def FitTOD(tod, ra, dec, obs, clon, clat, prefix='', destripe=False):
# Beam solid angle aken from James' report on wiki Optics
nubeam = np.array([26., 33., 40.])
srbeam = np.array([2.1842e-6, 1.6771e-6, 1.4828e-6])
pmdl = interp1d(nubeam, srbeam) # straight interpolation
nHorns = tod.shape[0]
nSidebands = tod.shape[1]
nChans = tod.shape[2]
nSamps = tod.shape[3]
nParams = 5 + 0# 2
wcs, xr, yr = Mapping.DefineWCS(naxis, cdelt, crval)
# Crossing indices
crossings = np.zeros(nHorns)
# Calculate RMS from adjacent pairs
splitTOD = (tod[:,:,:,:(nSamps//2) * 2:2] - tod[:,:,:,1:(nSamps//2)*2:2])
rms = np.std(splitTOD,axis=3)/np.sqrt(2)
t = np.arange(nSamps)
P1 = np.zeros((nHorns, nSidebands, nChans, nParams+1))
errors = np.zeros((nHorns, nSidebands, nChans, nParams))
fig = pyplot.figure(figsize=(16,16))
# Rotate the ra/dec
width = 4.
pixwidth = 2./60.
nbins = int(width/pixwidth)
xygrid = [np.linspace(-width/2, width/2,nbins+1), np.linspace(-width/2, width/2.,nbins+1)]
xgrid, ygrid = np.meshgrid(np.linspace(-width/2, width/2,nbins)+pixwidth/2., np.linspace(-width/2, width/2.,nbins) + pixwidth/2.)
for i in range( nHorns):
x, y = Pointing.Rotate(ra[i,:], dec[i,:], clon, clat, 0)
# Bin the map up, apply a filter to remove spikes, to give first guess at pixel centre.
hmap = np.histogram2d(y,x, xygrid)[0]
todTemp = np.median(np.median(tod[i,:,:,:],axis=0),axis=0)
rmsTemp = np.std(todTemp[1:todTemp.size//2 *2:2] - todTemp[:todTemp.size//2 * 2:2])/np.sqrt(2)
smap = np.histogram2d(y,x, xygrid, weights = todTemp)[0]
xpix = (x+width/2)//pixwidth
ypix = (y+width/2)//pixwidth
#pyplot.plot(xpix, ypix, 'o')
#pyplot.figure()
#pyplot.plot(x, y, 'o')
#pyplot.show()
if destripe:
pixels = (ypix + nbins*xpix).astype(int)
offset = 300
gd = (pixels > 0) & (pixels < nbins*nbins-1)
m, offsets = Mapping.Destripe(todTemp[gd], pixels[gd], obs[gd], offset, nbins*nbins)
m = np.reshape(m, (nbins, nbins)).T
#m = smap/hmap
else:
#h, wx, wy = np.histogram2d(x,y, (xygrid[0], xygrid[1]))
#s, wx, wy = np.histogram2d(x,y, (xygrid[0], xygrid[1]), weights=todTemp)
#m = s/h
offsets =0
m, hits = Mapping.MakeMapSimple(todTemp, x, y, wcs)
m = m/hits
m -= np.nanmedian(m)
#m /= rmsTemp
m[np.isnan(m)] = 0.
#pyplot.plot(ra[i,:], todTemp-offsets,'.')
#pyplot.figure()
#pyplot.plot(dec[i,:], todTemp-offsets,'.')
#pyplot.show()
r = np.sqrt((xgrid)**2 + (ygrid)**2)
d1 = ImagePeaks(m, r, 4)
# if len(coords1) > 0:
# ximax, yimax = coords1[0]
# else:
# m = median_filter(m, size=(2,2))
# Remove background?
xgrid2,ygrid2 = np.meshgrid(np.linspace(-d1.shape[0]/2, d1.shape[0]/2, d1.shape[0]), np.linspace(-d1.shape[1]/2, d1.shape[1]/2, d1.shape[1]))
background = np.sqrt((xgrid2 - 0)**2 + (ygrid2 - 0)**2) > 25
print(d1.shape, xgrid.shape)
d1[background] = 0
ximax, yimax = np.unravel_index(np.argmax(d1), m.shape)
print(ximax, yimax, np.max(d1))
#pyplot.imshow(m)
#pyplot.plot(yimax, ximax,'ow')
#pyplot.figure()
#pyplot.imshow(d1)
#pyplot.plot(yimax, ximax,'ow')
#pyplot.show()
# +/- 180
x[x > 180] -= 360
#pyplot.plot(x,y)
#pyplot.figure()
#pyplot.plot(az[i,:], el[i,:])
#pyplot.show()
#xbins = np.linspace(-4, 4, 60*8+1)
#ybins = np.linspace(-4, 4, 60*8+1)
# Just select the near data
y0,x0 = -xr[ximax,yimax], yr[ximax,yimax]
print(x0, y0)
#print(x0, y0)
#pyplot.figure(figsize=(6,6))
#pyplot.plot(yr.flatten(),m.flatten(),'-')
#pyplot.axvline(y0,color='r')
#pyplot.show()
background = np.sqrt((x - x0)**2 + (y - y0)**2) > 30./60.
#x0, y0 = 0, 0
#pyplot.plot(x,tod[0,0,0,:])
#pyplot.axvline(x0)
#pyplot.axvline(x[np.argmax(tod[0,0,0,:])],color='r')
#pyplot.figure()
#pyplot.plot(tod[0,0,0,:])
#pyplot.show()
r = np.sqrt((x-x0)**2 + (y-y0)**2)
close = (r < 12.5/60.)
near = (r < 25./60.) & (r > 15/60.)
far = (r > 15./60.)
fitselect = (r < 20./60.)
time = np.arange(tod.shape[-1])
#pyplot.plot(time[:], tod[0,0,0,:])
#pyplot.plot(time[fitselect], tod[0,0,0,fitselect])
#pyplot.show()
#extent = [-naxis[0]/2. * cdelt[0], naxis[0]/2. * cdelt[0],
# -naxis[1]/2. * cdelt[1], naxis[1]/2. * cdelt[1]]
#pyplot.figure(figsize=(6,6))
#m2 = m*0
#r2 = np.sqrt((xr-x0)**2 + (yr-y0)**2)
#m2 = (r2 < 10./60.)
#pyplot.imshow(m.T,extent=extent,origin='lower')
#pyplot.scatter(x0,y0, marker='o',color='r')
#pyplot.figure()
#pyplot.imshow(m2.T,extent=extent,origin='lower')
#pyplot.scatter(x0,y0, marker='o',color='r')
#pyplot.show()
#pyplot.plot(time[:],todTemp[:],'.')
#pyplot.plot(time[fitselect],todTemp[fitselect],'.')
#pyplot.show()
plotselect = (r < 120./60.)
for j in range(nSidebands):
for k in range(nChans):
rmdl = np.poly1d(np.polyfit(time[background], tod[i,j,k,background]-offsets,11))
todBackground =RemoveBackground(tod[i,j,k,:], rms[i,j,k], close, sampleRate=50, cutoff=1.)
if destripe:
m, offsets = Mapping.Destripe(tod[i,j,k,gd], pixels[gd], obs[gd], offset, nbins*nbins)
m = np.reshape(m, (nbins,nbins)).T
else:
m, hits = Mapping.MakeMapSimple(tod[i,j,k,:], x, y, wcs)
m = m/hits
#tod[i,j,k,:] = tod[i,j,k,:] -offsets #-(rmdl(ra[i,:])+dmdl(dec[i,:]))
tod[i,j,k,:] -= todBackground#np.median(tod[i,j,k,:])
fitdata = tod[i,j,k,fitselect]
fitra = x[fitselect]
fitdec= y[fitselect]
amax = np.argmax(fitdata)
P0 = [np.max(fitdata) -np.median(fitdata) ,
4./60./2.355,
4./60./2.355,
np.median(fitdata),
x0,
y0,
0.,
0., 0.]#,0.,0., 0., 0.]
P0 = [np.max(fitdata) -np.median(fitdata) ,
4./60./2.355,
x0,
y0,
np.median(fitdata),
0., 0.]#,0.,0., 0., 0.]
P0 = [np.max(fitdata) -np.median(fitdata) ,
4./60./2.355,
x0,
y0,
np.median(fitdata)]#,0.,0., 0., 0.]
#P1[i,j,k,:nParams], cov_x, info, mesg, s = leastsq(Error, P0, args=(fitra, fitdec, fitdata, 0,0), full_output=True)
#fout = fmin(ErrorFmin, P0, maxfun=3000, args=(fitra, fitdec, fitdata, 0,0), full_output=True)
#fout = leastsq(ErrorLstSq, P0, args=(fitra, fitdec, fitdata, 0,0), full_output=True)
ndim, nwalkers = len(P0), 100
pos = [np.array(P0) + 1e-4*np.random.randn(ndim) for iwalker in range(nwalkers)]
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprobNoGrad, args=(fitra, fitdec, fitdata, rms[i,j,k]))
sampler.run_mcmc(pos, 1200)
samples = sampler.chain[:, 500:sampler.chain.shape[1]:3, :].reshape((-1, ndim))
fout = np.mean(samples,axis=0),
P1[i,j,k,:nParams] = fout[0]
print(fout[0])
print (P0)
print(x0, y0)
#import corner
#pyplot.clf()
#fig = corner.corner(samples)
#pyplot.show()
P2 = P1[i,j,k,:nParams]*1.
P2[0] = 0.
ntod = Gauss2dNoGrad(P1[i,j,k,:nParams], x, y, 0,0)
nulltod = Gauss2dNoGrad(P2, x, y, 0.,0.)
otod = tod[i,j,k,:]
omaps, hits = Mapping.MakeMapSimple(otod, x, y, wcs)
nmaps, hits = Mapping.MakeMapSimple(ntod, x, y, wcs)
nulls, hits = Mapping.MakeMapSimple(nulltod, x, y, wcs)
xgrid, ygrid = np.meshgrid(np.arange(naxis[0]), np.arange(naxis[1]))
xgrid = (xgrid-naxis[0]/2.)
ygrid = (ygrid-naxis[1]/2.)
rgrid = np.sqrt(xgrid**2 + ygrid**2)
getchi = (rgrid < 5)
nearChi = (rgrid > 7.5) & (rgrid < 15)
maprms = np.nanstd((nmaps[nearChi]-omaps[nearChi])/hits[nearChi])
rmap = (omaps[getchi]-nmaps[getchi])/hits[getchi]
mapChi = np.nansum((rmap-np.mean(rmap))**2/maprms**2)/(nParams-1.)
mapOrig = np.nansum(((omaps[getchi]-nulls[getchi])/hits[getchi])**2/maprms**2)/(nParams-1.)
origChi = np.sum((tod[i,j,k,plotselect] - Gauss2dNoGrad(P2, x[plotselect], y[plotselect], 0.,0.))**2/rms[i,j,k]**2)/(nParams-1.)
reducedChi = np.sum((tod[i,j,k,plotselect] - Gauss2dNoGrad(P1[i,j,k,:nParams], x[plotselect], y[plotselect], 0.,0.))**2/rms[i,j,k]**2)/(nParams-1.)
#print('CHI2', origChi, reducedChi, mapChi, mapOrig, reducedChi/origChi)
P1[i,j,k,nParams] = rms[i,j,k]
if (k >= 0):
#r2 = np.sqrt((x-P1[i,j,k,2])**2 + (y-P1[i,j,k,3])**2)
#plotselect = (r2 < 15./60.)
ax = fig.add_subplot(3,1,1)
todPlot = tod[i,j,k,plotselect]
todPlot -= np.median(todPlot)
pyplot.plot(todPlot-Gauss2dNoGrad(P1[i,j,k,:nParams], x[plotselect], y[plotselect], 0,0))
pyplot.title('Horn {}, Sideband {}, Avg. Channel {} \n {}'.format(i,j,k, prefix))
pyplot.xlabel('Sample')
pyplot.ylabel('Detector Units')
pyplot.text(0.9,0.9,r'$rms$ = '+'{:.3f}'.format(rms[i,j,k]), ha='right', transform=ax.transAxes)
ax = fig.add_subplot(3,1,2)
pyplot.plot(todPlot)
P3 = P1[i,j,k,:nParams]*1.
P3[3] = 0
P3[7:8] = 0
p3Model = Gauss2dNoGrad(P3, x[plotselect], y[plotselect], 0,0)
pyplot.plot(Gauss2dNoGrad(P1[i,j,k,:nParams], x[plotselect], y[plotselect], 0,0))#p3Model - np.nanmedian(p3Model))
pyplot.plot(Gauss2dNoGrad(P2, x[plotselect], y[plotselect], 0,0))
pyplot.xlabel('Sample')
pyplot.ylabel('Detector Units')
pyplot.text(0.9,0.9,r'$\chi^2$ = '+'{:.3f}'.format(mapChi), ha='right', transform=ax.transAxes)
extent = [-naxis[0]/2. * cdelt[0], naxis[0]/2. * cdelt[0],
-naxis[1]/2. * cdelt[1], naxis[1]/2. * cdelt[1]]
ax = fig.add_subplot(3,3,7)
ax.imshow(m, aspect='auto', extent=extent)
ax.scatter(P1[i,j,k,2], P1[i,j,k,3], marker='o',color='r')
ax = fig.add_subplot(3,3,8)
ax.imshow(nmaps/hits, extent=extent)
ax.scatter(P1[i,j,k,2], P1[i,j,k,3], marker='o',color='r')
ax = fig.add_subplot(3,3,9)
ax.imshow(nmaps/hits-m, extent=extent)
ax.scatter(P1[i,j,k,2], P1[i,j,k,3], marker='o',color='r')
#pyplot.tight_layout(True)
#pyplot.show()
pyplot.savefig('TODResidPlots/TODResidual_{}_H{}_S{}_C{}.png'.format(prefix, i,j,k), bbox_inches='tight')
pyplot.clf()
cross = np.argmax(Gauss2dNoGrad(np.median(P1[i,0,:,:nParams],axis=0), x, y, 0,0))
print( cross, nHorns)
crossings[i] = cross
return P1, errors, crossings
# for j in range(nsidebands):
# for k in range(nchans):
# print(tod.shape, pix.shape, obs.shape)
# m[i,j,k,:],a0 = Mapping.Destripe(tod[i,j,k,:], pix, obs, int(5/0.02), int(naxis[0]*naxis[1]))
# Merge the horns?
pix = Mapping.ang2pixWCS(wcs, dec.flatten(),
ra.flatten()).astype('int')
for j in range(nsidebands):
for k in range(nchans):
print(tod.shape, pix.shape, obs.shape)
todTemp = tod[:, j, k, :].flatten()
obsTemp = np.repeat(obs, tod.shape[0])
m[0, j,
k, :], a0 = Mapping.Destripe(todTemp, pix, obsTemp,
int(5 / 0.02),
int(naxis[0] * naxis[1]))
m[m == 0] = np.nan
m = np.reshape(
m, (m.shape[0], m.shape[1], m.shape[2], naxis[0], naxis[1]))
m = m[:, :, :, :, ::-1]
print(m.shape)
pyplot.subplot(1, 2, 1, projection=wcs)
pyplot.imshow(m[0, 0, 0, :, :], origin='lower',
aspect='auto') #,(int(naxis[0]), int(naxis[1]) ) ))
pyplot.colorbar()
pyplot.plot(ra[0, :],
dec[0, :],
transform=pyplot.gca().get_transform('world'),
alpha=0.1,
