def PowerSpectrum(data): # power spectrum smooth_data = SmoothFunction(data) freqs = numpy.array(numpy.fft.fftfreq(n=len(data), d=dt))/c fft = numpy.array(numpy.fft.fft(smooth_data)) fft = fft * freqs spectrum = abs(fft) * abs(fft) smooth_spectrum = Smoothing.window_smooth(spectrum, window='hamming', window_len=15) return (freqs,spectrum,smooth_spectrum)
def PlotSpans (files, axs, clr): cdfs = [CDF(f) for f in files] data = [c[0] for c in cdfs] data = reduce(operator.add, data) hist,edges = numpy.histogram(data, range=(0.0,400.0), bins=200) hist = Smoothing.window_smooth(hist) if clr == 'b': plt.bar([i-width/2 for i in range(len(new_bins[:-1]))], hist, width, color=clr, align='center') else: plt.bar([i+width/2 for i in range(len(new_bins[:-1]))], hist, width, color=clr, align='center')
def getLifespan(file,axs=None): cdf = CDF(file) # raw data data = cdf[0] # smooth out the data and digitize above a certain threshold smooth = Smoothing.window_smooth(numpy.array(data), window_len=40, window='blackman') smooth = smooth - 0.18 new_data = numpy.ceil(smooth) smooth = smooth + 0.18 if axs: axs.plot(data[1200:2800], linewidth=2.5, color='k', linestyle=':', alpha=0.7, label=r'$C(t)$') axs.plot(smooth[1200:2800], linewidth=2.0, color='r', linestyle='-', alpha=0.7, label=r'$C_s(t)$') axs.plot(new_data[1200:2800], linewidth=3.5, color='g', linestyle='-', alpha=0.8, label=r'$f(t)$') return [c for c in CutUpCycleBits(new_data)]
fig = plt.figure(num=None, facecolor='w', edgecolor='w', frameon=True)
axs = fig.add_subplot(1,1,1)
axs.set_ylabel(r'Spectrum', size='xx-large')
axs.set_xlabel(r'Frequency / cm$^{-1}$', size='xx-large')
# use the same axis for all the spectra
freq_axis = FreqAxis(len(tcfs[0])/2,dt)
fft_tcfs_x = [FFT(t) for t in tcfs_x]
fft_tcfs_y = [FFT(t) for t in tcfs_y]
fft_tcfs_z = [FFT(t) for t in tcfs_z]
fft_tcfs = [(x+y+z)/3.0 for x,y,z in zip(fft_tcfs_x,fft_tcfs_y,fft_tcfs_z)]
#avg_fft = reduce(operator.add, fft_tcfs_z)/len(fft_tcfs_z)
avg_fft = FFT(avg_tcf)
smooth = Smoothing.window_smooth(avg_fft)
#for f in fft_tcfs:
#axs.plot(freq_axis, f, linewidth=2.0)
axs.plot(freq_axis, smooth, linewidth=4.0, linestyle='-', color='k')
vector_tcf_ffts = [FFT(t) for t in vector_tcfs]
avg_vector_fft = numpy.array(reduce(operator.add,vector_tcf_ffts))/len(vector_tcf_ffts)
smooth_vector_fft = Smoothing.window_smooth(avg_vector_fft)
vector_freq_axis = FreqAxis(correlation_tau,dt)
print len(vector_freq_axis)
print len(smooth_vector_fft)
axs.plot(vector_freq_axis, smooth_vector_fft, linewidth=4.0, linestyle='-', color='r')
