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")
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 < 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"
