except: pmdec = 0.0
try: parallax = h[0].header['PARLAX1']
except: parallax = 0.0
try: rv = h[0].header['RV1']
except: rv = 0.0
jdutcs[i] = midflux
zmeas = np.nanmedian(chrec['z'])#utils.robust_mean(chrec['z'],3)
rverr = utils.robust_sigma(chrec['z'])*c
if zmeas == 0.0 or midflux < 2457469:
rvs[i] = np.nan
else:
if objname == 'daytimeSky':
rvs[i] = zmeas*c
else:
rvs[i] = utils.barycorr(midflux, ra, dec, pmra=pmra, pmdec=pmdec, parallax=parallax, rv=rv, zmeas=zmeas)
print fitsname, midflux, zmeas, rvs[i], rverr
i += 1
print objname, np.nanstd(rvs)
plt.plot(jdutcs-2457389,rvs-np.nanmean(rvs),'bo')
plt.title(objname)
plt.xlabel('Days since UT 2016-01-01')
plt.ylabel('RV (m/s)')
# plt.xlim([90,160])
plt.savefig(objname + '.png')
plt.close()
#plt.show()
def vank(objname, weightthresh=1.0,chithresh=99.0, sigmaithresh=1.0):
c = 299792458.0
filenames = glob.glob('/Data/kiwispec-proc/n20160[4,5,6]*/*' + objname + '*.chrec.npy')
nobs = len(filenames)
if nobs <= 3: return
vji = []
wji = []
chiji = []
ctsji = []
jdutcs = np.array([])
for filename in filenames:
chrec = np.load(filename)
fitsname = os.path.splitext(os.path.splitext(filename)[0])[0] + '.fits'
h = fits.open(fitsname)
t = Time(h[0].header['DATE-OBS'], format='isot',scale='utc')
midflux = t.jd + h[0].header['EXPTIME']/2.0/86400.0
jdutcs = np.append(jdutcs,midflux)
# reject chunks with bad DSST (Step 1)
bad = np.where(chrec['wt'] == 0)
chrec['z'][bad] = np.nan
# reject chunks where fitter failed to converge (rchi2 == 0 or 100)
# (Step 2)
bad = np.where(chrec['chi'] == 0.0)
bad2 = np.where(chrec['chi'] == 100.0)
chrec['z'][bad] = np.nan
if len(bad2[0]) != 0: ipdb.set_trace()
# reject chunks with the lowest DSST weight (Step 3)
lowweight = np.percentile(chrec['wt'],weightthresh)
bad = np.where(chrec['wt'] <= lowweight)
chrec['z'][bad] = np.nan
# rverr = utils.robust_sigma(chrec['z'])*c
ra = h[0].header['TARGRA1']
dec = h[0].header['TARGDEC1']
try: pmra = h[0].header['PMRA1']
except: pmra = 0.0
try: pmdec = h[0].header['PMDEC1']
except: pmdec = 0.0
try: parallax = h[0].header['PARLAX1']
except: parallax = 0.0
try: rv = h[0].header['RV1']
except: rv = 0.0
result = utils.barycorr(midflux, ra, dec, pmra=pmra, pmdec=pmdec, parallax=parallax, rv=rv, zmeas=0.0)
zb = result/c
rvs = c*((1.0+chrec['z'])*(1.0+zb)-1.0)
if len(vji) == 0: vji = rvs
else: vji = np.vstack((vji,rvs))
if len(wji) == 0: wji = chrec['wt']
else: wji = np.vstack((wji,chrec['wt']))
if len(chiji) == 0: chiji = chrec['chi']
else: chiji = np.vstack((chiji,chrec['wt']))
if len(ctsji) == 0: ctsji = chrec['cts']
else: ctsji = np.vstack((ctsji,chrec['cts']))
nchunks = len(chrec)
vij = np.transpose(vji)
wij = np.transpose(wji)
chiij = np.transpose(chiji)
ctsij = np.transpose(ctsji)
snr = np.sqrt(np.sum(ctsij,axis=0))
# reject chunks with the worst fits (step 4)
chimed = np.nanmedian(chiij,axis=1)
hichi = np.percentile(chimed,chithresh)
bad = np.where(chimed >= hichi)
vij[bad,:] = np.nan
# adjust all chunks to have the same RV zero points (step 5)
# subtract the mean velocity of all observations from each chunk
vij -= np.transpose(np.tile(np.nanmean(vij,axis=1),(nobs,1)))
# compute chunk weights (step 6):
# median velocities for each observation
vjmed = np.nanmedian(vij,axis=0) # eq 2.9
# compute the matrix of velocity differences
Deltaij = vij - np.tile(vjmed,(nchunks,1)) # eq 2.8
sigmai = np.nanstd(Deltaij,axis=1) # eq 2.6
# reject the highest sigmai (step 7)
bad = np.where(sigmai == 0.0)
sigmai[bad] = np.inf
hisigma = np.percentile(sigmai,sigmaithresh)
bad = np.where(sigmai >= hisigma)
sigmai[bad] = np.inf
# compute rj (eq 2.7)
rj = np.nanmedian(np.abs(Deltaij)*np.transpose(np.tile(sigmai,(nobs,1))),axis=0) # eq 2.7
sigmaij = np.transpose(np.tile(sigmai,(nobs,1)))*np.tile(rj,(nchunks,1)) # eq 2.5
# prevent nans
bad = np.where(sigmaij == 0.0)
sigmaij[bad] = np.inf
wij = 1.0/sigmaij**2
vj = np.nansum(vij*wij,axis=0)/np.nansum(wij,axis=0) # eq 2.10
sigmavj = 1.0/np.sqrt(np.nansum(wij,axis=0)) # eq 2.12
print objname + " RMS: " + str(np.nanstd(vj))
# plot scatter vs time
plt.plot(jdutcs-2457389,vj,'bo')
plt.title(objname)
plt.xlabel('Days since UT 2016-01-01')
plt.ylabel('RV (m/s)')
plt.savefig(objname + '.png')
plt.close()
# create a histogram of scatter within a night
mindate = int(math.floor(min(jdutcs)))
maxdate = int(math.ceil(max(jdutcs)))
jdbin = []
rvbin = []
errbin = []
sigmabin = []
for i in range(mindate,maxdate):
match = np.where((jdutcs >= i) & (jdutcs < (i+1)))
if len(match[0]) > 1:
jdbin.append(mindate)
rvbin.append(np.mean(vj[match]))
errbin.append(np.std(vj[match]))
sigmabin.append(np.mean(sigmavj[match]))
# print jdbin, rvbin, errbin, sigmabin
hist, bins = np.histogram(errbin, bins=20)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.xlabel('Intranight RMS (m/s)')
plt.ylabel('# of Nights')
plt.savefig(objname + '.hist.png')
plt.close()
# plot scatter within a night vs SNR
plt.plot(sigmabin, errbin,'bo')
plt.title(objname)
plt.xlabel('Sigma_v_j')
plt.ylabel('Intranight RMS (m/s)')
plt.savefig(objname + '.SNR.png')
plt.close()
# subtract a linear trend from vj
good = np.where(np.isfinite(vj))
t0 = np.nanmedian(jdutcs[good])
coeffs = np.polyfit(jdutcs[good]-t0,vj[good],1)
vjtrend = coeffs[1] + (jdutcs[good]-t0)*coeffs[0]
# plot scatter minus trend vs time
plt.plot(jdutcs[good]-2457389,vj[good]-vjtrend,'bo')
plt.title(objname)
plt.xlabel('Days since UT 2016-01-01')
plt.ylabel('RV (m/s)')
plt.savefig(objname + '.detrended.png')
plt.close()
print objname + " Detrended RMS: " + str(np.nanstd(vj[good]-vjtrend))
def vank(objname, weightthresh=10.0,chithresh=0.0, sigmaithresh=0.0):
c = 299792458.0
if socket.gethostname() == 'Main':
filenames = glob.glob('/Data/kiwispec-proc/n20160[5,6]*/*' + objname + '*.chrec' + exten + '.npy')
else:
filenames = glob.glob('/n/home12/jeastman/minerva/data/n2016051[4-9]/*' + objname + '*.chrec' + exten + '.npy') +\
glob.glob('/n/home12/jeastman/minerva/data/n201605[2-3]?/*' + objname + '*.chrec' + exten + '.npy') +\
glob.glob('/n/home12/jeastman/minerva/data/n2016060?/*' + objname + '*.chrec' + exten + '.npy') +\
glob.glob('/n/home12/jeastman/minerva/data/n2016061[0-2]/*' + objname + '*.chrec' + exten + '.npy')
ntel = 4
nobs = len(filenames)*ntel
if nobs <= 3: return
vji = []
wji = []
chiji = []
ctsji = []
alphaji = []
ipsigmaji = []
slopeji = []
jdutcs = np.array([])
telescope = np.array([])
for filename in filenames:
st = os.stat(filename)
# if (st.st_size != 121120): continue
if (st.st_size == 0.0):
nobs -= 4
continue
chrec = np.load(filename)
fitsname = os.path.splitext(os.path.splitext(filename)[0])[0] + '.fits'
h = pyfits.open(fitsname,mode='update')
# # reject chunks with bad DSST (Step 1)
# bad = np.where(chrec['wt' + str(i)] == 0)
# chrec['z' + str(i)][bad] = np.nan
# reject chunks where fitter failed to converge (rchi2 == 0 or 100)
# (Step 2)
for i in range(1,ntel+1):
if h[0].header['FLUXMID' + str(i)] == 'UNKNOWN':
t = Time(h[0].header['DATE-OBS'], format='isot',scale='utc')
midflux = t.jd+h[0].header['EXPTIME']/2.0/86400.0
h[0].header['FLUXMID' + str(i)] = midflux
else:
midflux = h[0].header['FLUXMID' + str(i)]
# very noisy data can find great fits
bad = np.where(chrec['chi' + str(i)] <= 0.3)
chrec['z' + str(i)][bad] = np.nan
# bad fits will have bad chi^2
bad = np.where(chrec['chi' + str(i)] >= 5)
chrec['z' + str(i)][bad] = np.nan
# very noisy data can find great fits
bad = np.where((chrec['alpha' + str(i)] <= -0.95) | (chrec['alpha' + str(i)] >= 0.95))
chrec['z' + str(i)][bad] = np.nan
# very noisy data can find great fits
bad = np.where((chrec['sigma' + str(i)] <= 0.25) | (chrec['sigma' + str(i)] >= 1.25))
chrec['z' + str(i)][bad] = np.nan
# reject chunks with the lowest DSST weight (Step 3) or bad weights (Step 1)
lowweight = np.nanpercentile(chrec['wt' + str(i)],weightthresh)
bad = np.where((chrec['wt' + str(i)] <= lowweight) | (chrec['wt' + str(i)] == 0))
chrec['z' + str(i)][bad] = np.nan
if 'BARYCOR' + str(i) not in h[0].header.keys():
# do the barycentric correction
ra = h[0].header['TARGRA' + str(i)]
dec = h[0].header['TARGDEC' + str(i)]
try: pmra = h[0].header['PMRA' + str(i)]
except: pmra = 0.0
try: pmdec = h[0].header['PMDEC' + str(i)]
except: pmdec = 0.0
try: parallax = h[0].header['PARLAX' + str(i)]
except: parallax = 0.0
try: rv = h[0].header['RV' + str(i)]
except: rv = 0.0
result = utils.barycorr(midflux, ra, dec, pmra=pmra, pmdec=pmdec, parallax=parallax, rv=rv, zmeas=0.0)
zb = result/c
h[0].header['BARYCOR' + str(i)] = zb
elif h[0].header['BARYCOR' + str(i)] == 'UNKNOWN':
# do the barycentric correction
ra = h[0].header['TARGRA' + str(i)]
dec = h[0].header['TARGDEC' + str(i)]
try: pmra = h[0].header['PMRA' + str(i)]
except: pmra = 0.0
try: pmdec = h[0].header['PMDEC' + str(i)]
except: pmdec = 0.0
try: parallax = h[0].header['PARLAX' + str(i)]
except: parallax = 0.0
try: rv = h[0].header['RV' + str(i)]
except: rv = 0.0
result = utils.barycorr(midflux, ra, dec, pmra=pmra, pmdec=pmdec, parallax=parallax, rv=rv, zmeas=0.0)
zb = result/c
h[0].header['BARYCOR' + str(i)] = zb
else:
zb = h[0].header['BARYCOR' + str(i)]
# active = np.append(active ,h[0].header['FAUSTAT' + str(i)] == 'GUIDING')
if h[0].header['FAUSTAT' + str(i)] == 'GUIDING' or 'daytimeSky' in h[0].header['OBJECT' + str(i)]:
rvs = c*((1.0+chrec['z' + str(i)])*(1.0+zb)-1.0)
# if all chunks are bad, skip it
if len(np.where(np.isnan(rvs))[0]) == len(rvs):
nobs-=1
continue
jdutcs = np.append(jdutcs,midflux)
telescope = np.append(telescope,i)
if len(vji) == 0: vji = rvs
else: vji = np.vstack((vji,rvs))
if len(wji) == 0: wji = chrec['wt' + str(i)]
else: wji = np.vstack((wji,chrec['wt' + str(i)]))
if len(chiji) == 0: chiji = chrec['chi' + str(i)]
else: chiji = np.vstack((chiji,chrec['chi' + str(i)]))
if len(ctsji) == 0: ctsji = chrec['cts' + str(i)]
else: ctsji = np.vstack((ctsji,chrec['cts' + str(i)]))
if len(alphaji) == 0: alphaji = chrec['alpha' + str(i)]
else: alphaji = np.vstack((alphaji,chrec['alpha' + str(i)]))
if len(ipsigmaji) == 0: ipsigmaji = chrec['sigma' + str(i)]
else: ipsigmaji = np.vstack((ipsigmaji,chrec['sigma' + str(i)]))
if len(slopeji) == 0: slopeji = chrec['slope' + str(i)]
else: slopeji = np.vstack((slopeji,chrec['slope' + str(i)]))
else: nobs-=1
h.flush()
h.close()
nchunks = len(chrec)
vij = np.transpose(vji)
wij = np.transpose(wji)
chiij = np.transpose(chiji)
ctsij = np.transpose(ctsji)
alphaij = np.transpose(alphaji)
ipsigmaij = np.transpose(ipsigmaji)
slopeij = np.transpose(slopeji)
snr = np.sqrt(np.nansum(ctsij,axis=0))
# reject chunks with the worst fits (step 4)
chimed = np.nanmedian(chiij,axis=1)
hichi = np.nanpercentile(chimed,100.0-chithresh)
bad = np.where(chimed >= hichi)
vij[bad,:] = np.nan
# adjust all chunks to have the same RV zero points (step 5)
# subtract the mean velocity of all observations from each chunk
vij -= np.transpose(np.tile(np.nanmean(vij,axis=1),(nobs,1)))
# compute chunk weights (step 6):
# median velocities for each observation
vjmed = np.nanmedian(vij,axis=0) # eq 2.9
# compute the matrix of velocity differences
Deltaij = vij - np.tile(vjmed,(nchunks,1)) # eq 2.8
sigmai = np.nanstd(Deltaij,axis=1) # eq 2.6
# reject the highest sigmai (step 7)
bad = np.where(sigmai == 0.0)
sigmai[bad] = np.inf
hisigma = np.nanpercentile(sigmai,100.0-sigmaithresh)
bad = np.where(sigmai >= hisigma)
sigmai[bad] = np.inf
# compute rj (eq 2.7)
rj = np.nanmedian(np.abs(Deltaij)*np.transpose(np.tile(sigmai,(nobs,1))),axis=0) # eq 2.7
sigmaij = np.transpose(np.tile(sigmai,(nobs,1)))*np.tile(rj,(nchunks,1)) # eq 2.5
# prevent nans
bad = np.where(sigmaij == 0.0)
sigmaij[bad] = np.inf
wij = 1.0/sigmaij**2
vj = np.nansum(vij*wij,axis=0)/np.nansum(wij,axis=0) # eq 2.10
sigmavj = 1.0/np.sqrt(np.nansum(wij,axis=0)) # eq 2.12
print objname + " RMS: " + str(np.nanstd(vj))
# plot scatter vs time
colors = ['','r','g','b','orange']
for i in range(1,ntel+1):
match = np.where(telescope == i)
plt.plot(jdutcs[match]-2457389,vj[match],'o',color=colors[i])
plt.title(objname)
plt.xlabel('Days since UT 2016-01-01')
plt.ylabel('RV (m/s)')
plt.savefig(objname + '.' + exten + '.png')
plt.close()
nightlyrvs = {'all':np.array([]),
'T1':np.array([]),
'T2':np.array([]),
'T3':np.array([]),
'T4':np.array([])}
# bin per telescope per night
for jd in range(2457480,2457571):
for i in range(1,ntel+1):
match = np.where((jdutcs >= jd) & (jdutcs < (jd+1)) & np.isfinite(vj) & (telescope==i))
night = np.mean(jdutcs[match])-2457389
nightlyrv = np.sum(vj[match]/sigmavj[match]**2)/np.sum(1.0/sigmavj[match]**2)
plt.plot([night],[nightlyrv],'o',color=colors[i])
nightlyrvs['T' + str(i)] = np.append(nightlyrvs['T' + str(i)],nightlyrv)
match = np.where((jdutcs >= jd) & (jdutcs < (jd+1)) & np.isfinite(vj))
night = np.mean(jdutcs[match])-2457389
nightlyrv = np.sum(vj[match]/sigmavj[match]**2)/np.sum(1.0/sigmavj[match]**2)
plt.plot([night],[nightlyrv],'ko')
nightlyrvs['all'] = np.append(nightlyrvs['all'],nightlyrv)
plt.title(objname)
plt.xlabel('Days since UT 2016-01-01')
plt.ylabel('Nightly binned RV (m/s)')
plt.savefig(objname + '.' + exten + '.binned.png')
plt.close()
print objname + ' nightly binned RMS:'
print "All: " + str(np.nanstd(nightlyrvs['all']))
print "T1: " + str(np.nanstd(nightlyrvs['T1']))
print "T2: " + str(np.nanstd(nightlyrvs['T2']))
print "T3: " + str(np.nanstd(nightlyrvs['T3']))
print "T4: " + str(np.nanstd(nightlyrvs['T4']))
# create a histogram of scatter within a night
mindate = int(math.floor(min(jdutcs)))
maxdate = int(math.ceil(max(jdutcs)))
jdbin = []
rvbin = []
errbin = []
sigmabin = []
for i in range(mindate,maxdate):
match = np.where((jdutcs >= i) & (jdutcs < (i+1)) & (np.isfinite(vj)))
if len(match[0]) > 1:
jdbin.append(mindate)
rvbin.append(np.mean(vj[match]))
errbin.append(np.std(vj[match]))
sigmabin.append(np.mean(sigmavj[match]))
# print jdbin, rvbin, errbin, sigmabin
hist, bins = np.histogram(errbin, bins=20)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.xlabel('Intranight RMS (m/s)')
plt.ylabel('# of Nights')
plt.savefig(objname + '.' + exten + '.hist.png')
plt.close()
# plot median sigma as a function of chunk number
for tel in range(1,ntel+1):
match = np.where(telescope == tel)
for chunk in range(np.shape(ipsigmaij)[0]):
plt.plot(chunk,np.nanmedian(ipsigmaij[chunk,match]),'o',color=colors[tel])
sigmawav.append()
sigmaall.append(np.nanmedian(ipsigmaij[chunk,match]))
sigmatel.append(tel)
plt.title(objname)
plt.xlabel('Chunk number')
plt.ylabel('sigma')
plt.savefig(objname + '.' + exten + '.sigmavchunk.png')
plt.close()
# plot median alpha as a function of chunk number
for tel in range(1,ntel+1):
match = np.where(telescope == tel)
for chunk in range(np.shape(alphaij)[0]):
plt.plot(chunk,np.nanmedian(alphaij[chunk,match]),'o',color=colors[tel])
plt.title(objname)
plt.xlabel('Chunk number')
plt.ylabel('alpha')
plt.savefig(objname + '.' + exten + '.alphavchunk.png')
plt.close()
# plot median slope as a function of chunk number
for tel in range(1,ntel+1):
match = np.where(telescope == tel)
for chunk in range(np.shape(slopeij)[0]):
plt.plot(chunk,np.nanmedian(slopeij[chunk,match]),'o',color=colors[tel])
plt.title(objname)
plt.xlabel('Chunk number')
plt.ylabel('slope')
plt.savefig(objname + '.' + exten + '.slopevchunk.png')
plt.close()
# plot sigma for chunk 150 over time
chunk = 150
for t in jdutcs:
for tel in range(1,ntel+1):
match = np.where((telescope == tel) & (jdutcs == t))
plt.plot(t-2457389,np.nanmedian(ipsigmaij[chunk,match]),'o',color=colors[tel])
plt.title(objname)
plt.xlabel('Days since UT 2016-01-01')
plt.ylabel('sigma')
plt.savefig(objname + '.' + exten + '.sigma150vtime.png')
plt.close()
# plot scatter within a night vs SNR
plt.plot(sigmabin, errbin,'bo')
plt.title(objname)
plt.xlabel('Sigma_v_j')
plt.ylabel('Intranight RMS (m/s)')
plt.savefig(objname + '.' + exten + '.SNR.png')
plt.close()
# subtract a linear trend from vj
good = np.where(np.isfinite(vj))
t0 = np.nanmedian(jdutcs[good])
coeffs = np.polyfit(jdutcs[good]-t0,vj[good],1)
vjtrend = coeffs[1] + (jdutcs[good]-t0)*coeffs[0]
# output text files of JD, rv, rverr
f = open(objname + '.dat','w')
for i in range(len(vj)):
if np.isfinite(vj[i]):
f.write('{0:f} {1:f} {2:f} T{3:d} {4:s}'.format(jdutcs[i],vj[i],sigmavj[i],int(telescope[i]),'\n'))
f.close()
# plot scatter minus trend vs time
plt.plot(jdutcs[good]-2457389,vj[good]-vjtrend,'bo')
plt.title(objname)
plt.xlabel('Days since UT 2016-01-01')
plt.ylabel('RV (m/s)')
plt.savefig(objname + '.' + exten + '.detrended.png')
plt.close()
print objname + " Detrended RMS: " + str(np.nanstd(vj[good]-vjtrend))
for i in range(1,ntel+1):
match = np.where((telescope == i) & np.isfinite(vj))
# rate = 1.0/(jdutcs[match]-np.roll(jdutcs[match],1))
# taus = np.logspace(0, 3, 50)
taus = np.arange(1,len(vj[match])/3)
(t2, ad, ade, adn) = allantools.oadev(vj[match], rate=1, data_type="freq", taus=taus)
plt.loglog(t2,ad,'o',color=colors[i])
# overplot the line
y = np.asarray([pow(tt,-0.5) for tt in taus])*ad[0]
plt.loglog(taus,y,'-',color=colors[i])
match = np.where(np.isfinite(vj))
taus = np.arange(1,len(vj[match])/3)
(t2, ad, ade, adn) = allantools.oadev(vj[match], rate=1, data_type="freq", taus=taus)
plt.loglog(t2,ad,'ko')
# overplot the line
y = np.asarray([pow(tt,-0.5) for tt in taus])*ad[0]
plt.loglog(taus,y,'k-')
plt.xlabel('N bin')
plt.ylabel('Precision (m/s)')
plt.savefig(objname + '.' + exten + '.allan.png')
plt.close()
