def test_template(data_type,data_spectrum,teff,logg,feh,start_lambda,end_lambda,shift):
### Import a template spectrum - test
template_spectrum = "template_" + str(teff) + "_" + str(logg) + "_" + str(feh) + ".dat"
if data_type=="flux":
template_spectrum = functions.read_ascii(model_path_flux + template_spectrum)
if data_type=="norm":
template_spectrum = functions.read_ascii(model_path_norm + template_spectrum)
template_spectrum = functions.read_table(template_spectrum)
template_spectrum = transpose(array(template_spectrum))
if data_type=="flux":
template_spectrum = spectype_functions.normalise(template_spectrum,flux_normalise_w1,flux_normalise_w2)
### Chop both spectra
data_region = spectype_numerical_functions.chop_spectrum(data_spectrum,start_lambda,end_lambda)
template_region = spectype_numerical_functions.chop_spectrum(template_spectrum,start_lambda,end_lambda)
### Conform template spectrum to data spectrum -> same wavelength scale
template_region = spectype_numerical_functions.conform_spectrum(data_region,template_region)
### Find shift
data_region_shifted,template_region_shifted = spectype_functions.shift_spectrum(data_region,template_region,shift)
chisq = spectype_numerical_functions.chisq(data_region_shifted,template_region_shifted)
# plt.clf()
# plt.plot(data_region_shifted[0],data_region_shifted[1])
# plt.plot(template_region_shifted[0],template_region_shifted[1])
# plt.title(data_type + " " + str(teff) + " " + str(logg) + " " + str(feh) +" " + str(chisq))
# plt.show()
return chisq
def find_shift(data_type,data_spectrum,teff,logg,feh,start_lambda,end_lambda):
### Import the template spectrum
template_spectrum = "template_" + str(teff) + "_" + str(logg) + "_" + str(feh) + ".dat"
if data_type=="flux":
template_spectrum = functions.read_ascii(model_path_flux + template_spectrum)
if data_type=="norm":
template_spectrum = functions.read_ascii(model_path_norm + template_spectrum)
template_spectrum = functions.read_table(template_spectrum)
template_spectrum = transpose(array(template_spectrum))
if data_type=="flux":
template_spectrum =spectype_functions.normalise(template_spectrum,flux_normalise_w1,flux_normalise_w2)
### Chop both spectra
data_region = spectype_numerical_functions.chop_spectrum(data_spectrum,start_lambda,end_lambda)
template_region = spectype_numerical_functions.chop_spectrum(template_spectrum,start_lambda,end_lambda)
### Conform template spectrum to data spectrum -> same wavelength scale
template_region = spectype_numerical_functions.conform_spectrum(data_region,template_region)
### Find shift
chisq_shift = []
shift_limit = 20
shift = -1*shift_limit
while shift <= shift_limit:
data_region_shifted,template_region_shifted = spectype_functions.shift_spectrum(data_region,template_region,shift)
chisq_shift.append(spectype_numerical_functions.chisq(data_region_shifted,template_region_shifted))
shift = shift + 1
chisq_min = spectype_functions.find_min(chisq_shift)
best_shift = chisq_min - shift_limit
return best_shift
def query_hscand(query_entry):
### Write the mysql query csh script
command = "mysql --defaults-file=/home/gzhou/hscand.cfg HSCAND -e \"" + query_entry + "\" > /home/gzhou/query_result.txt"
mysql_query = open("mysql_query.csh","w")
mysql_query.write("#! /bin/csh \n")
mysql_query.write(command + "\n")
mysql_query.close()
### Copy the required scripts to hatsouth@princeton
os.system("chmod a+x mysql_query.csh")
print "copying files to hatsouth@princeton"
os.system("scp hscand.cfg [email protected]:/home/gzhou/")
os.system("scp mysql_query.csh [email protected]:/home/gzhou/")
### Execute the program and copy the results over
print "Executing .csh files on princeton via ssh"
os.system("ssh [email protected] '/home/gzhou/mysql_query.csh'")
os.system("scp [email protected]:/home/gzhou/query_result.txt .")
### Read query_result.txt in as a list
query_result = functions.read_ascii("query_result.txt")
query_result = functions.read_table(query_result)
os.system("rm query_result.txt")
os.system("rm mysql_query.csh")
return query_result
def main(file_path,file_name):
file_path_reduced = file_path+"reduced/"
file_path_temp = file_path+"temp/"
coo = functions.read_table(functions.read_ascii(file_path_temp+"master_coo"))
spatial = loadtxt(file_path_reduced+"spatial_"+file_name+".dat")
coord_fit = array(fit_2dgauss(coo,spatial))
savetxt(file_path+"reduced/coords_"+file_name+".dat",coord_fit,fmt="%.10f")
def plot_isochrones(program_dir,style,lwidth):
isochrones = functions.read_ascii(program_dir + "isochrone.dat")
isochrones = functions.read_table(isochrones)
isochrones = isochrones[:len(isochrones)-1]
isochrones = transpose(isochrones)
teff = 10**array(isochrones[4])
logg = array(isochrones[5])
plt.plot(teff,logg,style,linewidth=lwidth)
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 loop_input_spectrum(input_wave,input_flux,folder,teff_space,logg_space,feh_space,w1,w2,perform_normalise):
data = []
for teff in teff_space:
for logg in logg_space:
for feh in feh_space:
template_spectrum = "template_" + str(teff) + "_" + str(logg) + "_" + str(feh)+".dat"
#print folder + template_spectrum
template_spectrum = functions.read_ascii(folder+template_spectrum)
template_spectrum = functions.read_table(template_spectrum)
template_spectrum = transpose(array(template_spectrum))
if folder == model_path_flux:
template_spectrum = spectype_functions.normalise(template_spectrum,flux_normalise_w1,flux_normalise_w2)
i1 = w1 - min(input_wave)
i2 = w2 - min(input_wave)
input_wave_cropped = input_wave[i1:i2]
input_flux_cropped = input_flux[i1:i2]
template_spectrum = spectype_numerical_functions.chop_spectrum(template_spectrum,w1-10,w2+10)
template_interp = interpolate.splrep(template_spectrum[0],template_spectrum[1],s=0)
template_flux = interpolate.splev(input_wave_cropped,template_interp,der=0)
sigma = 3.0
if perform_normalise:
diff_flux = input_flux_cropped/median(input_flux_cropped) - template_flux/median(template_flux)
else:
diff_flux = input_flux_cropped - template_flux
diff_flux = clip(diff_flux,median(diff_flux) - sigma*std(diff_flux),median(diff_flux)+sigma*std(diff_flux))
rms = sqrt(sum(diff_flux**2) /float(len(input_wave_cropped)))
# plt.clf()
# plt.plot(input_wave_cropped,input_flux_cropped/median(input_flux_cropped))
# plt.plot(input_wave_cropped,template_flux/median(template_flux))
# plt.show()
# #sys.exit()
#print rms
data.append(rms)
return data
print RV_list[i]
RV_list = temp_list
if len(RV_list) > 0:
### Create string list RV_Standards to feed into iraf
RV_Standards = ""
for i in range(len(RV_list)):
RV_Standards = RV_Standards + RV_list[i] + ","
### Append VHELIO to all RV standard stars - this requires
### Ascii file RV_standard.dat to be present in program/
### Read in RV_standard.dat
RV_standard_dat = functions.read_ascii(program_dir + "RV_standard.dat")
RV_standard_dat = functions.read_table(RV_standard_dat)
### Hedit VHELIO by matching object_name to RV_standard_dat
for i in range(len(RV_list)):
### Find star name
file_location = file_path_reduced + RV_list[i]
hdulist = pyfits.open(file_location)
object_name = hdulist[0].header["OBJECT"]
hdulist.close()
### Find corresponding RV information in database
VHELIO = retrieve_RV(object_name, RV_standard_dat)
append_VHELIO(file_location, str(VHELIO))
############################
### Apply header changes ###
iraf.continpars.setParam("low_reject",2.0,check=1,exact=1)
iraf.continpars.setParam("high_reject",2.0,check=1,exact=1)
### Then apply fxcor to the stellar regions for RV measurement
os.system("rm fxcor_stellar*")
#region = "*"
#region = "a5700-6100"
region = "a5250-6815"
normalise(file_name)
run_fxcor("temp.fits","mdwarf_template_norm.fits",region,"fxcor_stellar",0,False)
os.system("cat fxcor_stellar.txt")
### Now calculate RV
data = functions.read_ascii("fxcor_stellar.txt")
data = functions.read_table(data)
rv = []
rverr = []
for i in data:
if functions.is_number(i[3]):
hjd = i[3]+50000
if functions.is_number(i[12]):
if abs(i[12]) < 500 and abs(i[13]) < 500:
rv.append(i[12])
rverr.append(i[13])
RV = median(rv)
RV_err = median(rverr)
def plot_spectrum(rms_data,input_spectrum):
rms_data = functions.read_ascii(rms_data)
rms_data = functions.read_table(rms_data)
rms_data = transpose(rms_data)
### Find min
for i in range(len(rms_data[0])):
if rms_data[3][i] == min(rms_data[3]):
teff_min = rms_data[0][i]
logg_min = rms_data[1][i]
feh_min = rms_data[2][i]
break
print teff_min,logg_min,feh_min
teff_list = []
logg_list = []
rms_list = []
for i in range(len(rms_data[0])):
if rms_data[2][i] == feh_min:
teff_list.append(rms_data[0][i])
logg_list.append(rms_data[1][i])
rms_list.append(rms_data[3][i])
plt.subplot(211)
cm = matplotlib.cm.get_cmap('jet')
sc = plt.scatter(teff_list, logg_list, c=rms_list, vmin=min(rms_list), vmax=max(rms_list), s=70, cmap=cm,edgecolor="w")
cbar = plt.colorbar(sc)
cbar.ax.set_ylabel("RMS")
plt.scatter(teff_min,logg_min,color="r",s=70,marker="+")
spectype_functions.plot_isochrones(program_dir,"r-",1)
plt.xlim(max(teff_list)+250,min(teff_list)-250)
plt.ylim(max(logg_list)+.25,min(logg_list)-0.25)
plt.xlabel("Teff (K)")
plt.ylabel("Logg")
plt.subplot(212)
data_spectrum = functions.read_ascii(input_spectrum)
data_spectrum = functions.read_table(data_spectrum)
data_spectrum = transpose(array(data_spectrum))
data_spectrum = spectype_functions.normalise(data_spectrum,flux_normalise_w1,flux_normalise_w2)
template_spectrum = "template_" + str(int(teff_min)) + "_" + str(logg_min) + "_" + str(feh_min)+".dat"
template_spectrum = functions.read_ascii(model_path_flux+template_spectrum)
template_spectrum = functions.read_table(template_spectrum)
template_spectrum = transpose(array(template_spectrum))
template_spectrum = spectype_functions.normalise(template_spectrum,flux_normalise_w1,flux_normalise_w2)
plt.plot(data_spectrum[0],data_spectrum[1],"b-")
plt.plot(template_spectrum[0],template_spectrum[1],"g-")
plt.xlim(3700,5800)
plt.xlabel("Wavelength (A)")
plt.ylabel("Normalised flux")
plt.show()
def find_shift(input1,input2,i1,i2,shift_range):
if abs(i2 - i1) < 300:
i1 = i1 - 150
i2 = i2 + 150
if i1 < 0:
i1 = 0
i2 = 300
if i2 > len(input1):
i2 = len(input1)
i1 = len(input1) - 300
### Use xcorr
currdir = os.getcwd()
os.chdir(file_path_reduced)
os.system("rm "+file_path_reduced+"shift_spec*")
input1_cropped = input1[i1:i2]/median(input1[i1:i2])
input2_cropped = input2[i1:i2]/median(input1[i1:i2])
wave_axis = arange(1,len(input1_cropped)+1)
shift_spec1 = open(file_path_reduced+"shift_spec1.txt","w")
functions.write_table(transpose([wave_axis,input1_cropped]),shift_spec1)
shift_spec1.close()
shift_spec2 = open(file_path_reduced+"shift_spec2.txt","w")
functions.write_table(transpose([wave_axis,input2_cropped]),shift_spec2)
shift_spec2.close()
iraf.rspectext(
input = file_path_reduced+"shift_spec1.txt",\
output = file_path_reduced+"shift_spec1.fits",\
title = "shift_spec1",\
flux = 0,\
dtype = "interp",\
crval1 = "",\
cdelt1 = "",\
fd1 = "",\
fd2 = "")
iraf.rspectext(
input = file_path_reduced+"shift_spec2.txt",\
output = file_path_reduced+"shift_spec2.fits",\
title = "shift_spec2",\
flux = 0,\
dtype = "interp",\
crval1 = "",\
cdelt1 = "",\
fd1 = "",\
fd2 = "")
time.sleep(0.5)
### Find shift
os.system("rm apshift*")
### Makesure keywpars is set at default
iraf.unlearn(iraf.keywpars)
cuton = len(input1_cropped)/25.
cutoff = len(input1_cropped)/2.5
iraf.filtpars.setParam("f_type","welch",check=1,exact=1)
iraf.filtpars.setParam("cuton",cuton,check=1,exact=1)
iraf.filtpars.setParam("cutoff",cutoff,check=1,exact=1)
run_fxcor(file_path_reduced+"shift_spec1.fits",file_path_reduced+"shift_spec2.fits","*","apshift",0,10,"gaussian","INDEF",0)
vel_shift = functions.read_ascii("apshift.txt")
vel_shift = functions.read_table(vel_shift)
vel_shift = vel_shift[0][6]
if vel_shift == "INDEF":
vel_shift = 0.0
if abs(vel_shift) > shift_range:
vel_shift = 0.0
print "best pixel shift of ",vel_shift
os.system("rm apshift*")
os.system("rm "+file_path_reduced+"shift_spec*")
os.chdir(currdir)
#if i1 < shift_range:
# i1 = shift_range
#if i2 > len(input1)-shift_range:
# i2 = len(input1)-shift_range
# shift_rms = []
# shift_list = []
# for shift in range(-1*shift_range,shift_range+1):
# input1_cropped = input1[i1+shift:i2+shift]
# input2_cropped = input2[i1:i2]
# diff = input1_cropped/median(input1_cropped) * input2_cropped/median(input2_cropped)
# rms = sum(diff)
# #rms = sqrt(sum(diff**2) /float(len(diff)))
# shift_rms.append(rms)
# shift_list.append(shift)
# for i in range(len(shift_rms)):
# if shift_rms[i] == max(shift_rms):
# break
# print "Applying a shift of ",shift_list[i]
# plt.clf()
# plt.plot(input1[i1+shift_list[i]:i2+shift_list[i]]/median(input1[i1+shift_list[i]:i2+shift_list[i]]),"b-")
# plt.plot(input1[i1:i2]/median(input1[i1:i2]),"r-")
# plt.plot(input2[i1:i2]/median(input2[i1:i2]),"g-")
# plt.show()
# return shift_list[i]
return int(round(vel_shift,0))
hsmso_connect = functions.read_config_file("HSMSO_CONNECT")
hscand_connect = functions.read_config_file("HSCAND_CONNECT")
default_teff = float(functions.read_config_file("TEFF_ESTIMATE"))
default_logg = float(functions.read_config_file("LOGG_ESTIMATE"))
teff_ini,logg_ini = functions.estimate_teff_logg(object_name,hsmso_connect,hscand_connect,default_teff,default_logg)
feh_ini = 0.0
print "Initial estimate of teff, logg: ",str(teff_ini),str(logg_ini)
### Change directory to reduced/
program_dir = os.getcwd() + "/" #Save the current working directory
os.chdir(file_path_reduced) #Change to ../reduced/ dir
### Load in spectra
flux_spectrum = functions.read_ascii("fluxcal_" + file_name + ".dat")
flux_spectrum = functions.read_table(flux_spectrum)
flux_spectrum = transpose(array(flux_spectrum))
flux_spectrum = spectype_functions.normalise(flux_spectrum,flux_normalise_w1,flux_normalise_w2)
norm_spectrum = functions.read_ascii("norm_" + file_name + ".dat")
norm_spectrum = functions.read_table(norm_spectrum)
norm_spectrum = transpose(array(norm_spectrum))
print "Using specific regions for spectral typing"
### Check the temp and define which logg sensitive regions to use
#if teff_ini > 4750 and teff_ini < 5750:
if teff_ini > 4750 and teff_ini < 6250:
#logg_regions = [[5140,5235]]
logg_regions = [[5100,5400]]
if teff_ini <= 4750 and teff_ini > 4250:
logg_regions = [[5100,5400]]
### Set program dir and change working directory
program_dir = os.getcwd() + "/" #Save the current working directory
os.chdir(file_path_reduced) #Change to ../temp/ dir
### Find info from the fits header
hdulist = pyfits.open(file_path_reduced+"spec_" + file_name)
object_name = hdulist[0].header["OBJECT"]
dateobs = hdulist[0].header["DATE-OBS"]
mjd = hdulist[0].header["MJD-OBS"]
exptime = hdulist[0].header["EXPTIME"]
comment = hdulist[0].header["NOTES"]
hdulist.close()
### Read info from text files in reduced/
spectype = functions.read_ascii("spectype.txt")
spectype = functions.read_table(spectype)
for entry in spectype:
if entry[1] == object_name and entry[0] == file_name:
teff = entry[2]
logg = entry[4]
feh = entry[6]
image_quality = functions.read_ascii("image_quality.dat")
image_quality = functions.read_table(image_quality)
sn = 0.
entry_found = False
for entry in image_quality:
if entry[0] == file_name and entry[1] == object_name:
sn = entry[5]
def detect_stars(input_image,se_path,no_stars):
image_data = pyfits.getdata(input_image)
oned = []
for i in range(len(image_data)):
for j in range(len(image_data)):
oned.append(image_data[i,j])
med = median(oned)
run_daofind(input_image,"master_coo",1)
os.system("rm coords.cat")
SEcommand = se_path+" "+input_image+" -c default.sex"
SEcommand = SEcommand+" -BACK_TYPE MANUAL -BACK_VALUE "+str(med)
os.system(SEcommand)
os.system("cat coords.cat")
SE_coo = functions.read_ascii("coords.cat")
SE_coo = functions.read_table(SE_coo)
temp = []
for i in SE_coo:
if i[0] < 36.:
temp.append(i)
SE_coo = temp
phot_coo = functions.read_ascii("master_coo")
phot_coo = functions.read_table(phot_coo)
temp = []
for i in phot_coo:
if i[0] < 36.:
temp.append(i)
phot_coo = temp
### Check if the objects in phot_coo exists also in SE_coo
confirmed_objects = []
for phot_obj in phot_coo:
phot_obj_x = phot_obj[0]
phot_obj_y = phot_obj[1]
for SE_obj in SE_coo:
SE_obj_x = SE_obj[0]
SE_obj_y = SE_obj[1]
SE_obj_fwhm = SE_obj[4]
SE_obj_fwhm = 6
# if SE_obj_fwhm < 5. or SE_obj_fwhm > 10.0:
# SE_obj_fwhm = 5
if abs(phot_obj_x-SE_obj_x)<SE_obj_fwhm and abs(phot_obj_y-SE_obj_y)<SE_obj_fwhm:
confirmed_objects.append(phot_obj)
break
if len(confirmed_objects) == 0 and len(SE_coo) > 0:
print "NO matching objects, using SE coordinates"
confirmed_objects = []
for SE_obj in SE_coo:
confirmed_objects.append([SE_obj[0],SE_obj[1],"INDEF",0.5,0.5,0.5,SE_obj[0]])
elif len(confirmed_objects) == 0 and len(phot_coo) > 0:
print "NO matching objects, using iraf.phot coordinates"
confirmed_objects = phot_coo
elif len(confirmed_objects)==0 and len(phot_coo)==0 and len(SE_coo)==0:
print "NO objects detected!!!"
sys.exit()
### Order by brightness
flux_list = []
for i in confirmed_objects:
aperture = circle(i[1]-1,i[0]-1,2.0,image_data)
flux = aperture*image_data - aperture*med
flux = flux.sum()
flux_list.append(flux)
flux_list_sorted = sorted(flux_list,reverse=True)
print "flux",flux_list_sorted
temp = []
for i in range(len(flux_list_sorted)):
j = flux_list.index(flux_list_sorted[i])
temp.append(confirmed_objects[j])
confirmed_objects = temp
### remove unwanted objects
if no_stars > 0:
confirmed_objects = confirmed_objects[:no_stars]
master_out = open("master_coo","w")
functions.write_table(confirmed_objects,master_out)
master_out.close()
########################
### Start of program ###
########################
file_path = sys.argv[1]
file_path_temp = file_path + "temp/"
file_path_reduced = file_path + "reduced/"
file_name = sys.argv[2]
print "This script uses iraf.fxcor to generate a CCF for " +file_name + " using synthetic templates"
program_dir = os.getcwd() + "/" #Save the current working directory
### Load fxcor RV measurements
fxcor_stellar = functions.read_ascii(file_path_reduced + "fxcor_stellar.txt")
fxcor_stellar = functions.read_table(fxcor_stellar)
### Load grating / camera settings
grating = functions.read_config_file("GRATING")
dichroic = functions.read_config_file("RT560")
region_w1 = functions.read_param_file(grating+"_"+dichroic+"_w1")
region_w2 = functions.read_param_file(grating+"_"+dichroic+"_w2")
### Load location of library
synthetic_library = functions.read_param_file("RV_SPECTRAL_LIBRARY")
### Load RV fxcor region
stellar_region = functions.read_param_file("STELLAR_REGION")
### Determine best aperture
object_mjd = hdulist[0].header['MJD-OBS']
hdulist.close()
camera = functions.read_config_file("CAMERA")
grating = functions.read_config_file("GRATING")
dichroic = functions.read_config_file("DICHROIC")
combine_aps = functions.read_config_file("COMBINE_APERTURES")
task = functions.read_config_file("TASK")
no_apertures = eval(functions.read_config_file("NO_APERTURES"))
print "This script applies NeAr arc image to calibrate the object spectrum " +file_name
### Get slice numbers and arc images to use
arc_list = functions.read_ascii(file_path_temp + "arcs_to_use.txt")
coo = functions.read_ascii(file_path_temp+"master_coo")
coo = functions.read_table(coo)
### Calculate the fractional weight of each arc
arc_weight = []
for arc_name in arc_list:
hdulist = pyfits.open(file_path + arc_name)
arc_mjd = hdulist[0].header['MJD-OBS']
hdulist.close()
arc_weight.append(abs(arc_mjd - object_mjd))
arc_weight = array(arc_weight)
arc_weight = arc_weight / sum(arc_weight)
### Define linelist to use
linelist = grating + "_linelist.dat"
###################################
### Load fxcor output txt files ###
###################################
### Set file_path
file_path = sys.argv[1]
file_path_temp = file_path + "temp/"
file_path_reduced = file_path + "reduced/"
file_name = sys.argv[2]
program_dir = os.getcwd() + "/" #Save the current working directory
### Load fxcor RV measurements
fxcor_stellar = functions.read_ascii(file_path_reduced + "fxcor_stellar.txt")
fxcor_stellar = functions.read_table(fxcor_stellar)
### Load grating / camera settings
grating = functions.read_config_file("GRATING")
dichroic = functions.read_config_file("DICHROIC")
region_w1 = functions.read_param_file(grating+"_"+dichroic+"_w1")
region_w2 = functions.read_param_file(grating+"_"+dichroic+"_w2")
########################
### Start of program ###
########################
### Find weights according flux of each aperture
aperture_weights = find_flux_weights(file_name)
print "weights for each aperture"
cand_txt.close()
else:
print "ERROR entries not found for " + object_name
if len(RV_points) > 1:
print "Calculating orbital solution"
### Extract candidate phase information from HSCAND
if object_name[:4]=="HATS":
print "Using HSCAND for candidate parameters"
query_entry = "select HATSE,HATSP,HATSq from HATS where HATSname=\'%s\' " % object_name_query
cand_params = mysql_query.query_hscand(query_entry)[1]
else:
### Try to find it in "candidatex.txt"
candidates_txt = functions.read_ascii(plots_folder + "candidates.txt")
candidates_txt = functions.read_table(candidates_txt)
object_found = False
for entry in candidates_txt:
print entry[0]
if entry[0] == object_name_query:
print "Using candidates.txt for candidate parameters"
object_found = True
cand_params = [entry[5],entry[6],entry[7]]
#break
if not object_found:
print "Using default candidate parameters"
RV_points = transpose(RV_points)
HJD_points = RV_points[0]
cand_params = [min(HJD_points),max(HJD_points)-min(HJD_points),0.1]
no_trials = 0
npoints = 20
while (not good_correction) and no_trials < 5:
os.system("rm apshift*")
### Makesure keywpars is set at default
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",10000,check=1,exact=1)
run_fxcor("norm_" + im_slice + "_" + file_name,"telluric.fits",telluric_region,"apshift",0,npoints,"gaussian")
vel_shift = functions.read_ascii("apshift.txt")
vel_shift = functions.read_table(vel_shift)
vel_shift = str(vel_shift[0][11])
if not vel_shift == "INDEF":
good_correction = True
else:
print "Fit did not converge, trying again with ",npoints,"n_points"
npoints = npoints + 10
no_trials = no_trials + 1
### IF it still doesn't work, use a the centre1d function
if vel_shift == "INDEF":
os.system("rm apshift*")
### Makesure keywpars is set at default
iraf.unlearn(iraf.keywpars)
import functions
import os
import sys
from numpy import *
import matplotlib.pyplot as plt
def rms(input_list):
input_list = array(input_list)
rms = sqrt(sum(input_list**2) / len(input_list))
return rms
RV = functions.read_ascii("aperture_RV_3aps.dat")
RV = functions.read_table(RV)
for i in RV:
ap1 = i[2]
ap1_RV = i[3]
ap2 = i[4]
ap2_RV = i[5]
plt.plot(array([ap1,ap2]) - min([ap1,ap2]),[ap1_RV,ap2_RV])
plt.show()
### Set program dir and change working directory
program_dir = os.getcwd() + "/" #Save the current working directory
os.chdir(file_path_reduced) #Change to ../temp/ dir
### Find info from the fits header
hdulist = pyfits.open(file_path_reduced+"normspec_" + file_name)
object_name = hdulist[0].header["OBJECT"]
dateobs = hdulist[0].header["DATE-OBS"]
mjd = hdulist[0].header["MJD-OBS"]
exptime = hdulist[0].header["EXPTIME"]
comment = hdulist[0].header["NOTES"]
hdulist.close()
### Read info from text files in reduced/
RV_dat = functions.read_ascii("RV.dat")
RV_dat = functions.read_table(RV_dat)
for entry in RV_dat:
if entry[0] == object_name and entry[1] == file_name:
if functions.is_number(entry[2]):
hjd = entry[2] + 50000
RV = entry[3]
RV_err = entry[4]
ccf_height = entry[5]
ccf_log = functions.read_ascii("ccf_log.txt")
ccf_log = functions.read_table(ccf_log)
ccf_fwhm = 0
bis = 0
bis_err = 0
program_dir = os.getcwd() + "/" #Save the current working directory
os.chdir(file_path_reduced) #Change to ../temp/ dir
os.system("mkdir ccf_pdfs")
os.system("rm ccf_pdfs/*" + file_name + "*")
################
### Load ccf ###
################
plt.clf()
hdulist = pyfits.open(file_path_reduced + "normspec_"+file_name)
candidate = hdulist[0].header["OBJECT"]
hdulist.close()
ccf = functions.read_ascii("ccf_" + file_name + ".txt")
ccf = functions.read_table(ccf)
### Find max
max_ccf = max(transpose(ccf)[1])
max_pos = 0
for i in range(len(ccf)):
if ccf[i][1] == max_ccf:
max_pos = i
break
#ccf = ccf[i-200:i+200]
ccf = ccf[i-40:i+40]
ccf = transpose(ccf)
### Define plotting axes
free_param_vals.append(temp_param_vals[i])
free_param_range.append(temp_param_range[i])
free_param_func.append("b")
print "FREE PARAMS"
for i in range(len(free_param_names)):
print free_param_names[i], free_param_vals[i], free_param_range[i]
print "FIXED PARAMS"
for i in range(len(fixed_param_names)):
print fixed_param_names[i], fixed_param_vals[i]
x0 = zeros(len(free_param_names))
free_param_vals = [functions.read_ascii("best_param_mcmc")[1]]
free_param_vals = array(functions.read_table(free_param_vals))[0]
print free_param_vals
phase, flux, err, model = [], [], [], []
for n in range(len(lclist)):
lc = lclist[n]
lc = loadtxt(lc)
phase_n, flux_n, err_n, model_n = fitting_functions.lc_chisq(
free_param_vals, free_param_names, fixed_param_names, fixed_param_vals,
lc, False, True, cadence[n])
phase += list(phase_n)
flux += list(flux_n)
err += list(err_n)
no_apertures = int(functions.read_config_file("NO_APERTURES"))
no_stars = int(functions.read_config_file("NO_STARS"))
se_path = functions.read_param_file("SE_PATH")
program_dir = os.getcwd()+"/"
### Set file_path
file_path = sys.argv[1]
file_path_temp = file_path + "temp/"
file_path_reduced = file_path + "reduced/"
file_name = sys.argv[2]
interactive = functions.read_config_file("INTERACT")
image_slices_list = functions.read_ascii(file_path_temp + "slice_" + file_name+".txt")
image_slices_list = functions.read_table(image_slices_list)
image_slices_list = image_slices_list[1:]
hdulist = pyfits.open(file_path + file_name)
object_name = hdulist[0].header['OBJECT']
hdulist.close()
os.chdir(file_path_temp)
########################################################
### Reconstruct array by reading in each image slice ###
########################################################
spatial_image = []
for image_slice in image_slices_list:
row_image = pyfits.getdata(image_slice[0])
value = redden,\
R = 3.1,\
type = "E(B-V)",\
apertures = "*",\
override = 1,\
uncorrect = 0,\
mode = "al")
### Create .dat file out of fits file redden_name
os.system("rm " + redden_name + ".dat")
iraf.wspectext(redden_name + "[*,1,1]", redden_name + ".dat")
spectrum = functions.read_ascii(redden_name + ".dat")
spectrum = functions.read_table(spectrum)
temp = []
for i in spectrum:
if len(i) == 2:
if functions.is_number(i[0]):
temp.append(i)
spectrum = temp
spectrum = spectrum[1:len(spectrum)-2]
output_spectrum = open(redden_name + ".dat","w")
functions.write_table(spectrum,output_spectrum)
output_spectrum.close()
os.system("mv " + redden_name + ".dat deredden")
os.system("mv " + redden_name + " deredden")
def plot_spectrum(rms_data,input_spectrum):
print "Plotting ",input_spectrum
rms_data = functions.read_ascii(rms_data)
rms_data = functions.read_table(rms_data)
rms_data = transpose(rms_data)
### Find min
for i in range(len(rms_data[0])):
if rms_data[3][i] == min(rms_data[3]):
teff_min = rms_data[0][i]
logg_min = rms_data[1][i]
feh_min = rms_data[2][i]
break
print teff_min,logg_min,feh_min,min(rms_data[3])
teff_list = []
logg_list = []
rms_list = []
for i in range(len(rms_data[0])):
if rms_data[2][i] == feh_min:
teff_list.append(rms_data[0][i])
logg_list.append(rms_data[1][i])
rms_list.append(rms_data[3][i])
### Create 2D space
teff_space = arange(min(teff_list),max(teff_list)+250,250)
logg_space = arange(min(logg_list),max(logg_list)+0.5,0.5)
rms_space = zeros([len(teff_space),len(logg_space)])
for i in range(len(rms_list)):
x_index = int((teff_list[i] - min(teff_list)) / 250.)
y_index = int((logg_list[i] - min(logg_list)) / 0.5)
rms_space[x_index,y_index] = rms_list[i]
### Crop 2D space to perform gaussian fit for min
teff_space_cropped,logg_space_cropped,rms_space_cropped=spectype_functions.chop_array(teff_space,logg_space,transpose(rms_space),teff_min,logg_min,250,0.5)
rms_space_cropped = -1*(rms_space_cropped - rms_space_cropped.max())
print rms_space_cropped
try:
gauss_fit = spectype_functions.fitgaussian(rms_space_cropped)
teff_min_fit = min(teff_space_cropped) + gauss_fit[2] * 250
logg_min_fit = min(logg_space_cropped) + gauss_fit[1] * 0.5
except TypeError:
print "Bad gaussian fit, using abs min"
teff_min_fit = teff_min
logg_min_fit = logg_min
if teff_min_fit < 3500:
teff_min_fit = 3500
if teff_min_fit > 9000:
teff_min_fit = 9000
if logg_min_fit < 0.0:
logg_min_fit = 0.0
if logg_min_fit > 5.0:
logg_min_fit = 5.0
teff_min = int(spectype_functions.round_value(teff_min_fit,250.))
logg_min = spectype_functions.round_value(logg_min_fit,0.5)
print teff_min,logg_min
### Plot teff_logg space
plt.figure(figsize=(7,5))
plt.subplot(211)
plt.title(object_name+" "+file_name+" "+str(int(round(teff_min_fit,0)))+" "+str(round(logg_min_fit,1))+" "+str(feh_min)+" \n RMS="+str(round(min(rms_data[3]),4)))
v_min = min(rms_list)
v_max = min(rms_list)+((max(rms_list)-min(rms_list))/3.)
#v_max = max(rms_list)
rms_space = clip(rms_space,v_min,v_max)
cm = matplotlib.cm.get_cmap('jet')
sc = plt.contourf(teff_space,logg_space,transpose(rms_space),100,cmap=cm)
#sc = plt.scatter(teff_list, logg_list, c=rms_list, vmin=min(rms_list), vmax=(max(rms_list)-min(rms_list))/3+min(rms_list), s=150, cmap=cm,edgecolor="w")
cbar = plt.colorbar(sc)
cbar.ax.set_ylabel("RMS")
plt.scatter(teff_min_fit,logg_min_fit,color="r",s=70,marker="+")
spectype_functions.plot_isochrones(program_dir,"r-",1)
plt.xlim(max(teff_list),min(teff_list))
plt.ylim(max(logg_list),min(logg_list))
#plt.xlim(max(teff_list)+250,min(teff_list)-250)
#plt.ylim(max(logg_list)+.25,min(logg_list)-0.25)
plt.xlabel("Teff (K)")
plt.ylabel("Logg")
### Plot spectrum
plt.subplot(212)
data_spectrum = functions.read_ascii(input_spectrum)
data_spectrum = functions.read_table(data_spectrum)
data_spectrum = transpose(array(data_spectrum))
data_spectrum = spectype_functions.normalise(data_spectrum,flux_normalise_w1,flux_normalise_w2)
template_spectrum = "template_" + str(int(teff_min)) + "_" + str(logg_min) + "_" + str(feh_min)+".dat"
template_spectrum = functions.read_ascii(model_path_flux+template_spectrum)
template_spectrum = functions.read_table(template_spectrum)
template_spectrum = transpose(array(template_spectrum))
template_spectrum = spectype_functions.normalise(template_spectrum,flux_normalise_w1,flux_normalise_w2)
data_wave = data_spectrum[0]
data_flux = data_spectrum[1]
template_wave = template_spectrum[0]
template_flux = template_spectrum[1]
c = 3.0 * 10**5
data_wave = data_wave / ((vel_shift / c) + 1)
data_interp = interpolate.splrep(data_wave,data_flux,s=0)
data_flux = interpolate.splev(master_flux_wave,data_interp,der=0)
template_interp = interpolate.splrep(template_wave,template_flux,s=0)
template_flux = interpolate.splev(master_flux_wave,template_interp,der=0)
plt.plot(master_flux_wave,data_flux,"b-",label="data")
plt.plot(master_flux_wave,template_flux,"g-",label="template")
plt.xlim(3600,5800)
ylim_range = max(template_flux)-min(template_flux)
plt.ylim(min(template_flux)-ylim_range*0.2,max(template_flux)+ylim_range*0.2)
plt.legend(loc="lower right",ncol=2)
plt.xlabel("Wavelength (A)")
plt.ylabel("Normalised flux")
os.system("rm "+file_path_reduced+"spectype_plots/"+object_name+"_"+file_name+".pdf")
plt.savefig(file_path_reduced+"spectype_plots/"+object_name+"_"+file_name+".pdf")
#plt.show()
return teff_min_fit,logg_min_fit,feh_min
#################
### Functions ###
#################
########################
### Start of program ###
########################
file_path = sys.argv[1]
file_path_temp = file_path + "temp/"
file_path_reduced = file_path + "reduced/"
file_name = sys.argv[2]
### Read in the correct image slice to analyse
slices = functions.read_ascii(file_path_temp + "stellar_apertures.txt")
slices = functions.read_table(slices)
slice_to_use = slices[0][0]
image_data = pyfits.getdata(file_path_temp + str(int(slice_to_use)) + "_" + file_name)
slice_data = image_data
### Chop the 200 columns in the centre of the image
image_data = transpose(image_data)
image_data = image_data[len(image_data)/2 - 100:len(image_data)/2 + 100]
image_data = transpose(image_data)
median_list = []
for i in image_data:
median_list.append(median(i))
for i in range(len(median_list)):
if median_list[i] == max(median_list):
########################
### Set file_path
file_path = sys.argv[1]
file_path_temp = file_path + "temp/"
file_path_reduced = file_path + "reduced/"
file_name = sys.argv[2]
biassubtracted_file_name = "out_ccdproc_" + file_name
### Load in the image slices table
### This table was created by define_image_slices.py
### and contains locations of the image slices
### according to a flat field frame
image_slices = functions.read_ascii(file_path_temp + "image_slice_table.txt")
image_slices = functions.read_table(image_slices)
### Loop through and cut out each slice
### save in individual files
print "Chopping image into its image slices"
os.chdir(file_path_temp)
slices_file_list = ""
for i in range(len(image_slices)):
start_column = int(image_slices[i][0])
end_column = int(image_slices[i][1])
region = '[1:4093,' + str(start_column) + ':'+str(end_column)+']'
print region
os.system("rm " + str(i) +"_"+ file_name)
free_param_range.append(temp_param_range[i])
free_param_func.append("b")
print "FREE PARAMS"
for i in range(len(free_param_names)):
print free_param_names[i],free_param_vals[i],free_param_range[i]
print "FIXED PARAMS"
for i in range(len(fixed_param_names)):
print fixed_param_names[i],fixed_param_vals[i]
x0 = zeros(len(free_param_names))
free_param_vals = [functions.read_ascii("best_param_mcmc")[1]]
free_param_vals = array(functions.read_table(free_param_vals))[0]
print free_param_vals
phase,flux,err,model = fitting_functions.lc_chisq(free_param_vals,free_param_names,fixed_param_names,fixed_param_vals,lc,False,True)
### Plot data
plt.clf()
plt.scatter(phase,flux,s=1,color="k")
plt.scatter(phase+1,flux,s=1,color="k")
plt.scatter(phase,model,s=2,color="r")
plt.scatter(phase+1,model,s=2,color="r")
plt.xlim(0.995,1.005)
plt.show()
### Set program dir and change working directory
program_dir = os.getcwd() + "/" #Save the current working directory
os.chdir(file_path_reduced) #Change to ../temp/ dir
### Find info from the fits header
hdulist = pyfits.open(file_path + file_name)
object_name = hdulist[0].header["OBJECT"]
dateobs = hdulist[0].header["DATE-OBS"]
mjd = hdulist[0].header["MJD-OBS"]
exptime = hdulist[0].header["EXPTIME"]
comment = hdulist[0].header["NOTES"]
hdulist.close()
image_quality = functions.read_ascii("image_quality.dat")
image_quality = functions.read_table(image_quality)
for entry in image_quality:
if entry[0] == file_name and entry[1] == object_name:
sn = entry[5]
import MySQLdb
sql_date = string.split(dateobs,"T")[0]
sql_time = string.split(dateobs,"T")[1]
print "Connecting to database"
db=MySQLdb.connect(host="marbles.anu.edu.au",user="******",passwd="h@ts0uthDB",db="daniel1")
c = db.cursor()
c.execute("""SELECT SPECid FROM SPEC WHERE SPECmjd=""" + str(mjd) + """ and SPECobject=\"%s\" """ % object_name)
#teff_regions = [[3800,5500]]
#logg_regions = [[]]
#teff_regions = [[3500,5900]]
##########################
### Start the analysis ###
##########################
### Change directory to reduced/
program_dir = os.getcwd() + "/" #Save the current working directory
os.chdir(file_path_reduced) #Change to ../reduced/ dir
### Load in spectra
norm_spectrum = functions.read_ascii("norm_" + file_name + ".dat")
norm_spectrum = functions.read_table(norm_spectrum)
norm_spectrum = transpose(array(norm_spectrum))
flux_spectrum = functions.read_ascii("fluxcal_" + file_name + ".dat")
flux_spectrum = functions.read_table(flux_spectrum)
flux_spectrum = transpose(array(flux_spectrum))
flux_spectrum = spectype_functions.normalise(flux_spectrum,flux_normalise_w1,flux_normalise_w2)
### Find shift
os.system("rm apshift*")
### Makesure keywpars is set at default
iraf.unlearn(iraf.keywpars)
iraf.filtpars.setParam("f_type","square",check=1,exact=1)