#! /usr/bin/env python
import numpy as np
import aipy as a
from capo.pspec import jy2T
from PolSim import *
from healpy import *
from pylab import *
Niter = 100
Nside = 64
aa = a.cal.get_aa('psa_null', np.array([0.165]))
D = Distributions.Dist('scripts/6C.cf', 0.76, 'data/Oppermann.copy.npz')
jy2T = jy2T(0.165)
Cell = []
for i in range(Niter):
print "Working on iteration %d/%d"%(i+1,Niter)
print "\t with %d sources"%D.Nsrc
prms = D.SimSkyParams()
x,y = prms['L'], prms['M']
z = np.sqrt(1.-x**2 - y**2)
m = np.zeros(nside2npix(Nside))
for pi,fi,xi,yi,zi in zip(prms['P'],prms['F'],x,y,z):
m[vec2pix(Nside, xi, yi, zi)] += jy2T*fi*pi
Cell.append(anafast(m, lmax=1000))
Cell = np.array(Cell)
dCell = np.std(Cell, axis=0)
Cell = np.mean(Cell, axis=0)
if n.round(w.max())%2:#if we had an odd number of points
dif += n.ma.array(D).squeeze() * S #sum with signs (opposing if --dt)
w += n.logical_not(f).astype(n.float).squeeze()
print "evening it up!"
print dif.shape
#if we diffed over baselines we should just have a spectrum
if opts.bl:
dif = dif.squeeze()
var = n.real(dif*n.conj(dif))
elif len(dif.shape)>1:
#if not, then we need to average over them
var = n.mean(dif*n.conj(dif),axis=0) #this should be the zero axis
else:
var = dif*n.conj(dif)
print len(freqs),len(var)
Trms = n.sqrt(var)/w.squeeze()*jy2T(freqs)
n.savez('diff_uv.npz',freqs=freqs,dif=dif.filled(0),weights=w,Trms=Trms.filled(0),mask=Trms.mask)
if opts.plot:
#theoretical noise level
F = n.load('diff_uv.npz')
freqs = F['freqs']
Trms = n.ma.masked_where(F['mask'],F['Trms'])
print Trms.shape,freqs.shape
plot(freqs*1e3,Trms,'k')
savefig('diff_uv.png')
grid()
ylabel('mK')
xlabel('MHz')
# show()
Nann = 3
for i in range(Nann):
if i == 0 or i+1 == Nann: continue
#if i%4 != 0: continue
nchan_i = int(Nchan/Nann)
ntaps = 3
ch1,ch2 = i*nchan_i,(i+1)*nchan_i
ch_in,ch_out = ch1-nchan_i, ch2+nchan_i
freqs_i = freqs[ch_in:ch_out]
dnu = freqs_i[1] - freqs_i[0]
eta = np.fft.fftshift(np.fft.fftfreq(nchan_i,dnu))
z_mid = np.median(pspec.f2z(freqs_i))
k = np.abs(eta*pspec.dk_deta(z_mid))
V_nu = RMvis(freqs_i,RM)
V_nu *= pspec.jy2T(freqs_i)
Trms = pfb.pfb(V_nu,taps=ntaps,window=window,fft=np.fft.ifft)
Trms = np.fft.fftshift(Trms)
DELTA = k3pk(Trms,k=k,fq=np.mean(freqs_i),B=0.1/Nann)
valid = k!=0.
DELTA = np.extract(valid,DELTA)
DELTA /= DELTA.sum()
k = np.extract(valid,k)
_k,_DELTA = rebin_log(k,DELTA)
ax = fig.add_subplot(len(RMs),1,j+1)
#ax.step(k,DELTA,where='mid',color='k',label='z = %2.1f'%z_mid)
D = n.ma.diff(D, axis=0) / n.sqrt(2)
if opts.v:
print "diffing baselines"
if opts.t:
D = n.ma.diff(D, axis=1) / n.sqrt(2)
if opts.f:
D = n.ma.diff(D, axis=2) / n.sqrt(2)
W = W[:, :, :
-1] #if we're diffing by freqs drop the topmost channel flag
var = n.abs(n.ma.sum(n.ma.sum(D * n.conj(D), axis=0),
axis=0)) #bl=0,time=1
w = n.sum(n.sum(W, axis=0), axis=0) #bl=0,time=1
Trms = var
Trms[w > 0] /= w[w > 0]
Trms = n.sqrt(Trms) * jy2T(freqs)
if opts.v:
mypoint = w.argmax()
print var[mypoint]
print w[mypoint]
print Trms[mypoint]
print "writing uv_rms.npz"
n.savez('uv_rms.npz',
freqs=freqs,
weights=w,
Trms=Trms.filled(0),
mask=Trms.mask,
label=opts.label)
if opts.v:
#see Eq 4.16 of Jacobs thesis
if opts.bl and not (D.shape[0]%2):
D = n.ma.diff(D,axis=0)/n.sqrt(2)
if opts.v:
print "diffing baselines"
if opts.t:
D = n.ma.diff(D,axis=1)/n.sqrt(2)
if opts.f:
D = n.ma.diff(D,axis=2)/n.sqrt(2)
W = W[:,:,:-1]#if we're diffing by freqs drop the topmost channel flag
var = n.abs(n.ma.sum(n.ma.sum(D*n.conj(D),axis=0),axis=0)) #bl=0,time=1
w = n.sum(n.sum(W,axis=0),axis=0) #bl=0,time=1
Trms = var
Trms[w>0] /= w[w>0]
Trms = n.sqrt(Trms)*jy2T(freqs)
if opts.v:
mypoint = w.argmax()
print var[mypoint]
print w[mypoint]
print Trms[mypoint]
print "writing uv_rms.npz"
n.savez('uv_rms.npz',freqs=freqs,weights=w,Trms=Trms.filled(0),mask=Trms.mask,label=opts.label)
if opts.v:
rows,cols = intsquare(D.shape[0])
for i in xrange(D.shape[0]):
subplot(rows,cols,i+1)
title(a.miriad.bl2ij(bls[i]))
imshow(n.abs(D[i])*jy2T(freqs),aspect='auto',vmin=0,vmax=30)
