) # 400 by 400 image, i.e. 200 boxes, with 4 pixels per box,freq space kmax=100, dk=0.5
X, Y, Z = img.get_top(center=(200, 200))
shape0 = X.shape
X, Y, Z = X.flatten(), Y.flatten(), Z.flatten()
ntop = n.array([X, Y, Z])
aa = a.cal.get_aa('psa6622_v001', n.array([.15]))
src = a.fit.RadioFixedBody(0, aa.lat, janskies=0., mfreq=.15, name='test')
# src=a.fit.RadioSpecial("Sun")
nants = 128
dt = 0.001
dt_fine = 43. / 3600 / 24
times_coarse = n.arange(2456240.3, 2456240.4, dt)
times_fine = n.arange(2456240.3, 2456240.4, dt_fine)
dist = 1. # size of cells to store in dictionary.
corr_tol = 5000. # cutoff of minimum correlation
bmp = export_beam.beam_real(aa[0], ntop, shape0, 'x')
freq, fbmamp = export_beam.beam_fourier(bmp, d, 400)
print 'Time to initialize:', sys_time.clock(), 'seconds'
d = select_pair.pair_coarse(aa, src, times_coarse, dist,
2.) #coarsely determine crossings
print 'Time after coarse selection:', sys_time.clock(), 'seconds'
# pairs_sorted = select_pair.pair_sort(d,freq,fbmamp) # sort crossings
# clos_app = select_pair.get_closest(pairs_sorted) # determine closest approach points
nproc = 4
nkey = len(d)
chunk, remainder = nkey / nproc, nkey % nproc
clos_app = select_pair.alter_clos(d, freq,
fbmamp) # determine closest approach points
print 'Found closest approach points after:', sys_time.clock(), 'seconds'
def weight_ext(dec, aa):
l,m = n.sin(0-aa.long), n.sin(dec-aa.lat); nn = n.sqrt(1-l*l-m*m)
ntop = n.array([l,m,nn])
wt = export_beam.beam_real(aa[0], ntop, pol='x', sq=False)
#print n.array(wt).shape()
return wt[0]
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import export_beam
# plots the beam and the Fourier transform of the beam squared
sz = 200
d = 1./sz
img = a.img.Img(200,res=0.5)
X,Y,Z = img.get_top(center=(200,200))
shape0 = X.shape
X,Y,Z = X.flatten(),Y.flatten(),Z.flatten()
aa = a.cal.get_aa('psa6622_v001',n.array([.15, .18]))
aa.set_jultime(2456240.2)
peak = []
sample_ant = 1
ntop = n.array([X,Y,Z]) #note all beams are the same
bmp_list = export_beam.beam_real(aa[sample_ant], ntop, shape0, 'x',sq=True)
bmp = bmp_list[0]
freq, fbmamp = export_beam.beam_fourier(bmp, d, 400)
rax = [-1,1,-1,1]
freq_pl = 5
mid = len(freq)/2
flim = (mid-freq_pl,mid+freq_pl)
freqax = [freq[flim[0]],freq[flim[1]], freq[flim[0]],freq[flim[1]]]
f_range = n.array(n.arange(flim[0],flim[1]))
m_range = n.arange(0, flim[1]-flim[0])
#peak.append(fbmamp[mid,mid])
fig = p.figure()
img = a.img.Img(200,res=0.5) # 400 by 400 image, i.e. 200 boxes, with 4 pixels per box,freq space kmax=100, dk=0.5
X,Y,Z = img.get_top(center=(200,200))
shape0 = X.shape
X,Y,Z = X.flatten(),Y.flatten(),Z.flatten()
ntop = n.array([X,Y,Z])
aa = a.cal.get_aa('psa6622_v001',n.array([.15]))
src = a.fit.RadioFixedBody(0, aa.lat, janskies=0., mfreq=.15, name='test')
# src=a.fit.RadioSpecial("Sun")
nants = 128
dt = 0.001
dt_fine = 43./3600/24
times_coarse = n.arange(2456240.3,2456240.4, dt)
times_fine = n.arange(2456240.3,2456240.4, dt_fine)
dist = 1. # size of cells to store in dictionary.
corr_tol = 5000. # cutoff of minimum correlation
bmp = export_beam.beam_real(aa[0], ntop, shape0, 'x')
freq, fbmamp = export_beam.beam_fourier(bmp, d, 400)
print 'Time to initialize:', sys_time.clock(), 'seconds'
d = select_pair.pair_coarse(aa, src,times_coarse,dist,2.) #coarsely determine crossings
print 'Time after coarse selection:', sys_time.clock(), 'seconds'
# pairs_sorted = select_pair.pair_sort(d,freq,fbmamp) # sort crossings
# clos_app = select_pair.get_closest(pairs_sorted) # determine closest approach points
nproc = 4
nkey = len(d)
chunk,remainder = nkey/nproc, nkey%nproc
clos_app = select_pair.alter_clos(d,freq,fbmamp) # determine closest approach points
print 'Found closest approach points after:', sys_time.clock(), 'seconds'
pairs_final = select_pair.pair_fin(clos_app,dt,aa,src,freq,fbmamp,corr_tol) # output final sorted pairs
print 'Total time:', sys_time.clock(), 'seconds'
import export_beam
# plots the beam and the Fourier transform of the beam squared
sz = 200
d = 1. / sz
img = a.img.Img(200, res=0.5)
X, Y, Z = img.get_top(center=(200, 200))
shape0 = X.shape
X, Y, Z = X.flatten(), Y.flatten(), Z.flatten()
aa = a.cal.get_aa('psa6622_v001', n.array([.15, .18]))
aa.set_jultime(2456240.2)
peak = []
sample_ant = 1
ntop = n.array([X, Y, Z]) #note all beams are the same
print X
print Z
bmp_list = export_beam.beam_real(aa[sample_ant], ntop, shape0, 'x', sq=True)
bmp = bmp_list[0]
freq, fbmamp = export_beam.beam_fourier(bmp, d, 400)
rax = [-1, 1, -1, 1]
freq_pl = 5
mid = len(freq) / 2
flim = (mid - freq_pl, mid + freq_pl)
freqax = [freq[flim[0]], freq[flim[1]], freq[flim[0]], freq[flim[1]]]
f_range = n.array(n.arange(flim[0], flim[1]))
m_range = n.arange(0, flim[1] - flim[0])
#peak.append(fbmamp[mid,mid])
fig = p.figure()
ax1 = fig.add_subplot(121)
im1 = ax1.imshow(bmp, interpolation='nearest', extent=rax)
