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serval.py
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serval.py
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#! /usr/bin/env python
__author__ = 'Mathias Zechmeister'
__version__ = '2019-02-17'
description = '''
SERVAL - SpEctrum Radial Velocity AnaLyser (%s)
by %s
''' % (__version__, __author__)
import argparse
import copy
import ctypes
from ctypes import c_void_p, c_double, c_int
import csv
# from datetime import datetime imported with read_spec!
import glob
import os
import resource
import stat as os_stat
import sys
import time
import numpy as np
from numpy import std,arange,zeros,where, polynomial,setdiff1d,polyfit,array, newaxis,average
from scipy import interpolate, optimize
from scipy.optimize import curve_fit
from gplot import *
from pause import pause, stop
from wstat import wstd, wmean, wrms, rms, mlrms, iqr, wsem, nanwsem, nanwstd, naniqr, quantile
from read_spec import * # flag, sflag, def_wlog
from calcspec import *
from targ import Targ
import cubicSpline
import cspline as spl
import masktools
import phoenix_as_RVmodel
from chi2map import Chi2Map
gplot2 = Gplot() # for a second plot window
gplot.bar(0).colors('classic')
if 'gplot_set' in locals():
raise ImportError('Please update new gplot.py.')
if tuple(map(int,np.__version__.split('.'))) > (1,6,1):
np.seterr(invalid='ignore', divide='ignore') # suppression warnings when comparing with nan.
def lam2wave(l, wlog=def_wlog):
return np.log(l) if wlog else l
def wave2lam(w, wlog=def_wlog):
return np.exp(w) if wlog else w
servalsrc = os.path.dirname(os.path.realpath(__file__)) + os.sep
servaldir = os.path.dirname(os.path.realpath(servalsrc)) + os.sep
servallib = servaldir + 'lib' + os.sep
os.environ['ASTRO_DATA'] = servalsrc
ptr = np.ctypeslib.ndpointer
_pKolynomial0 = ctypes.CDLL(servalsrc+'polyregression.so')
_pKolynomial0.polyfit.restype = c_double
_pKolynomial = np.ctypeslib.load_library(servalsrc+'polyregression', '.')
_pKolynomial.polyfit.restype = c_double
_pKolynomial.polyfit.argtypes = [
ptr(dtype=np.float), # x2
ptr(dtype=np.float), # y2
ptr(dtype=np.float), # e_y2
ptr(dtype=np.float), # fmod
#ptr(dtype=np.bool), # ind
c_double, # ind
c_int, c_double, c_int, # n, wcen, deg
ptr(dtype=np.float), # p
ptr(dtype=np.float), # lhs
ptr(dtype=np.float) # pstat
]
_pKolynomial.interpol1D.argtypes = [
ptr(dtype=np.float), # xn
ptr(dtype=np.float), # yn
ptr(dtype=np.float), # x
ptr(dtype=np.float), # y
c_int, c_int # nn, n
]
c = 299792.4580 # [km/s] speed of light
def nans(*args, **kwargs):
return np.nan * np.empty(*args, **kwargs)
# default values
postiter = 3 # number of iterations for post rvs (postclip=3)
debug = 0
sp, fmod = None, None # @getHalpha
apar = zeros(3) # parabola parameters
astat = zeros(3*2-1)
alhs = zeros((3, 3))
def Using(point, verb=False):
usage = resource.getrusage(resource.RUSAGE_SELF)
if verb: print '%s: usertime=%s systime=%s mem=%s mb' % (point,usage[0],usage[1],
(usage[2]*resource.getpagesize())/1000000.0 )
return (usage[2]*resource.getpagesize())/1000000.0
class Logger(object):
def __init__(self):
self.terminal = sys.stdout
self.logfile = None # open(logfilename, "a")
self.logbuf = ''
def flush(self):
# dummy for function astropy iers download; progress bar will not be shown
pass
def write(self, message): # fork the output to stdout and file
self.terminal.write(message)
if self.logfile:
self.logfile.write(message)
else:
self.logbuf += message
def logname(self, logfilename):
self.logfile = open(logfilename, 'a')
self.logfile.write(self.logbuf)
print 'logging to', logfilename
def minsec(t): return '%um%.3fs' % divmod(t, 60) # format time
class interp:
"""interpolation similar to interpolate.interp1d but faster
array must be sorted; 1D arrays only !!!
"""
def __init__(self, x, y) :
self.x = 1 * x # we like real arrays
self.y = 1 * y
def __call__(self, xx):
yy = 0 * xx
_pKolynomial.interpol1D(self.x, self.y, xx, yy, self.x.size, xx.size)
return yy
class Tpl:
def __init__(self, wk, fk, initfunc, evalfunc, mask=False, berv=None):
'''
wk : barycentric corrected wavelength
'''
ii = slice(None)
if mask:
ii = np.isfinite(fk)
self.wk = wk[ii]
self.fk = fk[ii]
self.berv = berv
self.funcarg = initfunc(self.wk, self.fk)
self.evalfunc = evalfunc
def __call__(self, w, der=0):
return self.evalfunc(w, self.funcarg, der=der)
def mskatm(self, w, msk):
# mask regions (atm, stellar) in template
# need target velocity and velocity range
if msk and self.berv:
# bb[o][tellmask(barshift(ww[o],-spt.berv))>0.01] |= flag.atm # mask as before berv correction
# bb[o][skymsk(barshift(ww[o],-spt.berv))>0.01] |= flag.sky # mask as before berv correction
# mask as before berv correction
return msk(barshift(w,-self.berv)) > 0.01
else:
return slice(0,0) # empty
def analyse_rv(obj, postiter=1, fibsuf='', oidx=None, safemode=False, pdf=False):
"""
"""
print obj+'/'+obj+'.rvc'+fibsuf+'.dat'
allrv = np.genfromtxt(obj+'/'+obj+'.rvo'+fibsuf+'.dat')
allerr = np.genfromtxt(obj+'/'+obj+'.rvo'+fibsuf+'.daterr')
sbjd = np.genfromtxt(obj+'/'+obj+'.rvo'+fibsuf+'.dat', dtype=('|S33'), usecols=[0]) # as string
snr = np.genfromtxt(obj+'/'+obj+'.snr'+fibsuf+'.dat')
if np.size(allrv) == 1:
return # just one line, e.g. drift
bjd, RVc_old, e_RVc_old, RVd, e_RVd, RV_old, e_RV_old, BRV, RVsa = np.genfromtxt(obj+'/'+obj+'.rvc'+fibsuf+'.dat', dtype=None).T
orders, = np.where(np.sum(allerr[:,5:]>0, 0)) # orders with all zero error values
if oidx is not None:
omiss = set(oidx) - set(orders)
if omiss: pause('WARNING: orders', omiss,'missing')
else: orders = np.array(sorted(set(orders) & set(oidx)))
omap = orders[:,newaxis]
rv, e_rv = allrv[:,5+orders], allerr[:,5+orders]
# 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)
# post RVs (re-weightening)
ok = e_rv > 0
ordmean = average(rv*0, axis=0)
gplot.key('tit "'+obj+'"')
for i in range(1+postiter): # centering, first loop to init, other to clip
RVp, e_RVp = nanwsem(rv-ordmean, e=e_rv*ok, axis=1)
orddisp = rv - ordmean - RVp[:,newaxis]
ordstd, d_ordmean = wstd(orddisp, e_rv*ok, axis=0)
ordmean += d_ordmean # update ordmean
ok &= np.abs(orddisp-d_ordmean) <= 3*ordstd # clip and update mask
if np.isnan(RVp.sum()) or np.isnan(e_RVp.sum()):
print 'WARNING: nan in post RVs. Maybe to few measurements. Re-setting to originals.\n'
RVp, e_RVp = RV, e_RV
break
else:
gplot(orddisp.T, ' matrix us (%s-1):3 t ""' % "".join(['$1==%s?%s:' % io for io in enumerate(orders)]))
ogplot(orders, ordstd, ' w lp lt 3 t "", "" us 1:(-$2) w lp t ""')
if 0: pause(i)
rvp = rv - ordmean
pdf=0
if pdf:
gplot.term('pdfcairo; set out "%s.pdf"'% obj)
if 1: # chromatic slope
gplot.xlabel('"BJD - 2 450 000"').ylabel('"chromatic index [m/s/Np]"')
gplot('"'+obj+'/'+obj+'.srv'+fibsuf+'.dat" us ($1-2450000):4:5 w e pt 7')
if not safemode: pause('chromatic index')
gplot.xlabel('"RV [m/s]"').ylabel('"chromatic index [m/s/Np]"')
gplot('"" us 2:4:3:5:($1-2450000) w xyerr pt 7 palette')
if not safemode: pause('correlation RV - chromatic index')
snro = snr[:,2+orders]
print "total SNR:", np.sum(snro**2)**0.5
if 1: # plot SNR
gplot.reset().xlabel('"Order"; set ylabel "SNR"; set ytics nomirr; set y2label "total SNR"; set y2tics; set yrange[0:]; set y2range[0:]')
gplot(snro, 'matrix us ($2+%i):3' % np.min(orders), flush='')
ogplot(np.sum(snro**2,axis=1)**0.5,' us (%i):1 axis x1y2 t "total SNR"'% (np.min(orders)+len(orders)))
if not safemode: pause()
gplot.reset()
# store post processed rvs
RVpc = RVp - np.nan_to_num(RVd) - np.nan_to_num(RVsa)
e_RVpc = np.sqrt(e_RVp**2 + np.nan_to_num(e_RVd)**2)
#unit_rvp = [open(obj+'/'+obj+'.post'+fibsuf+'.dat', 'w'), open(obj+'/'+obj+'.post.badrv'+fibsuf+'.dat', 'w')]
np.savetxt(obj+'/'+obj+'.post'+fibsuf+'.dat', zip(sbjd, RVpc, e_RVpc, RVp, e_RVp, RVd, e_RVd, BRV, RVsa), fmt='%s')
print 'Statistic on dispersion in RV time series for', obj
print ' %10s %10s %10s %10s %10s'% ('mlrms [m/s]', 'std [m/s]', 'wstd [m/s]', 'iqr [m/s]', 'wiqr [m/s]')
print 'RVmed: %10.4f' % std(allrv[:,3])
print 'RV: '+' %10.4f'*5 % (mlrms(RV,e_RV)[0], std(RV), wstd(RV,e_RV)[0], iqr(RV,sigma=True), iqr(RV, w=1/e_RV**2, sigma=True))
print 'RVp: '+' %10.4f'*5 % (mlrms(RVp,e_RVp)[0], std(RVp), wstd(RVp,e_RVp)[0], iqr(RVp,sigma=True), iqr(RVp, w=1/e_RVp**2, sigma=True))
print 'RVc: '+' %10.4f'*5 % (mlrms(RVc,e_RVc)[0], std(RVc), wstd(RVc,e_RVc)[0], iqr(RVc,sigma=True), iqr(RVc, w=1/e_RVc**2, sigma=True))
print 'RVpc: '+' %10.4f'*5 % (mlrms(RVpc,e_RVpc)[0], nanwstd(RVpc), nanwstd(RVpc,e=e_RVpc), naniqr(RVpc,sigma=True), iqr(RVpc, w=1/e_RVpc**2, sigma=True))
print 'median internal precision', np.median(e_RV)
print 'Time span [d]: ', bjd.max()-bjd.min()
gplot.reset().xlabel('"BJD - 2 450 000"; set ylabel "RV [m/s]"')
gplot('"'+obj+'/'+obj+'.rvc'+fibsuf+'.dat" us ($1-2450000):2:3 w e pt 7 t "rvc %s"'%obj)
if not safemode: pause('rvc')
gplot(bjd, allrv[:,3],' us ($1-2450000):2 t "RVmedian" lt 4', flush='')
ogplot(bjd, RV, e_RV, ' us ($1-2450000):2:3 w e t "RV"', flush='')
ogplot(bjd, RVp, e_RVp, ' us ($1-2450000):2:3 w e t "RVp"', flush='')
ogplot(bjd, RVc, e_RVc, ' us ($1-2450000):2:3 w e pt 7 lt 1 t "RVc"', flush='')
ogplot(bjd, RVpc, e_RVpc,' us ($1-2450000):2:3 w e pt 6 lt 7 t "RVpc"')
if not safemode: pause('rv')
if 0: # error comparison
gplot_set('set xlabel "Order"')
gplot(orders, ordstd, ' t "external error"')
ogplot(orders, np.mean(e_rv, axis=0), ' t "internal error"')
if not safemode: pause('ord disp')
if 1: # Order dispersion
gplot.reset().xlabel('"Order"')
#gplot('"'+filename,'" matrix every ::%i::%i us ($1-5):3' % (omin+5,omax+5))
#ogplot(allrv[:,5:71],' matrix every ::%i us 1:3' %omin)
# create ord, rv,e_rv, bb
#ore = [(o+omin,orddisp[n,o],e_rv[n,o],~ok[n,o]) for n,x in enumerate(orddisp[:,0]) for o,x in enumerate(orddisp[0,:])]
#ore = [(o,)+x for row in zip(orddisp,e_rv,~ok) for o,x in enumerate(zip(*row),omin)]
ore = [np.tile(orders,orddisp.shape).ravel(), orddisp.ravel(), e_rv.ravel(), ~ok.ravel()]
gplot(*ore + ['us 1:2:($3/30) w xe'], flush='')
if not ok.all(): ogplot('"" us 1:2:($3/30/$4) w xe', flush='') # mark 3-sigma outliners
#gplot(orddisp,' matrix us ($1+%i):3' % omin, flush='')
#gplot('"'+filename,'" matrix every ::'+str(omin+5)+' us ($1-5):3')
#ogplot(ordmean,' us ($0+%i):1 w lp lt 3 pt 3 t "ord mean"' %omin, flush='')
ogplot(orders, ordstd,' w lp lt 3 pt 3 t "1 sigma"', flush='')
ogplot('"" us 1:(-$2) w lp lt 3 pt 3 t ""', flush='')
ogplot('"" us 1:($2*3) w lp lt 4 pt 3 t "3 sigma"', flush='')
ogplot('"" us 1:(-$2*3) w lp lt 4 pt 3 t ""', flush='')
ogplot('"" us ($1+0.25):(0):(sprintf("%.2f",$2)) w labels rotate t""', flush='')
ogplot(*ore+[ 'us 1:2:($3) w point pal pt 6'])
if not safemode: pause('ord scatter')
print 'mean ord std:',np.mean(ordstd), ', median ord std:',np.median(ordstd)
if pdf:
gplot.out()
return allrv
def nomask(x): # dummy function, tellurics are not masked
return 0. * x
def lineindex(l, r1, r2):
if np.isnan(r1[0]) or np.isnan(r2[0]):
return np.nan, np.nan
s = l[0] / (r1[0]+r2[0]) * 2
# error propagation
e = s * np.sqrt((l[1]/l[0])**2 + (r1[1]**2+r2[1]**2)/(r1[0]+r2[0])**2)
return s, e
def getHalpha(v, typ='Halpha', inst='HARPS', rel=False, plot=False):
"""
v [km/s]
sp,fmod as global variables !
deblazed sp should be used !
"""
wcen, dv1, dv2 = {
'Halpha': (6562.808, -15.5, 15.5), # Kuerster et al. (2003, A&A, 403, 1077)
'Halpha': (6562.808, -40., 40.),
'Haleft': (6562.808, -300., -100.),
'Harigh': (6562.808, 100, 300),
'Haleft': (6562.808, -500., -300.),
'Harigh': (6562.808, 300, 500),
'CaI': (6572.795, -15.5, 15.5), # Kuerster et al. (2003, A&A, 403, 1077)
'CaH': (3968.470, -1.09/2./3968.470*c, 1.09/2./3968.470*c), # Lovis et al. (2011, arXiv1107.5325)
'CaK': (3933.664, -1.09/2./3933.664*c, 1.09/2./3933.664*c), # Lovis et al.
'CaIRT1': (8498.02, -15., 15.), # NIST + my definition
'CaIRT1a': (8492, -40, 40), # My definition
'CaIRT1b': (8504, -40, 40), # My definition
'CaIRT2': (8542.09, -15., 15.), # NIST + my definition
'CaIRT2a': (8542.09, -300, -200), # My definition
'CaIRT2b': (8542.09, 250, 350), # My definition, +50 due telluric
'CaIRT3': (8662.14, -15., 15.), # NIST + my definition
'CaIRT3a': (8662.14, -300, -200), # NIST + my definition
'CaIRT3b': (8662.14, 200, 300), # NIST + my definition
'NaD1': (5889.950943, -15., 15.), # NIST + my definition
'NaD2': (5895.924237, -15., 15.), # NIST + my definition
'NaDref1': (5885, -40, 40), # My definition
'NaDref2': ((5889.950+5895.924)/2, -40, 40), # My definition
'NaDref3': (5905, -40, 40) # My definition
}[typ]
wcen = lam2wave(airtovac(wcen))
o = None
if inst == 'HARPS':
if typ in ['Halpha', 'Haleft', 'Harigh', 'CaI']: o = 67
if typ in ['CaK']: o = 5
if typ in ['CaH']: o = 7
elif inst == 'CARM_VIS':
if typ in ['Halpha', 'Haleft', 'Harigh', 'CaI']: o = 25
if 'CaIRT1' in typ: o = 46
if 'CaIRT2' in typ: o = 46
if 'CaIRT3' in typ: o = 47
if 'NaD' in typ: o = 14
if o is None: return np.nan, np.nan
ind = o, (wcen+(v-sp.berv+dv1)/c < sp.w[o]) & (sp.w[o] < wcen+(v-sp.berv+dv2)/c)
if rel: # index relative to template
# relative index is not a good idea for variable lines
if np.isnan(fmod[ind]).all(): return np.nan, np.nan
if np.isnan(fmod[ind]).any(): pause()
mod = fmod[ind]
res = sp.f[ind] - mod
I = np.mean(res/fmod[ind])
e_I = rms(np.sqrt(sp.f[ind])/fmod[ind]) / np.sqrt(np.sum(ind[1])) # only for photon noise
else: # mean absolute flux ("box fitting" .... & ref reg => absolute index EW width)
I = np.mean(sp.f[ind])
e_I = 1. / ind[1].sum() * np.sqrt(np.sum(sp.e[ind]**2))
mod = I + 0*sp.f[ind]
if plot==True or typ in plot:
print typ, I, e_I
gplot(sp.w[o], fmod[o], sp.f[o], 'us 1:2 w l lt 3 t "template", "" us 1:3 lt 1 t "obs"')
ogplot(sp.w[ind], sp.f[ind], mod, 'lt 1 pt 7 t "'+typ+'", "" us 1:3 w l t "model", "" us 1:($2-$3) t "residuals"')
pause()
return I, e_I
def polyreg(x2, y2, e_y2, v, deg=1, retmod=True): # polynomial regression
"""Returns polynomial coefficients and goodness of fit."""
fmod = calcspec(x2, v, 1.) # get the shifted template
if 0: # python version
ind = fmod>0.01 # avoid zero flux, negative flux and overflow
p,stat = polynomial.polyfit(x2[ind]-calcspec.wcen, y2[ind]/fmod[ind], deg-1, w=fmod[ind]/e_y2[ind], full=True)
SSR = stat[0][0]
else: # pure c version
pstat = np.empty(deg*2-1)
p = np.empty(deg)
lhs = np.empty((deg, deg))
# _pKolynomial.polyfit(x2, y2, e_y2, fmod, ind, ind.size,globvar.wcen, rhs.size, rhs, lhs, pstat, chi)
ind = 0.0001
# no check for zero division inside _pKolynomial.polyfit!
#pause()
SSR = _pKolynomial.polyfit(x2, y2, e_y2, fmod, ind, x2.size, calcspec.wcen, deg, p, lhs, pstat)
if SSR < 0:
ii, = np.where((e_y2<=0) & (fmod>0.01))
print 'WARNING: Matrix is not positive definite.', 'Zero or negative yerr values for ', ii.size, 'at', ii
p = 0*p
pause(0)
if 0: #abs(v)>200./1000.:
gplot(x2,y2,calcspec(x2, v, *p), ' w lp,"" us 1:3 w l, "" us 1:($2-$3) t "res [v=%f,SSR=%f]"'%(v, SSR))
pause('v, SSR: ',v,SSR)
if retmod: # return the model
return p, SSR, calcspec(x2, v, *p, fmod=fmod)
return p, SSR
def gauss(x, a0, a1, a2, a3):
z = (x-a0) / a2
y = a1 * np.exp(-z**2 / 2) + a3 #+ a4 * x + a5 * x**2
return y
def optidrift(ft, df, f2, e2=None):
"""
Scale derivative to the residuals.
Model:
f(v) = A*f - A*df/dv * v/c
"""
# pre-normalise
#A = np.dot(ft, f2) / np.dot(ft, ft) # unweighted (more robust against bad error estimate)
A = np.dot(1/e2**2*ft, f2) / np.dot(1/e2**2*ft, ft)
fmod = A * ft
v = -c * np.dot(1/e2**2*df, f2-fmod) / np.dot(1/e2**2*df, df) / A #**2
if 0:
# show RV contribution of each pixel!
print 'median', np.median(-(f2-fmod)/df*c/A*1000), ' v', v*1000
gplot(-(f2-fmod)/df*c/A*1000, e2/df*c/A*1000, 'us 0:1:2 w e, %s' % (v*1000))
pause()
e_v = c / np.sqrt(np.dot(1/e2**2*df, df)) / A
fmod = fmod - A*df*v/c
#gplot(f2, ',', ft*A, ',', f2-fmod,e2, 'us 0:1:2 w e')
#gplot(f2, ',', ft*A, ',', f2-A*ft,e2, 'us 0:1:2 w e')
#gplot(f2, ft*A, A*ft-A*df/c*v, A*ft-A*df/c*0.1,' us 0:1, "" us 0:2, "" us 0:3, "" us 0:4, "" us 0:($1-$3) w lp, "" us 0:($1-$4) w lp lt 7')
return type('par',(),{'params': np.append(v,A), 'perror': np.array([e_v,1.0])}), fmod
def CCF(wt, ft, x2, y2, va, vb, e_y2=None, keep=None, plot=False, ccfmode='trapeze'):
# CCF is on the same level as the least square routine fit_spec
if keep is None: keep = np.arange(len(x2))
if e_y2 is None: e_y2 = np.sqrt(y2)
vgrid = np.arange(va, vb, v_step)
# CCF is a data compression/smoothing/binning
SSR = np.arange(vgrid.size, dtype=np.float64)
def model_box(x, v):
idx = np.searchsorted(wt+v/c, x)
return ft[idx] > 0
for i in SSR:
if ccfmode == 'box':
# real boxes (zero order interpolation)
#idx = np.searchsorted(wt+vgrid[i]/c, x2[keep])
#SSR[i] = np.dot(y2[keep], ft[idx]>0) / np.sum(ft[idx]>0)
y2mod = model_box(x2[keep], vgrid[i])
SSR[i] = np.dot(y2[keep], y2mod) / np.sum(y2mod)
# gplot(np.exp(x2), y2, ft[idx]*y2.max(),'w lp, "" us 1:3 w l lt 3')
if ccfmode == 'trapeze':
stop()
# gplot(np.exp(x2), y2,'w lp,', np.exp(x2[keep]),ft[idx]*5010, 'w l lt 3')
# Peak analysis with gaussian fit
# The Gaussian is a template for the second level product CCF
try:
params, covariance = curve_fit(gauss, vgrid, SSR, p0=(0., SSR.max()-SSR.min(), 2.5, SSR.min()))
perror = np.diag(covariance) if np.isfinite(covariance).min() else 0*params
SSRmod = gauss(vgrid, *params)
SSRmin = gauss(params[0], *params)
except:
params = [0,0,0,0]
perror = [0,0,0,0]
SSRmod = 0
SSRmin = 0
if 0:
# old CCF_binless version
# uses just the mask which has a fixed window size
idx = np.searchsorted(wt, x2[keep]) #v=0
ind = (ft[idx]>0) & (ft[idx-1]>0)
iidx = idx[ind] - 1 # previous/lower index
xv2 = (x2[keep[ind]]-0.5*(wt[iidx+1,0]+wt[iidx])) * c
yv2 = y2[keep[ind]] / ft[iidx]
ev2 = e_y2[keep[ind]] / ft[iidx]
elif ccfmode == 'binless':
# get the line center from the mask
# grab the data around the line center with the window size
# phase fold all windows to velocity space by subtracting the line center
# assume windows do not overlap
wsz = 6.0 # [km/s] 3.0 for HARPS, 6.0 for FEROS
linecen = 0.5 * (wt[1::4]+wt[2::4]) # get the linecenter form the mask
lineflux = 0.5 * (ft[1::4]+ft[2::4])
idx = np.searchsorted(linecen, x2[keep]) # find index of nearest largest line
idx = idx - ((linecen[idx]-x2[keep]) > (x2[keep]-linecen[idx-1])) # index of the nearest (smaller or larger) line
ind = np.abs(x2[keep]-linecen[idx]) < wsz/c # index for values with the window
xv2 = (x2[keep[ind]]-linecen[idx[ind]]) * c # phase fold
yv2 = y2[keep[ind]] / lineflux[idx[ind]]
ev2 = e_y2[keep[ind]] / lineflux[idx[ind]]
# normalise window flux with the flux mean in each window
iidx = idx[ind] - 1
lidx = iidx - iidx.min()
nlines = lidx.max() + 1
linenorm = np.zeros((nlines,2))
for i,y in zip(lidx, yv2): linenorm[i] += np.array([y,1])
linenorm = (linenorm[:,0] / linenorm[:,1])[lidx] # mean = sum / n
yv2norm = yv2 / linenorm
#yv2 = yv2norm
fmod = 0
if 0 or plot and ccfmode=='binless':
# try a robust gaussfit via iterative clipping
iidx = idx[ind] - 1
fmod = 0 * y2
try:
#par2, cov2 = curve_fit(gauss, xv2, yv2, p0=(0,1,2.5,0))
nclip = 1
good = np.arange(len(xv2))
for it in range(nclip+1):
par2, cov2 = curve_fit(gauss, xv2[good], yv2norm[good], p0=(0,1,2.5,0))
perr2 = np.diag(cov2)
SSR2mod = gauss(vgrid, *par2)
yv2mod = gauss(xv2, *par2)
normfac = params[1] / par2[1]
fmod[keep[ind]] = gauss(xv2, *par2)
# clip
kap = 3 * np.std(yv2norm[good]-yv2mod[good])
out = np.abs(yv2norm - yv2mod) > kap
rml = np.unique(lidx[out])
good = np.sum(lidx == rml[:,newaxis],0)==0 # ind for lines not reject (good windows)
if plot:
gplot(xv2, yv2norm, yv2mod,good, ', "" us 1:3, "" us 1:($2/($4==0)),', vgrid,SSR2mod,SSR2mod-kap,SSR2mod+kap, 'w l lt 7, "" us 1:3 w l lt 1, "" us 1:4 w l lt 1')
#pause()
except:
par2 = [0]
perr2 = [0,0,0]
normfac = 0
SSR2mod = vgrid*0
if plot&1:
gplot(vgrid, SSR, SSRmod, 'w lp pt 7 t "CCF", "" us 1:3 w l lt 3 lw 3 t"CCF fit %.2f +/- %.2f m/s"'%(params[0]*1000,perror[0]*1000))
if plot&1 and ccfmode=='binless':
# insert a -1 row for broken lines in plot
gg = []
for g,diff in zip(np.dstack((xv2, normfac*yv2norm, normfac*ev2, iidx-iidx.min()))[0], np.ediff1d(iidx, to_begin=0)): gg += [g*0-1,g] if diff else [g]
gg = np.array(gg)
gplot.palette('model HSV rgbformulae 3,2,2; set bar 0')
gplot(gg, 'us 1:($2/($4>-1)):4 w lp palette pt 7, "" us 1:($2/($4>-1)):3 w e pt 1 lt 1,', vgrid, normfac*SSR2mod, 'w l lc 3 lw 3 t"%.2f +/- %.2f m/s"'%(par2[0]*1000,perr2[2]*1000))
#gplot(xv2, normfac*yv2norm, normfac*ev2, iidx-iidx.min(), np.ediff1d(iidx, to_begin=0), 'us 1:2:4 w lp palette pt 7, "" us 1:2:3 w e pt 1 lt 1,', vgrid, normfac*SSR2mod, 'w l lc 0 lw 3 t"%.2f +/- %.2f m/s"'%(par2[0]*1000,perr2[2]*1000))
ogplot(vgrid, SSR, SSRmod, 'w lp lt 7 pt 7 t "CCF", "" us 1:3 w l lt 7 lw 3 t"CCF fit %.2f +/- %.2f m/s"'%(params[0]*1000,perror[0]*1000))
pause(params[0]*1000, perror[0]*1000)
print params[0]*1000, perror[0]*1000
if plot&1: pause()
if plot&2:
idx = np.searchsorted(wt, x2)
idx0 = np.searchsorted(wt-vgrid[0]/c, x2)
gplot(np.exp(x2), y2, ft[idx]/10, y2*(ft[idx]>0), y2*(ft[idx0]>0), 'w lp t "x2,y2 input spectrum", "" us 1:3 w l lt 3 t "mask (v=0)", "" us 1:4 lt 3 t "x2,y2*mask", "" us 1:5 lt 4 pt 1 t "x2,y2*mask(v=va)')
pause()
stat = {'std': 1, 'snr': 0}
params[0] *= -1
return type('par',(),{'params': params, 'perror':perror, 'ssr':SSRmin, 'niter':0}), fmod, (vgrid, SSR, SSRmod), stat
def SSRstat(vgrid, SSR, dk=1, plot='maybe'):
# 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 a0+a1*x+a2*x**2 (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
v = vgrid[k] - a[1]/2./a[2] # position of 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
def opti(va, vb, x2, y2, e_y2, p=None, vfix=False, plot=False):
""" vfix to fix v for RV constant stars?
performs a mini CCF; the grid stepping
returns best v and errors from parabola curvature
"""
vgrid = np.arange(va, vb, v_step)
nk = len(vgrid)
SSR = np.empty(nk)
for k in range(nk):
p, SSR[k] = polyreg(x2, y2, e_y2, vgrid[k], len(p), retmod=False)
# analyse the CCF peak fitting
v, e_v, a = SSRstat(vgrid, SSR, plot=(not safemode)*(1+plot))
if np.isnan(e_v):
v = vgrid[nk/2] # actually it should be nan, but may the next clipping loop or plot use vcen
print " Setting v=" % v
if vfix: v = 0.
p, SSRmin, fmod = polyreg(x2, y2, e_y2, v, len(p)) # final call with v
if 1 and (np.isnan(e_v) or plot) and not safemode:
gplot(x2, y2, fmod, ' w lp, "" us 1:3 w lp lt 3')
pause(v)
return type('par', (), {'params': np.append(v,p), 'perror': np.array([e_v,1.0]), 'ssr': (vgrid,SSR)}), fmod
def fitspec(tpl, w2, f2, e_y=None, v=0, vfix=False, clip=None, nclip=1, keep=None, indmod=np.s_[:], v_step=True, df=None, plot=False, deg=3, chi2map=False):
"""
Performs the robust least square fit via iterative clipping.
vfix : boolean, optional
v is fixed. For RV constant stars or in coadding when only the background polynomial is computed.
indmod : Index range to be finally calculated-
clip : Kappa sigma clipping value.
nclip : Number of clipping iterations (default: 0 if clip else 1).
df : Derivative for drift measurement.
v_step : Number of v steps (only background polynomial => v_step = false).
"""
if keep is None: keep = np.arange(len(w2))
if e_y is None: e_y = np.mean(f2)**0.5 + 0*f2 # mean photon noise
if clip is None: nclip = 0 # number of clip iterations; default 1
calcspec.wcen = np.mean(w2[keep])
calcspec.tpl = tpl
p = np.array([v, 1.] + [0]*deg) # => [v,1,0,0,0]
fMod = np.nan * w2
#fres = 0.*w2 # same size
for n in range(nclip+1):
if df is not None:
'''drift mode: scale derivative to residuals'''
par, fModkeep = optidrift(ft.take(keep,mode='clip'), df.take(keep,mode='clip'), f2.take(keep,mode='clip'),
e_y.take(keep,mode='clip'))
elif v_step:
'''least square mode'''
par, fModkeep = opti(v+v_lo, v+v_hi, w2.take(keep,mode='clip'), f2.take(keep,mode='clip'),
e_y.take(keep,mode='clip'), p[1:], vfix=vfix, plot=plot)
ssr = par.ssr
else:
'''only background polynomial'''
p, SSR, fModkeep = polyreg(w2.take(keep,mode='clip'), f2.take(keep,mode='clip'), e_y.take(keep,mode='clip'), v, len(p)-1)
par = type('par',(),{'params': np.append(v,p), 'ssr': SSR})
p = par.params
par.niter = n
if 1:
# exclude model regions with negative flux
# can occur e.g. in background in ThAr, or due to tellurics
ind = fModkeep > 0
keep = keep[ind] # ignore the pixels modelled with negative flux
fModkeep = fModkeep[ind]
# all might be negative (for low/zero S/N data)
#fres[keep] = (f2[keep] - fMod[keep]) / fMod[keep]**0.5
#res_std = rms(fres[keep]) # residual noise / photon noise
# use error predicted by model
#fres = (f2.take(keep,mode='clip')-fModkeep) / fModkeep**0.5
# use external errors
fres = (f2.take(keep,mode='clip')-fModkeep) / e_y.take(keep,mode='clip')
res_std = rms(fres) # residual noise / photon noise
if n < nclip:
ind = np.abs(fres) <= clip*res_std
nreject = len(keep) - np.sum(ind)
if nreject: keep = keep[ind] # prepare next clip loop
# else: break
if len(keep)<10: # too much rejected? too many negative values?
print "too much rejected, skipping"
break
if 0 and np.abs(par.params[0]*1000)>20:
if df:
fMod = ft * p[1] # compute also at bad pixels
else:
fMod = calcspec(w2, *p) # compute also at bad pixels
gplot.y2tics().ytics('nomir; set y2range [-5:35];')
gplot(w2,fMod,' w lp pt 7 ps 0.5 t "fmod"')
gplot+(w2[keep],fMod[keep],' w lp pt 7 ps 0.5 t "fmod[keep]"')
gplot+(w2,f2,' w lp pt 7 ps 0.5 t "f2"')
#ogplot(w2[keep],fres[keep],' w lp pt 7 ps 0.5 lc rgb "red" axis x1y2 t "residuals"',flush='')
ogplot(w2[keep],fres,' w lp pt 7 ps 0.5 lc rgb "black" axis x1y2, 0 w l lt 2 axis x1y2 t"", '+str(res_std)+' w l lt 1 axis x1y2, '+str(-res_std)+ ' w l lt 1 t "" axis x1y2')
pause('large RV', par.params[0]*1000)
stat = {"std": res_std, 'ssrmin': fres.sum(), "snr": np.mean(fModkeep)/np.mean(np.abs(f2.take(keep,mode='clip')-fModkeep))}
#pause(stat["snr"], wmean(fModkeep)/wrms(f2.take(keep,mode='clip')-fModkeep), np.median(fModkeep)/np.median(np.abs(f2.take(keep,mode='clip')-fModkeep)) )
if df is not None:
fMod[indmod] = ft[indmod]*p[1] - df[indmod]*p[1]*p[0]/c # compute also at bad pixels
else:
fMod[indmod] = calcspec(w2[indmod], *p) # compute also at bad pixels
if chi2map:
return par, fMod, keep, stat, ssr
else:
return par, fMod, keep, stat
def serval(*argv):
sys.stdout = Logger()
global obj, targ, oset, coadd, coset, last, tpl, sa, tplrv, debug, sp, fmod, reana, inst, fib, look, looki, lookt, lookp, lookssr, pmin, pmax, debug, pspllam, kapsig, nclip, atmfile, skyfile, atmwgt, omin, omax, ptmin, ptmax, driftref, deg, targrv
if not argv: argv = sys.argv # python shell start
else: argv = ['module '] + list(argv)
if len(argv)>1:
outdir = obj + '/'
fibsuf = '_B' if inst=='FEROS' and fib=='B' else ''
else:
print "object missing"
print "Example: "+sys.argv[0]+" gj699 dir_or_inputlist=/home/zechmeister/data/harps/gj699/path/ -restore omin=20 nset='1::2'"
return 1
print description
### SELECT INSTRUMENTAL FORMAT ###
# general default values
pat = '*tar' # default search pattern
maskfile = servallib + 'telluric_mask_atlas_short.dat'
# instrument specific default values
if inst == 'CARM_VIS':
if fib == '': fib = 'A'
iomax = 61 # NAXIS2
# pat = '*pho*_x2d_'+fib+'.fits'
pat = '*-vis_' + fib + '.fits'
maskfile = servallib + 'telluric_mask_carm_short.dat'
elif inst == 'CARM_NIR':
if fib == '': fib = 'A'
iomax = 28
iomax *= 2 # reshaping
pat = '*-nir_' + fib + '.fits'
maskfile = servallib + 'telluric_mask_carm_short.dat'
elif 'HARP' in inst:
if fib == '': fib = 'A'
if fib == 'A': iomax = 72
if fib == 'B': iomax = 71
if inst=='HARPN': iomax = 68
elif inst == 'FEROS':
iomax = 38
if fib == '': fib = 'A'
if fib == 'B': maskfile = servallib + 'feros_mask_short.dat'
ptomin = np.array([1800, 2000, 1800, 2000, 2000, 1600, 1500, 1400, 1100, 1000,
1000, 1000, 1000, 900, 800, 800, 800, 600, 500, 500,
400, 400, 300, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100])
ptomax = np.array([3100, 4000, 4000, 4000, 4000, 4500, 4600, 4600, 4800, 4800,
5000, 5200, 5200, 5300, 5600, 5700, 5800, 6000, 6100, 6300,
6500, 6600, 6700, 6800, 7100, 7200, 7400, 7700, 7900, 8100,
8400, 8600, 8900, 9200, 9500, 9800,10200,10600,11100])
pomin = ptomin + 300
pomax = ptomax - 300
pmin = pomin.min()
pmax = pomin.max()
elif inst == 'FTS':
iomax = 70
pmin = 300
pmax = 50000/5 - 500
ptmin = pmin - 100 # oversize the template
ptmax = pmax + 100
orders = np.arange(iomax)[oset]
corders = np.arange(iomax)[coset]
orders = sorted(set(orders) - set(o_excl))
corders = sorted(set(corders) - set(co_excl))
omin = min(orders)
omax = max(orders)
comin = min(corders)
comax = max(corders)
if reana:
x = analyse_rv(obj, postiter=postiter, fibsuf=fibsuf, oidx=orders)
exit()
os.system('mkdir -p '+obj)
t0 = time.time()
print "start time:", time.strftime("%Y-%m-%d %H:%M:%S %a", time.localtime())
use_drsberv = False
if fib == 'B' or (ccf is not None and 'th_mask' in ccf):
pass
### SELECT TARGET ###
targ = type('TARG', (), dict(name=targ, plx=targplx, rv=targrv, sa=float('nan')))
targ.ra, targ.de = targrade
targ.pmra, targ.pmde = targpm
if targ.name == 'cal':
print 'no barycentric correction (calibration)'
elif targ.ra and targ.de or targ.name:
targ = Targ(targ.name, targrade, targpm, plx=targplx, rv=targrv, cvs=obj+'/'+obj+'.targ.cvs')
print ' using sa=', targ.sa, 'm/s/yr', 'ra=', targ.ra, 'de=', targ.de, 'pmra=', targ.pmra, 'pmde=', targ.pmde
else:
print 'using barycentric correction from DRS'
# choose the interpolation type
spltype = 3 # 3=> fast version
spline_cv = {1: interpolate.splrep, 2: cubicSpline.spl_c, 3: cubicSpline.spl_cf }[spltype]
spline_ev = {1: interpolate.splev, 2: cubicSpline.spl_ev, 3: cubicSpline.spl_evf}[spltype]
print dir_or_inputlist
print 'tpl=%s pmin=%s nset=%s omin=%s omax=%s' % (tpl, pmin, nset, omin, omax)
''' SELECT FILES '''
files = sorted(glob.glob(dir_or_inputlist+os.sep+pat))
isfifo = os_stat.S_ISFIFO(os.stat(dir_or_inputlist).st_mode)
if os.path.isfile(dir_or_inputlist) or isfifo:
if dir_or_inputlist.endswith(('.txt', '.lis')) or isfifo:
files = []
with open(dir_or_inputlist) as f:
print 'getting filenames from file (',dir_or_inputlist,'):'
for line in f:
line = line.split() # remove comments
if line:
if os.path.isfile(line[0]):
files += [line[0]]
print len(files), files[-1]
else:
print 'skipping:', line[0]
if fib == 'B':
files = [f.replace('_A.fits','_B.fits') for f in files]
print 'renaming', files
else:
# case if dir_or_inputlist is one fits-file
files = [dir_or_inputlist]
# handle tar, e2ds, fox
if 'HARP' in inst and not files:
files = sorted(glob.glob(dir_or_inputlist+'/*e2ds_'+fib+'.fits'))
if not files:
files = sorted(glob.glob(dir_or_inputlist+'/*e2ds_'+fib+'.fits.gz'))
drs = bool(len(files))
if 'HARPS' in inst and not drs: # fox
files = sorted(glob.glob(dir_or_inputlist+'/*[0-9]_'+fib+'.fits'))
if 'CARM' in inst and not files:
files = sorted(glob.glob(dir_or_inputlist+'/*pho*_'+fib+'.fits'))
if 'FEROS' in inst:
files += sorted(glob.glob(dir_or_inputlist+'/f*'+('1' if fib=='A' else '2')+'0001.mt'))
if 'FTS' in inst:
files = sorted(glob.glob(dir_or_inputlist+'/*ap08.*_ScSm.txt'))
files = [s for s in files if '20_ap08.1_ScSm.txt' not in s and '20_ap08.2_ScSm.txt' not in s and '001_08_ap08.193_ScSm.txt' not in s ]
files = np.array(files)[nset]
nspec = len(files)
if not nspec:
print "no spectra found in", dir_or_inputlist, 'or using ', pat, inst
exit()
# expand slices to index arrays
if look: look = np.arange(iomax)[look]
if lookt: lookt = np.arange(iomax)[lookt]
if lookp: lookp = np.arange(iomax)[lookp]
if lookssr: lookssr = np.arange(iomax)[lookssr]
if outfmt or outchi: os.system('mkdir -p '+obj+'/res')
with open(outdir+'lastcmd.txt', 'w') as f:
print >>f, ' '.join(argv)
with open('cmdhistory.txt', 'a') as f:
print >>f, ' '.join(argv)
badfile = file(outdir + obj + '.flagdrs' + fibsuf + '.dat', 'w')
infofile = file(outdir + obj + '.info' + fibsuf + '.cvs', 'w')
bervfile = open(outdir + obj + '.brv' + fibsuf + '.dat', 'w')
prefile = outdir + obj + '.pre' + fibsuf + '.dat'
rvofile = outdir + obj + '.rvo' + fibsuf + '.dat'
snrfile = outdir + obj + '.snr' + fibsuf + '.dat'
chifile = outdir + obj + '.chi' + fibsuf + '.dat'
halfile = outdir + obj + '.halpha' + fibsuf + '.dat'
nadfile = outdir + obj + '.nad' + fibsuf + '.dat'
irtfile = outdir + obj + '.cairt' + fibsuf + '.dat'
dfwfile = outdir + obj + '.dlw' + fibsuf + '.dat'
# (echo 0 0 ; awk '{if($2!=x2){print x; print $0}; x=$0; x2=$2;}' telluric_mask_atlas.dat )> telluric_mask_atlas_short.dat
#################################
### Loading and prepare masks ###
#################################
mask = None
tellmask = nomask
skymsk = nomask
if fib == 'B':
if atmfile == 'auto':
atmfile = None
if skyfile == 'auto':
skyfile = None
if ccf and 'th_mask' not in ccf:
atmfile = None
if atmfile:
if atmfile != 'auto':
maskfile = atmfile
if 'mask_ne' in atmfile:
maskfile = servallib + atmfile
print 'maskfile', maskfile
mask = np.genfromtxt(maskfile, dtype=None)
if 'telluric_mask_atlas_short.dat' in maskfile:
lcorr = 0.000009 # Guillems mask needs this shift of 2.7 km/s
mask[:,0] = airtovac(mask[:,0]) * (1-lcorr)
if 'th_mask' in maskfile: # well, it is not atmosphere, but ...
mask[:,1] = mask[:,1] == 0 # invert mask; actually the telluric mask should be inverted (so that 1 means flagged and bad)
if skyfile:
if skyfile=='auto' and inst=='CARM_NIR':
skyfile = servallib + 'sky_carm_nir'
sky = np.genfromtxt(skyfile, dtype=None)
skymsk = interp(lam2wave(sky[:,0]), sky[:,1])
msksky = [0] * iomax
if 1 and inst=='CARM_VIS':
try:
import pyfits
except:
import astropy.io.fits as pyfits
msksky = flag.atm * pyfits.getdata(servallib + 'carm_vis_tel_sky.fits')
if msklist: # convert line list to mask
mask = masktools.list2mask(msklist, wd=mskwd)
mask[:,1] = mask[:,1] == 0 # invert mask; actually the telluric mask should be inverted (so that 1 means good)
if mask is None:
print 'using telluric mask: NONE'
else:
# make the discrete mask to a continuous mask via linear interpolation
tellmask = interp(lam2wave(mask[:,0]), mask[:,1])
print 'using telluric mask: ', maskfile
if 0:
mask2 = np.genfromtxt('telluric_add.dat', dtype=None)
# DO YOU NEED THIS: mask2[:,0] = airtovac(mask2[:,0]) ??
i0 = 0 #where(mask[:,0]<mask2[0][0])[0][-1]
for ran in mask2:
while (mask[i0,0]<ran[0]): i0 += 1
#if mask[i0-1,0]==0:
mask = np.insert(mask,i0,[ran[0]-0.0000001,0.0],axis=0) # insert
mask = np.insert(mask,i0+1,[ran[0],1.0],axis=0) # insert
#while (mask[i0,0]<ran[1]): np.delete(mask,i0,axis=0)
#if mask[i0-1,0]==0:
mask = np.insert(mask,i0+2,[ran[1],1.0],axis=0) # insert
mask = np.insert(mask,i0+3,[ran[1]+0.0000001,0.0],axis=0) # insert
################################
### READ FITS FILES ############
################################
splist = []
spi = None
SN55best = 0.
print " # %*s %*s OBJECT BJD SN DRSBERV DRSdrift flag calmode" % (-len(inst)-6, "inst_mode", -len(os.path.basename(files[0])), "timeid")
infowriter = csv.writer(infofile, delimiter=';', lineterminator="\n")
for n,filename in enumerate(files): # scanning fitsheader
print '%3i/%i' % (n+1, nspec),
sp = Spectrum(filename, inst=inst, pfits=2 if 'HARP' in inst else True, drs=drs, fib=fib, targ=targ, verb=True)
splist.append(sp)
if use_drsberv:
sp.bjd, sp.berv = sp.drsbjd, sp.drsberv
sp.sa = targ.sa / 365.25 * (sp.bjd-splist[0].bjd)
sp.header = None # saves memory(?), but needs re-read (?)
if inst == 'HARPS' and drs: sp.ccf = read_harps_ccf(filename)
if sp.sn55 < snmin: sp.flag |= sflag.lowSN
if sp.sn55 > snmax: sp.flag |= sflag.hiSN
if distmax and sp.ra and sp.de:
# check distance for mis-pointings
# yet no proper motion included
ra = (targ.ra[0] + (targ.ra[1]/60 + targ.ra[2]/3600)* np.sign(targ.ra[0]))*15 # [deg]
de = targ.de[0] + (targ.de[1]/60 + targ.de[2]/3600)* np.sign(targ.de[0]) # [deg]
dist = np.sqrt(((sp.ra-ra)*np.cos(np.deg2rad(sp.de)))**2 + (sp.de-de)**2) * 3600
if dist > distmax: sp.flag |= sflag.dist
if not sp.flag:
if SN55best < sp.sn55 < snmax:
SN55best = sp.sn55
spi = n
else:
print >>badfile, sp.bjd, sp.ccf.rvc, sp.ccf.err_rvc, sp.timeid, sp.flag
print >>bervfile, sp.bjd, sp.berv, sp.drsbjd, sp.drsberv, sp.drift, sp.timeid, sp.tmmean, sp.exptime, sp.berv_start, sp.berv_end
infowriter.writerow([sp.timeid, sp.bjd, sp.berv, sp.sn55, sp.obj, sp.exptime, sp.ccf.mask, sp.flag, sp.airmass, sp.ra, sp.de])
#print >>infofile, sp.timeid, sp.bjd, sp.berv, sp.sn55, sp.obj, sp.exptime, sp.ccf.mask, sp.flag
badfile.close()
bervfile.close()
infofile.close()
sys.stdout.logname(obj+'/log.'+obj)