def info_bis(wbis):
if len(wbis) > 0:
with fits.open(wbis) as image:
info = pd.DataFrame({"BJD":[image[0].header['HIERARCH ESO DRS BJD']],
"CCF_RVC":[image[0].header['HIERARCH ESO DRS CCF RVC']],
"FWHM":[image[0].header['HIERARCH ESO DRS CCF RVC']],
"MASK":[image[0].header['HIERARCH ESO DRS CCF MASK']]})
key_check=image[0].header.get('HIERARCH ESO DRS DVRMS')
if key_check == None:
info["DVRMS"]=np.nan
else:
info["DVRMS"]=image[0].header['HIERARCH ESO DRS DVRMS'] #Estimated RV uncertainty [m/s]
else:
a=wbis.find('untar/')
b=wbis.find('bis')
iraf.fxcor(wbis[0:b]+"s1d_A.fits", "Sun.fits",output=wbis[0:a+6]+"RVs",interactive="no", observatory="esovlt")
RVel=Table.read(wbis[0:a+6]+"RVs.txt", format='ascii')
info = pd.DataFrame({"BJD":[np.nan],
"CCF_RVC":[RVel['col12'][0]],
"FWHM":[np.nan],
"MASK":[np.nan],
"DVRMS":[RVel['col14'][0]]})
os.remove(wbis[0:a+6]+"RVs.txt")
os.remove(wbis[0:a+6]+"RVs.gki")
os.remove(wbis[0:a+6]+"RVs.log")
return info
def run_fxcor(input_file,input_rv,lines,output,fitshead_update,npoints,functiontype):
iraf.fxcor(
objects = input_file, \
templates = input_rv, \
apertures = "*", \
cursor = "",\
continuum = "both",\
filter = "both",\
rebin = "smallest",\
pixcorr = 0,\
osample = lines,\
rsample = lines,\
apodize = 0.2,\
function = functiontype,\
width = npoints,\
height= 0.,\
peak = 0,\
minwidth = npoints,\
maxwidth = npoints,\
weights = 1.,\
background = "INDEF",\
window = 30,\
wincenter = 0,\
output = output,\
verbose = "long",\
imupdate = fitshead_update,\
graphics = "stdgraph",\
interactive = 0,\
autowrite = 1,\
ccftype = "image",\
observatory = "sso",\
continpars = "",\
filtpars = "",\
keywpars = "")
def run_fxcor(lstl, lstu, tpl, tpu, chip):
if chip == 'l':
templ = tpl
lst = lstl
else:
templ = tpu
lst = lstu
lista = '@' + lst
chip_par = 'rvout_' + chip
iraf.noao()
iraf.rv()
iraf.fxcor(lista,
templ,
output=chip_par,
verbose='txtonly',
interac='no',
observa='paranal')
rv = []
rvlist = open(chip_par + '.txt', "r")
for cols in (raw.strip().split() for raw in rvlist):
vobs_index = len(cols) - 3
if cols[0][0] != '#':
rv.append(float(cols[vobs_index]))
rvlist.close()
return rv
def run_fxcor(input_file,input_rv,lines):
iraf.unlearn(iraf.keywpars)
iraf.filtpars.setParam("f_type","square",check=1,exact=1)
iraf.filtpars.setParam("cuton",50,check=1,exact=1)
iraf.filtpars.setParam("cutoff",2000,check=1,exact=1)
os.system("rm fxcor_shift*")
iraf.fxcor(
objects = input_file, \
templates = input_rv, \
apertures = "*", \
cursor = "",\
continuum = "both",\
filter = "both",\
rebin = "smallest",\
pixcorr = 0,\
osample = lines,\
rsample = lines,\
apodize = 0.2,\
function = "gaussian",\
width = 15,\
height= 0.,\
peak = 0,\
minwidth = 15,\
maxwidth = 15,\
weights = 1.,\
background = "INDEF",\
window = "INDEF",\
wincenter = "INDEF",\
output = "fxcor_shift",\
verbose = "long",\
imupdate = 0,\
graphics = "stdgraph",\
interactive = 0,\
autowrite = 1,\
ccftype = "image",\
observatory = "sso",\
continpars = "",\
filtpars = "",\
keywpars = "")
vel_shift = functions.read_ascii("fxcor_shift.txt")
vel_shift = functions.read_table(vel_shift)
vel_shift = str(vel_shift[0][11])
if vel_shift == "INDEF":
vel_shift = 0
print "shifting by ",vel_shift,"km/s"
return vel_shift
def run_fxcor(input_file, input_rv, lines, output, fitshead_update, write_ccf):
if write_ccf:
ccf_command = program_dir + "fxcor_cursor_command.txt"
interact = 1
else:
ccf_command = ""
interact = 0
iraf.fxcor(
objects=input_file,
templates=input_rv,
apertures="*",
cursor=ccf_command,
# cursor = "",\
continuum="both",
filter="both",
rebin="smallest",
pixcorr=0,
osample=lines,
rsample=lines,
apodize=0.2,
function="gaussian",
width=9.0,
height=0.0,
peak=0,
minwidth=9.0,
maxwidth=9.0,
weights=1.0,
background="INDEF",
window="INDEF",
wincenter="INDEF",
# window = 500,\
# wincenter = 0.0,\
output=output,
verbose="long",
imupdate=fitshead_update,
graphics="stdgraph",
interactive=interact,
# interactive = 1,\
autowrite=1,
ccftype="text",
observatory="sso",
continpars="",
filtpars="",
keywpars="",
)
def run_fxcor(input_file,input_rv,lines,output,fitshead_update,write_ccf):
if write_ccf:
ccf_command = program_dir + "fxcor_cursor_command.txt"
if best_aperture == 1:
use_apertures = "*"
else:
use_apertures = str(best_aperture)
interact = 1
else:
ccf_command = ""
use_apertures = "*"
interact = 0
iraf.fxcor(
objects = input_file, \
templates = input_rv, \
apertures = use_apertures, \
cursor = ccf_command,\
continuum = "both",\
filter = "both",\
rebin = "smallest",\
pixcorr = 0,\
osample = lines,\
rsample = lines,\
apodize = 0.2,\
function = "gaussian",\
width = 7.0,\
height= 0.,\
peak = 0,\
minwidth = 7.0,\
maxwidth = 7.0,\
weights = 1.,\
background = "INDEF",\
window = "INDEF",\
wincenter = "INDEF",\
output = output,\
verbose = "long",\
imupdate = fitshead_update,\
graphics = "stdgraph",\
interactive = interact,\
autowrite = 1,\
ccftype = "text",\
observatory = "sso",\
continpars = "",\
filtpars = "",\
keywpars = "")
def fxcor(spec, task, template_spec, spec_list_file, fxcor_output_file, fxcor_range="4173-5447", interactive='no'):
"""
Run fxcor on the binned spectra to determine the relative velocity (with respect to the template spectra)
INPUT: BIN_SCI, FXCOR_TEMPLATE
OUTPUT: FXCOR_BIN_LIST, FXCOR_FILE(s)
Note: make sure you select the parameters for your desired use. Refer to the IRAF rv package help for description
of what each parameter is/does
"""
if os.path.exists(fxcor_output_file + '.txt'):
print('File {} already exists'.format(fxcor_output_file + '.txt'))
return
assert task == "abs" or task == "ems", "'task' is neight 'ab' or 'ems'"
spec_list = []
for i in range(len(glob.glob(spec.format('*')))):
spec_list.append(spec.format(i))
assert len(spec_list) > 0, 'Input files {} do not exist'.format(spec.format('*'))
np.array(spec_list).tofile(spec_list_file, sep='\n')
from pyraf import iraf
iraf.noao()
iraf.rv()
if task == 'ems':
iraf.fxcor('@{}'.format(spec_list_file), spec.format(template_spec), output=fxcor_output_file, continuum="both",
interactive=interactive, order=1, high_rej=2, low_rej=2, osample=fxcor_range, rsample=fxcor_range,
rebin="smallest", imupdate="no", pixcorr="no", filter="both", f_type="welch", cuton=20, cutoff=1000,
fullon=30, fulloff=800, ra="RA", dec="DEC", ut="UTSTART", epoch="EQUINOX", verbose="txtonly")
elif task == 'abs':
# Run interactively to make sure not fitting noise features, adapet osample/rsample to avoid such noise
iraf.fxcor('@{}'.format(spec_list_file), spec.format(template_spec), output=fxcor_output_file, continuum="both",
interactive=interactive, order=1, high_rej=2, low_rej=2, osample=fxcor_range, rsample=fxcor_range,
rebin="smallest", imupdate="no", pixcorr="no", filter="both", f_type="welch", cuton=20, cutoff=800,
fullon=30, fulloff=800, ra="RA", dec="DEC", ut="UTSTART", epoch="EQUINOX", verbose="txtonly")
assert os.path.exists(fxcor_output_file + '.txt'), 'Error in iraf.fxcor: File {} was not created'.format(
fxcor_output_file + '.txt')
def run_fxcor(st_names, templ, setup):
arq = open(setup + '/' + 'speclist', 'w')
for i in range(len(st_names)):
arq.write(st_names[i] + '.fits\n')
arq.close()
lista = '@' + setup + '/' + 'speclist'
clean_cr(lista)
outname = setup + '/' + 'rvout_' + setup
iraf.noao()
iraf.rv()
iraf.fxcor(lista,
templ,
output=outname,
verbose='txtonly',
interac='no',
observa='paranal')
rv = []
rvlist = open(outname + '.txt', "r")
for cols in (raw.strip().split() for raw in rvlist):
vobs_index = len(cols) - 3
if cols[0][0] != '#':
rv.append(float(cols[vobs_index]))
rvlist.close()
return rv
def get_RV(WORK_DIR, multi_ref=False, multi_sample=False):
# list RV ref files
if multi_ref:
rv_ref_list = glob.glob('../../*_Echelle_RVS.fits')
rv_ref_list = [rf.split('/')[-1].split('.')[0] for rf in rv_ref_list]
else:
rv_ref_list = ['solar']
# rv_ref_list = ['solar_spectra_conv']
# print ' RV ref files list:', rv_ref_list
for obj in observations[WORK_DIR]['objects']:
for rv_ref in rv_ref_list:
ts = time.time()
rvs = []
e_rvs = []
# determine barycentric RV
with open('RVS_'+obj+'_'+rv_ref+'.txt', 'w+') as file:
for order in np.arange(2, 31, 1):
try:
iraf.wspectext(input=obj+'_vh_norm.fits[*,'+str(order)+',1]', output='template.txt', header='no')
template = np.loadtxt('template.txt')
wav = template[:, 0]
mid1 = int(len(wav)/4.0)
mid2 = int(len(wav)/12.0)
wav1 = wav[mid1*1]
wav2 = wav[mid1*3]
#print 'WAV:', wav, mid1, wav1,wav2
# create multiple wav1, wav2 ranges if requested
if multi_sample:
wav_ranges = [[wav[mid2*2], wav[mid2*7]], [wav[mid2*4], wav[mid2*8]], [wav[mid2*5], wav[mid2*10]]]
else:
wav_ranges = [[wav1, wav2]]
for wav_r in wav_ranges:
iraf.unlearn('fxcor')
try:
os.remove('test.txt')
os.remove('test.log')
os.remove('test.gki')
except: pass
try:
os.remove('template.txt')
except: pass
iraf.fxcor(obj+'.ec.vh', '../../'+rv_ref, apertures=order, pixcorr='no', continuum='both',
osample='a %s %s' % (wav_r[0], wav_r[1]), rsample='a %s %s' % (wav_r[0], wav_r[1]),
interactive='no', output='test', function = 'gaussian',
apodize = 0.0, rebin = "smallest")
fields = np.loadtxt('test.txt', comments='#', dtype='str')
# print wav_r
# print fields
vrel, verr = fields[-3], fields[-1]
# vrel, verr = fields[-3], 0.
if vrel == 'INDEF' or np.float(verr) > 10. or np.abs(np.float(vrel)) > 350.:
vrel = np.nan
verr = np.nan
print order, vrel, verr
file.write("%s %s %s\n" % (order, vrel, verr))
rvs.append(float(vrel))
e_rvs.append(float(verr))
except:
print_exception()
# export all values in a list
# file.write('\n')
# file.write("'rv_echelle':[" + ','.join([str(v) for v in rvs]) + "]\n")
# file.write("'e_rv_echelle':[" + ','.join([str(v) for v in e_rvs]) + "]\n")
print '%s: RV median: %s, RV mean: %s, RV sigma: %s' % (obj, np.nanmedian(rvs), np.nanmean(rvs), np.nanstd(rvs))
print 'RV fit time: '+str((time.time()-ts)/60.)+' min'
print ''
def rv_plot(filelist_new):
rv_directory = os.path.dirname(filelist_new[0])
os.chdir(rv_directory)
template = "telutemplate.fits"
basedir = os.path.dirname(filelist_new[0])
templatefile = os.path.join(basedir, template)
if os.path.exists("telushift.txt"):
os.remove("telushift.txt")
if os.path.exists("rvshift.txt"):
os.remove("rvshift.txt")
iraf.fxcor(objects=os.path.join(basedir,"*merged.fits"), templates = templatefile, continuum = "both", filter="none", rebin="smallest", \
function = "gaussian", width = "10.0", height="0.0", minwidth ="3.0", maxwidth = "20.0", window = "200.0", background= "0.2", \
output = "telushift", verbose = "txtonly", osample = "5880:5975", rsample = "5880:5975", interactive = "no")
rvs = []
time = []
with open("telushift.txt", "r") as defaultfile:
for line in defaultfile:
if "#" in line:
pass
else:
rvs.append(line[-30:-21])
for i in range(len(filelist_new)):
hdulist = fits.open(filelist_new[i])
time.append(hdulist[0].header['JD-START'])
plt.plot(time, rvs, '.')
template = "mascara_megatemplate.fits"
templatefile = os.path.join(basedir, template)
### For A Type Stars
iraf.fxcor(objects=os.path.join(basedir,"*rvcorrect_TAC.fits"), templates = templatefile, continuum = "both", filter="template", rebin="smallest", \
function = "center1d", width = "200.0", height="0.0", minwidth ="3.0", maxwidth = "200.0", window = "1000.0", \
output = "rvshift", verbose = "txtonly", osample = "4610:4830, 4893:5679, 6019:6524, 6598:6860", rsample = "4610:4830, 4893:5679, 6019:6524, 6598:6860", interactive = "no")
### For G Type Stars
# iraf.fxcor(objects=os.path.join(basedir,"*rvcorrect_TAC.fits"), templates = templatefile, continuum = "both", filter="both", rebin="smallest", \
# function = "gaussian", width = "20.0", height="0.2", minwidth ="10.0", maxwidth = "200.0", window = "200.0", \
# output = "rvshift", verbose = "txtonly", osample = "4900:6450", rsample = "4900:6450", interactive = "no")
rvs2 = []
time2 = []
with open("rvshift.txt", "r") as defaultfile:
for line in defaultfile:
if "#" in line:
pass
else:
rvs2.append(line[-30:-21])
# iraf.fxcor(objects=os.path.join(basedir,"*rvcorrect_TAC.fits"), templates = templatefile, continuum = "both", filter="both", rebin="smallest", \
# function = "gaussian", width = "20.0", height="0.2", minwidth ="10.0", maxwidth = "200.0", window = "200.0", \
# output = "rvshift", verbose = "txtonly", osample = "4900:6450", rsample = "4900:6450", interactive = "no")
# rvs3 = []
# with open("rvshift.txt", "r") as defaultfile:
# for line in defaultfile:
# if "#" in line:
# pass
# else:
# rvs3.append(line[-30:-21])
for i in range(len(filelist_new)):
hdulist = fits.open(filelist_new[i])
time2.append(hdulist[0].header['JD-START'])
# iraf.dopcor(filelist_new[i], filelist_new[i].replace("merged_TAC","starvelocity"), redshift= rvs2[i], isvelocity = "yes")
with open(os.path.join(basedir, "rvshift.dat"), "w") as rvfile:
with open(os.path.join(basedir, "telushift.dat"), "w") as telufile:
with open(os.path.join(basedir, "rvtelushift.dat"),
"w") as rvtelufile:
for i in range(len(rvs)):
rvfile.write(str(time[i]) + "," + rvs2[i] + "\n")
telufile.write(str(time[i]) + "," + rvs[i] + "\n")
final_shift = float(rvs2[i]) + float(rvs[i])
rvtelufile.write(
str(time[i]) + "," + str(final_shift) + "\n")
plt.plot(time2, rvs2, '.')
plt.show()
obj.append(fits.open(i)[0].header['OBJECT'])
try:
template_loc=obj.index(ref_star_name)
except ValueError:
print "Reference star %s not found, exiting..." % (ref_star_name)
sys.exit(1)
template_image_id=t[template_loc]
print "Ref star %s found as spectrum %s" % (ref_star_name,template_image_id)
# loop over the spectra
for i in range(0,len(t)):
image_id=t[i]
if args.blends == 'nblends' and n_traces[i] == 1:
outfile="%s.out" % (image_id)
iraf.fxcor(objects=image_id,template=template_image_id,osample='6600-6800',rsample='6600-6800',output=outfile)
logfile="%s.log" % (outfile)
com=''
while (com.upper() not in coms):
com=raw_input('Single Peaked = SP - Double Peaked = DP - Broad Peak = BP - No Peak = NP\nEnter comment: ')
done=updateOutput(logfile,image_id,com)
# add this back into the loop later
# code up the BLENDs later when analysed the first time
if args.blends=='yblends' and n_traces[i] > 1:
for j in range(1,n_traces[i]+1):
outfile="%s.out%d" % (image_id,j)
logfile="%s.log" % (outfile)
iraf.fxcor(objects=image_id,template=template_image_id,apertures=str(j),osample='6600-6800',rsample='6600-6800',output=outfile)
def gen_ar():
#cargo resultado de f2c
temps, pasos, intensidad = f2c(res_tem="S", step_tem=100)
x = pasos
y = temps
z = intensidad
print(x[z.index(max(z))], y[z.index(max(z))])
os.chdir("../")
# obtengo parametros para para fxcor
obfx = "aB" + str(x[z.index(max(z))])
temp = str(int(y[z.index(max(z))]))
if len(temp) == 4:
temp = "0" + temp
iraf.imdel("ss")
iraf.imdel("tem")
iraf.sarit("T" + temp, "-", 1, "ss")
iraf.sarit("ss", "*", "amortigua", "tem")
fig = plt.figure(figsize=(12, 9.0))
fig.subplots_adjust(hspace=0.4,
bottom=0.06,
top=0.94,
left=0.12,
right=0.94)
#ejecuto y ploteo fxcor
ax2 = plt.subplot2grid((4, 1), (0, 0)) # grafico la func, de correlacion
program_dir = os.getcwd() + "/"
ccf_command = program_dir + "fxcor_cursor_command.txt" #comando para extraeer los datos del grafico
interact = 1
iraf.imdel("new_ccf.txt")
iraf.fxcor(object=obfx,
templates="tem",
cursor=ccf_command,
ccftype="text",
interactive=interact) #fxcor de irad
data = asciitable.read("new_ccf.txt") #archivo de salida del fxcor
ax2.set_xlim(-2000, 2000)
plt.axhline(linewidth=2, color='r')
plt.plot(data["col1"], data["col2"], "-", color="black")
plt.axvspan(-100, 100, facecolor='grey', alpha=0.5)
#plt.axhspan(0.9, 1.00, facecolor='grey', alpha=0.5)
ax2.yaxis.set_major_locator(plt.MaxNLocator(5))
ax2.set_xlabel(r'Velocity', fontsize=18)
ax2.set_ylabel(r'Correlation', fontsize=18)
#datos para scale
iraf.imdel("scale")
iraf.imdel("tplsca")
obscale = "B" + str(x[z.index(max(z))])
temp = "T" + temp
w1 = 5900
w2 = 6490
wi = 300
iraf.imdel("scale.txt")
iraf.imdel("tplsca.txt")
iraf.scale(spec=obscale, tpl=temp, w1=w1, w2=w2, width=wi)
iraf.wspectext("scale.fits", "scale.txt", header="no")
iraf.wspectext("tplsca.fits", "tplsca.txt", header="no")
iraf.wspectext(obscale, "obscale.txt", header="no")
#ploteo resultados de f2c
ax1 = plt.subplot2grid((4, 1), (1, 0), rowspan=3)
# define grid.
xi = np.linspace(min(x), max(x), 1000)
yi = np.linspace(min(y), max(y), 1000)
# grid the data.
zi = griddata((x, y), z, (xi[None, :], yi[:, None]), method='linear')
# contour the gridded data, plotting dots at the randomly spaced data points.
CS = plt.contour(xi, yi, zi, 15, linewidths=0.5, colors='k')
CS = plt.contourf(xi, yi, zi, 15, cmap=plt.cm.jet)
cb = plt.colorbar(orientation='horizontal', pad=0.1,
aspect=30) # draw colorbar
cb.set_label('Intensity', fontsize=18)
# plot data points.
plt.scatter(x, y, marker='o', c='b', s=5)
plt.scatter(x[z.index(max(z))],
y[z.index(max(z))],
marker='o',
color="w",
s=20)
plt.xlabel('Mass ratio')
plt.ylabel('Secondary Temperature (K)')
ax1.set_xlabel(r'Mass ratio', fontsize=18)
ax1.set_ylabel(r'Secondary Temperature (K)', fontsize=18)
ax1.set_xlim(min(pasos), max(pasos))
ax1.set_ylim(min(temps), max(temps))
ax1.yaxis.set_major_locator(plt.MaxNLocator(6))
ax1.xaxis.set_major_locator(plt.MaxNLocator(8))
ax2.text(0.03,
0.96,
"T=%s K" % y[z.index(max(z))],
ha='left',
va='top',
transform=ax2.transAxes,
fontsize=20)
ax2.text(0.04,
0.5,
"q=%s" % x[z.index(max(z))],
ha='left',
va='top',
transform=ax2.transAxes,
fontsize=20)
#plot scale
fig = plt.figure(figsize=(12, 9.0))
fig.subplots_adjust(hspace=0.4,
bottom=0.06,
top=0.94,
left=0.12,
right=0.94)
ax2 = plt.subplot2grid((4, 1), (0, 0))
data = asciitable.read("tplsca.txt") #archivo de salida del scale
#ax2.set_xlim(min(data["col1"]),max(data["col1"]))
plt.plot(data["col1"], data["col2"], "-", color="red")
data = asciitable.read("obscale.txt")
plt.plot(data["col1"], data["col2"], "-", color="black")
ax2.yaxis.set_major_locator(plt.MaxNLocator(5))
ax2.set_xlabel(r'Wavelength ($\AA$)', fontsize=18)
ax2.set_ylabel(r'Intensity', fontsize=18)
data = asciitable.read("scale.txt") #archivo de salida del scale
ax2.set_xlim(min(data["col1"]), max(data["col1"]) - 1)
plt.plot(data["col1"], data["col2"] * 100, "-", color="black")
ax2.yaxis.set_major_locator(plt.MaxNLocator(5))
ax2.set_xlabel(r'Wavelength ($\AA$)', fontsize=18)
ax2.set_ylabel(r'$I_2/I_T$ (%)', fontsize=18)
#plt.savefig('mass_temp.pdf',format="pdf", bbox_inches='tight',dpi = 300)
plt.show()
if analyse:
a = []
for h in np.arange(len(analyse["CCF_RVC"])):
a.append(np.float(analyse["CCF_RVC"][h]))
analyse["CCF_RVC"] = a
w = np.where(np.abs(analyse["CCF_RVC"]) > 1000.)
num = len(analyse["CCF_RVC"][w[0]])
if num > 0:
for j in w[0]:
print analyse['NAME_S1D'][
j], 'I am calculating the RV shift'
iraf.fxcor(star_folders[i] + "/untar/" +
analyse['NAME_S1D'][j],
"Sun.fits",
output=star_folders[i] + "/untar/RVs",
interactive="no",
observatory="esovlt")
RVel = Table.read(star_folders[i] + "/untar/RVs.txt",
format='ascii')
if RVel['col12'][0] == 'INDEF':
RVel['col12'][0] = -9999
RVel['col14'][0] = -9999
analyse["CCF_RVC"][j] = RVel['col12'][0]
analyse["FWHM"][j] = np.nan
analyse["DVRMS"][j] = RVel['col14'][0]
os.remove(star_folders[i] + "/untar/" "RVs.txt")
os.remove(star_folders[i] + "/untar/" "RVs.gki")
os.remove(star_folders[i] + "/untar/" "RVs.log")
f = analyse["CCF_RVC"] != -9999
