savefile=True,
saveall=False,
diffimg=False,
path=None,
timit=False)
#####################################################################################################################################################
# (3) ORDER TRACING #################################################################################################################################
# find orders roughly
P, tempmask = find_stripes(MW,
deg_polynomial=2,
min_peak=0.05,
gauss_filter_sigma=3.,
simu=False)
# assign physical diffraction order numbers (this is only a dummy function for now) to order-fit polynomials and bad-region masks
P_id = make_P_id(P)
mask = make_mask_dict(tempmask)
# extract stripes of user-defined width from the science image, centred on the polynomial fits defined in step (1)
MW_stripes, MW_indices = extract_stripes(MW,
P_id,
return_indices=True,
slit_height=5)
#####################################################################################################################################################
###
#if we want to determine spatial profiles, then we should remove cosmics and background from MW like so:
# cosmic_cleaned_MW = remove_cosmics(MW, ronmask, obsname, path, Flim=3.0, siglim=5.0, maxiter=1, savemask=False, savefile=False, save_err=False, verbose=True, timit=True)
# bg_corrected_MW = remove_background(cosmic_cleaned_MW, P_id, obsname, path, degpol=5, slit_height=5, save_bg=False, savefile=False, save_err=False, exclude_top_and_bottom=True, verbose=True, timit=True)
#before doing the following:
MW_stripes, MW_stripe_indices = extract_stripes(MW,
choice = 'r'
if os.path.isfile(path + 'P_id.npy') and os.path.isfile(path + 'mask.npy'):
choice = raw_input("INITIAL ORDER TRACING has already been done for " + date + " ! Do you want to skip this step or recreate it? ['s' / 'r']")
if choice.lower() == 's':
print('Loading initial order traces for ' + date + '...')
P_id = np.load(path + 'P_id.npy').item()
mask = np.load(path + 'mask.npy').item()
else:
# find rough order locations
#P,tempmask = find_stripes(MW, deg_polynomial=2, min_peak=0.05, gauss_filter_sigma=3., simu=False)
P,tempmask = find_stripes(MW, deg_polynomial=2, min_peak=0.05, gauss_filter_sigma=10., simu=False, maskthresh = 400)
# if the bad pixel column is found as an order:
# del P[5]
# tempmask = tempmask[np.r_[0:5, 6:40],:]
# assign physical diffraction order numbers (this is only a dummy function for now) to order-fit polynomials and bad-region masks
P_id_dum = make_P_id(P)
assert len(P_id_dum) == 39, 'ERROR: not exactly 39 orders found!!!'
mask = make_mask_dict(tempmask)
P_id = copy.deepcopy(P_id_dum)
# for the dates where fibre 8 had a ~20-30% drop in throughput, do NOT subtract 2
if int(date) == 20190414:
for o in P_id.keys():
P_id[o][0] -= 1.
elif (int(date) <= 20190203) or (int(date) >= 20190619):
for o in P_id.keys():
P_id[o][0] -= 2.
np.save(path + 'P_id.npy', P_id)
np.save(path + 'mask.npy', mask)
# now redo the master white properly, incl background removal, and save to file
def make_fibparms_by_fib(savefile=True):
path = '/Users/christoph/OneDrive - UNSW/fibre_profiles/'
fp_files = glob.glob(path + "sim/" + "fibre_profiles*.npy")
#mask_files = glob.glob(path+"masks/"+"mask*.npy")
fibparms = {}
for file in fp_files:
fibnum = file[-6:-4]
fib = 'fibre_' + fibnum
fp = np.load(file).item()
mask = np.load(path + "masks/" + "mask_" + fibnum + ".npy").item()
flatname = '/Volumes/BERGRAID/data/simu/veloce_flat_t70000_single_fib' + fibnum + '.fit'
flat = pyfits.getdata(flatname)
img = flat + 1.
#we need the flux later only to weight the fits
P, tempmask = find_stripes(flat, deg_polynomial=2)
P_id = make_P_id(P)
#mask = make_mask_dict(tempmask)
stripes = extract_stripes(img, P_id, slit_height=10)
fibparms[fib] = {}
for ord in sorted(fp.keys()):
fibparms[fib][ord] = {}
mu = np.array(fp[ord]['mu'])
#amp = np.array(fp[ord]['amp'])
sigma = np.array(fp[ord]['sigma'])
beta = np.array(fp[ord]['beta'])
#offset = np.array(fp[ord]['offset'])
#slope = np.array(fp[ord]['slope'])
onchip = mu >= 0
good = np.logical_and(mu >= 0, mask[ord])
stripe = stripes[ord]
sc, sr = flatten_single_stripe(stripe, slit_height=10, timit=False)
fluxsum = np.sum(sc, axis=0)
w = np.sqrt(
fluxsum
) #use relative error so that the order centres receive the largest weight-contribution
xx = np.arange(len(mu))
mu_fit = np.poly1d(np.polyfit(xx[good], mu[good], 5, w=w[good]))
# xarr = xx*4*np.pi/np.max(xx) - 2*np.pi
# amp_popt,amp_pcov = curve_fit(blaze,xarr,amp,sigma=w,p0=(0.2,np.max(amp),0.))
sigma_fit = np.poly1d(
np.polyfit(xx[good], sigma[good], 2, w=w[good]))
beta_fit = np.poly1d(np.polyfit(xx[good], beta[good], 2,
w=w[good]))
#offset_fit = np.poly1d(np.polyfit(xx[good], offset[good], 0, w=w[good]))
#offset_fit = np.average(offset, weights=w)
fibparms[fib][ord]['mu_fit'] = mu_fit
fibparms[fib][ord]['sigma_fit'] = sigma_fit
fibparms[fib][ord]['beta_fit'] = beta_fit
#fibparms[fib][ord]['offset_fit'] = offset_fit
fibparms[fib][ord]['onchip'] = onchip
if savefile:
np.save(
'/Users/christoph/OneDrive - UNSW/fibre_profiles/sim/fibparms_by_fib.npy',
fibparms)
return fibparms