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 integrated_B(shot, num_probes=16, probe=3):
"""
There are different way that I've seen the data
integrated: this will plot the different ones, along with
a variety of different moving averages
--KG 06/13/19
"""
# data = [[0] *3 for i in range(num_probes)]
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
data = hdr.getquikData(shot)
btime = data.time
Bz_nBT_15 = cumtrapz(
data.unCalibData[2, probe, :] - mj.get_gaussian_moving_avg(
data.time, data.unCalibData[2, probe, :], 15),
data.time) * -1.103810335732411829e+02
Bz_nBT_100 = cumtrapz(
data.unCalibData[2, probe, :] - mj.get_gaussian_moving_avg(
data.time, data.unCalibData[2, probe, :], 100),
data.time) * -1.103810335732411829e+02
Bz_nBT_500 = cumtrapz(
data.unCalibData[2, probe, :] - mj.get_gaussian_moving_avg(
data.time, data.unCalibData[2, probe, :], 500),
data.time) * -1.103810335732411829e+02
# num_probes = 1 #only plot the first probe
# probe_locs = get_probeLocs_calib_setup(shot)
# Bz=sp.signal.detrend(magdata.fullData[2,probe_num,:])
# Bx=sp.signal.detrend(magdata.fullData[0,probe_num,:])
# By=sp.signal.detrend(magdata.fullData[1,probe_num,:])
# Bz=cumtrapz(data.unCalibData[2,1,:]-mj.get_gaussian_moving_avg(data.time, data.unCalibData[2,1,:], 1), data.time)
Bz = sp.signal.detrend(cumtrapz(data.unCalibData[2, probe, :], data.time))
btime = btime[:len(Bz)]
ax1.plot(btime, Bz, label='Detrend, integrated')
ax1.plot(btime, Bz_nBT_15, label='-moving avg, win = 15')
ax1.plot(btime, Bz_nBT_100, label='-moving avg, win = 100')
ax1.plot(btime, Bz_nBT_500, label='-moving avg, win = 500')
ax1.legend()
Bdot25 = np.zeros([3, 25, 8192])
timeB = data.time[801:]
Bdot25[0, 3, :] = data.Bdot[0, 3, :]
Bdot25[1, 3, :] = data.Bdot[1, 3, :]
Bdot25[2, 3, :] = data.Bdot[2, 3, :]
bzero = pyl.detrend_linear(Bdot25[2, 3, 800:])
bint = sp.integrate.cumtrapz(bzero, data.time[800:])
ax2.plot(btime, Bz_nBT_15, label='-moving avg, win = 15')
ax2.plot(btime, Bz_nBT_500, label='-moving avg, win = 500')
ax2.plot(timeB, bint, label="detrend LINEAR and integrated time shifted")
# ax2.set_title("Current")
plt.legend()
plt.show()
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 process_mjmag_data(shot):
data = hdr.getMJMagData(shot)
#print data.settings
#recast array into [3,25]
Bdot25 = np.zeros([3, 25, 8192])
#1
Bdot25[0, 0, :] = data.Bdot[0, 0, :]
Bdot25[1, 0, :] = data.Bdot[1, 0, :]
#2
Bdot25[0, 1, :] = data.Bdot[0, 1, :]
Bdot25[1, 1, :] = data.Bdot[1, 1, :]
#3
Bdot25[0, 2, :] = data.Bdot[0, 2, :]
Bdot25[1, 2, :] = data.Bdot[1, 2, :]
#4
Bdot25[0, 3, :] = data.Bdot[0, 3, :]
Bdot25[1, 3, :] = data.Bdot[1, 3, :]
#5
Bdot25[0, 4, :] = data.Bdot[0, 4, :]
Bdot25[1, 4, :] = data.Bdot[1, 4, :]
#6
Bdot25[0, 5, :] = data.Bdot[0, 5, :]
Bdot25[1, 5, :] = data.Bdot[1, 5, :]
#7
Bdot25[0, 6, :] = data.Bdot[0, 6, :]
Bdot25[1, 6, :] = data.Bdot[1, 6, :]
#8
Bdot25[0, 7, :] = data.Bdot[0, 7, :]
Bdot25[1, 7, :] = data.Bdot[1, 7, :]
#9
Bdot25[0, 8, :] = data.Bdot[0, 8, :]
Bdot25[1, 8, :] = data.Bdot[1, 8, :]
#10
Bdot25[0, 9, :] = data.Bdot[0, 9, :]
Bdot25[1, 9, :] = data.Bdot[1, 9, :]
#11
Bdot25[0, 10, :] = data.Bdot[0, 10, :]
Bdot25[1, 10, :] = data.Bdot[1, 10, :]
#12
Bdot25[0, 11, :] = data.Bdot[0, 11, :]
Bdot25[1, 11, :] = data.Bdot[1, 11, :]
#13
Bdot25[0, 12, :] = data.Bdot[0, 12, :]
Bdot25[1, 12, :] = data.Bdot[1, 12, :]
Bdot25[2, 12, :] = data.Bdot[2, 2, :]
#14
Bdot25[0, 13, :] = data.Bdot[0, 13, :]
Bdot25[1, 13, :] = data.Bdot[1, 13, :]
Bdot25[2, 13, :] = data.Bdot[2, 1, :]
#15
Bdot25[0, 14, :] = data.Bdot[0, 14, :]
Bdot25[1, 14, :] = data.Bdot[1, 14, :]
Bdot25[2, 14, :] = data.Bdot[2, 0, :]
#16
Bdot25[0, 15, :] = data.Bdot[0, 15, :]
Bdot25[1, 15, :] = data.Bdot[1, 15, :]
#17
Bdot25[0, 16, :] = data.Bdot[2, 3, :]
Bdot25[1, 16, :] = data.Bdot[2, 12, :]
#18
Bdot25[0, 17, :] = data.Bdot[2, 4, :]
Bdot25[1, 17, :] = data.Bdot[2, 13, :]
#19
Bdot25[0, 18, :] = data.Bdot[2, 5, :]
Bdot25[1, 18, :] = data.Bdot[2, 14, :]
#20
Bdot25[0, 19, :] = data.Bdot[2, 6, :]
Bdot25[1, 19, :] = data.Bdot[2, 15, :]
#21
Bdot25[0, 20, :] = data.Bdot[2, 7, :]
#22
Bdot25[0, 21, :] = data.Bdot[2, 8, :]
#23
Bdot25[0, 22, :] = data.Bdot[2, 9, :]
#24
Bdot25[0, 23, :] = data.Bdot[2, 10, :]
#25
Bdot25[0, 24, :] = data.Bdot[2, 11, :]
#reintegrate with later start times
B25 = np.zeros([3, 25, 7391])
timeB = data.time[801:]
for i in np.arange(3):
for j in np.arange(25):
#bzero = Bdot25[i,j,:]-np.mean(Bdot25[i,j,800:8000])
bzero = plt.detrend_linear(Bdot25[i, j, 800:])
bint = sp.cumtrapz(bzero, data.time[800:])
B25[i, j, :] = bint
#compute Bmod
Bmod25 = np.zeros([25, 7391])
for j in np.arange(25):
Bmod25[j, :] = np.sqrt(B25[0, j, :]**2 + B25[1, j, :]**2 +
B25[2, j, :]**2)
return data.time, Bdot25, timeB, B25, Bmod25, data
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)
