ds.spec['rr'], 1, 1 / rms**2
) # compute variance-weighted RR tseries (so RFI doesn't dominate)
t = ds.time.mjds()
print 't0:', ds.t0()
print 'Saving tseries to', savefile
save(savefile, tseries)
print 'Saving times to', timefile, '\n'
save(timefile, t)
else:
t = load(timefile)
tseries = load(savefile)
t = mjds_to_hjds(t, sb)
ind = find(tseries != 0)
times[band] += t[ind].tolist()
flux[band] += tseries[ind].tolist()
plot(t[ind] - t[0], tseries[ind])
leg.append(obs + band)
legend(leg)
filename = 'tseries/' + band + 'band_tseries.dat'
savetxt(filename, column_stack((times[band], flux[band])))
savefig('tseries/tseries_band.pdf', bbox_inches='tight')
figure()
for band in bands:
plot(times[band], flux[band])
legend(bands)
savefig('tseries/long.pdf', bbox_inches='tight')
# frequency range to search w/ LS periodogram
Pmin = 1. # hrs
Pmax = 25.
yfit_imag = intercept_imag + slope_imag * f
clf()
#dsR.plot_dynspec(plot_params={'pol':'rr','smin':0.01})
#plot(f/1.e3,treal,'.b')
#plot(f/1.e3,yfit,'-b')
drift_rate = 1 / slope # MHz/sec
print 'Drift rate:', drift_rate, 'MHz/sec'
#show()
tseries1 = dsR.tseries(fmin=1.6e9, fmax=1.8e9)
tseries2 = dsR.tseries(fmin=1.8e9, fmax=2.0e9)
t = tseries1.get_tlist() * tseries1.dt()
plot(t, tseries1.spec['rr'])
plot(t, tseries2.spec['rr'])
xlabel('Time in seconds')
ylabel('Flux (mJy) in RCP')
legend(('1.6-1.8 GHz', '1.8-2 GHz'))
savefig('plots/drift.pdf', bbox_inches='tight')
### LCP portion of burst ###
dsL = ds_bin.clip(tmin=0, tmax=8, fmin=1.6e9, fmax=4.0e9)
dsL = dsL.bin_dynspec(nt=2, nf=2)
clf()
dsL.plot_dynspec(
plot_params={
'pol': 'v',
'smin': -0.015,
SI = spec.spec['i']
SI_err = spec_err['i']
rc_mask = ~ (~SI.mask * ~SV.mask)
rc_min = ma.masked_array(zeros(len(SV)),mask=rc_mask)
rc_best = ma.masked_array(zeros(len(SV)),mask=rc_mask)
rc_max = ma.masked_array(zeros(len(SV)),mask=rc_mask)
for i in range(len(SV)):
if ~rc_mask[i]:
rc_min[i],rc_max[i],rc_best[i] = rc_conf_int({'I':real(SI[i]),'Ierr':SI_err[i],'V':real(SV[i]),'Verr':SV_err[i]},conf=0.68)
f = spec.f/1.e9
close('all')
figure(figsize=(3,4))
subplot(211)
plot(f,real(rr),'b')
fill_between(f, real(rr)-rr_err, real(rr)+rr_err,alpha=0.3, facecolor='b',linewidth=0)
plot(f,real(ll),'g')
fill_between(f, real(ll)-ll_err, real(ll)+ll_err,alpha=0.3, facecolor='g',linewidth=0)
#plot(f,imag(rr),'b--')
#plot(f,imag(ll),'g--')
xlim([1,4])
legend(('RCP','LCP'))
gca().axhline(0,color='k')
ylabel('Time-avg Flux Density (mJy)')
#axis([1,4,-18,18])
#show()
subplot(212)
#plot(f,rc_best,'k')
fill_between(f, rc_min, rc_max, alpha=0.5, facecolor='k',linewidth=0)
RA_hms=RA_hms_UVCet,
dec_dms=dec_dms_UVCet,
site_name='vla',
doprint=True) # just to print light travel time
t_TDB_hours = array([t.value * 24. for t in t_TDB])
# select only the times that are not masked
ind = find(~tseries.spec[pol].mask)
# add those points to the times and flux lists for all obs together (for this band)
times[band] += t_TDB_hours[ind].tolist()
flux[band] += real(tseries.spec[pol])[ind].tolist()
# plot this obs/band's tseries so I can see what they all look like
# (time axis is relative to obs start time so they won't be phased)
plot(t_TDB_hours[ind] - t_TDB_hours[0], real(tseries.spec[pol])[ind])
obs = dsfile.split('/')[5]
leg.append(obs + band)
# label the plot of all time series from this band
legend(leg)
# save a time series of all observations from this band together
# ! look at this file ! this may already be what I need.
tseries_filename = mydir + 'tseries/' + band + 'band_' + pol + '_doclip' + str(
do_clip) + '_tseries.dat'
savetxt(tseries_filename, column_stack((times[band], flux[band])))
# save a figure of the time series in both bands (one subplot per band)
savefig(mydir + 'tseries/tseries_band_' + pol + '_doclip' + str(do_clip) +
'.pdf',
if ~rc_mask[i]:
rc_min[i], rc_max[i], rc_best[i] = rc_conf_int(
{
'I': real(SI[i]),
'Ierr': SI_err[i],
'V': real(SV[i]),
'Verr': SV_err[i]
},
conf=0.68)
f = spec.f / 1.e9
close('all')
figure(figsize=(3, 4))
subplot(211)
plot(f, real(rr), 'b')
fill_between(f,
real(rr) - rr_err,
real(rr) + rr_err,
alpha=0.3,
facecolor='b',
linewidth=0)
plot(f, real(ll), 'g')
fill_between(f,
real(ll) - ll_err,
real(ll) + ll_err,
alpha=0.3,
facecolor='g',
linewidth=0)
#plot(f,imag(rr),'b--')
#plot(f,imag(ll),'g--')
def flin(C, x):
return C[0] * x
linear = odrpack.Model(flin)
sx = std(imag(rr)) * ones(len(x))
sy = std(imag(ll)) * ones(len(y))
mydata = odrpack.RealData(x, y, sx=sx, sy=sy)
myodr = odrpack.ODR(mydata, linear, beta0=[1.]) #,0.])
myoutput = myodr.run()
C = myoutput.beta
Cerr = myoutput.sd_beta
print 'chi2, I think (\"residual variance\"):', myoutput.res_var
plot(x, y, '.')
xmax = max(ma.max(real(rr)), ma.max(real(ll)))
xmin = min(ma.min(real(rr)), ma.min(real(ll)))
dx = (xmax - xmin) / 10
xmax += dx
xmin -= dx
ylin1 = C[0] * xmin #+ C[1]
ylin2 = C[0] * xmax #+ C[1]
plot([xmin, xmax], [ylin1, ylin2], 'k-')
axis([xmin, xmax, xmin, xmax])
gca().set_aspect(1)
rc = (1 - C[0]) / (1 + C[0])
print 'rc:', rc
xplt = sort(x)
mA = C[0] + Cerr[0]
rms = std(ds.spec['ll'],0) # rms for each frequency
tseries = average(ds.spec['rr'],1,1/rms**2) # compute variance-weighted RR tseries (so RFI doesn't dominate)
t = ds.time.mjds()
print 't0:',ds.t0()
print 'Saving tseries to',savefile
save(savefile,tseries)
print 'Saving times to',timefile,'\n'
save(timefile,t)
else:
t = load(timefile)
tseries = load(savefile)
t = mjds_to_hjds(t,sb)
ind = find( tseries != 0 )
times[band] += t[ind].tolist()
flux[band] += tseries[ind].tolist()
plot(t[ind]-t[0],tseries[ind])
leg.append(obs+band)
legend(leg)
filename='tseries/'+band+'band_tseries.dat'
savetxt(filename,column_stack((times[band],flux[band])))
savefig('tseries/tseries_band.pdf',bbox_inches='tight')
figure()
for band in bands:
plot(times[band],flux[band])
legend(bands)
savefig('tseries/long.pdf',bbox_inches='tight')
# frequency range to search w/ LS periodogram
Pmin = 1. # hrs
Pmax = 25.
yfit_imag = intercept_imag + slope_imag * f
clf()
#dsR.plot_dynspec(plot_params={'pol':'rr','smin':0.01})
#plot(f/1.e3,treal,'.b')
#plot(f/1.e3,yfit,'-b')
drift_rate = 1/slope # MHz/sec
print 'Drift rate:', drift_rate, 'MHz/sec'
#show()
tseries1 = dsR.tseries(fmin=1.6e9,fmax=1.8e9)
tseries2 = dsR.tseries(fmin=1.8e9,fmax=2.0e9)
t = tseries1.get_tlist() * tseries1.dt()
plot(t,tseries1.spec['rr'])
plot(t,tseries2.spec['rr'])
xlabel('Time in seconds')
ylabel('Flux (mJy) in RCP')
legend(('1.6-1.8 GHz','1.8-2 GHz'))
savefig('plots/drift.pdf',bbox_inches='tight')
### LCP portion of burst ###
dsL = ds_bin.clip(tmin=0,tmax=8,fmin=1.6e9,fmax=4.0e9)
dsL = dsL.bin_dynspec(nt=2,nf=2)
clf()
dsL.plot_dynspec(plot_params={'pol':'v','smin':-0.015,'smax':0.015,'df':0.1,'dx':0.5,'dy':0.1,'scale':'linear'})
savefig('plots/dsL.pdf',bbox_inches='tight')
x = real(rr).compressed()
y = real(ll).compressed()
def flin(C,x):
return C[0]*x
linear = odrpack.Model(flin)
sx = std(imag(rr)) * ones(len(x))
sy = std(imag(ll)) * ones(len(y))
mydata = odrpack.RealData(x,y,sx=sx,sy=sy)
myodr = odrpack.ODR(mydata,linear,beta0=[1.]) #,0.])
myoutput = myodr.run()
C = myoutput.beta
Cerr = myoutput.sd_beta
print 'chi2, I think (\"residual variance\"):', myoutput.res_var
plot(x,y,'.')
xmax = max(ma.max(real(rr)),ma.max(real(ll)))
xmin = min(ma.min(real(rr)),ma.min(real(ll)))
dx = (xmax-xmin)/10
xmax += dx
xmin -= dx
ylin1 = C[0]*xmin #+ C[1]
ylin2 = C[0]*xmax #+ C[1]
plot([xmin,xmax],[ylin1,ylin2],'k-')
axis([xmin,xmax,xmin,xmax])
gca().set_aspect(1)
rc = (1-C[0])/(1+C[0])
print 'rc:', rc
xplt = sort(x)
mA = C[0] + Cerr[0]