def applywavelengths(wavefile, applyfile, newname):
#Read in file with wavelength solution and get header info
wave = fits.open(wavefile)
n_fr = float(wave[0].header['LINDEN'])
n_fd = float(wave[0].header['CAMFUD'])
fl = float(wave[0].header['FOCLEN'])
zPnt = float(wave[0].header['ZPOINT'])
#Read in file to apply wavelength solution and update header
spec_data = fits.getdata(applyfile)
spec_header = fits.getheader(applyfile)
rt.Fix_Header(spec_header)
spec_header.append(('LINDEN', n_fr, 'Line Desity for Grating Eq.'),
useblanks=True,
bottom=True)
spec_header.append(
('CAMFUD', n_fd, 'Camera Angle Correction Factor for Grat. Eq.'),
useblanks=True,
bottom=True)
spec_header.append(('FOCLEN', fl, 'Focal Length for Grat Eq.'),
useblanks=True,
bottom=True)
spec_header.append(('ZPOINT', zPnt, 'Zero Point Pixel for Grat Eq.'),
useblanks=True,
bottom=True)
NewspecHdu = fits.PrimaryHDU(data=spec_data, header=spec_header)
#See if new file already exists
mylist = [True for f in os.listdir('.') if f == newname]
exists = bool(mylist)
clob = False
if exists:
print 'File %s already exists.' % newname
nextstep = raw_input(
'Do you want to overwrite or designate a new name (overwrite/new)? '
)
if nextstep == 'overwrite':
clob = True
exists = False
elif nextstep == 'new':
newname = raw_input('New file name: ')
exists = False
else:
exists = False
NewspecHdu.writeto(newname, output_verify='warn', clobber=clob)
complete task
'''
import numpy as np
import ReduceSpec_tools as rt
import spectools as st
import warnings
#import pyfits as fits
import astropy.io.fits as fits
task = raw_input('What would you like to do? (bias, flat, normalize, lacosmic, combine, trim, wavelength, details) ')
if task == 'combine':
files = raw_input('Name of file containing images to combine: ')
filelist = rt.Read_List(files)
output_file = raw_input('Output file name: ')
method = raw_input('Method to combine (median,average,sum): ')
low_sig = float(raw_input('Low sigma clipping threshold: '))
low_sig = np.abs(low_sig) #Ensure that this value is positive
high_sig = float(raw_input('High sigma clipping threshold: '))
rt.imcombine(filelist,output_file,method,lo_sig = low_sig, hi_sig = high_sig)
if task == 'trim':
files = raw_input('Name of file to trim: ')
rt.Trim_Spec(files)
if task == 'lacosmic':
files = raw_input('Name of file containing images to combine: ')
filelist = rt.Read_List(files)
import ReduceSpec_tools as rt
import spectools as st
import warnings
task = raw_input('What would you like to do? (bias, flat, normalize, combine, trim, wavelength) ')
if task == 'combine':
files = raw_input('Name of file containing images to combine: ')
filelist = rt.Read_List(files)
output_file = raw_input('Output file name: ')
method = raw_input('Method to combine (median,average,sum): ')
low_sig = float(raw_input('Low sigma clipping threshold: '))
low_sig = np.abs(low_sig) #Ensure that this value is positive
high_sig = float(raw_input('High sigma clipping threshold: '))
rt.imcombine(filelist,output_file,method,lo_sig = low_sig, hi_sig = high_sig)
if task == 'trim':
files = raw_input('Name of file to trim: ')
rt.Trim_Spec(files)
if task == 'wavelength':
Wavefile = raw_input('Name of file with wavelength solution: ')
files = raw_input('Name of file to apply wavelength solution to: ')
outputfile = raw_input('Output name of file: ')
st.applywavelengths(Wavefile,files,outputfile)
if task == 'normalize':
files = raw_input('Name of flat file to normalize: ')
rt.Norm_Flat_Poly(files)
method = 'median' # method used to combine images
# If overwrite special comand is given #
if nargs >= 6:
overwrite = args[5]
warnings.filterwarnings('ignore', category=UserWarning, append=True)
# If low_sigma and high_sigma values are given #
if nargs >= 8:
lo_sig = float(args[6])
hi_sig = float(args[7])
# If method is given #
if nargs >= 9:
method = args[8]
#Set up array to save for diagnostics. This is defined in rt.init()
rt.init()
# The rest of the code runs the reduction procces up to apall # =========
# Combine Zeros #
comb_zero = rt.imcombine(zero_lists[0], zero_names[0], 'average', lo_sig= 10,
hi_sig= 3, overwrite= overwrite)
# Bias Subtract Flats #
nf= len(flat_lists) # number of flats
b_flat_lists= []
i= 0
while i < nf:
b_flat_lists.append( rt.Bias_Subtract(flat_lists[i], comb_zero ) )
i= i+1
# Combine Bias Subtracted Flats #
def reduce_now(args):
nargs = len(args)
if (nargs < 4):
print("\n====================\n")
print("\nNot Enough Inputs.")
print("Need at least 4 inputs: listZero, listFlat, listSpec, listFe")
print("Optional inputs: overwrite= , low_sig= , high_sig= ")
print("Example:")
print("\n>>> python imcombine.py listZero listFlat listSpec listFe \n")
print("\n====================\n")
# Unpack list from command line and combe trough them for diffrent observations #
scriptname = args[0]
zero_lists = rt.List_Combe(rt.Read_List(args[1]))
flat_lists = rt.List_Combe(rt.Read_List(args[2]))
spec_lists = rt.List_Combe(rt.Read_List(args[3]))
# Select names from the first image of each observation #
zero_names = []
for zero in zero_lists:
zero_names.append(zero[0][5:])
flat_names = []
for flat in flat_lists:
flat_names.append(flat[0][5:])
spec_names = []
for spec in spec_lists:
spec_names.append(spec[0][5:])
# Default values for special commands if none are given these dont change #
overwrite = True # dont give imcombine permision to overwrite files #
lo_sig = 10
hi_sig = 3
method = 'median' # method used to combine images
# If overwrite special comand is given #
if nargs >= 6:
overwrite = args[5]
warnings.filterwarnings('ignore', category=UserWarning, append=True)
# If low_sigma and high_sigma values are given #
if nargs >= 8:
lo_sig = float(args[6])
hi_sig = float(args[7])
# If method is given #
if nargs >= 9:
method = args[8]
#Set up array to save for diagnostics. This is defined in rt.init()
rt.init()
# The rest of the code runs the reduction procces up to apall # =========
# Combine Zeros #
comb_zero = rt.imcombine(zero_lists[0],
zero_names[0],
'average',
lo_sig=10,
hi_sig=3,
overwrite=overwrite)
# Bias Subtract Flats #
nf = len(flat_lists) # number of flats
b_flat_lists = []
i = 0
while i < nf:
b_flat_lists.append(rt.Bias_Subtract(flat_lists[i], comb_zero))
i = i + 1
# Combine Bias Subtracted Flats #
i = 0
comb_flat = []
while i < nf:
comb_flat.append(
rt.imcombine(b_flat_lists[i],
'b.' + flat_names[i],
'median',
lo_sig=10,
hi_sig=3,
overwrite=overwrite))
i = i + 1
# Normalize Flat #
i = 0
nb_flat = []
while i < nf:
nb_flat.append(rt.Norm_Flat_Avg(
comb_flat[i])) # (divide by average of counts)
i = i + 1
print('tennisten')
# Bias Subtract Spec #
i = 0
b_spec_list = []
nsp = len(spec_lists)
# number of spectra
while i < nsp:
b_spec_list.append(rt.Bias_Subtract(spec_lists[i], comb_zero))
i = i + 1
# Flat Field Individual Spectra #
i = 0
ftb_spec_list = []
tb_spec_list = rt.List_Combe(b_spec_list)
print(tb_spec_list[i])
print(type(tb_spec_list[i]))
#tb_spec_list = rt.List_Combe(b_spec_list)
tb_spec_list = b_spec_list
while i < nsp:
ftb_spec_list.append(rt.Flat_Field(tb_spec_list[i], nb_flat[0]))
i = i + 1
'''
blueindex = [i for i, s in enumerate(nb_flat) if 'blue' in s.lower()]
nbflatblue = nb_flat[blueindex[0]]
redindex = [i for i, s in enumerate(nb_flat) if 'red' in s.lower()]
if len(redindex) > 0:
nbflatred = nb_flat[redindex[0]]
i= 0
ftb_spec_list = []
tb_spec_list = rt.List_Combe(b_spec_list)
while i < nsp:
if tb_spec_list[i][0].lower().__contains__('blue') == True:
ftb_spec_list.append( rt.Flat_Field(tb_spec_list[i], nbflatblue) )
elif tb_spec_list[i][0].lower().__contains__('red') == True:
ftb_spec_list.append( rt.Flat_Field(tb_spec_list[i], nbflatred) )
else:
print("Problem applying the Flats.")
print("Could not identify blue or red setup.")
i= i+1
tb_spec_list = rt.List_Combe(b_spec_list)
print(tb_spec_list[i])
print(type(tb_spec_list[i]))
'''
# Save all diagnostic info
rt.save_diagnostic()
#LA Cosmic
i = 0
cftb_spec = []
cftb_mask = []
while i < nsp:
m = 0
while m < len(ftb_spec_list[i]):
lacos_spec, lacos_mask = rt.lacosmic(ftb_spec_list[i][m])
cftb_spec.append(lacos_spec)
cftb_mask.append(lacos_mask)
m += 1
i += 1
cftb_spec_list = rt.List_Combe(cftb_spec)
cftb_mask_list = rt.List_Combe(cftb_mask)
print("Done. Ready for Apeture Extraction.\n")
def calibrate_now(lamp,
zz_specname,
fit_zpoint,
zzceti,
offset_file,
plotall=True):
# Read Lamp Data and Header #
lamp_data = fits.getdata(lamp)
lamp_header = fits.getheader(lamp)
# Check number of image slices, and select the spectra #
if lamp_header["NAXIS"] == 2:
lamp_spec = lamp_data[0]
elif lamp_header["NAXIS"] == 3:
lamp_spec = lamp_data[0][0]
else:
print("\nDont know which data to unpack.")
print("Check the array dimensions\n")
# plt.figure(1)
# plt.plot(lamp_spec)
# plt.title('Raw')
# plt.show()
# Find the pixel number offset due to trim reindexing #
trim_sec = lamp_header["CCDSEC"]
trim_offset = float(trim_sec[1:len(trim_sec) - 1].split(':')[0]) - 1
# Find Bining #
try:
bining = float(lamp_header["PARAM18"])
except:
bining = float(lamp_header["PG3_2"])
# Get Pixel Numbers #
nx = np.size(lamp_spec)
Pixels = bining * (np.arange(0, nx, 1) + trim_offset)
# Select Set of Parameters to use #
global parm
if lamp.lower().__contains__('red'):
parm = Param_930_20_40
line_list = WaveList_Fe_930_20_40
elif lamp.lower().__contains__('blue'):
parm = Param_930_12_24
line_list = WaveList_Fe_930_12_24
else:
print "Could not detect setup!"
# Calculate Initial Guess Solution # ========================================
alpha = float(lamp_header["GRT_TARG"])
theta = float(lamp_header["CAM_TARG"])
Wavelengths = DispCalc(Pixels, alpha, theta, parm[0], parm[1], parm[2],
parm[3])
# Ask for offset # ===========================================================
print offset_file
if offset_file:
print 'Using offset file: ', offset_file
offsets = np.genfromtxt(offset_file, dtype='d')
if offsets.size == 1:
offsets = np.array([offsets])
#print offsets
if 'blue' in lamp.lower():
offset = offsets[0]
elif 'red' in lamp.lower():
offset = offsets[1]
Wavelengths = [w + offset for w in Wavelengths]
else:
# Plot Dispersion #
plt.figure(1)
plt.plot(Wavelengths, lamp_spec)
plt.hold('on')
for line in line_list[1]:
if (Wavelengths[0] <= line <= Wavelengths[-1]):
plt.axvline(line, color='r', linestyle='--')
plt.title(
"Initial Dispersion Inspection Graph. \nClose to Calculate Offset")
plt.xlabel("Wavelengths")
plt.ylabel("Counts")
plt.hold('off')
plt.show()
print "\nWould You like to set Offset?"
yn = raw_input('yes/no? >>> ')
#yn= 'yes'
if yn == 'yes':
global ax, fig, coords
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(Wavelengths, lamp_spec)
plt.hold('on')
for line in line_list[1]:
if (Wavelengths[0] <= line <= Wavelengths[-1]):
plt.axvline(line, color='r', linestyle='--')
plt.title(
"First click known line(red), then click coresponding peak near center\n Then close graph."
)
plt.xlabel("Wavelengths (Ang.)")
plt.ylabel("Counts")
if lamp.__contains__('blue'):
plt.xlim(4700., 4900.)
elif lamp.__contains__('red'):
plt.xlim(6920., 7170.)
plt.hold('off')
coords = []
cid = fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
k_line = find_near(
coords[0][0], line_list[1]) # Nearest line to click cordinates
k_peak = find_near(coords[1][0],
Wavelengths) # Nearest Peak to click cordinates
i_peak = Wavelengths.index(k_peak)
X = Wavelengths[i_peak - 7:i_peak + 7]
Y = lamp_spec[i_peak - 7:i_peak + 7]
amp, center, width, b = fit_Gauss(X, Y)
offset = (k_line - center)
##########
#Save the offset
print '\n Would you like to save the offset?'
save_offset = raw_input('yes/no? >>> ')
if save_offset == 'yes':
print 'Saving offset to offsets.txt'
g = open('offsets.txt', 'a')
g.write(str(offset) + '\n')
g.close()
##########
Wavelengths = [w + offset for w in Wavelengths]
plt.figure(1)
plt.plot(Wavelengths, lamp_spec)
plt.hold('on')
for line in line_list[1]:
if (Wavelengths[0] <= line <= Wavelengths[-1]):
plt.axvline(line, color='r', linestyle='--')
plt.title("Offset Applied.")
plt.xlabel("Wavelengths (Ang.)")
plt.ylabel("Counts")
plt.hold('off')
plt.show()
else:
offset = 0.
# Ask Refit # ===============================================================
yn = 'yes'
while yn == 'yes':
#print "\nWould you like to refit and recalculate dispersion?"
#yn= raw_input('yes/no? >>> ')
yn = 'yes'
if yn == 'yes':
#print "\nOffset to apply to Grating Angle?"
#alpha_offset= float( raw_input('Offset Value? >>>') )
alpha_offset = 0.
#alpha= alpha + alpha_offset
'''
#Uncomment this part if you would like to select lines to use by hand. Otherwise, all lines in the above line lists are used.
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(Wavelengths, lamp_spec)
plt.hold('on')
lines_in_range= []
for line in line_list[1]:
if (Wavelengths[0] <= line <= Wavelengths[-1]):
lines_in_range.append(line)
plt.axvline(line, color= 'r', linestyle= '--')
plt.title("Click on The Peaks You Want to Use to Refit \n Then close graph.")
plt.xlim([np.min(lines_in_range)-50, np.max(lines_in_range)+50])
plt.ylim([np.min(lamp_spec)-100, np.max(lamp_spec)/2])
plt.xlabel("Wavelengths (Ang.)")
plt.ylabel("Counts")
plt.hold('off')
coords= []
cid = fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
'''
###n_pnt, n_cor= np.shape(coords)
###coord_x= [coords[i][0] for i in range(0,n_pnt)]
coord_x = line_list[
1] #Use all lines in the line lists for the refitting.
n_pnt = len(coord_x)
peak_x = []
for i in range(0, n_pnt):
x = find_near(coord_x[i], Wavelengths)
peak_x.append(x)
centers_in_wave = find_peak_centers(peak_x, Wavelengths, lamp_spec)
centers_in_wave = [w - offset for w in centers_in_wave]
centers_in_pix = PixCalc(centers_in_wave, alpha, theta, parm[0],
parm[1], parm[2], parm[3])
known_waves = []
for i in range(0, n_pnt):
x = find_near(coord_x[i], line_list[1])
known_waves.append(x)
#Create array to save data for diagnostic purposes
global savearray, n_fr, n_fd, n_zPnt
savearray = np.zeros([len(Wavelengths), 8])
#n_fr, n_fd, n_zPnt= fit_Grating_Eq(centers_in_pix, known_waves, alpha, theta, parm)
par, rmsfit = fit_Grating_Eq(centers_in_pix,
known_waves,
alpha,
theta,
parm,
plotalot=plotall)
n_fr, n_fd, n_zPnt = par
n_Wavelengths = DispCalc(Pixels, alpha - alpha_offset, theta, n_fr,
n_fd, parm[2], n_zPnt)
if plotall:
plt.figure(1)
plt.plot(n_Wavelengths, lamp_spec)
plt.hold('on')
for line in line_list[1]:
if (n_Wavelengths[0] <= line <= n_Wavelengths[-1]):
plt.axvline(line, color='r', linestyle='--')
plt.title("Refitted Solution")
plt.xlabel("Wavelengths (Ang.)")
plt.ylabel("Counts")
plt.hold('off')
savearray[0:len(n_Wavelengths), 2] = n_Wavelengths
savearray[0:len(lamp_spec), 3] = lamp_spec
savearray[0:len(np.array(line_list[1])),
4] = np.array(line_list[1])
'''
plt.figure(2)
Diff= [ (Wavelengths[i]-n_Wavelengths[i]) for i in range(0,np.size(Wavelengths)) ]
plt.plot(Diff, '.')
plt.title("Diffence between old and new solution.")
plt.xlabel("Pixel")
plt.ylabel("old-new Wavelength (Ang.)")
'''
plt.show()
if ('blue' in lamp.lower()) and (rmsfit > 1.0):
coord_list_short = line_list[0][1:]
wave_list_short = line_list[1][1:]
line_list = np.array([coord_list_short, wave_list_short])
print 'Refitting without first line.'
yn = 'yes'
else:
yn = 'no' #Don't refit again
# Save parameters in header and write file #
#print "\nWrite solution to header?"
#yn= raw_input("yes/no? >>>")
print '\n Writing solution to header'
yn = 'yes'
if yn == "yes":
newname = 'w' + lamp
mylist = [True for f in os.listdir('.') if f == newname]
exists = bool(mylist)
clob = False
if exists:
print 'File %s already exists.' % newname
nextstep = raw_input(
'Do you want to overwrite or designate a new name (overwrite/new)? '
)
if nextstep == 'overwrite':
clob = True
exists = False
elif nextstep == 'new':
newname = raw_input('New file name: ')
exists = False
else:
exists = False
rt.Fix_Header(lamp_header)
lamp_header.append(('LINDEN', n_fr, 'Line Desity for Grating Eq.'),
useblanks=True,
bottom=True)
lamp_header.append(
('CAMFUD', n_fd, 'Camera Angle Correction Factor for Grat. Eq.'),
useblanks=True,
bottom=True)
lamp_header.append(('FOCLEN', parm[2], 'Focal Length for Grat Eq.'),
useblanks=True,
bottom=True)
lamp_header.append(('ZPOINT', n_zPnt, 'Zero Point Pixel for Grat Eq.'),
useblanks=True,
bottom=True)
lamp_header.append(('RMSWAVE', rmsfit, 'RMS from Wavelength Calib.'),
useblanks=True,
bottom=True)
NewHdu = fits.PrimaryHDU(data=lamp_data, header=lamp_header)
NewHdu.writeto(newname, output_verify='warn', clobber=clob)
#Save parameters to ZZ Ceti spectrum#
#print "\nWrite solution to header of another spectrum?"
#yn= raw_input("yes/no? >>>")
if zz_specname:
#specname = raw_input("Filename: ")
#fitspectrum = raw_input('Would you like to fit a new zero point using a spectral line? (yes/no) ')
if fit_zpoint == 'yes':
newzeropoint = WaveShift(zz_specname, zzceti, plotall)
else:
newzeropoint = n_zPnt
spec_data = fits.getdata(zz_specname)
spec_header = fits.getheader(zz_specname)
rt.Fix_Header(spec_header)
spec_header.append(('LINDEN', n_fr, 'Line Desity for Grating Eq.'),
useblanks=True,
bottom=True)
spec_header.append(
('CAMFUD', n_fd, 'Camera Angle Correction Factor for Grat. Eq.'),
useblanks=True,
bottom=True)
spec_header.append(('FOCLEN', parm[2], 'Focal Length for Grat Eq.'),
useblanks=True,
bottom=True)
spec_header.append(
('ZPOINT', newzeropoint, 'Zero Point Pixel for Grat Eq.'),
useblanks=True,
bottom=True)
spec_header.append(('RMSWAVE', rmsfit, 'RMS from Wavelength Calib.'),
useblanks=True,
bottom=True)
NewspecHdu = fits.PrimaryHDU(data=spec_data, header=spec_header)
newname = 'w' + zz_specname
mylist = [True for f in os.listdir('.') if f == newname]
exists = bool(mylist)
clob = False
if exists:
print 'File %s already exists.' % newname
nextstep = raw_input(
'Do you want to overwrite or designate a new name (overwrite/new)? '
)
if nextstep == 'overwrite':
clob = True
exists = False
elif nextstep == 'new':
newname = raw_input('New file name: ')
exists = False
else:
exists = False
NewspecHdu.writeto(newname, output_verify='warn', clobber=clob)
#Save arrays for diagnostics
now = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M")
endpoint = '.ms'
with open(
'wavecal_' + zz_specname[4:zz_specname.find(endpoint)] + '_' +
now + '.txt', 'a') as handle:
header = lamp + ',' + zz_specname + '\n First 2 columns: fitted wavelengths, residuals \n Next 3 columns: wavelengths, flux, lambdas fit \n Final 3 columns: wavelengths, sky flux, fit to line for recentering'
np.savetxt(handle, savearray, fmt='%f', header=header)
def reduce_now(args):
nargs = len(args)
if (nargs < 5):
print "\n====================\n"
print "\nNot Enough Inputs."
print "Need at least 4 inputs: listZero, listFlat, listSpec, listFe"
print "Optional inputs: overwrite= , low_sig= , high_sig= "
print "Example:"
print "\n>>> python imcombine.py listZero listFlat listSpec listFe \n"
print "\n====================\n"
# Unpack list from command line and combe trough them for diffrent observations #
scriptname = args[0]
zero_lists = rt.List_Combe(rt.Read_List(args[1]))
flat_lists = rt.List_Combe(rt.Read_List(args[2]))
spec_lists = rt.List_Combe(rt.Read_List(args[3]))
fe_lists = rt.List_Combe(rt.Read_List(args[4]))
# Select names from the first image of each observation #
zero_names = []
for zero in zero_lists:
zero_names.append(zero[0][5:])
flat_names = []
for flat in flat_lists:
flat_names.append(flat[0][5:])
spec_names = []
for spec in spec_lists:
spec_names.append(spec[0][5:])
fe_names = []
for lamp in fe_lists:
fe_names.append(lamp[0][5:])
# Default values for special commands if none are given these dont change #
overwrite = False # dont give imcombine permision to overwrite files #
lo_sig = 10
hi_sig = 3
method = 'median' # method used to combine images
# If overwrite special comand is given #
if nargs >= 6:
overwrite = args[5]
warnings.filterwarnings('ignore', category=UserWarning, append=True)
# If low_sigma and high_sigma values are given #
if nargs >= 8:
lo_sig = float(args[6])
hi_sig = float(args[7])
# If method is given #
if nargs >= 9:
method = args[8]
#Set up array to save for diagnostics. This is defined in rt.init()
rt.init()
#Check ADC status during observations
adc_status = rt.adcstat(spec_lists[0][0])
# The rest of the code runs the reduction procces up to apall # =========
# Combine Zeros #
comb_zero = rt.imcombine(zero_lists[0],
zero_names[0],
'average',
lo_sig=10,
hi_sig=3,
overwrite=overwrite)
# Bias Subtract Flats #
nf = len(flat_lists) # number of flats
b_flat_lists = []
i = 0
while i < nf:
b_flat_lists.append(rt.Bias_Subtract(flat_lists[i], comb_zero))
i = i + 1
# Combine Bias Subtracted Flats #
i = 0
comb_flat = []
while i < nf:
comb_flat.append(
rt.imcombine(b_flat_lists[i],
'b.' + flat_names[i],
'median',
lo_sig=10,
hi_sig=3,
overwrite=overwrite))
i = i + 1
#Trim flats#
tcomb_flat = []
i = 0
while i < nf:
tcomb_flat.append(rt.Trim_Spec(comb_flat[i]))
i = i + 1
'''
# Normalize Flat #
i= 0
nb_flat1= []
nb_flat= []
while i < nf:
nb_flat.append( rt.Norm_Flat_Poly(tcomb_flat[i], 4.) ) # (divide by average of counts)
#nb_flat.append(rt.Norm_Flat_Boxcar(nb_flat1[0]))
i= i+1
'''
# Normalize Flat #
i = 0
nb_flat = []
while i < nf:
if 'blue' in tcomb_flat[i].lower():
nb_flat.append(
rt.Norm_Flat_Boxcar_Multiples(tcomb_flat[i],
adc_stat=adc_status))
else:
if 'quartz' in tcomb_flat[i].lower():
nb_flat.append(rt.Norm_Flat_Poly(tcomb_flat[i], 4.))
else:
flat_temp = []
flat_temp.append(rt.Norm_Flat_Poly(tcomb_flat[i], 3.))
nb_flat.append(rt.Norm_Flat_Boxcar(flat_temp[0]))
#nb_flat.append( rt.Norm_Flat_Poly(tcomb_flat[i]) ) # (divide by average of counts)
#nb_flat.append(rt.Norm_Flat_Boxcar(nb_flat1[i]))
#nb_flat.append(rt.Norm_Flat_Boxcar_Multiples(tcomb_flat[i]))
i = i + 1
# Bias Subtract Spec #
i = 0
b_spec_list = []
nsp = len(spec_lists)
# number of spectra
while i < nsp:
b_spec_list.append(rt.Bias_Subtract(spec_lists[i], comb_zero))
i = i + 1
#Trim Spectra#
tb_spec_list = []
i = 0
while i < nsp:
for x in range(0, len(b_spec_list[i])):
tb_spec_list.append(rt.Trim_Spec(b_spec_list[i][x]))
i = i + 1
# Flat Field Individual Spectra #
blueindex = [i for i, s in enumerate(nb_flat) if 'blue' in s.lower()]
nbflatblue = nb_flat[blueindex[0]]
redindex = [i for i, s in enumerate(nb_flat) if 'red' in s.lower()]
if len(redindex) > 0:
nbflatred = nb_flat[redindex[0]]
i = 0
ftb_spec_list = []
tb_spec_list = rt.List_Combe(tb_spec_list)
while i < nsp:
if tb_spec_list[i][0].lower().__contains__('blue') == True:
ftb_spec_list.append(rt.Flat_Field(tb_spec_list[i], nbflatblue))
elif tb_spec_list[i][0].lower().__contains__('red') == True:
ftb_spec_list.append(rt.Flat_Field(tb_spec_list[i], nbflatred))
else:
print("Problem applying the Flats.")
print("Could not identify blue or red setup.")
i = i + 1
# Save all diagnostic info
rt.save_diagnostic()
#LA Cosmic
i = 0
cftb_spec = []
cftb_mask = []
while i < nsp:
m = 0
while m < len(ftb_spec_list[i]):
lacos_spec, lacos_mask = rt.lacosmic(ftb_spec_list[i][m])
cftb_spec.append(lacos_spec)
cftb_mask.append(lacos_mask)
m += 1
i += 1
cftb_spec_list = rt.List_Combe(cftb_spec)
cftb_mask_list = rt.List_Combe(cftb_mask)
# Combine Spectra #
i = 0
comb_fb_spec = []
while i < nsp:
rt.checkspec(cftb_spec_list[i])
comb_fb_spec.append(
rt.imcombine(cftb_spec_list[i],
'cftb.' + spec_names[i],
'average',
lo_sig=10,
hi_sig=3,
overwrite=overwrite,
mask=cftb_mask_list[i]))
i = i + 1
print "\n====================\n"
#########################################
# Combine Fe lamps #
print "Combining and trimming Fe lamps."
nf = len(fe_lists) #number of fe lamps
i = 0
comb_lamp = []
while i < nf:
comb_lamp.append(
rt.imcombine(fe_lists[i],
fe_names[i],
'average',
lo_sig=lo_sig,
hi_sig=hi_sig,
overwrite=overwrite))
i = i + 1
# Trim lamps #
i = 0
while i < nf:
rt.Trim_Spec(comb_lamp[i])
i = i + 1
########################################
print "Done. Ready for Apeture Extraction.\n"
