def spaxel_scale(datacube, head, spaxel):
'''Function that takes 3D datacube or 2D image and
rebins it to chosen spaxel scale. Reads spaxel scale
of input datacube from header keywords CDELT1/2.
Inputs:
datacube: 3D datacube or 2D image
head: header file
spaxel: spaxel scale mas (x, y)
Outputs:
new_cube: datacube (or 2D image) rebinned to chosen spaxel scale
new_head: updated header file
'''
print 'Spaxel scale'
try:
z, y, x = datacube.shape
except:
y, x = datacube.shape
cdelt1 = head["CDELT1"]
cdelt2 = head["CDELT2"]
#total field of view in mas
x_field = cdelt1 * x
y_field = cdelt2 * y
x_newsize = int(n.round(x_field / float(spaxel[0]), 0))
y_newsize = int(n.round(y_field / float(spaxel[1]), 0))
try:
newcube = n.zeros((z, y_newsize, x_newsize), dtype=n.float64)
for i in xrange(z):
newcube[i, :, :] = frebin(datacube[i, :, :],
(x_newsize, y_newsize),
total=True)
except:
newcube = frebin(datacube, (x_newsize, y_newsize), total=True)
head['CDELT1'] = (spaxel[0], "mas")
head['CDELT2'] = (spaxel[1], "mas")
head['NAXIS1'] = int(x_newsize)
head['NAXIS2'] = int(y_newsize)
print 'Spaxel scale - done!'
return newcube, head
def spaxel_scale(datacube, head, spaxel):
'''Function that takes 3D datacube or 2D image and
rebins it to chosen spaxel scale. Reads spaxel scale
of input datacube from header keywords CDELT1/2.
Inputs:
datacube: 3D datacube or 2D image
head: header file
spaxel: spaxel scale mas (x, y)
Outputs:
new_cube: datacube (or 2D image) rebinned to chosen spaxel scale
new_head: updated header file
'''
print 'Spaxel scale'
try:
z, y, x = datacube.shape
except:
y, x = datacube.shape
cdelt1 = head["CDELT1"]
cdelt2 = head["CDELT2"]
#total field of view in mas
x_field = cdelt1*x
y_field = cdelt2*y
x_newsize = n.round(x_field/float(spaxel[0]),0)
y_newsize = n.round(y_field/float(spaxel[1]),0)
try:
newcube = n.zeros((z, y_newsize, x_newsize), dtype=n.float64)
for i in xrange(z):
newcube[i,:,:] = frebin(datacube[i,:,:], (x_newsize, y_newsize), total=True)
except:
newcube = frebin(datacube, (x_newsize, y_newsize), total=True)
head['CDELT1'] = (spaxel[0], "mas")
head['CDELT2'] = (spaxel[1], "mas")
head['NAXIS1'] = int(x_newsize)
head['NAXIS2'] = int(y_newsize)
print 'Spaxel scale - done!'
return newcube, head
def create_psf_channel(params, iteration, psfspax, output_spax, array_dim,
aperture, res_jitter):
'''Function that creates an AO PSF image given the
parameter values and sampling scale.
Inputs:
psfparams: Dictionary containing parameter arrays
iteration: Iteration value through wavelength array
psfspax: PSF sampling scale [mas/spaxel]
output_spax: Output sampling scale tuple (x, y) [mas/spaxel]
array_dim: Size of PSF array
aperture: List containing telescope diameter [m] and obsc. ratio
res_jitter: value of residual telescope jitter [mas]
Outputs:
psf = 2D PSF array
'''
#array = r_values([array_dim, array_dim], [(psfspax/1000.)*array_dim,(psfspax/1000.)*array_dim])
array = dist_arr(array_dim, psfspax / 1000.)
# airy = obsc_airy(array, params['oh'][iteration], params['ow'][iteration]*rad_conv, aperture[1])
# moff1 = moffat(array, params['mh'][iteration], params['mw'][iteration]*rad_conv, params['mq'][iteration])
# lor = lorentz(array, params['lh'][iteration], params['lp'][iteration]*rad_conv, params['lw'][iteration]*rad_conv)
# moff2 = moffat(array, params['m2h'][iteration], params['m2w'][iteration]*rad_conv, params['m2q'][iteration])
airy = obsc_airy(array, params['oh'][iteration], params['ow'] * rad_conv,
aperture[1])
moff1 = moffat(array, params['mh'][iteration],
params['mw'][iteration] * rad_conv, params['mq'][iteration])
lor = lorentz(array, params['lh'][iteration],
params['lp'][iteration] * rad_conv,
params['lw'][iteration] * rad_conv)
moff2 = moffat(array, params['m2h'][iteration],
params['m2w'][iteration] * rad_conv,
params['m2q'][iteration])
channel = (airy + moff1 + lor + moff2)
#Apply residual jitter
channel /= channel.sum() #normalise
psf = residual_jitter(channel, psfspax, res_jitter)
#Frebin PSF up to same sampling as datacube channels
psf /= psf.sum() #normalise
#total field of view in mas
x_field = array_dim * psfspax
y_field = array_dim * psfspax
x_newsize = x_field / float(output_spax[0])
y_newsize = y_field / float(output_spax[1])
psf = frebin(psf, (x_newsize, y_newsize), total=True)
import numpy
return psf
def create_instpsf(array_dim, psfspax, output_spax, psfoutspax):
'''Function that creates an instrument PSF array given the
parameter values and sampling scale.
Inputs:
array_dim: Size of PSF array
psfspax: PSF sampling scale [mas/spaxel]
output_spax: Output sampling scale tuple (x, y) [mas/spaxel]
psfoutspax: PSF convolution sampling scale to match datacube: tuple (x, y) [mas/spaxel]
Outputs:
instpsf = 2D PSF array
'''
#FWHM of Instrument PSF depending on output spaxel scale
#Factors taken into account:
#design image quality, manufacturing and assembly tolerances, vibration, flexure, diff refraction,
#Also interpolating the data back onto a regular grid.
#The interpolation adds another contribution of 0.8-1.8 PIXEL FWHM.
#So a reasonable assumption: 1.1 pix FWHM.
#FWHMs given in mas according to output spaxel scale
instpsf_data = {
(30., 60.): 65.,
(20., 20.): 34.,
(10., 10.): 17.,
(4., 4.): 6.
}
#print 'SPAX = ', output_spax
try:
FWHM = instpsf_data[output_spax]
#print 'FWHM = ', FWHM
instpsf = Gauss2D(array_dim, FWHM / float(psfspax))
except:
#print 'Non-HARMONI spaxel scale - no instrument PSF'
instpsf = n.zeros([array_dim, array_dim], dtype=float)
instpsf[array_dim / 2, array_dim / 2] = 1.0
#total field of view in mas
x_field = array_dim * psfspax
y_field = array_dim * psfspax
x_newsize = x_field / float(psfoutspax[0])
y_newsize = y_field / float(psfoutspax[1])
instpsf = frebin(instpsf, (x_newsize, y_newsize), total=True)
return instpsf
def create_instpsf(array_dim, psfspax, output_spax, psfoutspax):
'''Function that creates an instrument PSF array given the
parameter values and sampling scale.
Inputs:
array_dim: Size of PSF array
psfspax: PSF sampling scale [mas/spaxel]
output_spax: Output sampling scale tuple (x, y) [mas/spaxel]
psfoutspax: PSF convolution sampling scale to match datacube: tuple (x, y) [mas/spaxel]
Outputs:
instpsf = 2D PSF array
'''
#FWHM of Instrument PSF depending on output spaxel scale
#Factors taken into account:
#design image quality, manufacturing and assembly tolerances, vibration, flexure, diff refraction,
#Also interpolating the data back onto a regular grid.
#The interpolation adds another contribution of 0.8-1.8 PIXEL FWHM.
#So a reasonable assumption: 1.1 pix FWHM.
#FWHMs given in mas according to output spaxel scale
instpsf_data = {(30.,60.): 65.,
(20.,20.): 34.,
(10.,10.):17.,
(4.,4.):6.}
#print 'SPAX = ', output_spax
try:
FWHM = instpsf_data[output_spax]
#print 'FWHM = ', FWHM
instpsf = Gauss2D(array_dim, FWHM/float(psfspax))
except:
#print 'Non-HARMONI spaxel scale - no instrument PSF'
instpsf = n.zeros([array_dim, array_dim], dtype=float)
instpsf[array_dim/2,array_dim/2] = 1.0
#total field of view in mas
x_field = array_dim*psfspax
y_field = array_dim*psfspax
x_newsize = x_field/float(psfoutspax[0])
y_newsize = y_field/float(psfoutspax[1])
instpsf = frebin(instpsf, (x_newsize, y_newsize), total=True)
return instpsf
def create_psf_channel(params, iteration, psfspax, output_spax, array_dim, aperture, res_jitter):
'''Function that creates an AO PSF image given the
parameter values and sampling scale.
Inputs:
psfparams: Dictionary containing parameter arrays
iteration: Iteration value through wavelength array
psfspax: PSF sampling scale [mas/spaxel]
output_spax: Output sampling scale tuple (x, y) [mas/spaxel]
array_dim: Size of PSF array
aperture: List containing telescope diameter [m] and obsc. ratio
res_jitter: value of residual telescope jitter [mas]
Outputs:
psf = 2D PSF array
'''
#array = r_values([array_dim, array_dim], [(psfspax/1000.)*array_dim,(psfspax/1000.)*array_dim])
array = dist_arr(array_dim, psfspax/1000.)
airy = obsc_airy(array, params['oh'][iteration], params['ow'][iteration]*rad_conv, aperture[1])
moff1 = moffat(array, params['mh'][iteration], params['mw'][iteration]*rad_conv, params['mq'][iteration])
lor = lorentz(array, params['lh'][iteration], params['lp'][iteration]*rad_conv, params['lw'][iteration]*rad_conv)
moff2 = moffat(array, params['m2h'][iteration], params['m2w'][iteration]*rad_conv, params['m2q'][iteration])
channel = (airy + moff1 + lor + moff2)
#Apply residual jitter
channel /= channel.sum() #normalise
psf = residual_jitter(channel, psfspax, res_jitter)
#Frebin PSF up to same sampling as datacube channels
psf /= psf.sum() #normalise
#total field of view in mas
x_field = array_dim*psfspax
y_field = array_dim*psfspax
x_newsize = x_field/float(output_spax[0])
y_newsize = y_field/float(output_spax[1])
psf = frebin(psf, (x_newsize, y_newsize), total=True)
return psf
def spaxel_scale(datacube, head, spaxel):
"""Function that takes datacube and rebins it to chosen
spaxel scale. Reads spaxel scale of input datacube
from header keywords CDELT1/2.
Inputs:
datacube: science datacube
head: header file
spaxel: spaxel scale mas (x, y)
Outputs:
new_cube: datacube rebinned to chosen spaxel scale
new_head: updated header file
"""
print "Spaxel scale"
z, y, x = datacube.shape
cdelt1 = head["CDELT1"]
cdelt2 = head["CDELT2"]
# total field of view in mas
x_field = cdelt1 * x
y_field = cdelt2 * y
x_newsize = n.round(x_field / float(spaxel[0]), 0)
y_newsize = n.round(y_field / float(spaxel[1]), 0)
newcube = n.zeros((z, y_newsize, x_newsize), dtype=n.float64)
for i in xrange(z):
newcube[i, :, :] = frebin(datacube[i, :, :], (x_newsize, y_newsize), total=True)
head["CDELT1"] = (spaxel[0], "mas")
head["CDELT2"] = (spaxel[1], "mas")
head["NAXIS1"] = int(x_newsize)
head["NAXIS2"] = int(y_newsize)
print "Spaxel scale - done!"
return newcube, head
def create_Gausspsf_channel(wave, seeing, aperture, res_jitter, array_dim, Nyquist, psfspax, output_spax):
'''Function that creates a 2D spatial Gaussian PSF.
The FWHM of the Gaussian is depends on wavelength in the following way:
FWHM(radians) = 0.98 Lambda/r_0
r_0 = A Lambda^(6/5.)
FWHM(arcsec) = (0.98/A)/Lambda^(1/5.)
Inputs:
wave: wavelength [microns]
seeing: FWHM value of the seeing at 500nm (V-band) in arcseconds.
aperture: List containing [diameter of telescope, obscuration ratio].
Diameter only used when Nyquist=True.
array_dim: Spatial size of PSF arrays.
Nyquist: Boolean - True returns Nyquist sampling for each wavelength channel.
- False uses value of spaxel.
psfspax: Initial spatial sampling value [mas].
output_spax: Output spatial sampling [mas].
Output:
psf: PSF array for given wavelength.
'''
## lam = 500.E-9
## #Compute r_0 from given seeing value
## r_0 = 0.98*lam/float(seeing/206265.)
##
## #Use r_0 to find A
## A = r_0/lam**(6/5.)
##
## #Use A to find constant for FWHM propto Lambda**(-1/5) equation
## B = 0.98*206265./A
r0 = 0.976*500.E-9/float(seeing)*(180./n.pi*3600.)*(wave/0.5)**1.2 #in m
fwhm = seeing*(wave/0.5)**(-0.2)*n.sqrt(1.+(1./(1.+300.*aperture[0]/23.)-1)*2.183*(r0/23.)**(0.356)) #in arcsec
if Nyquist:
#fwhm = B/float((wave*(1.E-6))**(1/5.)) #in arcsec
diff = wave*(1.E-6)/(2.*float(aperture[0]))
diff *= 206265 #in arcsec/spaxel
channel = Gauss2D(array_dim, fwhm/diff)
#Apply residual jitter
psf = residual_jitter(channel, diff, res_jitter)
elif not Nyquist:
spax = psfspax / 1000. #in arcsec/spaxel
#fwhm = B/float((wave*(1.E-6))**(1/5.)) #in arcsec
channel = Gauss2D(array_dim, fwhm/spax)
#Apply residual jitter
psf = residual_jitter(channel, spax, res_jitter)
#Normalise PSF
psf /= psf.sum()
#Frebin PSF up to same sampling as datacube channels
#total field of view in mas
x_field = array_dim*psfspax
y_field = array_dim*psfspax
x_newsize = x_field/float(output_spax[0])
y_newsize = y_field/float(output_spax[1])
psf = frebin(psf, (x_newsize, y_newsize), total=True)
return psf
def create_Gausspsf_channel(wave, seeing, aperture, res_jitter, array_dim,
Nyquist, psfspax, output_spax):
'''Function that creates a 2D spatial Gaussian PSF.
The FWHM of the Gaussian is depends on wavelength in the following way:
FWHM(radians) = 0.98 Lambda/r_0
r_0 = A Lambda^(6/5.)
FWHM(arcsec) = (0.98/A)/Lambda^(1/5.)
Inputs:
wave: wavelength [microns]
seeing: FWHM value of the seeing at 500nm (V-band) in arcseconds.
aperture: List containing [diameter of telescope, obscuration ratio].
Diameter only used when Nyquist=True.
array_dim: Spatial size of PSF arrays.
Nyquist: Boolean - True returns Nyquist sampling for each wavelength channel.
- False uses value of spaxel.
psfspax: Initial spatial sampling value [mas].
output_spax: Output spatial sampling [mas].
Output:
psf: PSF array for given wavelength.
'''
## lam = 500.E-9
## #Compute r_0 from given seeing value
## r_0 = 0.98*lam/float(seeing/206265.)
##
## #Use r_0 to find A
## A = r_0/lam**(6/5.)
##
## #Use A to find constant for FWHM propto Lambda**(-1/5) equation
## B = 0.98*206265./A
r0 = 0.976 * 500.E-9 / float(seeing) * (180. / n.pi *
3600.) * (wave / 0.5)**1.2 #in m
fwhm = seeing * (wave / 0.5)**(-0.2) * n.sqrt(
1. + (1. / (1. + 300. * aperture[0] / 23.) - 1) * 2.183 *
(r0 / 23.)**(0.356)) #in arcsec
if Nyquist:
#fwhm = B/float((wave*(1.E-6))**(1/5.)) #in arcsec
diff = wave * (1.E-6) / (2. * float(aperture[0]))
diff *= 206265 #in arcsec/spaxel
channel = Gauss2D(array_dim, fwhm / diff)
#Apply residual jitter
psf = residual_jitter(channel, diff, res_jitter)
elif not Nyquist:
spax = psfspax / 1000. #in arcsec/spaxel
#fwhm = B/float((wave*(1.E-6))**(1/5.)) #in arcsec
channel = Gauss2D(array_dim, fwhm / spax)
#Apply residual jitter
psf = residual_jitter(channel, spax, res_jitter)
#Normalise PSF
psf /= psf.sum()
#Frebin PSF up to same sampling as datacube channels
#total field of view in mas
x_field = array_dim * psfspax
y_field = array_dim * psfspax
x_newsize = x_field / float(output_spax[0])
y_newsize = y_field / float(output_spax[1])
psf = frebin(psf, (x_newsize, y_newsize), total=True)
return psf
