def fftplt1(x,Mch,dtrnd=True,demean=True): fs=1 if demean: x = x - x.mean() if dtrnd: Pout = plt.psd(x, NFFT=Mch, detrend=pylab.detrend_linear, noverlap=Mch/2, Fs=fs) else: Pout = plt.psd(x, NFFT=Mch, detrend=pylab.detrend_none, noverlap=Mch/2, Fs=fs) xdtrnd = pylab.detrend_linear(x) xauto = mcmath.acorr_mlab(xdtrnd,2) rhoxauto = (xauto[1] + np.sqrt(abs(xauto[2])))/2 R = mcmath.redspec(rhoxauto,np.arange(Mch/2),Mch) P = Pout[0][:R.size] F = Pout[1][:R.size] Psum = P.sum() Rsum = R.sum() PRratio = Psum/Rsum Rcmp = R*PRratio plt.figure() plt.plot(F,P) plt.plot(F,Rcmp) return (F,P,Rcmp)
def fftplt3(x,Mch,pval=0.1,dtrnd=True,demean=True,titlestr='',Dt=0.01):
"""Differs from fftplt3 solely by plotting the power spectral density
in log-form, 10*log10(P)."""
fs=1
titl_plc = (1.,1.)
if demean:
x = x - x.mean()
if dtrnd:
Pout = plt.psd(x, NFFT=Mch, detrend=pylab.detrend_linear, noverlap=Mch/2, Fs=fs)
else:
Pout = plt.psd(x, NFFT=Mch, detrend=pylab.detrend_none, noverlap=Mch/2, Fs=fs)
xdtrnd = pylab.detrend_linear(x)
xauto = mcmath.acorr_mlab(xdtrnd,2)
rhoxauto = (xauto[1] + np.sqrt(abs(xauto[2])))/2
R = mcmath.redspec(rhoxauto,np.arange(Mch/2),Mch)
P = Pout[0][:R.size]
F = Pout[1][:R.size]
Psum = P.sum()
Rsum = R.sum()
PRratio = Psum/Rsum
Rcmp = R*PRratio
dof = (x.size / (Mch/2.)) * 1.2
Fval = stats.f.isf(pval,dof,dof)
tst = P / Rcmp
pass1 = np.where(tst > Fval)[0]
maxs = mcmath.extrema_find(P,'max',t=F,dt=Dt)
max_ind = np.intersect1d(maxs,pass1)
Fmaxs = F[max_ind]
Fmaxs2 = np.append(Fmaxs,0.1)
Fmaxs2.sort()
Tmaxs = 1/(Fmaxs2)
Tmaxs = np.round(Tmaxs,2)
ax = plt.gca()
ax.plot(F,10*np.log10(Rcmp))
ax.set_xticks(Fmaxs2)
ax.set_xticklabels(Tmaxs)
my_fmt_xdate(ax,rot=90,hal='center')
multiline(Fmaxs,c='red',ls='--')
xtl = ax.get_xticklabels()
ytl = ax.get_yticklabels()
plt.setp(xtl,'size',10)
plt.setp(ytl,'size',9)
ppct = int((1 - pval)*100)
titl_str = '%s FFT (chunksize: %d) :: peak CL: %d%%' % (titlestr,Mch,ppct)
plt.text(titl_plc[0],titl_plc[1],titl_str,ha='right',va='bottom',size=12,transform=ax.transAxes)
plt.xlabel('Peak period (yrs)',size=11)
return (F,P,Rcmp)
def fftplt1(x, Mch, dtrnd=True, demean=True):
fs = 1
if demean:
x = x - x.mean()
if dtrnd:
Pout = plt.psd(x,
NFFT=Mch,
detrend=pylab.detrend_linear,
noverlap=Mch / 2,
Fs=fs)
else:
Pout = plt.psd(x,
NFFT=Mch,
detrend=pylab.detrend_none,
noverlap=Mch / 2,
Fs=fs)
xdtrnd = pylab.detrend_linear(x)
xauto = mcmath.acorr_mlab(xdtrnd, 2)
rhoxauto = (xauto[1] + np.sqrt(abs(xauto[2]))) / 2
R = mcmath.redspec(rhoxauto, np.arange(Mch / 2), Mch)
P = Pout[0][:R.size]
F = Pout[1][:R.size]
Psum = P.sum()
Rsum = R.sum()
PRratio = Psum / Rsum
Rcmp = R * PRratio
plt.figure()
plt.plot(F, P)
plt.plot(F, Rcmp)
return (F, P, Rcmp)
def fftplt2(x,Mch,pval=0.1,dtrnd=True,demean=True,titlestr='',Dt=0.01):
"""Differs from fftplt1 by returning a plot highlighting sig peaks, with their
freq axis tick marks notated by their period. Also allows adjustment of sig level and title string."""
fs=1
titl_plc = (1.,1.)
if demean:
x = x - x.mean()
if dtrnd:
Pout = plt.psd(x, NFFT=Mch, detrend=pylab.detrend_linear, noverlap=Mch/2, Fs=fs)
else:
Pout = plt.psd(x, NFFT=Mch, detrend=pylab.detrend_none, noverlap=Mch/2, Fs=fs)
xdtrnd = pylab.detrend_linear(x)
xauto = mcmath.acorr_mlab(xdtrnd,2)
rhoxauto = (xauto[1] + np.sqrt(abs(xauto[2])))/2
R = mcmath.redspec(rhoxauto,np.arange(Mch/2),Mch)
P = Pout[0][:R.size]
F = Pout[1][:R.size]
Psum = P.sum()
Rsum = R.sum()
PRratio = Psum/Rsum
Rcmp = R*PRratio
dof = (x.size / (Mch/2.)) * 1.2
Fval = stats.f.isf(pval,dof,dof)
tst = P / Rcmp
pass1 = np.where(tst > Fval)[0]
maxs = mcmath.extrema_find(P,'max',t=F,dt=Dt)
max_ind = np.intersect1d(maxs,pass1)
Fmaxs = F[max_ind]
Fmaxs2 = np.append(Fmaxs,0.1)
Fmaxs2.sort()
Tmaxs = 1/(Fmaxs2)
Tmaxs = np.round(Tmaxs,2)
fig = plt.figure(figsize=(7,5))
ax = fig.add_axes((0.1,0.14,0.88,0.81))
ax.plot(F,P,)
ax.plot(F,Rcmp)
ax.set_xticks(Fmaxs2)
ax.set_xticklabels(Tmaxs)
my_fmt_xdate(ax,rot=90,hal='center')
multiline(Fmaxs,c='red',ls='--')
xtl = ax.get_xticklabels()
ytl = ax.get_yticklabels()
plt.setp(xtl,'size',10)
plt.setp(ytl,'size',9)
ppct = int((1 - pval)*100)
titl_str = '%s FFT (chunksize: %d) :: peak CL: %d%%' % (titlestr,Mch,ppct)
plt.text(titl_plc[0],titl_plc[1],titl_str,ha='right',va='bottom',size=12,transform=ax.transAxes)
plt.xlabel('Peak period (yrs)',size=11)
return (F,P,Rcmp)
def fftplt3(x, Mch, pval=0.1, dtrnd=True, demean=True, titlestr='', Dt=0.01):
"""Differs from fftplt3 solely by plotting the power spectral density
in log-form, 10*log10(P)."""
fs = 1
titl_plc = (1., 1.)
if demean:
x = x - x.mean()
if dtrnd:
Pout = plt.psd(x,
NFFT=Mch,
detrend=pylab.detrend_linear,
noverlap=Mch / 2,
Fs=fs)
else:
Pout = plt.psd(x,
NFFT=Mch,
detrend=pylab.detrend_none,
noverlap=Mch / 2,
Fs=fs)
xdtrnd = pylab.detrend_linear(x)
xauto = mcmath.acorr_mlab(xdtrnd, 2)
rhoxauto = (xauto[1] + np.sqrt(abs(xauto[2]))) / 2
R = mcmath.redspec(rhoxauto, np.arange(Mch / 2), Mch)
P = Pout[0][:R.size]
F = Pout[1][:R.size]
Psum = P.sum()
Rsum = R.sum()
PRratio = Psum / Rsum
Rcmp = R * PRratio
dof = (x.size / (Mch / 2.)) * 1.2
Fval = stats.f.isf(pval, dof, dof)
tst = P / Rcmp
pass1 = np.where(tst > Fval)[0]
maxs = mcmath.extrema_find(P, 'max', t=F, dt=Dt)
max_ind = np.intersect1d(maxs, pass1)
Fmaxs = F[max_ind]
Fmaxs2 = np.append(Fmaxs, 0.1)
Fmaxs2.sort()
Tmaxs = 1 / (Fmaxs2)
Tmaxs = np.round(Tmaxs, 2)
ax = plt.gca()
ax.plot(F, 10 * np.log10(Rcmp))
ax.set_xticks(Fmaxs2)
ax.set_xticklabels(Tmaxs)
my_fmt_xdate(ax, rot=90, hal='center')
multiline(Fmaxs, c='red', ls='--')
xtl = ax.get_xticklabels()
ytl = ax.get_yticklabels()
plt.setp(xtl, 'size', 10)
plt.setp(ytl, 'size', 9)
ppct = int((1 - pval) * 100)
titl_str = '%s FFT (chunksize: %d) :: peak CL: %d%%' % (titlestr, Mch,
ppct)
plt.text(titl_plc[0],
titl_plc[1],
titl_str,
ha='right',
va='bottom',
size=12,
transform=ax.transAxes)
plt.xlabel('Peak period (yrs)', size=11)
return (F, P, Rcmp)
def fftplt2(x, Mch, pval=0.1, dtrnd=True, demean=True, titlestr='', Dt=0.01):
"""Differs from fftplt1 by returning a plot highlighting sig peaks, with their
freq axis tick marks notated by their period. Also allows adjustment of sig level and title string."""
fs = 1
titl_plc = (1., 1.)
if demean:
x = x - x.mean()
if dtrnd:
Pout = plt.psd(x,
NFFT=Mch,
detrend=pylab.detrend_linear,
noverlap=Mch / 2,
Fs=fs)
else:
Pout = plt.psd(x,
NFFT=Mch,
detrend=pylab.detrend_none,
noverlap=Mch / 2,
Fs=fs)
xdtrnd = pylab.detrend_linear(x)
xauto = mcmath.acorr_mlab(xdtrnd, 2)
rhoxauto = (xauto[1] + np.sqrt(abs(xauto[2]))) / 2
R = mcmath.redspec(rhoxauto, np.arange(Mch / 2), Mch)
P = Pout[0][:R.size]
F = Pout[1][:R.size]
Psum = P.sum()
Rsum = R.sum()
PRratio = Psum / Rsum
Rcmp = R * PRratio
dof = (x.size / (Mch / 2.)) * 1.2
Fval = stats.f.isf(pval, dof, dof)
tst = P / Rcmp
pass1 = np.where(tst > Fval)[0]
maxs = mcmath.extrema_find(P, 'max', t=F, dt=Dt)
max_ind = np.intersect1d(maxs, pass1)
Fmaxs = F[max_ind]
Fmaxs2 = np.append(Fmaxs, 0.1)
Fmaxs2.sort()
Tmaxs = 1 / (Fmaxs2)
Tmaxs = np.round(Tmaxs, 2)
fig = plt.figure(figsize=(7, 5))
ax = fig.add_axes((0.1, 0.14, 0.88, 0.81))
ax.plot(
F,
P,
)
ax.plot(F, Rcmp)
ax.set_xticks(Fmaxs2)
ax.set_xticklabels(Tmaxs)
my_fmt_xdate(ax, rot=90, hal='center')
multiline(Fmaxs, c='red', ls='--')
xtl = ax.get_xticklabels()
ytl = ax.get_yticklabels()
plt.setp(xtl, 'size', 10)
plt.setp(ytl, 'size', 9)
ppct = int((1 - pval) * 100)
titl_str = '%s FFT (chunksize: %d) :: peak CL: %d%%' % (titlestr, Mch,
ppct)
plt.text(titl_plc[0],
titl_plc[1],
titl_str,
ha='right',
va='bottom',
size=12,
transform=ax.transAxes)
plt.xlabel('Peak period (yrs)', size=11)
return (F, P, Rcmp)