/
kergain_sim_phase.py
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/
kergain_sim_phase.py
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import numpy as np
import matplotlib.pyplot as plt
import pysco
from pysco.core import *
import fitsio
from k2_epd_george import print_time
from time import time as clock
from old_diffract_tools import *
import pymultinest
from pysco.diffract_tools import shift_image_ft
from pysco.common_tasks import shift_image
from swiftmask import swiftpupil
import matplotlib as mpl
from astropy.table import Table
mpl.style.use('seaborn-colorblind')
mpl.rcParams['figure.figsize']=(8.0,6.0) #(6.0,4.0)
mpl.rcParams['font.size']= 16 #10
mpl.rcParams['savefig.dpi']=200 #72
mpl.rcParams['axes.labelsize'] = 14
mpl.rcParams['xtick.labelsize'] = 10
mpl.rcParams['ytick.labelsize'] = 10
shift = np.fft.fftshift
fft = np.fft.fft2
ifft = np.fft.ifft2
fftfreq = np.fft.fftfreq
fftn = np.fft.fftn
rfftn = np.fft.rfftn
dtor = np.pi/180.0
'''------------------------------------------------------------
kergain_sim.py
Automate a simulation of the effectiveness of raw visibility
fitting versus kernel amplitudes
------------------------------------------------------------'''
pupil = 'plain'
try:
a = pysco.kpi('./geometry/'+pupil+'model.pick')
print 'Loaded kernel phase object'
except:
a = pysco.kpi('./geometry/'+pupil+'.txt')
a.name = 'Test'
a.save_to_file('./geometry/'+pupil+'model.pick')
nbuv, nbh = a.nbuv, a.nbh
try:
KerGain = np.loadtxt('KerGain_plain.csv')
print 'Loaded kernel amplitude matrix'
except:
gtfm = np.abs(a.TFM)
U, S, Vh = np.linalg.svd(gtfm.T, full_matrices=1)
S1 = np.zeros(nbuv)
S1[0:nbh-1] = S
nkg = np.size(np.where(abs(S1) < 1e-3))
print nkg
KGCol = np.where(abs(S1) < 1e-3)[0]
KerGain = np.zeros((nkg, nbuv)) # allocate the array
for i in range(nkg):
KerGain[i,:] = (Vh)[KGCol[i],:]
np.savetxt('KerGain_plain.csv',KerGain)
print 'saved'
###-----------------------------------------
### now initialize a simulation
###-----------------------------------------
'''------------------------------
First, set all your parameters.
------------------------------'''
print '\nSimulating a basic PSF'
wavel = 2.5e-6
rprim = 5.093/2.#36903.e-3/2.
rsec= 1.829/2.
pos = [0,0] #m, deg
spaxel = 36.
piston = 0
nimages = 200
reso = rad2mas(wavel/(2*rprim))
print 'Minimum Lambda/D = %.3g mas' % reso
image, imagex = diffract(wavel,rprim,rsec,pos,piston=piston,spaxel=spaxel,seeing=None,verbose=False,\
show_pupil=False,mode=None)
# image = recenter(image,sg_rad=25)
imsz = image.shape[0]
images = np.zeros((nimages,imsz,imsz))
psfs = np.zeros((nimages,imsz,imsz))
k=0
show=False
'''----------------------------------------
Loop over a range of contrasts
----------------------------------------'''
# contrast_list = [10,50,100,150,200,250,300,350,400,450,500]
contrast_list = [10,50,75,100,125,150,175,200,250,300,350,400,450,500,600,700,800,900,1000,1100,1200,1300,1400,1500]
# contrast_list = [10,50,100,200,300,400,500
contrast_list = np.linspace(10,2000,19)
ncalcs = len(contrast_list)
kseps, kthetas, kcons = np.zeros(ncalcs), np.zeros(ncalcs), np.zeros(ncalcs)
dkseps, dkthetas, dkcons = np.zeros(ncalcs), np.zeros(ncalcs), np.zeros(ncalcs)
vseps, vthetas, vcons = np.zeros(ncalcs), np.zeros(ncalcs), np.zeros(ncalcs)
dvseps, dvthetas, dvcons = np.zeros(ncalcs), np.zeros(ncalcs), np.zeros(ncalcs)
t0 = clock()
sep, theta = 48, 45
xb,yb = np.cos(theta*np.pi/180)*sep/spaxel, np.sin(theta*np.pi/180)*sep/spaxel
print 'x',xb,',y',yb
seeingamp = 0.75
try:
dummy = fitsio.FITS('psf_cube_phase_%.2f_wavel_%.2f.fits' % (seeingamp,wavel*1e6))
psfs = dummy[0][:,:,:]
print 'Loaded PSFs'
except:
print 'Creating PSFs'
for j in range(nimages):
psfs[j,:,:], imagex = diffract(wavel,rprim,rsec,pos,piston=piston,spaxel=spaxel,verbose=False,\
show_pupil=show,mode='phase',
perturbation=None,amp=0.0,seeingamp=seeingamp)
fitsio.write('psf_cube_phase_%.2f_wavel_%.2f.fits' % (seeingamp,wavel*1e6),psfs)
print_time(clock()-t0)
rev = 1
ac = shift(fft(shift(image)))
ac /= (np.abs(ac)).max() / a.nbh
'''----------------------------------------
Initialise pysco with a pupil model
----------------------------------------'''
# meter to pixel conversion factor
scale = 1.0
m2pix = mas2rad(spaxel) * imsz/ wavel * scale
uv_samp = a.uv * m2pix + imsz/2 # uv sample coordinates in pixels
x = a.mask[:,0]
y = a.mask[:,1]
uv_samp_rev=np.cast['int'](np.round(uv_samp))
uv_samp_rev[:,0]*=rev
data_cplx=ac[uv_samp_rev[:,1], uv_samp_rev[:,0]]
vis2 = np.abs(data_cplx)
vis2 /= vis2.max() #normalise to the origin
mvis = a.RED/a.RED.max().astype('float')
'''----------------------------------------
Loop over contrasts
----------------------------------------'''
for trial, contrast in enumerate(contrast_list):
print '\nSimulating for contrast %f' % contrast
thistime = clock()
for j in range(nimages):
images[j,:,:] = psfs[j,:,:] + shift_image_ft(psfs[j,:,:],[-yb,-xb])/contrast#shift_image(psf,x=x,y=y,doRoll=True)/contrast
imsz = image.shape[0]
show=False
k+=1
'''----------------------------------------
Extract Visibilities
----------------------------------------'''
# kpd_phase = np.angle(data_cplx)/dtor
# kpd_signal = np.dot(a.KerPhi, kpd_phase)
kervises=np.zeros((nimages/2,KerGain.shape[0]))
vis2s = np.zeros((nimages/2,vis2.shape[0]))
vis2_cals = np.zeros((nimages/2,vis2.shape[0]))
kpd_signals = np.zeros((nimages/2,a.KerPhi.shape[0]))
# phases = np.zeros((nimages,vis2.shape[0]))
randomGain = np.random.randn(np.shape(KerGain)[0],np.shape(KerGain)[1])
for j in range(nimages/2):
image3 = psfs[j,:,:]
ac3 = shift(fft(shift(image3)))
ac3 /= (np.abs(ac3)).max() / a.nbh
data_cplx3=ac3[uv_samp_rev[:,1], uv_samp_rev[:,0]]
vis2c = np.abs(data_cplx3)
vis2c /= vis2c.max() #normalise to the origin
# vi2sc[vis2c>1] = 1
vis2_cals[j,:]=vis2c
vis2cal = np.mean(vis2_cals,axis=0)
for j in range(nimages/2):
image2 = images[j+nimages/2,:,:]
ac2 = shift(fft(shift(image2)))
ac2 /= (np.abs(ac2)).max() / a.nbh
data_cplx2=ac2[uv_samp_rev[:,1], uv_samp_rev[:,0]]
vis2b = np.abs(data_cplx2)
vis2b /= vis2b.max() #normalise to the origin
# vis2b[vis2b>1.] = 1.
vis2s[j,:]= vis2b
# log_data_complex_b = np.log(np.abs(data_cplx2))+1.j*np.angle(data_cplx2)
# phases[j,:] = np.angle(data_cplx2)/dtor
kervises[j,:] = np.dot(KerGain,vis2b/vis2cal-1.)
# kervises[j,:] = np.dot(KerGain,np.sqrt(vis2b/vis2cal)**2-1)
# kervises[j,:] = np.dot(randomGain, np.sqrt(vis2b)-mvis)
# kpd_signals[j,:] = np.dot(a.KerPhi,np.angle(data_cplx2))/dtor
# kercomplexb = np.dot(KerBispect,log_data_complex_b)
# kervises_cplx[j,:] = np.abs(kercomplexb)
paramlimits = [20.,80.,30.,60.,contrast/2.,contrast*2.]
hdr = {'tel':'HST',
'filter':wavel,
'orient':0}
def myprior(cube, ndim, n_params,paramlimits=paramlimits):
cube[0] = (paramlimits[1] - paramlimits[0])*cube[0]+paramlimits[0]
cube[1] = (paramlimits[3] - paramlimits[2])*cube[1]+paramlimits[2]
for k in range(2,ndim):
cube[k] = (paramlimits[5] - paramlimits[4])*cube[k]+paramlimits[4]
def vis_loglikelihood(cube,vdata,ve,kpi):
'''Calculate chi2 for single band vis2 data.
Used both in the MultiNest and MCMC Hammer implementations.'''
vises = pysco.binary_model(cube[0:3],kpi,hdr,vis2=True)
chi2 = np.sum(((vdata-vises)/ve)**2)
return -chi2/2.
'''-----------------------------------------------
First do kernel amplitudes
-----------------------------------------------'''
my_observable = np.mean(kervises,axis=0)
# raw_data = np.dot(KerGain,np.sqrt((vis2s/vis2cal)**2).T-1.).T
# mycov = np.cov(raw_data.T) # calculate statistically independent KA
# my_eigs, my_s_matrix = np.linalg.eigh(mycov) # hermitian
# thismatrix = np.dot(my_s_matrix,KerGain)
thismatrix = KerGain
my_observable = np.mean(np.dot(thismatrix,(vis2s/vis2cal).T-1.),axis=1)
def kg_loglikelihood(cube,kgd,kge,kpi):
'''Calculate chi2 for single band kernel amplitude data.
Used both in the MultiNest and MCMC Hammer implementations.'''
vises = np.sqrt(pysco.binary_model(cube[0:3],kpi,hdr,vis2=True))
kergains = np.dot(thismatrix,vises-1)
chi2 = np.sum(((kgd-kergains)/kge)**2)
return -chi2/2.
# addederror = np.std(my_observable) # in case there are bad frames
addederror = 1e-5
my_error = np.sqrt(np.std(kervises,axis=0)**2+addederror**2)
# my_error = np.sqrt(my_eigs**2+addederror**2)
print 'Error:', my_error
def myloglike_kg(cube,ndim,n_params):
try:
loglike = kg_loglikelihood(cube,my_observable,my_error,a)
return loglike
except:
return -np.inf
parameters = ['Separation','Position Angle','Contrast']
n_params = len(parameters)
resume=False
eff=0.3
multi=True,
max_iter= 0
ndim = n_params
pymultinest.run(myloglike_kg, myprior, n_params, wrapped_params=[1],
verbose=True,resume=False)
thing = pymultinest.Analyzer(n_params = n_params)
s = thing.get_stats()
this_j = trial
kseps[this_j], dkseps[this_j] = s['marginals'][0]['median'], s['marginals'][0]['sigma']
kthetas[this_j], dkthetas[this_j] = s['marginals'][1]['median'], s['marginals'][1]['sigma']
kcons[this_j], dkcons[this_j] = s['marginals'][2]['median'], s['marginals'][2]['sigma']
stuff = thing.get_best_fit()
best_params = stuff['parameters']
model_vises = np.sqrt(pysco.binary_model(best_params,a,hdr,vis2=True))
model_kervises = np.dot(KerGain,model_vises-1.)
plt.clf()
# plt.errorbar(my_observable,model_kervises,xerr=my_error,
# ls='',markersize=10,linewidth=2.5)
plt.plot(my_observable,model_kervises,'.',
ls='',markersize=10,linewidth=2.5)
plt.xlabel('Measured Kernel Amplitudes')
plt.ylabel('Model Kernel Amplitudes')
plt.title('Model Fit: Kernel Amplitudes, Contrast %.1f' % contrast)
plt.savefig('kpfit_bin_phase_%.1f_con.png' % contrast)
print 'Kernel amplitudes done'
print_time(clock()-thistime)
print ''
'''-----------------------------------------------
Now do visibilities
-----------------------------------------------'''
my_observable = np.mean((vis2s/vis2cal)**2,axis=0)
print '\nDoing raw visibilities'
addederror = 0.000001
my_error = np.sqrt(np.std((vis2s/vis2cal)**2,axis=0)**2+addederror**2)
print 'Error:', my_error
def myloglike_vis(cube,ndim,n_params):
try:
loglike = vis_loglikelihood(cube,my_observable,my_error,a)
return loglike
except:
return -np.inf
thistime = clock()
pymultinest.run(myloglike_vis, myprior, n_params, wrapped_params=[1],
verbose=True,resume=False)
thing = pymultinest.Analyzer(n_params = n_params)
s = thing.get_stats()
this_j = trial
vseps[this_j], dvseps[this_j] = s['marginals'][0]['median'], s['marginals'][0]['sigma']
vthetas[this_j], dvthetas[this_j] = s['marginals'][1]['median'], s['marginals'][1]['sigma']
vcons[this_j], dvcons[this_j] = s['marginals'][2]['median'], s['marginals'][2]['sigma']
stuff = thing.get_best_fit()
best_params = stuff['parameters']
model_vises = pysco.binary_model(best_params,a,hdr,vis2=True)
plt.clf()
# plt.errorbar(my_observable,model_vises,xerr=my_error,
# ls='',markersize=10,linewidth=2.5)
plt.plot(my_observable,model_vises,'.',
ls='',markersize=10,linewidth=2.5)
plt.xlabel('Measured Visibilities')
plt.ylabel('Model Visibilities')
plt.title('Model Fit: Visibilities, Contrast %.1f' % contrast)
plt.savefig('vis2_bin_phase_%.1f_con.png' % contrast)
print 'Visibilities done'
print_time(clock()-thistime)
'''------------------------------------
Now save!
------------------------------------'''
cmin, cmax = np.min(contrast_list), np.max(contrast_list)
vdata = Table({'Seps':vseps,
'Thetas':vthetas,
'Cons':vcons,
'Dseps':dvseps,
'Dthetas':dvthetas,
'Dcons':dvcons})
vdata.write('raw_vis_sims_phase_%.0f_%.0f.csv' % (cmin,cmax))
print 'Visibility fits saved to raw_vis_sims_phase_%.0f_%.0f.csv' % (cmin,cmax)
kdata = Table({'Seps':kseps,
'Thetas':kthetas,
'Cons':kcons,
'Dseps':dkseps,
'Dthetas':dkthetas,
'Dcons':dkcons})
kdata.write('kernel_amplitude_sims_phase_%.0f_%.0f.csv' % (cmin,cmax))
print 'Kernel amplitude fits saved to kernel_amplitude_sims_phase_%.0f_%.0f.csv' \
% (cmin,cmax)
print 'Finished contrast loop'
print_time(clock()-t0)