def flag_tell(w, f, w_tpl, f_tpl, bp, o):
# another way of flagging tellurics
# more testing needed
x = np.arange(len(f_tpl))
ba = (np.load("lib/CRIRES/blaze_K2166.npy"))[o - 1]
bandp = (np.poly1d(ba)(x))
# print(len(x),len(bandp))
# exit()
good = np.where(
f_tpl / np.nanmean(f_tpl) > 0.8 * bandp / np.nanmean(bandp))
bp[f_tpl / np.nanmean(f_tpl) < 0.8 * bandp / np.nanmean(bandp)] |= 64
# good = np.where((f_tpl/np.nanmean(f_tpl) > 0.9*bandp/np.nanmean(bandp)) & (f/np.nanmean(f) > 0.9*bandp/np.nanmean(bandp)))
# bp[(f_tpl/np.nanmean(f_tpl) < 0.9*bandp/np.nanmean(bandp)) & (f/np.nanmean(f) < 0.9*bandp/np.nanmean(bandp))] |= 64
w1 = w * 1.
gplot(w, f / np.nanmean(f), 'w l t "data flag",', w_tpl,
f_tpl / np.nanmean(f_tpl), 'w l t "tpl flag",', w1,
bandp / np.mean(bandp) * 0.85, 'w l t "blaze"')
pause()
w = w[good]
f = f[good]
w_tpl = w_tpl[good]
f_tpl = f_tpl[good]
# return w,f,w_tpl,f_tpl
return bp
def qfacmask(lnw, f, sz=26, thres=3000., plot=False):
'''mask the parts of the spectrum which have a a Q-factor higher than thres
calculate moving Q factor'''
lnf = np.log(f)
qi = (np.gradient(lnf) /
np.gradient(lnw))**2 # ((lnf[1:]-lnf[:-1])/(lnw[1:]-lnw[:-1]))**2
Q = np.zeros(len(qi) + sz) # includes padded zeros
Q[sz / 2:-sz / 2] = np.cumsum(qi)
Q[-sz / 2:] = Q[-sz / 2 - 1] # pad the last; the first have zero
num = np.empty_like(qi)
num[sz / 2:-sz / 2] = sz
num[:sz / 2] = np.arange(sz / 2, sz)
num[-sz / 2:] = np.arange(sz / 2, sz)[::-1]
Qb = np.sqrt((Q[sz:] - Q[:-sz]) / num) # normalise
idx = Qb > thres # core index
reg = True * idx
for i, msk in enumerate(idx):
if msk: reg[max(i - sz / 2, 1):i + sz / 2] = True
if plot:
from gplot import *
gplot(
lnw, f, Qb, " ,'' us 1:3 axis x1y2, " + str(thres) +
" axis x1y2 t 'threshold'")
ogplot(lnw[reg], f[reg], ",", lnw[idx], f[idx])
pause()
return reg
def spaghetti(self):
o = np.tile(self.orders, (self.rvc.shape[0], 1))
bjd = np.tile(self.bjd, (self.rvc.shape[1], 1)).T
# color bjd with lc variable => same color for same floor(bjd)
#gplot(ovis, vis.rvc.ravel(), bjdvis.ravel(), ' lc variable pt 7 ps 0.5,', onir+60, nir.rvc.ravel(), bjdnir.ravel(), ' lc variable pt 7 ps 0.5')
gplot.key('tit "%s"' % self.keytitle)
gplot.palette('defined')
gplot.xlabel('"Order"').ylabel('"RV [m/s]"').cblabel(
'"BJD - 2 450 000"')
gplot(o.ravel(), self.rvc.ravel(), bjd.ravel(),
'us 1:2:($3-2450000) palette')
pause('spaghetti %s' % self.keytitle)
def __init__(self,
chi2map,
vrange,
RV,
e_RV,
rv,
e_rv,
orders,
keytitle,
rchi=None,
No=None,
name=''):
self.args = RV, e_RV, rv, e_rv, orders, keytitle, rchi
self.No = No # Number of orders
self.chi2map = chi2map
self.name = name
self.info = name.split('/')[-1]
self.vrange = v_lo, v_step = vrange
self.vgrid = v_lo + v_step * np.arange(chi2map.shape[1])
# normalised master map
self.mCCF = chi2map[orders].sum(axis=0)
# master CCF from rescaled order-wise CCF
if rchi is not None:
#self.rCCF
self.mCCF = (chi2map[orders] /
rchi[orders][:, np.newaxis]**2).sum(axis=0)
self.nmCCF = self.mCCF / self.mCCF.max()
self.mlRV, self.e_mlRV, _, _ = SSRstat(self.vgrid,
self.mCCF,
dk=1,
plot=0)
self.mlRV, self.e_mlRV = self.mlRV * 1000, self.e_mlRV * 1000
# Derive again the RV from the CCF minima
v, e_v, _, self.SSRv = zip(*[
SSRstat(self.vgrid, cmo, dk=1, plot=0) for cmo in chi2map[orders]
])
V, e_V = wsem(np.array(v), e=np.array(e_v), rescale=0)
V, e_V = wsem(np.array(v), e=np.array(e_v))
if 0:
print V, e_V
print RV, e_RV
gplot(rv, e_rv, 'us 0:1:2 w e,', v, e_v,
'us 0:1:2 w e lc 3, %s,%s' % (RV, V))
gplot(rv, e_rv, 'us 0:1:2 w e,', v, e_v * rchi[1:][orders],
'us 0:1:2 w e lc 3, %s,%s' % (RV, V))
pause()
def disp(self):
orddisp = self.rv - self.RV[:, np.newaxis]
#ok &= np.abs(orddisp-d_ordmean) <= 3*ordstd # clip and update mask
ordstd, d_ordmean = wstd(orddisp, self.e_rv, axis=0)
#ordmean += d_ordmean # update ordmean
#orddisp
gplot.reset()
gplot.tit('"Order dispersion %s";' % self.keytitle)
gplot.xlabel('"Order"').ylabel('"RV_{n,o} - RV_n [m/s]"')
gplot(
orddisp.T, ' matrix us (%s-1):3 t ""' %
"".join(['$1==%s?%s:' % io for io in enumerate(self.orders)]))
ogplot(self.orders, ordstd, ' w lp lt 3 t "", "" us 1:(-$2) w lp t ""')
pause()
def plot_dlwno(self):
'''Show RVs over order for each observation.'''
(bjd, dLW,
e_dLW), dlw, e_rv = self.dlw.T[[0, 1,
2]], self.dlw[:, 3:], self.allerr[:,
5:]
n = 0
while 0 <= n < self.N:
gplot.key('tit "%s %s %s"' % (n + 1, bjd[n], self.info[n]))
if 1:
gplot.multiplot('layout 2,1')
# bottom panel
gplot.xlabel('"BJD - 2 450 000"').ylabel('"dLW [1000(m/s)^2]"')
hypertext = ' "" us 1:2:(sprintf("No: %d\\nID: %s\\nBJD: %f\\ndLW: %f+/-%f",$0+1, stringcolumn(4), $1, $2, $3)) w labels hypertext point pt 0 lt 1 t "",'
gplot(bjd - 2450000,
dLW,
e_dLW,
self.info,
'us 1:2:3 w e pt 6,' + hypertext, [bjd[n] - 2450000],
[dLW[n]], [e_dLW[n]],
'us 1:2:3 w e pt 7 t""',
flush=' \n')
gplot.xlabel('"order"').ylabel('"dLW [m/s]"')
gplot(self.orders, dlw[n, self.orders], self.e_rv[n] * 0,
'us 1:2:3 w e pt 7')
''' , %s lt 3 t "%s +/- %sm/s", "+" us 1:(%s):(%s) w filledcurves lt 3 fs transparent solid 0.50 t ""' %(RVc[n], RVc[n], e_RVc[n], RVc[n]-e_RVc[n], RVc[n]+e_RVc[n]))
gplot.xlabel('"wavelength"').ylabel('"RV [m/s]"')
gplot.autoscale('noextend')
gplot.put('i=2; bind "$" "i = i%2+1; xlab=i==1?\\"order\\":\\"wavelength\\"; set xlabel xlab; set xra [*:*]; print i; if (i==1) {unset log} else {set log x} ; repl"')
#gplot(self.orders, lam_o[n,self.orders], self.rvc[n], self.e_rv[n], 'us i:3:4 w e pt 7, %s lt 3 t "%s +/- %sm/s", "+" us 1:(%s):(%s) w filledcurves lt 3 fs transparent solid 0.20 t "", %s*(log(x)-%s)+%s lt 2, "+" us 1:(%s*(log(x)-%s)+%s):(%s*(log(x)-%s)+%s) w filledcurves lt 2 fs transparent solid 0.20 t ""' %(RVc[n], RVc[n], e_RVc[n], RVc[n]-e_RVc[n], RVc[n]+e_RVc[n], crx[n], lnlv[n], RVc[n], crx[n]-e_crx[n], lnlv[n], RVc[n], crx[n]+e_crx[n], lnlv[n], RVc[n]))
RVmod = crx[n]*(np.log(lam_o[n,self.orders])-lnlv[n])+RVc[n]
RVlow = (crx[n]-e_crx[n])*(np.log(lam_o[n,self.orders])-lnlv[n])+RVc[n]
RVupp = (crx[n]+e_crx[n])*(np.log(lam_o[n,self.orders])-lnlv[n])+RVc[n]
gplot(self.orders, lam_o[n,self.orders], self.rvc[n], self.e_rv[n], RVmod, RVlow, RVupp,'us i:3:4 w e pt 7, "" us i:5 w l lt 2 t "CRX = %g +/- %g m/s/Np", "" us i:6:7 w filledcurves lt 2 fs transparent solid 0.20 t "", %s lt 3 t "%g +/- %g m/s", "+" us 1:(%s):(%s) w filledcurves lt 3 fs transparent solid 0.20 t ""' %(crx[n], e_crx[n], RVc[n], RVc[n], e_RVc[n], RVc[n]-e_RVc[n], RVc[n]+e_RVc[n]))
'''
gplot.unset('multiplot')
nn = pause('%i/%i %s %s' % (n + 1, self.N, bjd[n], self.info[n]))
try:
n += int(nn)
except:
if nn in ('-', "D", "B"):
n -= 1
elif nn in ('^'):
n = 0
elif nn in ('$'):
n = self.N - 1
else:
n += 1
def plotrv(self):
'''Show RVs over order for each observation.'''
bjd, RVc, e_RVc = self.bjd, self.RVc, self.e_RVc
arg = ''
if not self.has_d.all():
arg += 'us 1:2:3 w e pt 6 lt 7 t "RV no drift"'
if self.has_d.any():
if arg: arg += ', "" '
arg += 'us 1:($2/$4):3 w e pt 7 lt 7 t "RVc"'
hypertext = ', "" us 1:2:(sprintf("No: %d\\nID: %s\\nBJD: %f\\nRV: %f +/- %f",$0+1, stringcolumn(5),$1, $2, $3)) w labels hypertext point pt 0 lt 1 t ""'
arg += hypertext
gplot.key('tit "%s (rms = %.3g m/s)"' % (self.keytitle, self.mlrms[0]))
gplot.xlabel('"BJD - 2 450 000"').ylabel('"RV [m/s]"')
gplot(bjd - 2450000, RVc, e_RVc, self.has_d, self.info, arg)
pause('rv ', self.tag)
def mlc(self):
'''Show RVs over order for each observation.'''
_, mlRVc, e_mlRVc = self.tmlc.T[0:3]
bjd, RVc, e_RVc = self.bjd, self.RVc, self.e_RVc
if 1:
gplot.key('tit "%s"' % self.keytitle)
gplot.xlabel('"BJD - 2 450 000"').ylabel('"RV [m/s]"')
gplot(
bjd - 2450000, RVc, e_RVc,
' us 1:2:3 w e pt 5 lc 1 t "RVc (rms = %1.3g m/s)' %
self.mlrms[0])
ogplot(
bjd - 2450000, self.tmlc.T[1:],
'us 1:2:3 w e pt 7 lc 3 t "\\nmlRVc (rms = %1.3g m/s)\\nrms(diff)=%.2f m/s"'
% (mlrms(mlRVc, e=e_mlRVc)[0], mlrms(mlRVc - RVc, e_RVc)[0]))
pause('rv ', self.tag) # , "" us 1:2:($3/$4) w e pt 7 lt 1
def deconv_RL(f_tpl, w_tpl, bb=4):
# Deconvolution of f_tpl using Richardson Lucy algorithm
# needed for CRIRES data
psf = np.ones((1, bb)) / bb
f2 = f_tpl + 0.
f_tpl1 = np.reshape(f_tpl, (1, -1))
deconv_RL = restoration.richardson_lucy(f_tpl1 / np.max(f_tpl1) / 2.,
psf,
iterations=30)[0]
f_tpl = deconv_RL * np.max(f_tpl1) * 2
rv, ccc = pyasl.crosscorrRV(w_tpl[200:-200],
f_tpl[200:-200],
w_tpl[200:-200],
f2[200:-200],
-10.,
10.,
0.01,
skipedge=20)
pol = (np.poly1d(np.polyfit(rv, ccc, 15)))(rv)
shift = rv[np.argmax(pol)]
print('shift RL: ', shift, rv[np.argmax(ccc)])
# there seems to appear a shift in the spectra after the deconvolution
# why ???
# correction ???
# more testing needed, or another way of deconvolution
plot_RL = 0
if plot_RL:
gplot(rv, ccc, 'w l,', rv, pol, 'w l')
# gplot(w_tpl, f2, 'w l,', w_tpl /(1+shift/3e5),f_tpl,'w l')
pause()
# w_tpl /= (1+shift/3e5)
'''
bo = 10
f_tpl = f_tpl[bo:-bo]
w_tpl = w_tpl[bo:-bo]
f_ok = f_ok[bo:-bo]
w_ok = w_ok[bo:-bo]
x_ok = x_ok[bo:-bo]
'''
return f_tpl, w_tpl
def plot_halpha(self):
'''Show RVs over order for each observation.'''
bjd, halpha, e_halpha = self.halpha[0:3]
arg = ''
if not self.has_d.all():
arg += 'us 1:2:3 w e pt 6 lt 7 t "dLW no drift"'
if self.has_d.any():
if arg: arg += ', "" '
arg += 'us 1:2:($3/$4) w e pt 7 lt 7 t "dLW"'
hypertext = ', "" us 1:2:(sprintf("No: %d\\nID: %s\\nBJD: %f\\ndLW: %f +/- %f",$0+1, stringcolumn(5),$1, $2, $3)) w labels hypertext point pt 0 lt 1 t "",'
arg += hypertext
gplot.key('tit "%s"' % (self.keytitle))
gplot.xlabel('"BJD - 2 450 000"').ylabel('"Halpha index"')
gplot(bjd - 2450000, halpha, e_halpha, e_halpha, self.info, arg)
pause('halpha ', self.tag)
def example():
# a noisy time serie
x = np.arange(10490) / 40.0 + 0.1
yorg = np.sin(x * 1.65) + 0.1 * np.sin(x * 1.65 * 6.32)
y = yorg + 0.021 * np.random.randn(x.size)
mod, yfit = ucbspl_fit(x, y, w=1 + 0 * y, K=1800, lam=0.11, retfit=True)
gplot(x, y, yorg, ' lt 2,"" us 1:3 w l lt 1 t "org"')
ogplot(x, yfit, yfit - yorg, 'w l,"" us 1:3')
A = np.array([[20, -5, 0, 0], [-5, 15, -5, 0], [0, -5, 15, -5],
[0, 0, -5, 10]])
D = np.array([1100, 100, 100, 100])
print "SciPy - Solve Banded"
print "A:", A
print "D:", D
print "Result:", SolveBanded(A, D)
pause()
def prerv(self):
'''Show RVs over order for each observation.'''
prebjd, preRV, pree_RVc = self.tpre.T[0:3]
preRVc = preRV - np.nan_to_num(self.trvc[8])
bjd, RVc, e_RVc = self.bjd, self.RVc, self.e_RVc
if 1:
gplot.key('tit "%s"' % self.keytitle)
gplot.xlabel('"BJD - 2 450 000"').ylabel('"RV [m/s]"')
#gplot(';set ytics nomirr; set y2tics;', flush='')
gplot(
prebjd - 2450000, preRVc, pree_RVc,
'us 1:2:3 w e pt 5 lt 3 t "preRVc (rms = %1.3g m/s)"' %
mlrms(preRVc, e=pree_RVc)[0])
# ogplot(bjd-2450000, RVc-np.median(RVc)+np.median(drsRVc), e_RVc, ' us 1:2:3 w e pt 7 lt 1 t "RVc-med(RVc)+med(DRS)"')
ogplot(
bjd - 2450000, RVc - np.median(RVc) + np.median(preRVc), e_RVc,
' us 1:2:3 w e pt 7 lt 1 t "RVc (rms = %1.3g m/s)"' %
self.mlrms[0])
pause('rv ', self.tag) # , "" us 1:2:($3/$4) w e pt 7 lt 1
def drsrv(self):
'''Show RVs over order for each observation.'''
drsbjd, drsRVc, drse_RVc = self.drs.T[0:3]
drsRVc = drsRVc - np.nan_to_num(self.trvc[8])
bjd, RVc, e_RVc = self.bjd, self.RVc, self.e_RVc
if 1:
gplot.key('tit "%s"' % self.keytitle)
gplot.xlabel('"BJD - 2 450 000"').ylabel('"RV [m/s]"')
#gplot(';set ytics nomirr; set y2tics;', flush='')
gplot(
drsbjd - 2450000, drsRVc, drse_RVc,
'us 1:2:3 w e pt 7 lt 1 t "DRS (rms = %1.3g m/s)"' %
mlrms(drsRVc, e=drse_RVc)[0])
# ogplot(bjd-2450000, RVc-np.median(RVc)+np.median(drsRVc), e_RVc, ' us 1:2:3 w e pt 7 lt 1 t "RVc-med(RVc)+med(DRS)"')
ogplot(
bjd - 2450000, RVc - np.median(RVc) + np.nanmedian(drsRVc),
e_RVc,
' us 1:2:3 w e pt 5 lt 3 t "\\nRVc (rms = %1.3g m/s)\\nrms(diff) = %.2f m/s"'
% (self.mlrms[0], mlrms(drsRVc - RVc, e_RVc)[0]))
pause('rv ', self.tag) # , "" us 1:2:($3/$4) w e pt 7 lt 1
def plot_dlwo(self):
bjd, RVc, e_RVc, RVd, e_RVd, RV, e_RV, BRV, RVsa = self.trvc
allrv = self.allrv
bjd, RV, e_RV, rv, e_rv = allrv[:,
0], allrv[:,
1], allrv[:,
2], allrv[:,
5:], self.allerr[:,
5:]
dlw = self
(bjd, dLW,
e_dLW), dlw, e_rv = self.dlw.T[[0, 1,
2]], self.dlw[:, 3:], self.allerr[:,
5:]
bjdmap = np.tile(bjd[:, np.newaxis], rv.shape[1])
omap = np.tile(np.arange(len(self.dlw.T)), dlw.shape[0]).T
# Drift and sa yet no applied to rvo
# gplot(bjd, RV, e_RV, 'us 1:2:3 w e pt 7',)
# gplot(bjdmap.ravel(), rv.ravel(), e_rv.ravel(), omap.ravel(), 'us 1:2:4 w p pt 7 palette, "'+obj+'/'+obj+'.rvo.dat'+'" us 1:2:3 w e pt 7 lt 7')
rvc = rv - (np.nan_to_num(RVd) + np.nan_to_num(RVsa))[:, np.newaxis]
gplot.palette('define (0 "blue", 1 "green", 2 "red")').key(
'tit "%s"' % self.keytitle)
gplot.xlabel('"BJD - 2 450 000"').ylabel('"dLW [1000 (m/s)^2]')
# not drift corrected
#gplot(bjdmap.ravel(), rv.ravel(), e_rv.ravel(), omap.ravel(), 'us 1:2:4 w p pt 7 palette,', bjd, RV, e_RV, 'us 1:2:3 w e pt 7 lt 7')
# drift corrected
arg = ''
if not self.has_d.all():
arg += 'us 1:2:3 w e pt 6 lt 7 t "dLWo (no drift)"'
if self.has_d.any():
if arg: arg += ', "" '
arg += 'us 1:2:($3/$4) w e pt 7 lt 7 t "dLWo"'
gplot(
bjdmap.ravel() - 2450000, dlw.ravel(), e_rv.ravel(), omap.ravel(),
'us 1:2:4 w p pt 7 ps 0.5 palette t "RV_o",', bjd - 2450000, dLW,
e_dLW, self.has_d,
arg) # 'us 1:2:3 w e pt 6 lt 7, "" us 1:2:($3/$4) w e pt 7 lt 7')
pause('dLWo ', self.tag)
def plotrvo(self):
bjd, RVc, e_RVc, RVd, e_RVd, RV, e_RV, BRV, RVsa = self.trvc
allrv = self.allrv
bjd, RV, e_RV, rv, e_rv = allrv[:,
0], allrv[:,
1], allrv[:,
2], allrv[:,
5:], self.allerr[:,
5:]
bjdmap = np.tile(bjd[:, np.newaxis], rv.shape[1])
omap = np.tile(np.arange(len(rv.T)), rv.shape[0]).T
# Drift and sa yet no applied to rvo
# gplot(bjd, RV, e_RV, 'us 1:2:3 w e pt 7',)
# gplot(bjdmap.ravel(), rv.ravel(), e_rv.ravel(), omap.ravel(), 'us 1:2:4 w p pt 7 palette, "'+obj+'/'+obj+'.rvo.dat'+'" us 1:2:3 w e pt 7 lt 7')
rvc = rv - (np.nan_to_num(RVd) + np.nan_to_num(RVsa))[:, np.newaxis]
gplot.palette('define (0 "blue", 1 "green", 2 "red")').key(
'tit "%s"' % self.keytitle)
gplot.xlabel('"BJD - 2 450 000"').ylabel('"RV [m/s]')
# not drift corrected
#gplot(bjdmap.ravel(), rv.ravel(), e_rv.ravel(), omap.ravel(), 'us 1:2:4 w p pt 7 palette,', bjd, RV, e_RV, 'us 1:2:3 w e pt 7 lt 7')
# drift corrected
arg = ''
hypertext = ''
if not self.has_d.all():
arg += 'us 1:2:3 w e pt 6 lt 7 t "RV no drift"'
if self.has_d.any():
if arg: arg += ', "" '
arg += 'us 1:2:($3/$4) w e pt 7 lt 7 t "RVc"'
if 1:
hypertext = ' "" us 1:2:(sprintf("o: %d\\nBJD: %f\\nRV: %f", $4, $1, $2)) w labels hypertext point pt 0 lt 1 t "",'
arg += ', "" us 1:2:(sprintf("No: %d\\nID: %s\\nBJD: %f\\nRV: %f+/-%f",$0+1, stringcolumn(5),$1, $2, $3)) w labels hypertext point pt 0 lt 1 t ""'
gplot(
bjdmap.ravel() - 2450000, rvc.ravel(), e_rv.ravel(), omap.ravel(),
'us 1:2:4 w p pt 7 ps 0.5 palette t "RV_o",' + hypertext,
bjd - 2450000, RVc, e_RVc, self.has_d, self.info,
arg) # 'us 1:2:3 w e pt 6 lt 7, "" us 1:2:($3/$4) w e pt 7 lt 7')
pause('rvo ', self.tag)
def plot_input(*args):
RV, e_RV, rv, e_rv, orders, keytitle, rchi = args
gplot.palette('defined (0 "blue", 1 "green", 2 "red")')
mm = np.nanmean(chi2map, axis=0)
gplot.multiplot("layout 1,2")
# Plot order,RV,chi2map and overplot RV and master map
# easy to plot as image
gplot.xlabel('"order"').ylabel('"RV [km/s]"').cblabel(
'"{/Symbol c^2}"')
gplot(chi2map,
'matrix us 1:($2*%s+%s):3 w image, ' % (v_step, v_lo), rv,
e_rv, 'us 0:1:2 w e pt 6 lc 7, ', mm, mm,
'us (-$1-5):($2*%s+%s):3 matrix w image' % (v_step, v_lo),
',', [-5.5], [RV], [e_RV], 'us 1:2:3 w e pt 7 lc 7')
gplot.xlabel('"RV [km/s]"').ylabel('""').cblabel('"order"')
gplot(chi2map.T,
'matrix us ($1*%s+%s):3:2 w l palette , ' % (v_step, v_lo),
chi2map.min(axis=1), rv, e_rv, 'us 2:1:3 w xe pt 7, ', mm,
'us ($0*%s+%s):1 w l' % (v_step, v_lo), ' lc 7 lw 2,',
[mm.min()], [RV], [e_RV], 'us 2:1:3 w xe pt 7 lc 7 ')
gplot.unset("multiplot")
def read_spec(self, s, inst, plot=False, **kwargs):
#print s, inst
sp = inst.read(self, s, **kwargs)
return sp
if '.tar' in s: s = file_from_tar(s, inst=inst, fib=self.fib, **kwargs)
if 'HARP' in inst: sp = read_harps(self, s, inst=inst, **kwargs)
elif inst == 'CARM_VIS': sp = read_carm_vis(self, s, **kwargs)
elif inst == 'CARM_NIR': sp = read_carm_nir(self, s, **kwargs)
elif inst == 'FEROS': sp = read_feros(self, s, **kwargs)
elif inst == 'FTS': sp = read_fts(self, s, **kwargs)
elif inst == 'HPF': sp = read_hpf(self, s, **kwargs)
else:
return None
#if hasattr(s, 'close'):
#s.close()
if plot:
gplot.xlabel("'wavelength'").ylabel("'intensity'")
for o in range(len(sp.w)):
gplot(sp.w[o], sp.f[o], " w lp t 'order %i'" % o)
pause(o)
return sp
def SSRstat(vgrid, SSR, dk=1, plot='maybe'):
'''taken from serval.py and modified with SSRv (chi2 minimum)'''
v_step = vgrid[1] - vgrid[0]
# analyse peak
k = SSR[dk:-dk].argmin() + dk # best point (exclude borders)
vpeak = vgrid[k - dk:k + dk + 1]
SSRpeak = SSR[k - dk:k + dk + 1] - SSR[k]
# interpolating parabola (direct solution) through the three pixels in the minimum
a = np.array([
0, (SSR[k + dk] - SSR[k - dk]) / (2 * v_step),
(SSR[k + dk] - 2 * SSR[k] + SSR[k - dk]) / (2 * v_step**2)
]) # interpolating parabola for even grid
v = (SSR[k + dk] - SSR[k - dk]) / (SSR[k + dk] - 2 * SSR[k] +
SSR[k - dk]) * 0.5 * v_step
SSR_v = SSR[k] - a[1]**2 / a[
2] / 4. # a1*v+a2*v**2 # position of parabola minimum = a1*v+a2*v**2 = - a[1]^2/2./a[2]+a[1]^2/4./a[2] = -a[1]^2/a[2]/4.
v = vgrid[k] - a[1] / 2. / a[2] # parabola minimum
e_v = np.nan
if -1 in SSR:
print 'opti warning: bad ccf.'
elif a[2] <= 0:
print 'opti warning: a[2]=%f<=0.' % a[2]
elif not vgrid[0] <= v <= vgrid[-1]:
print 'opti warning: v not in [va,vb].'
else:
e_v = 1. / a[2]**0.5
if (plot == 1 and np.isnan(e_v)) or plot == 2:
gplot.yrange('[*:%f]' % SSR.max())
gplot(
vgrid, SSR - SSR[k], " w lp, v1=" + str(vgrid[k]) +
", %f+(x-v1)*%f+(x-v1)**2*%f," % tuple(a), [v, v], [0, SSR[1]],
'w l t "%f km/s"' % v)
ogplot(vpeak, SSRpeak, ' lt 1 pt 6; set yrange [*:*]')
pause(v)
return v, e_v, a, SSR_v
def plot(self):
RV, e_RV, rv, e_rv, orders, keytitle, rchi = self.args
chi2map = self.chi2map[orders]
# Plot all maps normalised
gplot.key('Left center rev top title "%s"' % keytitle)
gplot.palette('defined (0 "blue", 1 "green", 2 "red")')
gplot.xlabel(
'"v [km/s]"; set ylabel "chi^2 / max(chi^2)"; set cblabel "order"')
#gplot(chi2map, ' matrix us ($1*%s+%s):3:2 w l palette'%self.vrange)
gplot((chi2map / chi2map.max(axis=1)[:, np.newaxis]).T,
' matrix us (%s+$1*%s):3:2 w l palette t "%s"' % (self.vrange +
(self.info, )),
flush='')
#ogplot(self.rCCF/self.rCCF.max(), ' us (%s+$0*%s):1 w l lt 4 lw 2 t "total"'%self.vrange, flush='')
# the master CCF
ogplot(self.nmCCF,
' us (%s+$0*%s):1 w l lt 7 lw 2 t "lnL"' % self.vrange,
flush='')
# the parabola for the master
ogplot(
'((x-%s)**2/%s**2+%s)/%s lc 7 dt 2 t "lnL (parabola %.5g +/- %.5g m/s)"'
% (self.mlRV / 1000, self.e_mlRV / 1000., self.mCCF.min(),
self.mCCF.max(), self.mlRV, self.e_mlRV),
flush='')
ogplot("((x-%s)**2/%s**2+%s)/%s lc 9 dt 3 t 'RV,e\_RV (parabola)'" %
(RV, e_RV, self.mCCF.min(), self.mCCF.max()),
flush='')
ogplot([RV] * 2, [0, 1], [e_RV] * 2,
'us 1:2:3 w xerrorlines pt 2 lt 9 t "%.5g +/- %.5g m/s"' %
(RV * 1000, e_RV * 1000),
flush='')
ogplot(rv, (chi2map / chi2map.max(axis=1)[:, np.newaxis]).min(axis=1),
'us 1:2 lt 7 pt 1 t "chi^2_{min}"')
#! /usr/bin/ipython
import numpy as np
import ke
from gplot import *
from pause import *
ke._Ecs(0.0001, 1., n=15)
ke.E(0.0001, 1., n=15, typ='atr')
x=np.arange(0,10,0.001); y=1*x; ke._ke_newton.v_cos(x,y, x.size); gplot(x,y-np.cos(x))
x=np.arange(0,10,0.001); y=1*x; ke._ke_newton.v_sin(x,y, x.size); gplot(x,y-np.sin(x))
e = 1.0
E = np.arange(-10,6,0.001)
E = np.arange(0,3,0.001); M = ke.M(E, e)
if 0:
gplot(M, E, ke._E_newton(M, e), ke.E(M, e, n=55, typ='atr'), ' us 1:3, "" us 1:4')
gplot('"" us 1:($3-$2), "" us 1:($4-$2)')
gplot(M, np.log10(np.abs(ke._E(M, e, n=55)-E)))
pause()
M = np.arange(0, np.pi, np.pi/1000);
timeit ke._E(M, e, n=29)
#1000 loops, best of 3: 1.51 ms per loop
timeit ke.E(M, e, n=29)
#1000 loops, best of 3: 1.51 ms per loop
if not case:
import doctest
doctest.testmod()
elif case == 1:
# chi2map for a linear fit and comparison with curve_fit
from scipy.optimize import curve_fit
# model
trend = lambda x, a, b: a + b * x # = np.polynomial.polyval(x, a)
a0, a1 = 2, 2
# data
x = np.arange(20)
e = 2 + np.random.rand(20)
y = trend(x, a0, a1) + 2 * np.random.normal(0, e) # scale e by 2
gplot(x, y, e, "w e pt 7")
# curve_fit
a_cf, cov_cf = curve_fit(trend, x, y, [0.0, 0.0], e)
print(a_cf)
cov_cf
# paraboloid
# parameter and chi2 samples
P = [[1, 0], [0, 0], [-1, 0], [0, 1], [0, -1], [1, -1]]
# chi2map
z = [np.dot((y - trend(x, *p))**2, 1 / e**2) for p in P]
pause()
cov = covmat_fit(P, z, N=len(x))
#cov = covmat_fit(zip(*P), z)
def plotrvno(self):
'''Show RVs over order for each observation.'''
bjd, RVc, e_RVc = self.bjd, self.RVc, self.e_RVc
crx, e_crx = self.tcrx[1:3]
lam_o = np.exp(self.tcrx[6:].T)
lnlv = np.log(self.tcrx[5])
print "use '$' to toggle between orders and wavelength"
n = 0
while 0 <= n < self.N:
gplot.key('tit "%s %s %s"' % (n + 1, bjd[n], self.info[n]))
if 1:
gplot.multiplot('layout 2,1')
# top panel
gplot.xlabel('"BJD - 2 450 000"').ylabel('"RV [m/s]"')
gplot.unset('log')
arg = ''
if not self.has_d.all():
arg += 'us 1:2:3 w e pt 6 lt 7 t "RV no drift",'
if self.has_d.any():
if arg: arg += ' "" '
arg += 'us 1:($2/$4):3 w e pt 7 lt 7 t "RVc",'
hypertext = '"" us 1:2:(sprintf("No: %d\\nID: %s\\nBJD: %f\\nRV: %f +/- %f",$0+1, stringcolumn(5),$1, $2, $3)) w labels hypertext point pt 0 lt 1 t "",'
gplot(bjd - 2450000,
RVc,
e_RVc,
self.has_d,
self.info,
arg,
hypertext,
bjd[n] - 2450000,
RVc[n],
e_RVc[n],
'us 1:2:3 w e pt 7 t ""',
flush=' \n')
# bottom panel
gplot.xlabel('"order"').ylabel('"RV [m/s]"')
gplot.xlabel('"wavelength"').ylabel('"RV [m/s]"')
gplot.autoscale('noextend')
gplot.put(
'i=2; bind "$" "i = i%2+1; xlab=i==1?\\"order\\":\\"wavelength\\"; set xlabel xlab; set xra [*:*]; print i; if (i==1) {unset log} else {set log x} ; repl"'
)
RVmod = crx[n] * (np.log(lam_o[n, self.orders]) - lnlv[n]) + RVc[n]
RVlow = (crx[n] - e_crx[n]) * (np.log(lam_o[n, self.orders]) -
lnlv[n]) + RVc[n]
RVupp = (crx[n] + e_crx[n]) * (np.log(lam_o[n, self.orders]) -
lnlv[n]) + RVc[n]
hypertext = ', "" us i:3:(sprintf("No: %d\\nID: %s\\nBJD: %f\\nRV: %f +/- %f\\n' % (
n + 1, self.info[n], bjd[n], RVc[n], e_RVc[n]
) + 'o: %d\\nrv[o]: %f +/- %f", $1, $3, $4)) w labels hypertext point pt 0 lt 1 t ""'
gplot(
self.orders, lam_o[n, self.orders], self.rvc[n], self.e_rv[n],
RVmod, RVlow, RVupp, 'us i:3:4 w e pt 7' + hypertext +
', "" us i:5 w l lt 2 t "CRX = %g +/- %g m/s/Np", "" us i:6:7 w filledcurves lt 2 fs transparent solid 0.20 t "", %s lt 3 t "%g +/- %g m/s", "+" us 1:(%s):(%s) w filledcurves lt 3 fs transparent solid 0.20 t ""'
% (crx[n], e_crx[n], RVc[n], RVc[n], e_RVc[n],
RVc[n] - e_RVc[n], RVc[n] + e_RVc[n]))
gplot.unset('multiplot')
nn = pause('%i/%i %s %s' % (n + 1, self.N, bjd[n], self.info[n]))
try:
n += int(nn)
except:
if nn in ('-', "D", "B"):
n -= 1
elif nn in ('^'):
n = 0
elif nn in ('$'):
n = self.N - 1
else:
n += 1
def plot_fit(self):
RV, e_RV, rv, e_rv, orders, keytitle, rchi = self.args
x, xc, ind = self.xarg
vv = self.vv
lnL0 = np.array([cs_o(vv) for cs_o in self.cs])
gplot.palette('defined (0 "blue", 1 "green", 2 "red")')
gplot.multiplot("layout 1,2")
gplot.xlabel(
'"v [m/s]"; set ylabel "chi^2 / max(chi^2)"; set cblabel "order"')
nlnL0 = lnL0.T / lnL0.max(axis=1)
mm = nlnL0.sum(axis=1)
# left panel: the lnL parabolas in RV[km/s]
gplot(
nlnL0,
' matrix us ($1*%s+%s):3:2 w l palette,' % (vv[1] - vv[0], vv[0]),
nlnL0.min(axis=0),
rv,
e_rv,
'us 2:1:3 w xe pt 6 lc 7, ',
mm / mm.max(),
'us ($0*%s+%s):1 w l' % (vv[1] - vv[0], vv[0]),
' lc 7 lw 2,',
#[(mm/mm.max()).min()], [RV[i]/1000], [e_RVc[i]/1000], 'us 2:1:3 w xe pt 7 lc 7 '
)
# right panel: the map ln(lam) vs RV [m/s]
gplot.xlabel(
'"ln(wavelength)"; set ylabel "velocity v [m/s]"; set cblabel "chi^2 / max(chi^2)"'
)
gplot.yrange('[%s:%s]' % ((rv * 1000 - e_rv * 1000).min(), 1000 *
(rv + e_rv).max()))
# lnL as maps ,width shoul be addjust
# often some maps are not drawn
gplot(
"a='0 " + " ".join(map(str, self.ll)) + "'" +
', for [i=2:%s+1]' % len(lnL0),
vv * 1000,
lnL0 / lnL0.max(axis=1)[:, np.newaxis],
' us (word(a,i)+0):1:(0.002):(%s):i with boxxy palette fs solid t "",'
% ((vv[1] - vv[0]) * 1000 / 2),
self.ll,
rv * 1000,
e_rv * 1000,
" w e pt 7 lc 7 t 'RV_o',", #x,(x-xc)* self.crx+pval[0], 'w l lc 1,',
x,
((x - xc) * self.crx / 1000.) * 1000 + self.zz,
'w l lc 3 t "%s"' % self.crx)
gplot.unset("multiplot")
gplot.yrange('[*:*]')
#pause()
if 0:
gplot.xlabel(
'"v [m/s]"; set ylabel "chi^2/max(chi^2)"; set cblabel "order"'
)
#gplot(chi2map, ' matrix us ($1*%s+%s):3:2 w l palette'%(v_step, v_lo))
gplot(chi2map.T / chi2map.max(axis=1),
' matrix us ($1*%s+%s):3:2 w l palette' % (v_step, v_lo))
gplot(lnLvbint, 'matrix us 1:3:2 w l palette,', uu.sum(axis=0),
"us ($0-30):1")
#gplot(chi2map, 'matrix us 1:2:3 w l palette')
ij = 19
gplot(lnLvb[ij], 'matrix us 1:2:3 w image,',
np.argmin(lnLvb[ij], axis=1))
gplot.splot(np.log(np.array(lnLvbint).T - lnLvbint.min()),
'matrix us 1:2:3 w l palette')
def mlcrx(self, x, xc, ind):
'''Maximum likelihood'''
self.xarg = x, xc, ind
self.crx, self.e_crx = np.nan, np.nan
chi2map = self.chi2map #- self.chi2map.min(axis=1)[:,np.newaxis]
v_lo, v_step = self.vrange
vgrid = v_lo + v_step * np.arange(chi2map.shape[1])
if 0:
# test data
oo = ~np.isnan(chi2map[:, 0])
rv[i] = (x - 8.6) * 0.6 * 1000 + np.sin(x * 100) * 100 + 50
e_rv[i] = rv[i] * 0 + 10
rv[i][~oo] = np.nan
gplot(x, rv[i], e_rv[i], " w e")
#pval, cov = curve_fit(func, x[ind]-xc, rv[i][ind], np.array([0.0, 0.0]), e_rv[i][ind])
#crx[i] = pval[1]
#chi2map = (1./2*(vgrid[:,np.newaxis]-rv[i]/1000)**2/(e_rv[i]/1000)**2).T
def plot_input(*args):
RV, e_RV, rv, e_rv, orders, keytitle, rchi = args
gplot.palette('defined (0 "blue", 1 "green", 2 "red")')
mm = np.nanmean(chi2map, axis=0)
gplot.multiplot("layout 1,2")
# Plot order,RV,chi2map and overplot RV and master map
# easy to plot as image
gplot.xlabel('"order"').ylabel('"RV [km/s]"').cblabel(
'"{/Symbol c^2}"')
gplot(chi2map,
'matrix us 1:($2*%s+%s):3 w image, ' % (v_step, v_lo), rv,
e_rv, 'us 0:1:2 w e pt 6 lc 7, ', mm, mm,
'us (-$1-5):($2*%s+%s):3 matrix w image' % (v_step, v_lo),
',', [-5.5], [RV], [e_RV], 'us 1:2:3 w e pt 7 lc 7')
gplot.xlabel('"RV [km/s]"').ylabel('""').cblabel('"order"')
gplot(chi2map.T,
'matrix us ($1*%s+%s):3:2 w l palette , ' % (v_step, v_lo),
chi2map.min(axis=1), rv, e_rv, 'us 2:1:3 w xe pt 7, ', mm,
'us ($0*%s+%s):1 w l' % (v_step, v_lo), ' lc 7 lw 2,',
[mm.min()], [RV], [e_RV], 'us 2:1:3 w xe pt 7 lc 7 ')
gplot.unset("multiplot")
if 0:
plot_input(*self.args)
pause()
# Spline interpolate each chi2map
self.ll = ll = x[ind]
rchi = self.args[-1]
self.cs = cs = [
spl.ucbspl_fit(vgrid, chi_o, K=len(chi_o))
for chi_o in (chi2map[ind] - np.array(self.SSRv)[:, np.newaxis]) /
rchi[ind][:, np.newaxis]**2
]
self.vv = vv = np.arange(vgrid[30], vgrid[-30], 0.01)
if 0:
# discrete brute force minisation
lnLvb = []
bb = np.arange(-0.8, 0.8, 0.005)
for bbi in bb:
lnLv = []
for x_o, cs_o in zip(ll, cs):
lnLv += [cs_o(vv + (x_o - xc) * bbi)]
lnLvb += [lnLv]
lnLvb = np.array(lnLvb)
lnLvbint = lnLvb.sum(axis=1).T
ii = np.unravel_index(lnLvbint.argmin(), lnLvbint.shape)
crx_i = bb[ii[1]] * 1000 # km to m/s
zz = vv[ii[0]] * 1000
if 1:
# paraboloid
#imin,jmin = np.unravel_index(np.argmin(lnLvbint), lnLvbint.shape)
#sl = np.s_[imin-10:imin+11, jmin-10:jmin+11]
#X = np.tile(vv, bb.shape+(1,)).T[sl].ravel()
#Y = np.tile(bb, vv.shape+(1,))[sl].ravel()
#z = lnLvbint[sl].ravel() # ln L = chi^2/2
#cov = paraboloid.covmat_fit([X,Y], z, N=self.No[ind].sum())
#cov =bbi paraboloid.covmat_fit([X,Y], z)
try:
# setup a grid around the start guess mlRv and mlCRX=0
par = map(
np.ravel,
np.meshgrid(
np.arange(self.mlRV / 1000. - 1, self.mlRV / 1000. + 1,
0.2),
np.arange(-500, 500, 50) / 1000.))
# par = map(np.ravel, np.meshgrid(np.arange(self.mlRV/1000.-0.1, self.mlRV/1000.+.1,0.02), np.arange(-500,500,50)/1000.))
z = [
np.sum([
cs_o(vvi + (x_o - xc) * bbi)
for x_o, cs_o in zip(ll, cs)
]) for vvi, bbi in zip(*par)
]
# gplot.splot(par, z, ' palette')
#pause()
cov = paraboloid.covmat_fit(zip(*par), z, N=len(cs))
zmod = cov.p(zip(*par))
#pause("\n", cov.Va*1000**2, cov.Xc*1000)
#cov = paraboloid.covmat_fit(par, z)
#cov = paraboloid.covmat_fit(par, z, N=self.No[ind].sum())
v, b = cov.Xc
e_v, e_b = cov.e_a
#gplot.splot(par, z, ' palette,', v,b, cov.min); pause("v", v, "b", b)
#gplot.splot(par, z, zmod, ' palette, "" us 1:2:4 palette pt 6 , "" us 1:2:(($3-$4)*10),', v,b, cov.min); pause("v", v, "b", b)
if -1 < v < 1 and -0.5 < b < 0.5 and np.isfinite(
cov.e_a).all():
# refit closer to minimum
par = map(
np.ravel,
np.meshgrid(
np.arange(v - 3 * e_v, v + 3 * e_v, e_v / 2),
np.arange(b - 3 * e_b, b + 3 * e_b, e_b / 2)))
z = [
np.sum([
cs_o(vvi + (x_o - xc) * bbi)
for x_o, cs_o in zip(ll, cs)
]) for vvi, bbi in zip(*par)
]
zmod = cov.p(zip(*par))
cov = paraboloid.covmat_fit(zip(*par), z, N=len(cs))
self.crx = cov.Xc[1] * 1000
self.e_crx = cov.e_a[1] * 1000
except:
print "warning: mlCRX failed."
if 0:
# scipy optimisation
def lnL(a):
return np.sum([
cs_o(a[0] + a[1] * (x_o - xc))
for x_o, cs_o in zip(ll, cs)
])
from scipy.optimize import fmin
pp = fmin(lnL, [0, 0], disp=False)
#print pp * 1000
self.zz = pp[0] * 1000
self.crx = pp[1] * 1000
self.e_crx = None # e_crx[i]
#SSRstat(self.vgrid, self.mCCF, dk=1, plot=0)[0:2]
#ii = np.argmin(self.mCCF); ss = np.s_[ii-3:ii+3]
#uu = paraboloid.covmat_fit([self.vgrid[ss]], self.mCCF[ss])
#uu.Xc, uu.e_a
return self.crx, self.e_crx
def ls(self, suf='_A_mod.fits'):
'''Show last square fit form fits file.'''
#prebjd, preRV, pree_RVc = self.tpre.T[0:3]
#for n in range(self.N):
gplot.key("Left left rev bottom title '%s'" % self.keytitle)
'''
if def_wlog: w2 = np.exp(w2)
res = np.nan * f2
res[pmin:pmax] = (f2[pmin:pmax]-f2mod[pmin:pmax]) / e2[pmin:pmax] # normalised residuals
b = str(stat['std'])
#gplot_set('set key left Left rev samplen 2 tit "%s (o=%s, v=%.2fm/s)"'%(obj,o,rvo))
#gplot_set('set ytics nomirr; set y2tics; set y2range [-5*%f:35*%f]; set bar 0.5'%(rchi,rchi))
#gplot_set('i=1; bind "$" "i = i%2+1; xlab=i==1?\\"pixel\\":\\"wavelength\\"; set xlabel xlab; set xra [*:*]; print i; repl"')
#gplot('[][][][-5:35]',x2, w2, f2, e2.clip(0.,f2.max()), 'us (column(i)):3:4 w errorli t "'+sp.timeid+' all"', flush='')
ogplot(x2,w2, f2, ((b2==0)|(b2==flag.clip))*0.5, 1+4*(b2==flag.clip), 'us (column(i)):3:4:5 w p pt 7 lc var ps var t "'+sp.timeid+' telluric free"', flush='')
ogplot(x2,w2, f2mod,(b2==0)*0.5, 'us (column(i)):3:4 w lp lt 3 pt 7 ps var t "Fmod"', flush='')
ogplot(x2,w2, res, b2, "us (column(i)):3:4 w lp pt 7 ps 0.5 lc var axis x1y2 t 'residuals'", flush='')
# legend with translation of bpmap, plot dummy using NaN function
ogplot(", ".join(["NaN w p pt 7 ps 0.5 lc "+str(f) +" t '"+str(f)+" "+",".join(flag.translate(f))+"'" for f in np.unique(b2)]), flush='')
ogplot("0 axis x1y2 lt 3 t'',"+b+" axis x1y2 lt 1,-"+b+" axis x1y2 lt 1 t ''", flush='')
ogplot(x2,w2, ((b2&flag.atm)!=flag.atm)*40-5, 'us (column(i)):3 w filledcurve x2 fs transparent solid 0.5 noborder lc 9 axis x1y2 t "tellurics"', flush='')
ogplot(x2,w2, ((b2&flag.sky)!=flag.sky)*40-5, 'us (column(i)):3 w filledcurve x2 fs transparent solid 0.5 noborder lc 6 axis x1y2 t "sky"')
pause('large RV ' if abs(rvo/1000-rvguess+vref)>rvwarn else 'look ', o, ' rv = %.3f +/- %.3f m/s rchi = %.2f' %(rvo, e_rv[i,o],rchi2[i,o]))
# end loop over orders
'''
n = 0
o = 40
while True:
name = self.dir + '/res/' + self.info[n][:-5] + suf
hdu = pyfits.open(name)
resmap = hdu['res'].data
fmod = hdu['fmod'].data
e_f = hdu['err'].data
wave = hdu['wave'].data
flux = fmod + resmap
bpmap = 0 * wave
rvo = 1
if 1:
gplot_set('set xlabel "wavelength [A]"; set ylabel "flux"')
gplot_set(
'set key default left Left rev samplen 2 tit "%s (o=%s, v=%.2fm/s)"'
% (self.tag, o, rvo))
gplot_set(
'set ytics nomirr; set y2tics; set y2range [-5*%f:35*%f]; set bar 0.5'
% (1, 1))
gplot_set(
'i=1; bind "$" "i = i%2+1; xlab=i==1?\\"pixel\\":\\"wavelength\\"; set xlabel xlab; set xra [*:*]; print i; repl"'
)
x2 = wave[o]
w2 = wave[o]
f2 = flux[o]
f2mod = fmod[o]
e2 = e_f[o]
b2 = bpmap[o]
res = resmap[o] / f2
gplot('[][][][-5:35]',
x2,
w2,
f2,
e2.clip(0., f2.max()),
'us (column(i)):3:4 w errorli t "' + self.info[n] +
' all"',
flush='')
#ogplot(x2,w2, f2, ((b2==0)|(b2==flag.clip))*0.5, 1+4*(b2==flag.clip), 'us (column(i)):3:4:5 w p pt 7 lc var ps var t "'+sp.timeid+' telluric free"', flush='')
ogplot(x2,
w2,
f2mod, (b2 == 0) * 0.5,
'us (column(i)):3:4 w lp lt 3 pt 7 ps var t "Fmod"',
flush='')
ogplot(
x2, w2, res / e2, b2,
"us (column(i)):3:4 w lp pt 7 ps 0.5 lc var axis x1y2 t 'residuals'"
)
#pause()
# legend with translation of bpmap, plot dummy using NaN function
#ogplot(", ".join(["NaN w p pt 7 ps 0.5 lc "+str(f) +" t '"+str(f)+" "+",".join(flag.translate(f))+"'" for f in np.unique(b2)]), flush='')
no = pause('%i/%i %s %s %s' %
(n + 1, self.N, o, self.bjd[n], self.info[n]))
try:
o = int(no)
except:
if no in ('-', "D", "B"):
n -= 1
elif no in ('e'):
break
elif no in ('^'):
n = 0
elif no in ('$'):
n = self.N - 1
elif no in (')'):
o += 1
elif no in ('('):
o -= 1
else:
n += 1
n = np.clip(n, 0, self.N - 1)
o = np.clip(o, 0, resmap.shape[0] - 1)
def postrv(self,
postiter=1,
fibsuf='',
oidx=None,
safemode=False,
pdf=False,
plotrvo=True):
"""
"""
#pause()
rv, e_rv = self.allrv[:, 5 + self.orders], self.allerr[:,
5 + self.orders]
RV, e_RV = nanwsem(rv, e=e_rv, axis=1)
allrv = self.allrv
allerr = self.allerr
bjd, RVc, e_RVc, RVd, e_RVd, RV, e_RV, BRV, RVsa = self.trvc
bjd, RV, e_RV, rv, e_rv = allrv[:,
0], allrv[:,
1], allrv[:,
2], allrv[:,
5:], allerr[:,
5:]
bjdmap = np.tile(bjd[:, np.newaxis], rv.shape[1])
omap = np.tile(np.arange(len(rv.T)), rv.shape[0]).T
#gplot(bjd, RV, e_RV, 'us 1:2:3 w e pt 7',)
#gplot(bjdmap.ravel(), rv.ravel(), e_rv.ravel(), omap.ravel(), 'us 1:2:4 w p pt 7 palette, "'+obj+'/'+obj+'.rvo.dat'+'" us 1:2:3 w e pt 7 lt 7')
rvc = rv - (np.nan_to_num(RVd) + np.nan_to_num(RVsa))[:, np.newaxis]
gplot.palette('define (0 "blue", 1 "green", 2 "red")').key(
'tit "%s"' % self.keytitle)
gplot.xlabel('"BJD - 2 450 000"').ylabel('"RV [m/s]')
# not drift corrected
gplot(bjdmap.ravel() - 2450000, rv.ravel(), e_rv.ravel(), omap.ravel(),
'us 1:2:4 w p pt 7 palette,', bjd - 2450000, RV, e_RV,
'us 1:2:3 w e pt 7 lt 7')
# drift corrected
#gplot(bjdmap.ravel(), rvc.ravel(), e_rv.ravel(), omap.ravel(), 'us 1:2:4 w p pt 7 ps 0.5 palette,', bjd, RVc, e_RVc, 'us 1:2:3 w e pt 7 lt 7')
pause(self.tag)
# minimise (rv_io - rv_i - m_o)^2 / (e_rv_io^2 + e_rv_i^2 + e_rv_o^2)
e_n = 0
e_o = 0
m_o = 0
for e_n in [0, 0.1, 1, 2, 5, 10, 20, 30, 50, 100, 200]:
e_r = np.sqrt(e_rv**2 + e_n**2 + e_o**2)
RV, e_RV = nanwsem(rv - m_o, e=e_r, axis=1)
RV = RV[:, np.newaxis]
#stop()
m_o = (wmean(rv - RV, 1 / e_r**2, axis=0) *
0)[np.newaxis, :] # order offset
r = rv - RV - m_o # residuals
stop()
e_n = np.sqrt((np.nansum(
(r**2 - e_rv**2 - e_o**2) / e_r**2, axis=1) /
np.nansum(1 / e_r**2, axis=1)).clip(
min=0))[:, np.newaxis] # keepdims not in v1.6.1
e_o = np.sqrt(
(np.nansum((r**2 - e_rv**2 - e_n**2) / e_r**2, axis=0) /
np.nansum(1 / e_r**2, axis=0)).clip(min=0))[np.newaxis, :]
L = -0.5 * np.nansum(r**2 / e_r**2) - 0.5 * np.nansum(
np.log(2. * np.pi * e_r**2))
print L, e_n.ravel()
gplot.unset(' multiplot')
gplot.datafile('missing "nan"')
gplot.multiplot('layout 1,2;')
gplot.xlabel('"order"').ylabel('"rvo - RV [m/s]"')
gplot(rv - RV, ' matrix,', m_o.ravel(), 'us 0:1')
gplot.xlabel('"obs No."').ylabel('"rvo - RV [m/s]"')
gplot((rv - RV).T, ' matrix')
gplot.unset('multiplot')
pause()
# ind, = where(np.isfinite(e_rv[i])) # do not use the failed and last order
RV, e_RV = nanwsem(rv, e=e_rv, axis=1)
RVc = RV - np.nan_to_num(RVd) - np.nan_to_num(RVsa)
e_RVc = np.sqrt(e_RV**2 + np.nan_to_num(e_RVd)**2)
pause()
def ucbspl_fit(x,
y=None,
w=None,
K=10,
xmin=None,
xmax=None,
lam=0.,
pord=2,
mu=None,
e_mu=None,
nat=True,
retfit=False,
var=False,
e_yk=False,
cov=False,
plot=False,
c=True):
'''
Fit a uniform cubic spline to data.
Parameters
----------
x : array_like
Data position.
y : array_like
Data values.
w : array_like
Data weights (1/e_y^2).
K : integer
Number of uniform knots.
xmin : float
Position of the first knot (default minimum of x).
xmax : float
Position of the last knot (default maximum of x).
lam : float
Penality, smoothing value (default 0, i.e. spline regression).
pord : int
Penality order (default 2, i.e. second derivative/curvature).
mu : array_like
Analog to mean value of a Gaussian process.
e_mu : array_like
Deviation of mu.
nat : boolean
Natural spline. (Unclear how to treat this for pord!=2.)
e_yk : boolean
Error estimates for the knot values.
In unweighted case, it estimates the np.sqrt((N-1)/(K-1)).
retfit: boolean
If true the returned tuple contains the predicted values y(x) at the data position x.
var : boolean
Variance of the model prediction.
cov : boolean
If false band matrices are used for efficient solution of equation system with band solver.
If true covariances are estimated using matrix inversion.
Returns
-------
ucbspl
The spline model.
ucbspl, yfit, varmod, v_cspl
The content of tuple depends on keywords retfit, var, cov.
Examples
--------
>>> x = np.r_[0:100., 400:500, 600:700]
>>> xx = np.r_[0:x[-1]:0.1]
>>> y = (np.sin(0.1*x)+1)*1000
Comparison of simple and proper variance estimation
>>> spl, v_spl, c = ucbspl_fit(x, y, w=1./1000**2, K=50, lam=0.00000001, pord=1, nat=0, var=True, cov=True)
>>> gplot(xx, spl(xx), np.sqrt(v_spl(xx)), np.sqrt(c(xx)), ' us 1:($2-$3):($2+$3) with filledcurves fill transparent solid 0.5, "" us 1:($2-$4):($2+$4) with filledcurves fill transparent solid 0.5 lt 3, "" w l lt 7,', spl.xk, spl(), ' lt 7 ps 0.5 pt 7,', x, y, ' pt 7 lt 1')
>>> spl, v_spl = ucbspl_fit(x, y, w=0.00001, K=50, mu=800, e_mu=800, nat=0, var=True)
>>> gplot(x, y, ' pt 7')
>>> ogplot(xx, spl(xx), np.sqrt(v_spl(xx)), ' w l, "" us 1:($2-$3):($2+$3) with filledcurves fill transparent solid 0.5')
Comparison of penality order
>>> gplot(x, y, ' pt 7')
>>> for e_mu in [5, 1, 0.1]:
... spl = ucbspl_fit(x, y, K=50, mu=500, e_mu=e_mu)
... ogplot(xx, spl(xx), 'w l t "mu=0.5 +/- %s"'%e_mu)
>>> for lam in [0.1, 1, 10, 100, 10000]:
... spl = ucbspl_fit(x, y, K=50, lam=lam, pord=1, nat=0)
... ogplot(xx,spl(xx), 'w l t "lam=%s",' % lam, spl.xk, spl(), ' ps 0.3 pt 6 lt 3 t ""')
>>> for lam in [0.0000001, 0.01, 0.1, 1, 10, 100000]:
... spl = ucbspl_fit(x, y, K=50, lam=lam, pord=2, nat=0)
... ogplot(xx,spl(xx), 'w l t "lam=%s",' % lam, spl.xk, spl(), ' ps 0.3 pt 6 lt 3 t ""')
>>> for lam in [1/0.1**2, 1, 1/5.**2]:
... spl = ucbspl_fit(x, y, K=50, lam=lam, pord=0, nat=0)
... ogplot(xx, spl(xx), 'w l t "lam=%s"' % lam)
'''
if y is None: # uniform data
y = x
x = np.linspace(xmin or 0,
y.size - 1 if xmax is None else xmax,
num=y.size)
if w is None:
w = 1
wy = y
else:
wy = w * y
wy = np.ascontiguousarray(wy)
if xmin is None: xmin = x.min()
if xmax is None: xmax = x.max()
G, kk = [cbspline_Bk, _cbspline_Bk][c](x, K, xmin, xmax)
if nat:
Gorg = 1 * G
bk2bknat(G, kk, K)
nk = K + 2
BTy = np.zeros(nk)
BTBbnd = np.zeros((4, nk))
if 0:
# the slow way without band storage
print 'bspline2'
B = bspline2((x - x.min()) / (x.max() - x.min()) * K, K, D=3)
n = B.shape[1]
D = np.diff(np.eye(n), n=pord).T
print 'BTy'
BTy = np.dot(B.T, y)
print 'BTB'
BTB = np.dot(B.T, B) + lam * np.dot(D.T, D)
#BTB = B.T.dot(B)+lam*D.T.dot(D)
#a = np.linalg.solve(BTB, BTy) # too slow
a = solve_banded((3, 3), bandmat(BTB, bw=7), BTy)
print a
yfit = np.dot(B, a)
if c:
# C version for band matrix
_cbspline.rhs_fill(BTy, G, wy, kk, kk.size)
# w.size=1 not broadcasted in lhsbnd_fill
_cbspline.lhsbnd_fill(BTBbnd, G,
w if np.size(w) > 1 else np.full_like(y, w), kk,
kk.size, nk)
if nat:
BTy = 1 * BTy[1:K + 1]
BTBbnd = 1 * BTBbnd[:, 1:K + 1]
if lam:
_cbspline.add_DTD(BTBbnd, BTy.size, lam, pord)
else:
# Python version for band matrix
# compute sum_jk B_j(x_i) B_k(x_i) w_i
for k in range(4):
BTy += np.bincount(kk + k, wy * G[k], nk)
#for j in range(k+1):
#BTBbnd[3-k+j] += np.bincount(kk+k, w*G[k]*G[j], nk) # fill upper
#for j in range(k+1):
#BTBbnd[k-j] += np.bincount(kk+j, w*G[k]*G[j], nk) # fill lower
for j in range(k, 4):
BTBbnd[j - k] += np.bincount(kk + k, w * G[k] * G[j],
nk) # fill lower
if nat:
BTy = BTy[1:K + 1] * 1
BTBbnd = BTBbnd[:, 1:K + 1] * 1
if lam:
# Add penalty lam*DTD
print lam
if pord == 0:
# diagonal
BTBbnd[0] += lam
elif pord == 1:
# 1 2 ... 2 2 1
# -1 -1 ... -1 -1
# diagonal
BTBbnd[0, [0, -1]] += lam
BTBbnd[0, 1:-1] += 2 * lam
# subdiagonals
BTBbnd[1, :-1] -= lam
elif pord == 2:
# 1 5 6 ... 6 5 1
# -2 -4 -4 ... -4 -2
# 1 1 1 ... 1
# diagonal
BTBbnd[0, [0, -1]] += lam
BTBbnd[0, [1, -2]] += 5 * lam
BTBbnd[0, 2:-2] += 6 * lam
# first subdiagonal
BTBbnd[1, [0, -2]] -= 2 * lam
BTBbnd[1, 1:-2] -= 4 * lam
# second subdiagonal
BTBbnd[2, :-2] += lam
else:
# generic version
D = np.diff(np.eye(nk), n=pord)
DTD = lam * np.dot(D, D.T)
for k in range(4):
BTBbnd[k, :-k] += np.diag(DTD, k)
#ds9(BTBbnd)
#pause()
if mu is not None and e_mu:
# GP like penality with mu and variance
BTy += mu / e_mu**2
BTBbnd[0] += 1. / e_mu**2
if cov:
# invert matrix
BTB = np.diag(BTBbnd[0])
for i in range(1, 4):
BTB += np.diag(BTBbnd[i, :-i], i) + np.diag(BTBbnd[i, :-i], -i)
covmat = np.linalg.inv(BTB)
BTy = covmat.dot(BTy)
else:
if c: # python version
#BTB = np.diag(BTBbnd[0])
#tt = BTy*1
#for i in range(1,4): BTB += np.diag(BTBbnd[i,:-i], i) + np.diag(BTBbnd[i,:-i], -i)
#ds9(BTB)
#print _cbspline.cholsol(BTB, tt, BTy.size)
_cbspline.cholbnd(BTBbnd, BTy, BTy.size, 3)
#_cbspline.cholbnd_upper(gg, uu, BTy.size, 3)
elif 1: # python version
solveh_banded(BTBbnd,
BTy,
lower=True,
overwrite_ab=True,
overwrite_b=True)
else:
# old c version with Gauss elimination
# symmetric part
BTBbnd[4, :-1] = BTBbnd[2, 1:]
BTBbnd[5, :-2] = BTBbnd[1, 2:]
BTBbnd[6, :-3] = BTBbnd[0, 3:]
#pause()
_cbspline.bandsol(BTBbnd, BTy, BTy.size, 7)
# only works with "1*" (copy needed: np.ctypeslib.as_ctypes(BTBbnd): TypeError: strided arrays not supported)
a = BTy
if nat:
G = Gorg
a = np.r_[2 * a[0] - a[1], a, 2 * a[K - 1] - a[K - 2]]
mod = ucbspl(a, xmin, xmax)
out = mod
if retfit or var or cov:
out = out,
if retfit:
# same as yfit = mod(x), but re-using G[k]
yfit = 0.
for k in [0, 1, 2, 3]:
yfit += a[kk + k] * G[k]
out += yfit,
# error estimation for knot coefficients ak
# sig(ak)^-2 = sum_i Bk(x_i) * w_i
if var or e_yk:
wa = 0. # = sum_i Bk(x_i) * w_i = 1/var(ak)
for k, Gk in enumerate(
G): # short loop, but bincount could also have overhead
wa += np.bincount(kk + k, Gk * w, nk)
if mu is not None and e_mu:
wa += 1. / e_mu**2
with np.errstate(divide='ignore'): # yet lam is not handled
vara = 1. / wa
varmod = ucbspl(vara, xmin, xmax)
if var:
out += varmod,
if cov:
# compute sum_i,j B_j(x) * cov_jk B_k(x)
out += v_cspl(covmat, xmin, xmax),
#gplot(x, v_f(x), varmod(x), ', "" us 1:3,', varmod.xk, varmod(), v_f(varmod.xk),', "" us 1:3')
# error estimation for knot values
# var(yk) = 1/6 var(ak-1) + 4/6 var(ak) + 1/6 var(ak+1)
if e_yk:
mod.e_yk = np.sqrt(varmod())
if plot:
gplot(
mod.osamp(20),
' w l lt 1,', # oversample the knots
mod.xk,
mod(),
' lt 1 pt 7,',
x,
y,
mod(x),
' lt 3, "" us 1:3 w l lt 2')
return out
def read_feros(self, s, orders=None, pfits=True, verb=True):
"""
SYNTAX: read_feros(filename)
OUTPUT: namedtuple('spectrum', 'w f berv bjd blaze drift timeid sn55 ')
w - wavelength
f - flux
berv - Barycentric Earth Radial Velocity
bjd - Barycentric Julian Day
blaze - Blaze filename
drift - Used RV Drift
sn55 - S_N order center55
"""
hdulist = pyfits.open(s) # slow 30 ms
if orders is None or self.header is None:
HIERARCH = 'HIERARCH '
hdr = hdulist[0].header
self.inst = hdr['INSTRUME']
#data,hdr = fitsio.read(s,header=True)
indberv = [
i for i, s in enumerate(hdr.values()) if 'BARY_CORR' in str(s)
][0] + 1
self.drsberv = float(hdr[indberv])
# FEROS drsberv are wrong and already applied to the spectra
# remove it from the spectrum
self.drsbjd = hdr.get('MJD-OBS',
np.nan) #hdr.get(HIERARCH+'ESO DRS BJD',0)
if self.drsbjd: self.drsbjd += 2400000.5
if self.fib == 'B': self.drsberv = np.nan
self.tmmean = 0.5 # hdr['HIERARCH ESO INS DET1 TMMEAN'] not available
self.exptime = hdr['EXPTIME']
self.dateobs = hdr['ARCFILE'][6:-5] # hdr['DATE-OBS']
# FEROS has no S/N estimates; judge with airmass and seeing
#print hdr['DATASUM'], self.exptime, hdr['HIERARCH ESO TEL AIRM START'], hdr['HIERARCH ESO TEL AMBI FWHM START']
seefhwm = hdr['HIERARCH ESO TEL AMBI FWHM START'] # if -1 assume 3.0
self.sn55 = 100 / hdr['HIERARCH ESO TEL AIRM START'] / (
seefhwm if seefhwm > 0 else 3.0)
self.blaze = '' #hdr[HIERARCH+'ESO DRS BLAZE FILE']
self.drift = hdr.get(HIERARCH + 'ESO DRS DRIFT RV USED', np.nan)
#fileid = str(hdr.ascardlist()['MJD-OBS']) # read the comment
fileid = hdr.get('ARCFILE',
0) #fileid[fileid.index('(')+1:fileid.index(')')]
self.calmode = hdr.get(
'ORIGFILE',
0).split("_")[3] #fileid[fileid.index('(')+1:fileid.index(')')]
calmodedict = {'objcal': 'OBJ,ThAr', 'objsky': 'OBJ,SKY'}
if self.calmode in calmodedict:
self.calmode = calmodedict[self.calmode]
self.timeid = self.dateobs #fileid
if self.calmode != 'OBJ,ThAr' and hdr.get(
'FILENAME') != 'rebinned1.bdf':
self.flag = 1
# hdr['OBJECT'] = hdr.get('OBJECT','FOX')
self.header = hdr
hdr = self.header
if verb:
print "read_feros:", self.timeid, self.header[
'OBJECT'], self.drsbjd, self.sn55, self.drsberv, self.drift, self.flag, self.calmode
if orders is not None: # read order data
if self.filename.endswith('.mt'):
realname = os.path.realpath(self.filename)
data = [
pyfits.getdata(realname.replace('01.mt', '%02i.mt' % (o + 1)))
for o in range(39)
]
fsize = max([len(fo) for fo in data])
f = np.zeros((39, fsize)).astype(float)
for o, fo in enumerate(data):
f[o, 0:len(fo)] = fo * 100000.
else:
f = hdulist[0].data * 100000.
bpmap = np.isnan(f).astype(int) # flag 1 for nan
bpmap[f < 0.0001] |= flag.neg # flag 2 for zero and negative flux
# print " applying wavelength solution ", file
CRPIX1 = -49.
CDELT1 = 0.03
w = 0 * f.astype(float)
his = list(hdr.get_history())
wind = [i for i, x in enumerate(his) if 'WSTART' in x][0]
wstart = ' '.join(his[wind + 1:wind + 14])
wstart = np.fromstring(wstart, dtype=float, sep=' ')
for i in range(39):
#wstart = 3.527250000000000E+03
w[i] = wstart[i] + CDELT1 * np.arange(w[0].size)
c = 299792.4580 # [km/s]
w *= (1 - self.drsberv / c)
w = airtovac(w)
#from gplot import *; gplot(lam[i],data[i])
#pause()
#lam = hdulist['WAVE'].data
e = np.ones_like(f) * 100.
e[bpmap == 0] = 1.0 * 10. #np.sqrt(data[bpmap==0]) # slow 50 ms
if self.fib != 'B': # filter for cosmics
look = (None, )
kap = 4.5
for o in range(39):
idx, = np.where(bpmap[o] == 0)
if 1:
# using a robust 4.5 68 percentile clipping
#hh = np.argsort(f[o][idx])
#ii = hh[len(hh)*0.98:] # reject 2%
p1sig = (100 - 68.2) / 2
p16, p50, p84 = np.percentile(f[o][idx],
(p1sig, 50, 100 - p1sig))
rbsig = (p84 - p16) / 2 # robust five sigma
ii = idx[f[o][idx] > p50 + kap * rbsig]
if o in look:
gplot(
w[o], f[o], ',', w[o][idx], f[o][idx], ',',
w[o][ii], f[o][ii], ",%f, %f, %f, %f" %
(p16, p50, p84, p50 + kap * rbsig))
if 0:
# moving robust 4.5sigma clipping
csum = np.cumsum(f[o][idx])
mvmean = (csum[400:] - csum[:-400]) / 400
#mvmean = np.lib.pad(mvmean, (400,400), 'edge')
mvmean = np.concatenate((np.zeros(200) + mvmean[0], mvmean,
np.zeros(200) + mvmean[-1]))
csum = np.cumsum(abs(f[o][idx] - mvmean))
mvrbsig = (csum[400:] - csum[:-400]) / 400
mvrbsig = np.concatenate(
(np.zeros(200) + mvrbsig[0], mvrbsig,
np.zeros(200) + mvrbsig[-1]))
ii = idx[f[o][idx] > mvmean + kap * mvrbsig]
if o in look:
gplot(w[o], f[o], ',', w[o][idx], f[o][idx], mvmean,
mvmean + kap * mvrbsig, ',"" us 1:3, "" us 1:4,',
w[o][ii], sf[o][ii])
bpmap[o][ii] |= flag_cosm
if o in look:
pause(o)
hdulist.close()
return w, f, e, bpmap
hdulist.close()
def atm_model(w, f, berv, o):
# divides spectra by atmosphere model
# much more testing needed !
modelfile = 'inst/CRIRES_atm/stdAtmos_crires_airmass1.fits'
hdu = fits.open(modelfile, ignore_blank=True)
atm_model = hdu[1].data
w_atm2 = atm_model.field(0).astype(np.float64)
f_atm2 = atm_model.field(1).astype(np.float64)
# file_obs2 = "data/CRIRES/210220_PiOri/cr2res_obs_nodding_extracted_combined.fits"
file_obs2 = "/data/jana/VIPER/210220_tetVir/K2166/cr2res_obs_nodding_extracted_combined.fits"
x2, w_atm, f_atm, bp2, bjd2, berv2 = Spectrum(file_obs2, o=o)
bw = (np.load('wave_solution_tetvir.npy'))[o - 1]
w_atm = np.poly1d(bw[::-1])(x2)
# bb = [-0.457193, 314.784913, 5675608.219445]
ba = (np.load("lib/CRIRES/blaze_K2166.npy"))[o - 1]
bandp = np.poly1d(ba)(x2)
lmin = w[0]
lmax = w[-1]
lmin = max(w[0], w_atm[0])
lmax = min(w[-1], w_atm[-1])
smo = slice(*np.searchsorted(w_atm, [lmin, lmax]))
w_atm1 = w_atm[smo]
f_atm1 = f_atm[smo]
smo = slice(*np.searchsorted(w, [lmin, lmax]))
w = w[smo]
f = f[smo]
bandp = bandp[smo]
gplot(w_atm, f_atm / np.nanmean(f_atm), 'w l t "tell",', w,
f / np.nanmean(f), 'w l t "data",', w, bandp / np.mean(bandp) * 0.9,
'w l t "bandp"') #, w_atm1,f_atm1/np.nanmean(f_atm1),'w l')
pause()
# correction of shift between model and observed data
rv, ccc = pyasl.crosscorrRV(w,
f,
w_atm1,
f_atm1,
-5.,
5.,
0.005,
skipedge=20)
pol = (np.poly1d(np.polyfit(rv, ccc, 15)))(rv)
shift = rv[np.argmax(pol)]
print('shift atmmod: ', shift)
print(w, w_atm1)
w_atm /= (1 + shift / 3e5)
smo = slice(*np.searchsorted(w_atm, [lmin, lmax]))
w_atm1 = w_atm[smo]
f_atm1 = f_atm[smo]
print(w, w_atm1)
f_atm1 = np.interp(w, w_atm1, f_atm1)
# f_div = f/np.nanmean(f)/(f_atm1/np.mean(f_atm1))
f_div = f / f_atm1
print('mean', np.nanmean(abs(f / np.mean(f) - f_div / np.mean(f_div))))
i_flag = np.where(
abs(f_div) <= (np.median(f_div) + 2 * np.std(abs(f_div))))[0]
# i_flag = np.where(abs(f/np.nanmean(f)-f_div/np.nanmean(f_div)) <= 0.2)[0]
# f_div[f_div<=0] = 1000
# i_flag = np.where(abs(f_div) <= (np.median(f_div)*1.5))[0]
w1 = w * 1.
w = w[i_flag]
f = f[i_flag]
f_div = f_div[i_flag] #*np.median(f)
plot_tell = 1
if plot_tell:
print('mean', np.nanmean(abs(f / np.mean(f) - f_div / np.mean(f_div))))
# gplot(w,f/np.mean(f),'w l,', w,f_atm1/np.mean(f_atm1),'w l')
gplot(w, f / np.mean(f), 'w l t "data",', w, f_div / np.mean(f_div),
'w l t "div data",', w1, f_atm1 / np.mean(f_atm1),
'w l t "tell",', w,
abs(f / np.nanmean(f) - f_div / np.nanmean(f_div)),
'w p pt 7 ps 0.4 t "res"')
pause()
return w, f_div, i_flag #f_atm
