def make_mult_pseudo_obs(offsets=0.,
slitamps=1.,
nfib=19,
simpath='/Volumes/BERGRAID/data/simu/',
outpath='/Volumes/BERGRAID/data/simu/composite/',
norm_slitamps=False,
template=False,
add_laser=False,
savefile=True,
verbose=False,
debug_level=0,
timit=False):
"""
This routine creates simulated observations by adding together the individual-fibre spectra from my EchelleSimulator library using the appropriate relative intensities.
It does essentially the same as "make_pseudo_obs", but takes fibre-offsets and slitamps as user-defined inputs (ie as if 'fixed_offsets' and 'fixed_slitamps' were both
set to TRUE in "make_pseudo_obs", and no 'seeing' array was provided).
INPUT:
'offsets' : constant offset for all fibres (can be scalar (all fibres have same offset), or array of size (nobs,nfib))
'slitamps' : constant slitamps for all fibres (=relative intensities) (can be scalar (all fibres have same offset), or array of size (nobs,nfib))
'nfib' : the number of (stellar) fibres to use
'simpath' : path to my EchelleSimulator spectrum library
'outpath' : root-directory for the output files (sub-directories will be created every time this routine is run because of the random-normal pseudo-slit alignments)
'norm_slitamps' : boolean - do you want to 'normalize' the slitamps so they add up to 1? (only used if 'fixed_slitamps' is set to TRUE)
'template' : boolean - is this going to be a template? in that case use the 'maxsnr' spectra...
'add_laser' : boolean - do you want to add the LFC in fibre 1?
'savefile' : boolean - do you want to save the simulated spectra as FITS files?
'verbose' : boolean - for debugging...
'debug_level' : boolean - for debugging...
'timit' : boolean - do you want to clock the run-time?
OUTPUT:
'master_dict' : dictionary containing the simulated spectra for all seeing values (only if the 'savefile' keyword is not set, otherwise results are saved to FITS file(s))
TODO:
add SNR - for now this is for a given (fixed) observing time, i.e. the total flux captured by the IFU drops as the seeing increases
MODHIST:
02/07/2018 - CMB create (clone of "make_pseudo_obs")
"""
if timit:
start_time = time.time()
##### (1) #####
# fibre-offsets formatting
# (i) if a scalar is provided; nobs=1, use for all fibres
if len(get_iterable(offsets)) == 1:
nobs_from_offsets = 1
offsets = np.repeat(offsets, nfib)
#how can you do this for multiple obs?
if debug_level >= 1:
plot_pseudoslit(offsets)
# (ii) if a nfib-element array is provided; nobs=1
elif np.array(offsets).ndim == 1:
if offsets.shape[0] == nfib:
nobs_from_offsets = 1
offsets = np.array(offsets)
else:
print('ERROR: "offsets" has the wrong shape!!!')
return
#how can you do this for multiple obs?
if debug_level >= 1:
plot_pseudoslit(offsets)
# (iii) a (nx,nfib)-element array is provided; nobs=nx
else:
if offsets.shape[1] != nfib:
print('ERROR: "offsets" has the wrong shape!!!')
return
else:
nobs_from_offsets = offsets.shape[0]
##### (2) #####
# slitamps formatting
# (i) if a scalar is provided; nobs=1, use for all fibres
if len(get_iterable(slitamps)) == 1:
nobs_from_slitamps = 1
slitamps = np.repeat(slitamps, nfib).astype(float)
# (ii) if a nfib-element array is provided; nobs=1
elif np.array(slitamps).ndim == 1:
if slitamps.shape[0] == nfib:
nobs_from_slitamps = 1
else:
print('ERROR: "slitamps" has the wrong shape!!!')
return
# (iii) a (nx,nfib)-element array is provided; nobs=nx
else:
if slitamps.shape[1] != nfib:
print('ERROR: "slitamps" has the wrong shape!!!')
return
else:
nobs_from_slitamps = slitamps.shape[0]
#make sure we have consistent dimensions between "offsets" and "slitamps"
if nobs_from_offsets > nobs_from_slitamps:
if nobs_from_slitamps == 1:
print(
'WARNING: number of fibres inferred from "offsets" is LARGER than from "slitamps" !!!'
)
print('Using same slitamps for all observations...')
nobs = nobs_from_offsets
slitamps_1dim = slitamps.copy()
while nobs_from_slitamps < nobs_from_offsets:
slitamps = np.vstack((slitamps, slitamps))
nobs_from_slitamps += 1
else:
print(
'ERROR: dimensions of "offsets" and "slitamps" do not agree!!!'
)
return
elif nobs_from_offsets < nobs_from_slitamps:
if nobs_from_offsets == 1:
print(
'WARNING: number of fibres inferred from "offsets" is SMALLER than from "slitamps" !!!'
)
print('Using same offsets for all observations...')
nobs = nobs_from_slitamps
offsets_1dim = offsets.copy()
while nobs_from_offsets < nobs_from_slitamps:
offsets = np.vstack((offsets, offsets_1dim))
nobs_from_offsets += 1
else:
print(
'ERROR: dimensions of "offsets" and "slitamps" do not agree!!!'
)
return
else:
nobs = nobs_from_offsets
## reshape to accommodate the possibility of having multiple seeing conditions below
# slitamps = np.reshape(slitamps, (19,1))
if norm_slitamps:
slitsums = slitamps.sum(axis=1, keepdims=True)
slitamps = slitamps.astype(float) / slitsums
if savefile:
#create new sub-folder with info files containing info on the fibre offsets and the slitamps (=relative intensities)
datestring = get_datestring()
dum = 1
newpath = outpath + 'tests_' + datestring
dumpath = outpath + 'tests_' + datestring
while os.path.exists(dumpath):
dum += 1
dumpath = newpath + '_' + str(dum)
if dum > 1:
newpath = newpath + '_' + str(dum)
#create new folder
os.makedirs(newpath)
else:
master_dict = {}
#loop over all observations
for i in np.arange(nobs):
if verbose:
print('Simulating observation ' + str(i + 1) + '/' + str(nobs))
#some string manipulations for filenames in for loop below
strshifts = np.abs(offsets[i, :]).astype(int).astype(str)
redblue = np.empty(nfib).astype(str)
redblue[offsets[i, :] > 0] = 'red'
redblue[offsets[i, :] < 0] = 'blue'
redblue[offsets[i, :] == 0] = ''
if add_laser:
laserstr = '_laser'
else:
laserstr = ''
if template:
tstring = '_template'
maxsnr_string = '_maxsnr'
else:
tstring = ''
maxsnr_string = ''
#write OFFSETS file
outfn = newpath + '/' + 'offsets_' + str(i + 1).zfill(len(
str(nobs))) + '.txt'
outfile = open(outfn, 'w')
outfile.writelines(
["%s\n" % item for item in offsets[i, :].astype(str)])
outfile.close()
#write SLITAMPS file
outfn = newpath + '/' + 'slitamps_' + str(i + 1).zfill(len(
str(nobs))) + '.txt'
outfile = open(outfn, 'w')
outfile.writelines(
["%s\n" % item for item in slitamps[i, :].astype(str)])
outfile.close()
#use fibre-slots 6 to 24
for n in range(nfib):
fibslot = str(n + 6).zfill(2)
img = pyfits.getdata(simpath + 'fib' + fibslot + '_' + redblue[n] +
strshifts[n] + 'ms' + maxsnr_string + '.fit')
if n == 0:
master = (img.copy().astype(float) * slitamps[i, n]) + 1.
h = pyfits.getheader(simpath + 'fib' + fibslot + '_' +
redblue[n] + strshifts[n] + 'ms' +
maxsnr_string + '.fit')
else:
master += img * slitamps[i, n]
if add_laser:
laser_img = pyfits.getdata(simpath + 'veloce_laser_comb.fit')
master += laser_img / 5.
if savefile:
#save to file
pyfits.writeto(newpath + '/syntobs_' +
str(i + 1).zfill(len(str(nobs))) + laserstr +
tstring + '.fit',
master,
h,
clobber=True)
else:
master_dict['syntobs_' + str(i + 1)] = master
if timit:
print('Time elapsed: ' + str(np.round(time.time() - start_time, 1)) +
' seconds')
if savefile:
#print('Offsets: ',offsets)
return
else:
return master_dict
def make_pseudo_obs(seeing=None,
simpath='/Volumes/BERGRAID/data/simu/',
nfib=19,
outpath='/Volumes/BERGRAID/data/simu/composite/',
fixed_offsets=False,
fixoff=0.,
fixed_slitamps=False,
slitamps=1.,
norm_slitamps=False,
template=False,
add_laser=False,
savefile=True,
verbose=False,
debug_level=0,
timit=False):
"""
This routine creates simulated observations by
(1) calculating the flux-ratios / relative intensities in the individual fibres,
(2) simulating a pseudo-slit with a random normal distribution of relative offsets of the fibres relative to the centre (ie simulating alignment imperfections),
(3) adding together the individual-fibre spectra from my EchelleSimulator library using the appropriate relative intensities.
INPUT:
'seeing' : FWHM values of desired simulated seeing condition(s) - can be scalar or array
'simpath' : path to my EchelleSimulator spectrum library
'nfib' : the number of (stellar) fibres to use
'outpath' : root-directory for the output files (sub-directories will be created every time this routine is run because of the random-normal pseudo-slit alignments)
'fixed_offsets' : boolean - do you want to simulate fibre-offsets in dispersion direction for the pseudoslit? (set to TRUE if you don't want to simulate offsets, but rather use a constant offset 'fixoff' for all fibres)
'fixoff' : constant offset for all fibres (only used if 'fixed_offsets' is set to TRUE)
'fixed_slitamps' : boolean - do you want to simulate the effect of seeing or provide fixed relative intensities? (if TRUE, the relative intensities are taken from 'slitamps'; if set to FALSE
the relative intensities are calculated using the provided seeing values)
'slitamps' : constant slitamps for all fibres (=relative intensities) (only used if 'fixed_slitamps' is set to TRUE)
'norm_slitamps' : boolean - do you want to 'normalize' the slitamps so they add up to 1? (only used if 'fixed_slitamps' is set to TRUE)
'template' : boolean - is this going to be a template? in that case use the 'maxsnr' spectra...
'add_laser' : boolean - do you want to add the LFC in fibre 1?
'savefile' : boolean - do you want to save the simulated spectra as FITS files?
'verbose' : boolean - for debugging...
'debug_level' : boolean - for debugging...
'timit' : boolean - do you want to clock the run-time?
OUTPUT:
'master_dict' : dictionary containing the simulated spectra for all seeing values (only if the 'savefile' keyword is not set, otherwise results are saved to FITS file(s))
TODO:
add SNR - for now this is for a given (fixed) observing time, i.e. the total flux captured by the IFU drops as the seeing increases
MODHIST:
16/05/2018 - CMB create
08/06/2018 - CMB added 'add_laser' keyword; also changed the addition of 1 to the first image only
13/06/2018 - CMB added 'template' keyword (uses the 'maxsnr' simulated spectra)
14/06/2018 - CMB added "slitamps.txt" output file; also added 'fixed_slitamps', 'slitamps', and 'norm_slitamps' keywords
"""
if timit:
start_time = time.time()
##### (1) #####
if not fixed_slitamps:
if seeing is not None:
#first, calculate flux_ratios (ie relative intensities) for the different fibres from given seeing
fr = flux_ratios_from_seeing(seeing, verbose=verbose)
#then get an array of the relative intensities in the pseudo-slit
slitamps = get_pseudo_slitamps(fr['central'], fr['inner'],
fr['outer1'], fr['outer2'])
else:
print(
'ERROR: "fixed_slitamps" = False, but seeing not provided!!!')
return
else:
if len(get_iterable(slitamps)) == 1:
slitamps = np.repeat(slitamps, nfib).astype(float)
# #reshape to accommodate the possibility of having multiple seeing conditions below
# slitamps = np.reshape(slitamps, (19,1))
if norm_slitamps:
slitamps = slitamps.astype(float) / np.sum(slitamps)
##### (2) #####
#simulate fibre-offsets by using RV offsets
if not fixed_offsets:
offsets = make_pseudoslits()
else:
if len(get_iterable(fixoff)) == 1:
offsets = np.repeat(fixoff, nfib)
elif len(get_iterable(fixoff)) == nfib:
offsets = np.array(fixoff)
else:
print(
'ERROR! input values for fibre offsets do not have correct length'
)
return
if debug_level >= 1:
plot_pseudoslit(offsets)
##### (3) #####
#some string manipulations for filenames in for loop below
strshifts = np.abs(offsets).astype(int).astype(str)
redblue = np.empty(nfib).astype(str)
redblue[offsets > 0] = 'red'
redblue[offsets < 0] = 'blue'
redblue[offsets == 0] = ''
if add_laser:
laserstr = '_laser'
else:
laserstr = ''
if template:
tstring = '_template'
maxsnr_string = '_maxsnr'
else:
tstring = ''
maxsnr_string = ''
if savefile:
#create new sub-folder with info files containing info on the fibre offsets and the slitamps (=relative intensities)
datestring = get_datestring()
dum = 1
newpath = outpath + 'tests_' + datestring
dumpath = outpath + 'tests_' + datestring
while os.path.exists(dumpath):
dum += 1
dumpath = newpath + '_' + str(dum)
if dum > 1:
newpath = newpath + '_' + str(dum)
#create new folder
os.makedirs(newpath)
#write OFFSETS file
outfn = newpath + '/' + 'offsets.txt'
outfile = open(outfn, 'w')
outfile.writelines(["%s\n" % item for item in offsets.astype(str)])
outfile.close()
#write SLITAMPS file
outfn = newpath + '/' + 'slitamps.txt'
outfile = open(outfn, 'w')
outfile.writelines(["%s\n" % item for item in slitamps.astype(str)])
outfile.close()
else:
master_dict = {}
if seeing is not None:
for i, fwhm in enumerate(get_iterable(seeing)):
if verbose:
print('SEEING-LOOP: ', fwhm)
#use fibre-slots 6 to 24
for n in range(nfib):
fibslot = str(n + 6).zfill(2)
img = pyfits.getdata(simpath + 'fib' + fibslot + '_' +
redblue[n] + strshifts[n] + 'ms' +
maxsnr_string + '.fit')
if n == 0:
master = (img.copy().astype(float) * slitamps[n, i]) + 1.
h = pyfits.getheader(simpath + 'fib' + fibslot + '_' +
redblue[n] + strshifts[n] + 'ms' +
maxsnr_string + '.fit')
else:
master += img * slitamps[n, i]
if add_laser:
laser_img = pyfits.getdata(simpath + 'veloce_laser_comb.fit')
master += laser_img / 5.
if savefile:
#save to file
pyfits.writeto(newpath + '/seeing' + fwhm.astype(str) +
laserstr + tstring + '.fit',
master,
h,
clobber=True)
else:
master_dict['seeing' + fwhm.astype(str)] = master
else:
for n in range(nfib):
fibslot = str(n + 6).zfill(2)
img = pyfits.getdata(simpath + 'fib' + fibslot + '_' + redblue[n] +
strshifts[n] + 'ms' + maxsnr_string + '.fit')
if n == 0:
master = (img.copy().astype(float) * slitamps[n]) + 1.
h = pyfits.getheader(simpath + 'fib' + fibslot + '_' +
redblue[n] + strshifts[n] + 'ms' +
maxsnr_string + '.fit')
else:
master += img * slitamps[n]
if add_laser:
laser_img = pyfits.getdata(simpath + 'veloce_laser_comb.fit')
master += laser_img / 5.
if savefile:
#save to file
pyfits.writeto(newpath + '/syntobs' + laserstr + tstring + '.fit',
master,
h,
clobber=True)
else:
master_dict['syntobs' + tstring] = master
if timit:
print('Time elapsed: ' + str(np.round(time.time() - start_time, 1)) +
' seconds')
if savefile:
#print('Offsets: ',offsets)
return
else:
return master_dict
def make_pseudo_obs_with_noise(
seeing,
snrs=None,
RON=0.,
simpath='/Volumes/BERGRAID/data/simu/',
nfib=19,
outpath='/Volumes/BERGRAID/data/simu/composite/',
fixed_offsets=False,
fixoff=0.,
fixed_slitamps=False,
slitamps=1.,
norm_slitamps=False,
template=False,
add_laser=False,
savefile=True,
verbose=False,
debug_level=0,
timit=False):
"""
This routine creates simulated observations by
(1) calculating the flux-ratios / relative intensities in the individual fibres,
(2) simulating a pseudo-slit with a random normal distribution of relative offsets of the fibres relative to the centre (ie simulating alignment imperfections),
(3) adding together the individual-fibre spectra from my EchelleSimulator library using the appropriate relative intensities.
INPUT:
'seeing' : FWHM values of desired simulated seeing condition(s) - can be scalar or array
'snrs' : desired mean signal-to-noise ratio per extracted 1-dim pixel (WARNING: the seeing parameter does not renormalize to the flux captured by the IFU!!!)
floats can be provided for 'snrs', but it will be rounded to integer
'RON' : read-out noise
'simpath' : path to my EchelleSimulator spectrum library
'nfib' : the number of (stellar) fibres to use
'outpath' : root-directory for the output files (sub-directories will be created every time this routine is run because of the random-normal pseudo-slit alignments)
'fixed_offsets' : boolean - do you want to simulate fibre-offsets in dispersion direction for the pseudoslit? (set to TRUE if you don't want to simulate offsets, but rather use a constant offset 'fixoff' for all fibres)
'fixoff' : constant offset for all fibres (only used if 'fixed_offsets' is set to TRUE)
'fixed_slitamps' : boolean - do you want to simulate the effect of seeing or provide fixed relative intensities? (if TRUE, the relative intensities are taken from 'slitamps'; if set to FALSE
the relative intensities are calculated using the provided seeing values)
'slitamps' : constant slitamps for all fibres (=relative intensities) (only used if 'fixed_slitamps' is set to TRUE)
'norm_slitamps' : boolean - do you want to 'normalize' the slitamps so they add up to 1? (only used if 'fixed_slitamps' is set to TRUE)
'template' : boolean - is that going to be a template? in that case no noise is added...
'add_laser' : boolean - do you want to add the LFC in fibre 1?
'savefile' : boolean - do you want to save the simulated spectra as FITS files?
'verbose' : boolean - for debugging...
'debug_level' : boolean - for debugging...
'timit' : boolean - do you want to time the execution time?
OUTPUT:
'master_dict' : dictionary containing the simulated spectra for all seeing values (only if the 'savefile' keyword is not set, otherwise results are saved to FITS file(s))
TODO: add SNR - for now this is for a given (fixed) observing time, i.e. the total flux captured by the IFU drops as the seeing increases
MODHIST:
07/06/2018 - CMB create (clone of "make_pseudo_obs")
08/06/2018 - CMB added 'add_laser' keyword; also changed the addition of 1 to the first image only
14/06/2018 - CMB added "slitamps.txt" output file; also added 'fixed_slitamps', 'slitamps', and 'norm_slitamps' keywords
"""
if timit:
start_time = time.time()
##### (1) #####
if not fixed_slitamps:
if seeing is not None:
#first, calculate flux_ratios (ie relative intensities) for the different fibres from given seeing
fr = flux_ratios_from_seeing(seeing, verbose=verbose)
#then get an array of the relative intensities in the pseudo-slit
slitamps = get_pseudo_slitamps(fr['central'], fr['inner'],
fr['outer1'], fr['outer2'])
else:
print(
'ERROR: "fixed_slitamps" = False, but seeing not provided!!!')
return
else:
if len(get_iterable(slitamps)) == 1:
slitamps = np.repeat(slitamps, nfib)
#reshape to accommodate the possibility of having multiple seeing condittions below
slitamps = np.reshape(slitamps, (19, 1))
if norm_slitamps:
slitamps = slitamps / np.sum(slitamps)
##### (2) #####
#simulate fibre-offsets by using RV offsets
if not fixed_offsets:
#offsets = make_pseudoslits()
print('ERROR: offsets in pixel space have not been implemented yet!!!')
return
else:
if len(get_iterable(fixoff)) == 1:
offsets = np.repeat(fixoff, nfib)
elif len(get_iterable(fixoff)) == nfib:
offsets = np.array(fixoff)
else:
print(
'ERROR! input values for fibre offsets do not have correct length'
)
return
if not (offsets == 0).all():
print('ERROR: offsets in pixel space have not been implemented yet!!!')
return
if debug_level >= 1:
plot_pseudoslit(offsets)
##### (3) #####
#some string manipulations for filenames in for loop below
strshifts = np.abs(offsets).astype(int).astype(str)
redblue = np.empty(nfib).astype(str)
redblue[offsets > 0] = 'red'
redblue[offsets < 0] = 'blue'
redblue[offsets == 0] = ''
if add_laser:
laserstr = '_laser'
else:
laserstr = ''
if savefile:
#create new sub-folder with README file containing info on the fibre offsets
datestring = get_datestring()
dum = 1
newpath = outpath + 'tests_' + datestring
dumpath = outpath + 'tests_' + datestring
while os.path.exists(dumpath):
dum += 1
dumpath = newpath + '_' + str(dum)
if dum > 1:
newpath = newpath + '_' + str(dum)
#create new folder
os.makedirs(newpath)
#write README file
outfn = newpath + '/' + 'offsets.txt'
outfile = open(outfn, 'w')
outfile.writelines(["%s\n" % item for item in offsets.astype(str)])
outfile.close()
#write SLITAMPS file
outfn = newpath + '/' + 'slitamps.txt'
outfile = open(outfn, 'w')
outfile.writelines(["%s\n" % item for item in slitamps.astype(str)])
outfile.close()
else:
master_dict = {}
for i, (fwhm,
snr) in enumerate(zip(get_iterable(seeing), get_iterable(snrs))):
snr = int(np.round(snr))
if verbose:
print('SEEING: ', fwhm)
print('SNR: ', snr)
#use fibre-slots 6 to 24
for n in range(nfib):
fibslot = str(n + 6).zfill(2)
img = pyfits.getdata(simpath + 'fib' + fibslot + '_' + redblue[n] +
strshifts[n] + 'ms_maxsnr.fit') + 1.
if n == 0:
master = img.copy().astype(float) * slitamps[n, i]
h = pyfits.getheader(simpath + 'fib' + fibslot + '_' +
redblue[n] + strshifts[n] +
'ms_maxsnr.fit')
else:
master += img * slitamps[n, i]
#now add noise (a "maxsnr" spectrum has a mean SNR of ~555)
if not template:
fudge = (snr / 555.)**2.
scaled_master = master * fudge
err_amps = np.sqrt(scaled_master + RON * RON)
#add noise to the image
scaled_noise = make_scaled_white_noise(err_amps)
noisy_img = scaled_master + scaled_noise
else:
#don't want to add noise for the templates
noisy_img = master.copy()
#remove any negative pixels
noisy_img[noisy_img < 0] = 0.
if add_laser:
laser_img = pyfits.getdata(simpath + 'veloce_laser_comb.fit')
noisy_img += laser_img / 5.
if savefile:
#save to file
pyfits.writeto(newpath + '/seeing' + fwhm.astype(str) + '_snr_' +
str(snr) + laserstr + '.fit',
noisy_img,
h,
clobber=True)
else:
master_dict['seeing' + fwhm.astype(str)] = master
if timit:
print('Time elapsed: ' + str(np.round(time.time() - start_time, 1)) +
' seconds')
if savefile:
#print('Offsets: ',offsets)
return
else:
return master_dict
def flux_ratios_from_seeing(seeing, verbose=False):
'''
This routine calculates, as a function of seeing (which is approximated by a 2D Gaussian with FWHM = seeing in both directions),
the flux ratios that the Veloce IFU captures in total, as well as the ratios of the flux captured by the central fibre / inner-ring fibres / outer-ring fibres
to the total flux captured by the IFU (NOT the TOTAL total flux!!!)
INPUT:
'seeing' : can be scalar or array/list; the FWHM of the seeing disk (approximated as a 2-dim Gaussian)
KEYWORDS:
'verbose' : boolean - do you want verbose output printed to screen?
OUTPUT:
'flux_ratios' : a python dictionary containing the results
MODHIST:
Dec 2017 - CMB create
11/05/2018 - CMB modified output format and included possibility to iterate over 1-element seeing-array
'''
#initialise output dictionary
flux_ratios = {}
#inner-radius r of hexagonal fibre is 0.26", therefore outer-radius R is (2/sqrt(3))*0.26" = 0.30"
#what we really want is the "effective" radius though, for a regular hexagon that comes from A = 3*r*R = 2*sqrt(3)*r = pi * r_eff**2
#ie r_eff = sqrt( (2*sqrt(3)) / pi )
# fac = np.sqrt((2.*np.sqrt(3.)) / np.pi)
rc = 0.26
Rc = rc * 2. / np.sqrt(3.)
# ri = 0.78
# ro = 1.30
# reff = rc * fac
di = 0.52
do1 = 1.04
xo2 = di + rc
yo2 = 1.5 * Rc
# do2 = 3. * (2./np.sqrt(3.)) * 0.26
x = np.arange(-10, 10.01, .01)
y = np.arange(-10, 10.01, .01)
xx, yy = np.meshgrid(x, y)
#define constant (FWHM vs sigma, because FWHM = sigma * 2*sqrt(2*log(2))
cons = 2 * np.sqrt(np.log(2))
m1 = 1. / np.sqrt(3.) #slope1
m2 = -m1 #slope2
central = np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(np.abs(xx) <= rc, yy <= m1 * xx + Rc),
yy >= m1 * xx - Rc), yy <= m2 * xx + Rc), yy >= m2 * xx - Rc)
inner = np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(
np.abs(xx - di) <= rc, yy <= m1 * (xx - di) + Rc),
yy >= m1 * (xx - di) - Rc), yy <= m2 * (xx - di) + Rc),
yy >= m2 * (xx - di) - Rc), ~central)
outer1 = np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(
np.abs(xx - do1) <= rc, yy <= m1 * (xx - do1) + Rc),
yy >= m1 * (xx - do1) - Rc), yy <= m2 * (xx - do1) + Rc),
yy >= m2 * (xx - do1) - Rc), ~inner)
outer2 = np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(
np.logical_and(
np.abs(xx - xo2) <= rc,
yy <= m1 * (xx - xo2) + Rc - yo2),
yy >= m1 * (xx - xo2) - Rc - yo2),
yy <= m2 * (xx - xo2) + Rc - yo2),
yy >= m2 * (xx - xo2) - Rc - yo2), ~inner), ~outer1)
#########################################################
# this was the circular approximation:
# central = (np.sqrt(xx*xx + yy*yy) <= rc)
# inner = np.logical_and(np.sqrt(xx*xx + yy*yy) <= ri, ~central)
# outer = np.logical_and(np.sqrt(xx*xx + yy*yy) <= ro, np.logical_and(~central, ~inner))
# ifu = (np.sqrt(xx*xx + yy*yy) <= ro)
# central = (np.sqrt(xx*xx + yy*yy) <= reff)
# inner = np.sqrt((xx-di)*(xx-di) + yy*yy) <= reff
# outer1 = np.sqrt((xx-do1)*(xx-do1) + yy*yy) <= reff
# outer2 = np.sqrt((xx-do2)*(xx-do2) + yy*yy) <= reff
# ifu = (np.sqrt(xx*xx + yy*yy) <= ro*fac)
#########################################################
# if len(np.atleast_1d(seeing)) > 1:
#
# frac_ifu = []
# renorm_frac_c = []
# renorm_frac_i = []
# renorm_frac_o = []
#
# for fwhm in seeing:
#
# if verbose:
# print('Simulating '+str(fwhm)+'" seeing...')
#
# #calculate 2-dim Gaussian flux distribution as function of input FWHM
# fx = np.exp(-(np.absolute(xx) * cons / fwhm) ** 2.0)
# fy = np.exp(-(np.absolute(yy) * cons / fwhm) ** 2.0)
# f = fx * fy * 1e6
#
# frac_c = np.sum(f[central]) / np.sum(f)
# frac_i = 6. * np.sum(f[inner]) / np.sum(f)
# frac_o = 6. * ( np.sum(f[outer1]) + np.sum(f[outer2]) ) / np.sum(f)
# ifu_frac = frac_c + frac_i + frac_o #this is slightly overestimated (<1%) because they are not actually circular fibres
# frac_ifu.append(ifu_frac)
#
# rfc = frac_c / ifu_frac
# rfi = frac_i / ifu_frac
# rfo = frac_o / ifu_frac
#
# renorm_frac_c.append(rfc)
# renorm_frac_i.append(rfi)
# renorm_frac_o.append(rfo)
#
# if verbose:
# print('Total fraction of flux captured by IFU: '+str(np.round(ifu_frac * 100,1))+'%')
# print('----------------------------------------------')
# print('Contribution from central fibre: '+str(np.round(rfc * 100,1))+'%')
# print('Contribution from inner-ring fibres: '+str(np.round(rfi * 100,1))+'%')
# print('Contribution from outer-ring fibres: '+str(np.round(rfo * 100,1))+'%')
# print
#
# else:
#
# fwhm = seeing
# if verbose:
# print('Simulating '+str(fwhm)+'" seeing...')
# #calculate 2-dim Gaussian flux distribution as function of input FWHM
# fx = np.exp(-(np.absolute(xx) * cons / fwhm) ** 2.0)
# fy = np.exp(-(np.absolute(yy) * cons / fwhm) ** 2.0)
# f = fx * fy * 1e6
#
# frac_c = np.sum(f[central]) / np.sum(f)
# frac_i = 6. * np.sum(f[inner]) / np.sum(f)
# frac_o = 6. * ( np.sum(f[outer1]) + np.sum(f[outer2]) ) / np.sum(f)
# frac_ifu = frac_c + frac_i + frac_o #this is slightly overestimated (<1%) because they are not actually circular fibres
#
# renorm_frac_c = frac_c / frac_ifu
# renorm_frac_i = frac_i / frac_ifu
# renorm_frac_o = frac_o / frac_ifu
#
# if verbose:
# print('Total fraction of flux captured by IFU: '+str(np.round(frac_ifu * 100,1))+'%')
# print('----------------------------------------------')
# print('Contribution from central fibre: '+str(np.round(renorm_frac_c * 100,1))+'%')
# print('Contribution from inner-ring fibres: '+str(np.round(renorm_frac_i * 100,1))+'%')
# print('Contribution from outer-ring fibres: '+str(np.round(renorm_frac_o * 100,1))+'%')
# print
flux_ratios['seeing'] = []
flux_ratios['frac_ifu'] = []
flux_ratios['central'] = []
flux_ratios['inner'] = []
flux_ratios['outer'] = []
flux_ratios['outer1'] = []
flux_ratios['outer2'] = []
#stupid python!!!
for fwhm in get_iterable(seeing):
if verbose:
print('Simulating ' + str(fwhm) + '" seeing...')
#calculate 2-dim Gaussian flux distribution as function of input FWHM
fx = np.exp(-(np.absolute(xx) * cons / fwhm)**2.0)
fy = np.exp(-(np.absolute(yy) * cons / fwhm)**2.0)
#f = fx * fy
f = fx * fy * 1e6
frac_c = np.sum(f[central]) / np.sum(f)
frac_i = 6. * np.sum(f[inner]) / np.sum(f)
frac_o1 = 6. * np.sum(f[outer1]) / np.sum(f)
frac_o2 = 6. * np.sum(f[outer2]) / np.sum(f)
frac_o = 6. * (np.sum(f[outer1]) + np.sum(f[outer2])) / np.sum(f)
ifu_frac = frac_c + frac_i + frac_o #this is slightly overestimated (<1%) because they are not actually circular fibres
#renormalized fractions (renormalized to the total flux captured by the IFU, not the TOTAL total flux)
rfc = frac_c / ifu_frac
rfi = frac_i / ifu_frac
rfo = frac_o / ifu_frac
rfo1 = frac_o1 / ifu_frac
rfo2 = frac_o2 / ifu_frac
#fill the output dictionary
flux_ratios['seeing'].append(fwhm)
flux_ratios['frac_ifu'].append(ifu_frac)
flux_ratios['central'].append(rfc)
flux_ratios['inner'].append(rfi)
flux_ratios['outer'].append(rfo)
flux_ratios['outer1'].append(rfo1)
flux_ratios['outer2'].append(rfo2)
if verbose:
print('Total fraction of flux captured by IFU: ' +
str(np.round(ifu_frac * 100, 1)) + '%')
print('----------------------------------------------')
print('Contribution from central fibre: ' +
str(np.round(rfc * 100, 1)) + '%')
print('Contribution from inner-ring fibres: ' +
str(np.round(rfi * 100, 1)) + '%')
print('Contribution from outer-ring fibres: ' +
str(np.round(rfo * 100, 1)) + '%')
print
return flux_ratios