values
fdabs=abs(fd)
fdph=abs(py.unwrap(py.angle(fd)))
#calculate frequency axis
dfreq=py.fftfreq(tdData.num_points,tdData.dt)
#extrapolate phase to 0 and 0 frequency
fdph=self.removePhaseOffset(dfreq,fdph)
#set up the fdData array
t=py.column_stack((dfreq,fd.real,fd.imag,fdabs,fdph))
#this is so not nice!
self.fdData=t
unc=self.calculateFDunc()
t=self.getcroppedData(t,0,unc[-1,0])
#interpolate uncertainty
intpunc=interp1d(unc[:,0],unc[:,1:],axis=0)
unc=intpunc(t[:,0])
return py.column_stack((t,unc))
plt.grid(True)
w,h= sg.freqz(nmrz,dmrz,worN=512)
plt.plot((w/np.pi)*nyq_rate, np.absolute(h), linewidth=2)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Gain')
plt.title('Frequency Response')
plt.ylim(-0.05, 1.05)
# Upper inset plot.
ax1 = plt.axes([0.44, 0.56, .45, .25])
plt.plot((w/np.pi)*nyq_rate, np.absolute(h), linewidth=2)
plt.xlim(4000.0,15000.0)
plt.ylim(-0.01, 0.2)
plt.grid(True)
# Lower inset plot.
ax1 = plt.axes([0.44, 0.22, .45, .25])
plt.plot((w/np.pi)*nyq_rate, np.absolute(h), linewidth=2)
plt.xlim(4000.0,15000.0)
plt.ylim(0.96, 1.05)
plt.grid(True)
plt.figure(3)
plt.grid(True)
h_Phase = pl.unwrap(np.arctan2(np.imag(h),np.real(h)))
plt.plot(w/max(w),h_Phase)
plt.ylabel('Phase (radians)')
plt.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
plt.title(r'Phase response')
plt.show()
h_fir = h_ideal*h_kaiser
#+ term=True
print "FIR Filter Coefficients:\n",h_fir
#+ term=False
#' # Plot the FIR filter coefficients
plt.figure(1)
plt.grid(True)
plt.title('Filter Coefficients (%d taps)' % (2*N+1))
plt.plot(h_fir, 'bs-', linewidth=3)
#' # Plot frequency response
plt.figure(2)
plt.clf()
plt.grid(True)
w,h= sg.freqz(h_fir)
plt.plot((w/np.pi)*f_sampling*0.5, np.absolute(h), linewidth=2)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Gain')
plt.title('Frequency Response')
plt.show()
#' # Plot phase response
plt.figure(3)
plt.grid(True)
# Take tan inverse to find the phase plot
hPhase = pylab.unwrap(np.arctan2(np.imag(h),np.real(h)))
plt.plot(w/max(w),hPhase,linewidth=2)
plt.ylabel('Phase (radians)')
plt.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
plt.title(r'Phase response')
#http://www.labbookpages.co.uk/audio/firWindowing.html
