def LTAOpsfcube(lambs, seeing, aperture):
'''Function that creates a 3D spatial E-ELT LTAO PSF datacube by interpolating
between parameters of several analytical functions. Parameters are stored in
datafiles and are accessed by code.
Inputs:
lambs: Array of wavelengths corresponding to [start_wave, end_wave, del_wave].
seeing: FWHM value of the seeing. Value must be between 0.67" and 0.95" (21-12-13).
aperture: List containing [diameter of telescope, obscuration ratio]
Output:
psfcube: PSF cube of same spectral length as wavelength array
'''
print 'Generating LTAO PSF cube'
ks = 1
box = [0.4, 2.5, 0.6, 1.5]
###Load data from text file - wavelengths = vals[:,0]
#vals = n.loadtxt(psf_path+'LTAO/LTAOdata.txt', delimiter=',')
vals = n.loadtxt(os.path.join(psf_path, 'LTAO/LTAOdata.txt'),
delimiter=',')
#Seeing values
see_vals = n.array([0.67, 0.95])
params = []
#Interpolate for all height parameters (oh = 1, 12, 23, 34; mh = 3, 14, 25, 36; lh = 6, 17, 28, 39; m2h = 9, 20, 31, 42)
ohvals = n.array([vals[:, 1], vals[:, 11]])
ohinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
ohvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
yoh = ohinterp(lambs, seeing)
mhvals = n.array([vals[:, 2], vals[:, 12]])
mhinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
mhvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
ymh = mhinterp(lambs, seeing)
lhvals = n.array([vals[:, 5], vals[:, 15]])
lhinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
lhvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
ylh = lhinterp(lambs, seeing)
m2hvals = n.array([vals[:, 8], vals[:, 18]])
m2hinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
m2hvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
ym2h = m2hinterp(lambs, seeing)
params.append(yoh)
params.append(ymh)
params.append(ylh)
params.append(ym2h)
#Fit 6th order polynomial to Moffat (seeing) width and Lorentzian width parameters and then interpolate
for i in n.array([3, 7]):
p1, pn1 = curve_fit(x6, vals[:, 0], vals[:, i])
p2, pn2 = curve_fit(x6, vals[:, 0], vals[:, i + 10])
yp1 = x6(vals[:, 0], p1[0], p1[1], p1[2], p1[3], p1[4], p1[5], p1[6])
yp2 = x6(vals[:, 0], p2[0], p2[1], p2[2], p2[3], p2[4], p2[5], p2[6])
yps = n.array([yp1, yp2])
pinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
yps.transpose(),
kx=ks,
ky=ks,
bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Fit 1st order polynomial to Moffat (seeing) shape, Lorentz position
for i in n.array([4, 6]):
p1, pn1 = curve_fit(x1, vals[:, 0], vals[:, i])
p2, pn2 = curve_fit(x1, vals[:, 0], vals[:, i + 10])
yp1 = x1(vals[:, 0], p1[0], p1[1])
yp2 = x1(vals[:, 0], p2[0], p2[1])
yps = n.array([yp1, yp2])
pinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
yps.transpose(),
kx=ks,
ky=ks,
bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Interpolate for Moffat (core) width and shape as these are (effectively) step functions
for i in n.array([9, 10]):
yps = n.array([vals[:, i], vals[:, i + 10]])
pinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
yps.transpose(),
kx=ks,
ky=ks,
bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Airy width directly proportional to wavelength: width [mas] = (lambda[m]/(pi*D))*206265000.
yows = lambs * 1.E-6 * 206265000. / (n.pi * aperture[0])
params.append(yows)
#params = [oh, mh, lh, m2h, mw, lw, mq, lp, m2w, m2q, ow]
pdict = {
'oh': params[0],
'ow': params[10],
'mh': params[1],
'mw': params[4],
'mq': params[6],
'lh': params[2],
'lp': params[7],
'lw': params[5],
'm2h': params[3],
'm2w': params[8],
'm2q': params[9]
}
return pdict
def LTAOpsfcube(lambs, seeing, aperture):
'''Function that creates a 3D spatial E-ELT LTAO PSF datacube by interpolating
between parameters of several analytical functions. Parameters are stored in
datafiles and are accessed by code.
Inputs:
lambs: Array of wavelengths corresponding to [start_wave, end_wave, del_wave].
seeing: FWHM value of the seeing. Value must be between 0.67" and 0.95" (21-12-13).
aperture: List containing [diameter of telescope, obscuration ratio]
Output:
psfcube: PSF cube of same spectral length as wavelength array
'''
print 'Generating LTAO PSF cube'
ks = 1
box = [0.4,2.5,0.6,1.5]
###Load data from text file - wavelengths = vals[:,0]
#vals = n.loadtxt(psf_path+'LTAO/LTAOdata.txt', delimiter=',')
vals = n.loadtxt(os.path.join(psf_path,'LTAO/LTAOdata.txt'), delimiter=',')
#Seeing values
see_vals = n.array([0.67, 0.95])
params= []
#Interpolate for all height parameters (oh = 1, 12, 23, 34; mh = 3, 14, 25, 36; lh = 6, 17, 28, 39; m2h = 9, 20, 31, 42)
ohvals = n.array([vals[:,1], vals[:,11]])
ohinterp = s.RectBivariateSpline(vals[:,0], see_vals, ohvals.transpose(),kx=ks, ky=ks, bbox=box)
yoh = ohinterp(lambs, seeing)
mhvals = n.array([vals[:,2], vals[:,12]])
mhinterp = s.RectBivariateSpline(vals[:,0], see_vals, mhvals.transpose(),kx=ks, ky=ks, bbox=box)
ymh = mhinterp(lambs, seeing)
lhvals = n.array([vals[:,5], vals[:,15]])
lhinterp = s.RectBivariateSpline(vals[:,0], see_vals, lhvals.transpose(),kx=ks, ky=ks, bbox=box)
ylh = lhinterp(lambs, seeing)
m2hvals = n.array([vals[:,8], vals[:,18]])
m2hinterp = s.RectBivariateSpline(vals[:,0], see_vals, m2hvals.transpose(),kx=ks, ky=ks, bbox=box)
ym2h = m2hinterp(lambs, seeing)
params.append(yoh)
params.append(ymh)
params.append(ylh)
params.append(ym2h)
#Fit 6th order polynomial to Moffat (seeing) width and Lorentzian width parameters and then interpolate
for i in n.array([3, 7]):
p1, pn1 = curve_fit(x6, vals[:,0], vals[:,i])
p2, pn2 = curve_fit(x6, vals[:,0], vals[:,i+10])
yp1 = x6(vals[:,0], p1[0], p1[1], p1[2], p1[3], p1[4], p1[5], p1[6])
yp2 = x6(vals[:,0], p2[0], p2[1], p2[2], p2[3], p2[4], p2[5], p2[6])
yps = n.array([yp1, yp2])
pinterp = s.RectBivariateSpline(vals[:,0], see_vals, yps.transpose(), kx=ks, ky=ks, bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Fit 1st order polynomial to Moffat (seeing) shape, Lorentz position
for i in n.array([4, 6]):
p1, pn1 = curve_fit(x1, vals[:,0], vals[:,i])
p2, pn2 = curve_fit(x1, vals[:,0], vals[:,i+10])
yp1 = x1(vals[:,0], p1[0], p1[1])
yp2 = x1(vals[:,0], p2[0], p2[1])
yps = n.array([yp1, yp2])
pinterp = s.RectBivariateSpline(vals[:,0], see_vals, yps.transpose(), kx=ks, ky=ks, bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Interpolate for Moffat (core) width and shape as these are (effectively) step functions
for i in n.array([9, 10]):
yps = n.array([vals[:,i], vals[:,i+10]])
pinterp = s.RectBivariateSpline(vals[:,0], see_vals, yps.transpose(), kx=ks, ky=ks, bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Airy width directly proportional to wavelength: width [mas] = (lambda[m]/(pi*D))*206265000.
yows = lambs*1.E-6*206265000./(n.pi*aperture[0])
params.append(yows)
#params = [oh, mh, lh, m2h, mw, lw, mq, lp, m2w, m2q, ow]
pdict = {'oh':params[0], 'ow':params[10], 'mh':params[1],
'mw':params[4], 'mq':params[6], 'lh':params[2],
'lp':params[7], 'lw':params[5], 'm2h':params[3],
'm2w':params[8], 'm2q':params[9]}
return pdict
def SCAOpsfcube(lambs, seeing, aperture):
'''Function that creates a 3D spatial E-ELT SCAO PSF datacube by interpolating
between parameters of several analytical functions. Parameters are stored in
datafiles and are accessed by code.
Inputs:
lambs: Array of wavelengths.
seeing: FWHM value of the seeing at 500nm (V-band). Value must be between 0.67" and 1.10".
aperture: List containing [diameter of telescope, obscuration ratio]
Output:
psfcube: PSF cube of same spectral length as wavelength array
'''
print 'Generating SCAO PSF cube'
ks = 1
box = [0.4, 2.5, 0.6, 1.5]
###Load data from text file - wavelengths = vals[:,0]
#vals = n.loadtxt('/Users/SimonZ/Data/Sim_data/PSFs/SCAOdata.txt', delimiter=',')
#vals = n.loadtxt(psf_path+'SCAO/SCAOdata.txt', delimiter=',')
vals = n.loadtxt(os.path.join(psf_path, 'SCAO/SCAOdata.txt'),
delimiter=',')
#Seeing values
see_vals = n.array([0.67, 0.85, 0.95, 1.10])
params = []
#Interpolate for all height parameters (oh = 1, 12, 23, 34; mh = 3, 14, 25, 36; lh = 6, 17, 28, 39; m2h = 9, 20, 31, 42)
ohvals = n.array([vals[:, 1], vals[:, 12], vals[:, 23], vals[:, 34]])
ohinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
ohvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
yoh = ohinterp(lambs, seeing)
mhvals = n.array([vals[:, 3], vals[:, 14], vals[:, 25], vals[:, 36]])
mhinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
mhvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
ymh = mhinterp(lambs, seeing)
lhvals = n.array([vals[:, 6], vals[:, 17], vals[:, 28], vals[:, 39]])
lhinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
lhvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
ylh = lhinterp(lambs, seeing)
m2hvals = n.array([vals[:, 9], vals[:, 20], vals[:, 31], vals[:, 42]])
m2hinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
m2hvals.transpose(),
kx=ks,
ky=ks,
bbox=box)
ym2h = m2hinterp(lambs, seeing)
params.append(yoh)
params.append(ymh)
params.append(ylh)
params.append(ym2h)
#Fit 6th order polynomial to Moffat (seeing) width and Lorentzian width parameters and then interpolate
for i in n.array([4, 8]):
p1, pn1 = curve_fit(x6, vals[:, 0], vals[:, i])
p2, pn2 = curve_fit(x6, vals[:, 0], vals[:, i + 11])
p3, pn3 = curve_fit(x6, vals[:, 0], vals[:, i + 22])
p4, pn4 = curve_fit(x6, vals[:, 0], vals[:, i + 33])
yp1 = x6(vals[:, 0], p1[0], p1[1], p1[2], p1[3], p1[4], p1[5], p1[6])
yp2 = x6(vals[:, 0], p2[0], p2[1], p2[2], p2[3], p2[4], p2[5], p2[6])
yp3 = x6(vals[:, 0], p3[0], p3[1], p3[2], p3[3], p3[4], p3[5], p3[6])
yp4 = x6(vals[:, 0], p4[0], p4[1], p4[2], p4[3], p4[4], p4[5], p4[6])
yps = n.array([yp1, yp2, yp3, yp4])
pinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
yps.transpose(),
kx=ks,
ky=ks,
bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Fit 1st order polynomial to Airy width, Moffat (seeing) shape, Lorentz position, Moffat(core) width
for i in n.array([2, 5, 7, 10]):
p1, pn1 = curve_fit(x1, vals[:, 0], vals[:, i])
p2, pn2 = curve_fit(x1, vals[:, 0], vals[:, i + 11])
p3, pn3 = curve_fit(x1, vals[:, 0], vals[:, i + 22])
p4, pn4 = curve_fit(x1, vals[:, 0], vals[:, i + 33])
yp1 = x1(vals[:, 0], p1[0], p1[1])
yp2 = x1(vals[:, 0], p2[0], p2[1])
yp3 = x1(vals[:, 0], p3[0], p3[1])
yp4 = x1(vals[:, 0], p4[0], p4[1])
yps = n.array([yp1, yp2, yp3, yp4])
pinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
yps.transpose(),
kx=ks,
ky=ks,
bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Fit 2nd order polynomial to Moffat (core) shape
for i in n.array([11]):
p1, pn1 = curve_fit(x2, vals[:, 0], vals[:, i])
p2, pn2 = curve_fit(x2, vals[:, 0], vals[:, i + 11])
p3, pn3 = curve_fit(x2, vals[:, 0], vals[:, i + 22])
p4, pn4 = curve_fit(x2, vals[:, 0], vals[:, i + 33])
yp1 = x2(vals[:, 0], p1[0], p1[1], p1[2])
yp2 = x2(vals[:, 0], p2[0], p2[1], p2[2])
yp3 = x2(vals[:, 0], p3[0], p3[1], p3[2])
yp4 = x2(vals[:, 0], p4[0], p4[1], p4[2])
yps = n.array([yp1, yp2, yp3, yp4])
pinterp = s.RectBivariateSpline(vals[:, 0],
see_vals,
yps.transpose(),
kx=ks,
ky=ks,
bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#params = [oh, mh, lh, m2h, mw, lw, ow, mq, lp, m2w, m2q]
pdict = {
'oh': params[0],
'ow': params[6],
'mh': params[1],
'mw': params[4],
'mq': params[7],
'lh': params[2],
'lp': params[8],
'lw': params[5],
'm2h': params[3],
'm2w': params[9],
'm2q': params[10]
}
return pdict
def SCAOpsfcube(lambs, seeing, aperture):
'''Function that creates a 3D spatial E-ELT SCAO PSF datacube by interpolating
between parameters of several analytical functions. Parameters are stored in
datafiles and are accessed by code.
Inputs:
lambs: Array of wavelengths.
seeing: FWHM value of the seeing at 500nm (V-band). Value must be between 0.67" and 1.10".
aperture: List containing [diameter of telescope, obscuration ratio]
Output:
psfcube: PSF cube of same spectral length as wavelength array
'''
print 'Generating SCAO PSF cube'
ks = 1
box = [0.4,2.5,0.6,1.5]
###Load data from text file - wavelengths = vals[:,0]
#vals = n.loadtxt('/Users/SimonZ/Data/Sim_data/PSFs/SCAOdata.txt', delimiter=',')
#vals = n.loadtxt(psf_path+'SCAO/SCAOdata.txt', delimiter=',')
vals = n.loadtxt(os.path.join(psf_path,'SCAO/SCAOdata.txt'), delimiter=',')
#Seeing values
see_vals = n.array([0.67, 0.85, 0.95, 1.10])
params= []
#Interpolate for all height parameters (oh = 1, 12, 23, 34; mh = 3, 14, 25, 36; lh = 6, 17, 28, 39; m2h = 9, 20, 31, 42)
ohvals = n.array([vals[:,1], vals[:,12], vals[:,23], vals[:,34]])
ohinterp = s.RectBivariateSpline(vals[:,0], see_vals, ohvals.transpose(),kx=ks, ky=ks, bbox=box)
yoh = ohinterp(lambs, seeing)
mhvals = n.array([vals[:,3], vals[:,14], vals[:,25], vals[:,36]])
mhinterp = s.RectBivariateSpline(vals[:,0], see_vals, mhvals.transpose(),kx=ks, ky=ks, bbox=box)
ymh = mhinterp(lambs, seeing)
lhvals = n.array([vals[:,6], vals[:,17], vals[:,28], vals[:,39]])
lhinterp = s.RectBivariateSpline(vals[:,0], see_vals, lhvals.transpose(),kx=ks, ky=ks, bbox=box)
ylh = lhinterp(lambs, seeing)
m2hvals = n.array([vals[:,9], vals[:,20], vals[:,31], vals[:,42]])
m2hinterp = s.RectBivariateSpline(vals[:,0], see_vals, m2hvals.transpose(),kx=ks, ky=ks, bbox=box)
ym2h = m2hinterp(lambs, seeing)
params.append(yoh)
params.append(ymh)
params.append(ylh)
params.append(ym2h)
#Fit 6th order polynomial to Moffat (seeing) width and Lorentzian width parameters and then interpolate
for i in n.array([4, 8]):
p1, pn1 = curve_fit(x6, vals[:,0], vals[:,i])
p2, pn2 = curve_fit(x6, vals[:,0], vals[:,i+11])
p3, pn3 = curve_fit(x6, vals[:,0], vals[:,i+22])
p4, pn4 = curve_fit(x6, vals[:,0], vals[:,i+33])
yp1 = x6(vals[:,0], p1[0], p1[1], p1[2], p1[3], p1[4], p1[5], p1[6])
yp2 = x6(vals[:,0], p2[0], p2[1], p2[2], p2[3], p2[4], p2[5], p2[6])
yp3 = x6(vals[:,0], p3[0], p3[1], p3[2], p3[3], p3[4], p3[5], p3[6])
yp4 = x6(vals[:,0], p4[0], p4[1], p4[2], p4[3], p4[4], p4[5], p4[6])
yps = n.array([yp1, yp2, yp3, yp4])
pinterp = s.RectBivariateSpline(vals[:,0], see_vals, yps.transpose(), kx=ks, ky=ks, bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Fit 1st order polynomial to Airy width, Moffat (seeing) shape, Lorentz position, Moffat(core) width
for i in n.array([2, 5, 7, 10]):
p1, pn1 = curve_fit(x1, vals[:,0], vals[:,i])
p2, pn2 = curve_fit(x1, vals[:,0], vals[:,i+11])
p3, pn3 = curve_fit(x1, vals[:,0], vals[:,i+22])
p4, pn4 = curve_fit(x1, vals[:,0], vals[:,i+33])
yp1 = x1(vals[:,0], p1[0], p1[1])
yp2 = x1(vals[:,0], p2[0], p2[1])
yp3 = x1(vals[:,0], p3[0], p3[1])
yp4 = x1(vals[:,0], p4[0], p4[1])
yps = n.array([yp1, yp2, yp3, yp4])
pinterp = s.RectBivariateSpline(vals[:,0], see_vals, yps.transpose(), kx=ks, ky=ks, bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#Fit 2nd order polynomial to Moffat (core) shape
for i in n.array([11]):
p1, pn1 = curve_fit(x2, vals[:,0], vals[:,i])
p2, pn2 = curve_fit(x2, vals[:,0], vals[:,i+11])
p3, pn3 = curve_fit(x2, vals[:,0], vals[:,i+22])
p4, pn4 = curve_fit(x2, vals[:,0], vals[:,i+33])
yp1 = x2(vals[:,0], p1[0], p1[1], p1[2])
yp2 = x2(vals[:,0], p2[0], p2[1], p2[2])
yp3 = x2(vals[:,0], p3[0], p3[1], p3[2])
yp4 = x2(vals[:,0], p4[0], p4[1], p4[2])
yps = n.array([yp1, yp2, yp3, yp4])
pinterp = s.RectBivariateSpline(vals[:,0], see_vals, yps.transpose(), kx=ks, ky=ks, bbox=box)
ys = pinterp(lambs, seeing)
params.append(ys)
#params = [oh, mh, lh, m2h, mw, lw, ow, mq, lp, m2w, m2q]
pdict = {'oh':params[0], 'ow':params[6], 'mh':params[1],
'mw':params[4], 'mq':params[7], 'lh':params[2],
'lp':params[8], 'lw':params[5], 'm2h':params[3],
'm2w':params[9], 'm2q':params[10]}
return pdict
