def readspectext(self, InputFile, OutputFile, Fits_Folder, conf_dict = None, Suffix = ''):
if OutputFile == None:
OutputFile = self.outputNameGenerator(InputFile, Suffix)
self.deleteIrafFiles(Fits_Folder + OutputFile)
conf_dict = self.rspectextAttributes(InputFile, OutputFile, Fits_Folder)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('rspectext', conf_dict)
print '--Using command'
print Command
iraf.rspectext(**conf_dict)
return
def extract(image,trace,sigma,bk):
if sigma < 1.:
sigma = 1.
#sigma = sigma*2
#print sigma,"sigma"
original_file = pyfits.open(image)
original_header = iraf.imheader(images=image,longheader=1,Stdout=1)
image = pyfits.getdata(image)
image = transpose(image)
line = []
peak = []
step = 200
i = 0
while i < len(image):
crop = image[i]
bk_i = []
signal_i = []
### uncomment this to see where the extraction is taking place
# plt.plot(crop)
# plt.axvline(x=trace[i])
# plt.show()
x = arange(0,len(crop)+0.1-1,0.1)
y = interpolate.interp1d(arange(0,len(crop)),crop)
y = y(x)
weights = []
for j in range(len(x)):
for n in bk:
if x[j] > n[0] and x[j] < n[1]:
bk_i.append(y[j])
if x[j] > trace[i] - sigma and x[j] < trace[i] + sigma:
signal_i.append(y[j])
weights.append(gaussian([1,trace[i],sigma,0],x[j]))
if len(bk_i) > 0:
bk_i = median(bk_i)
else:
bk_i = 0
if len(signal_i) > 0:
pass
else:
signal_i = [0]
weights = [1]
signal_i = array(signal_i)
weights = array(weights)
line.append(i+1)
peak.append(sum(signal_i)-bk_i*len(signal_i))
#peak.append(sum(signal_i*weights)-sum(bk_i*weights))
i = i + 1
### Uncomment to plot spectrum
# plt.clf()
# plt.plot(line,peak)
# plt.show()
data = transpose(array([line,peak]))
f = open("spectrum.flux","w")
functions.write_table(data,f)
f.close()
os.system("rm spectrum.fits")
iraf.rspectext(
input = "spectrum.flux",\
output = "spectrum.fits",\
title = "",\
flux = 0,\
dtype = "interp")
update_fitsheader(original_header,original_file,"spectrum.fits")
original_file.close()
else:
# leave things in vacuum wavelengths
AIR = wl
newf = f
bfmod = model[:-4]+'.bf.apogee.txt'
# Various outfiles, some of which will be deleted at the end
textcont = model[:-4]+'_short.txt'
fitscont = model[:-4]+'_short.fits'
fitsnocont = model[:-4]+'_nocont.fits'
convtext = model[:-4]+'.cnv'
convout = model[:-4]+'.cnv.out'
# Continuum fit the spectrum so we can use IRAF's continuum function to remove it
np.savetxt(textcont, np.array([AIR,newf]).transpose())
iraf.rspectext(input=textcont, output=fitscont, flux='No', dtype='interp')
print('iraf rspectext complete')
iraf.continuum(input=fitscont, output=fitsnocont, lines='*',
type='ratio', replace=no, wavescale=yes, logscale=no,
listonly=no, interactive=no, sample='*',
naverage=-25, function='spline3', order=5, niterate=10,
low_reject=1.5, high_reject=5.0, markrej=no, grow=0,
override=yes)
print('iraf continuum complete')
print('friendly untouched by convspec FITS saved {0}'.format(fitsnocont))
# Create a text file correctly formatted for convspec
wave, data = read_fits(fitsnocont)
np.savetxt(convtext, np.array([wave, np.zeros(len(wave)), data]).transpose())
# Run convspec, the fortran code
#!/usr/bin/env python
from astropy.io import fits
from pyraf import iraf
import os
import sys
#filename = 'spec-0266-51602-0051.fits'
filename = sys.argv[1]
#import fits
ob = fits.open(filename)
#Get data and save wavelenft in x, and flux on y
dataob = ob[1].data
x = 10**(dataob['loglam'])
y = dataob['model']
#save to a .txt file
namenewfits = '{name}.txt'.format(name=filename.split('.')[0])
with open(namenewfits, 'w') as file:
for i in zip(x, y):
file.write('{}\t{}\n'.format(i[0], i[1]))
#Create fits file from text file interpolation.
iraffitsname = 'iraf' + filename.split('.')[0]
if os.path.exists(iraffitsname + '.fits'):
os.remove(iraffitsname + '.fits')
iraf.noao.onedspec()
iraf.dataio()
iraf.rspectext(namenewfits, iraffitsname, dtype='interp')
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))