def loadVisibilitiesAndSamples(dataFiles, pol, blList, redundancyDict):
"""Based on Zaki and Carina's omni_get_k.py"""
print 'Now reading all data files...'
data = {}
samples = {}
jds = []
lsts = []
files = []
conjugate = [(0, 101), (0, 62), (0, 100), (0, 97), (12, 43), (57, 64)]
for fl in dataFiles:
print ' Reading %s' % fl
meta, _, mdl, _ = omni.from_npz(fl)
jd = meta['jds']
lst = meta['lsts']
jds.append(jd)
lsts.append(lst)
files.append(fl.split('/')[-1]) #ignore the folder
for b in bls:
if b in conjugate: _d = np.conj(mdl[pol][b])
else: _d = mdl[pol][b]
if b not in data.keys(): data[b] = _d
else: data[b] = np.concatenate((data[b], _d), axis=0)
samples[b] = redundancyDict[b] * np.ones(data[b].shape)
samples[b][
data[b] ==
0] = 0 #TODO: we need a more sophisticated way of handling flagging than this!!!
return data, samples
def loadVisibilitiesAndSamples(dataFiles, pol, blList, redundancyDict, flags):
"""Loads visibilities and samples from omnical npzs. Applies flags."""
print 'Now reading all data files...'
data = {}
samples = {}
jds = []
lsts = []
files = []
conjugate = [(0,101), (0,62), (0,100), (0,97), (12,43), (57,64)]
for fl in dataFiles:
print ' Reading %s'%fl
meta,_,mdl,_ = omni.from_npz(fl)
jd = meta['jds']
lst = meta['lsts']
jds.append(jd)
lsts.append(lst)
files.append(fl.split('/')[-1]) #ignore the folder
for b in bls:
if b in conjugate: _d = np.conj(mdl[pol][b])
else: _d = mdl[pol][b]
if b not in data.keys(): data[b] = _d
else: data[b] = np.concatenate((data[b],_d), axis=0)
samples[b] = redundancyDict[b] * np.ones(data[b].shape)
samples[b][data[b]==0] = 0
if flags is not None:
for b in bls: data[b][flags==1] = 0 #apply flags
for b in bls: samples[b][flags==1] = 0 #apply flags
return data, samples
def loadVisibilitiesAndSamples(dataFiles, pol, blList, redundancyDict, flags):
"""Loads visibilities and samples from omnical npzs. Applies flags."""
print 'Now reading all data files...'
data = {}
samples = {}
jds = []
lsts = []
files = []
conjugate = [(0, 101), (0, 62), (0, 100), (0, 97), (12, 43), (57, 64)]
for fl in dataFiles:
print ' Reading %s' % fl
meta, _, mdl, _ = omni.from_npz(fl)
jd = meta['jds']
lst = meta['lsts']
jds.append(jd)
lsts.append(lst)
files.append(fl.split('/')[-1]) #ignore the folder
for b in bls:
if b in conjugate: _d = np.conj(mdl[pol][b])
else: _d = mdl[pol][b]
if b not in data.keys(): data[b] = _d
else: data[b] = np.concatenate((data[b], _d), axis=0)
samples[b] = redundancyDict[b] * np.ones(data[b].shape)
samples[b][data[b] == 0] = 0
if flags is not None:
for b in bls:
data[b][flags == 1] = 0 #apply flags
for b in bls:
samples[b][flags == 1] = 0 #apply flags
return data, samples
def flag_by_chisq(filenames, nsig=12, deg=8, outfile=False):
'''Use the omnical global chisq to flag the model visibilities.'''
m,g,v,x = omni.from_npz(filenames)
f = {}
for pol in g.keys():
if not pol in f: f[pol] = {}
for k in g[pol].keys():
chisq = m['chisq'+str(k)+pol[0]]
#iterate twice on flattening bandpass to find rfi
mask = n.zeros_like(chisq, dtype=n.bool)
#Run loop twice to get better fit after removing large rfi
for i in range(2):
wgts = n.logical_not(mask)
chisq *= wgts
med_chisq = n.median(chisq, axis=0)
w = n.median(chisq,axis=0)!=0.
fit = n.polyfit(n.arange(len(med_chisq))[w], n.log10(med_chisq[w]), deg=8)
flat_chisq = chisq/10**n.polyval(fit, n.arange(len(med_chisq)))
med = n.median(flat_chisq)
sig = n.sqrt(n.median(n.abs(flat_chisq-med)**2))
mask |= n.where(flat_chisq > (med + nsig*sig), True, False)
#import IPython; IPython.embed()
f[pol][k] = n.logical_not(mask)#weights for the data
if outfile:
pass
return m,g,v,x,f
def loadVisibilitiesAndSamples(dataFiles, pol, blList, redundancyDict):
"""Based on Zaki and Carina's omni_get_k.py"""
print 'Now reading all data files...'
data = {}
samples = {}
jds = []
lsts = []
files = []
conjugate = [(0,101), (0,62), (0,100), (0,97), (12,43), (57,64)]
for fl in dataFiles:
print ' Reading %s'%fl
meta,_,mdl,_ = omni.from_npz(fl)
jd = meta['jds']
lst = meta['lsts']
jds.append(jd)
lsts.append(lst)
files.append(fl.split('/')[-1]) #ignore the folder
for b in bls:
if b in conjugate: _d = np.conj(mdl[pol][b])
else: _d = mdl[pol][b]
if b not in data.keys(): data[b] = _d
else: data[b] = np.concatenate((data[b],_d), axis=0)
samples[b] = redundancyDict[b] * np.ones(data[b].shape)
samples[b][data[b]==0] = 0 #TODO: we need a more sophisticated way of handling flagging than this!!!
return data, samples
def getBaselines(freqs, intSeps, exampleDataFile, calFile='psa6622_v003'):
aa = a.cal.get_aa(calFile, freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
m, _, mdl, _ = omni.from_npz(exampleDataFile)
bls = [(0, i) for i in intSeps]
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0], ij[1])
try:
s = tuple(map(int, bl2sep[bl].split(',')))
except (KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1] - 1] = ij
return bls
def getBaselines(freqs, intSeps, exampleDataFile, calFile='psa6622_v003'):
aa = a.cal.get_aa(calFile, freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
m, _, mdl,_= omni.from_npz(exampleDataFile)
bls = [(0,i) for i in intSeps]
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0],ij[1])
try:
s = tuple(map(int,bl2sep[bl].split(',')))
except(KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1]-1] = ij
return bls
def loadVisibilitiesAndSamples(dataFile, pol, blList, redundancyDict, flags):
"""Loads visibilities and samples from an omnical npz. Applies flags."""
data, samples = {}, {}
jds, lsts = [], []
conjugate = [(0,101), (0,62), (0,100), (0,97), (12,43), (57,64)]
if verbose: print 'Reading %s'%dataFile
meta,_,mdl,_ = omni.from_npz(dataFile)
jd = meta['jds']
lst = meta['lsts']
jds.append(jd)
lsts.append(lst)
for b in bls:
if b in conjugate: _d = np.conj(mdl[pol][b])
else: _d = mdl[pol][b]
if b not in data.keys(): data[b] = _d
else: data[b] = np.concatenate((data[b],_d), axis=0)
samples[b] = redundancyDict[b] * np.ones(data[b].shape)
samples[b][data[b]==0] = 0
if flags is not None:
for b in bls: data[b][flags==1] = 0 #apply flags
for b in bls: samples[b][flags==1] = 0 #apply flags
return data, samples
alwaysFlaggedChannels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 169, 183, 185, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 75, 76, 77]
alsoFlagTheseChannels = [14, 15, 55, 101, 123, 179, 180, 181, 182, 184, 186]
flaggedChannels = sorted(list(set(alwaysFlaggedChannels).union(set(alsoFlagTheseChannels))))
seps = np.arange(1,16) #* 15m = baseline lengths
dx = 15.0
separations = dx*seps
freqs = np.arange(.1,.2,.1/203)
#freqs = np.arange(.1,.2,.1/203)[160:]
chans = range(len(freqs))
chan2FreqDict = {chan: freqs[chan] for chan in chans}
uTolerance = 15.0 * 15 * (freqs[1]-freqs[0]) / 3e8 * 1e9
aa = a.cal.get_aa('psa6622_v003', freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
m, _, mdl,_= omni.from_npz(dataFiles[0])
bls = [(0,i) for i in seps]
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0],ij[1])
try:
s = tuple(map(int,bl2sep[bl].split(',')))
except(KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1]-1] = ij
bl2SepDict = {bl: np.asarray([sep,0.0]) for bl,sep in zip(bls,separations)}
#bl2SepDict = {bl: np.asarray([sep]) for bl,sep in zip(bls,separations)}
def blRedundancyDictFromCalFile(calFile = 'psa6622_v003'):
""" Uses Omnical to figure out the number of redundant baselines for a given separation (as demarkated by a representative baseline tuple).
Returns redundancyDict and also saves it as an instance variable."""
#! /usr/bin/env python
import numpy as n
import aipy as a
import optparse, sys
import capo.omni as omni, capo.zsa as zsa, capo.arp as arp
import pylab as p, os
o = optparse.OptionParser()
a.scripting.add_standard_options(o, cal=True)
opts, args = o.parse_args(sys.argv[1:])
pol = 'xx'
freqs = n.arange(.1, .2, .1 / 203)
aa = a.cal.get_aa('psa6622_v003', freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
m, _, mdl, _ = omni.from_npz(args[0])
seps = n.arange(1, 16) #* 15m = baseline lengths
bls = [(0, i) for i in seps]
sep2ij = {}
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0], ij[1])
try:
s = tuple(map(int, bl2sep[bl].split(',')))
except (KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1] - 1] = ij
#print bls
conjugate = [(0, 101), (0, 62), (0, 100), (0, 97), (12, 43), (57, 64)]
import numpy as n
import ipdb as PDB
cal = 'psa6622_v002'
exec('from {cal} import prms'.format(cal=cal))
grid=grid2ij(prms['ant_layout'])[0]
def ij2grid(bl):
for sep in grid:
if bl in grid[sep].split(','):
return sep
def bltxt(bltuple):
return '%s_%s'%bltuple
print ij2grid('0_103')
seps = ['0,1']
data = {}
for filename in sys.argv[1:]:
meta, gains, vismdl, xtalk, jds, lsts, freqs = from_npz(filename)
#for select off my bls
for pol in vismdl:
try: data[pol]
except(KeyError): data[pol] = {}
for bl in vismdl[pol]:
blsep = ij2grid(bltxt(bl))
if blsep in seps:
try: data[pol][blsep].append(vismdl[pol][bl])
except(KeyError):data[pol][blsep] = [vismdl[pol][bl]]
datacount = 0
for pol in data:
for sep in data[pol]:
try:
data[pol][sep] = n.concatenate(data[pol][sep])
except(TypeError):
import aipy as a
import numpy as np
import optparse, sys, os
import capo.omni as omni, capo.zsa as zsa, capo.arp as arp
import healpy as hp
import matplotlib.pyplot as plt
#%%
meta, _, mdl, _ = omni.from_npz(
'./SimulatedVisibilities/simple.2456679.35658.xx.npz')
meta2, _, mdl2, _ = omni.from_npz('../Data/zen.2456679.35658.xx.npz')
#%%
data = mdl['xx']
data2 = mdl2['xx']
bls = [(0, 103), (1, 4), (0, 101), (0, 62), (0, 100), (1, 13),
(1, 70), (1, 56), (1, 71), (1, 59), (0, 97), (12, 43), (9, 71), (9, 59),
(57, 64)]
conjugate = [(0, 101), (0, 62), (0, 100), (0, 97), (12, 43), (57, 64)]
aa = a.cal.get_aa('psa6622_v003', 0.1 / 203, 0.1, 203)
blLengths = [
np.abs((np.round(
aa.get_baseline(blIndex[0], blIndex[1]) * a.const.c / 1.5e12))[0])
for blIndex in bls
]
freqs = aa.get_afreqs()
plt.figure(1)
import numpy as n
import aipy as a
import optparse,sys
import capo.omni as omni, capo.zsa as zsa, capo.arp as arp
import pylab as p, os
o = optparse.OptionParser()
a.scripting.add_standard_options(o, cal=True)
opts,args = o.parse_args(sys.argv[1:])
pol='xx'
freqs = n.arange(.1,.2,.1/203)
aa = a.cal.get_aa('psa6622_v003', freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
m, _, mdl,_= omni.from_npz(args[0])
seps = n.arange(1,16) #* 15m = baseline lengths
bls = [(0,i) for i in seps]
sep2ij = {}
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0],ij[1])
try:
s = tuple(map(int,bl2sep[bl].split(',')))
except(KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1]-1] = ij
#print bls
conjugate = [(0,101), (0,62), (0,100), (0,97), (12,43), (57,64)]
uTolerance = 15.0 * 15 * (freqs[1] - freqs[0]) / 3e8 * 1e9
o = optparse.OptionParser()
a.scripting.add_standard_options(o, cal=True)
opts, dataFiles = o.parse_args(sys.argv[1:])
if len(sys.argv[1:]) == 0: #This option is for testing
#dataFiles = ["./Data/" + file for file in os.listdir("./Data") if file.endswith("uvcRRE.npz")]
dataFiles = [
"./Data/" + file for file in os.listdir("./Data")
if file.endswith("2456943.57058.xx.uvcRRE.npz")
]
aa = a.cal.get_aa('psa6622_v003', freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
m, _, mdl, _ = omni.from_npz(dataFiles[0])
bls = [(0, i) for i in seps]
sep2ij = {}
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0], ij[1])
try:
s = tuple(map(int, bl2sep[bl].split(',')))
except (KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1] - 1] = ij
baselineFreqPairs = {}
print 'Now finding all baseline/frequency pairs...'
for ch1, f1 in enumerate(freqs):
dx = 15.0
separations = dx*seps
freqs = np.arange(.1,.2,.1/203)
uTolerance = 15.0 * 15 * (freqs[1]-freqs[0]) / 3e8 * 1e9
o = optparse.OptionParser()
a.scripting.add_standard_options(o, cal=True)
opts,dataFiles = o.parse_args(sys.argv[1:])
if len(sys.argv[1:]) == 0: #This option is for testing
#dataFiles = ["./Data/" + file for file in os.listdir("./Data") if file.endswith("uvcRRE.npz")]
dataFiles = ["./Data/" + file for file in os.listdir("./Data") if file.endswith("2456943.57058.xx.uvcRRE.npz")]
aa = a.cal.get_aa('psa6622_v003', freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
m, _, mdl,_= omni.from_npz(dataFiles[0])
bls = [(0,i) for i in seps]
sep2ij = {}
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0],ij[1])
try:
s = tuple(map(int,bl2sep[bl].split(',')))
except(KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1]-1] = ij
baselineFreqPairs = {}
print 'Now finding all baseline/frequency pairs...'
uv.rewind()
if opts.infile:
xfile = '%f.xtalk.rep.npz' % jd
if not os.path.exists(xfile): xfile = '%f.xtalk.npz' % jd
if not os.path.exists(xfile):
print xfile, 'does not exist. Skipping...'
continue
print ' using', xfile
xtalk = np.load(xfile)
else:
guess, cnt, xtalk = {}, {}, {}
if opts.omnimdl:
#if pass in omnical file, load in the mdl
omnifile = opts.omnimdl % '.'.join(
filename.split('/')[-1].split('.')[0:4])
m, _, mdl, _ = omni.from_npz(omnifile)
#make info object so that we can get a mapping of mdl bls to all bls.
aa = a.cal.get_aa(opts.cal, np.array([.150]))
info = omni.aa_to_info(aa)
redmapping = {}
for k in mdl[a.miriad.pol2str[uv['pol']]].keys():
for gp in info.get_reds():
if k in gp:
for kk in gp:
redmapping[kk] = k
for (uvw, t, (i, j)), d, f in uv.all(raw=True):
ti = np.where(m['jds'] == t)[0]
bl = str(a.pol.ijp2blp(i, j, uv['pol']))
if not guess.has_key(bl): guess[bl], cnt[bl] = 0, 0
if (i, j) in [(97, 80), (80, 97), (72, 96), (96, 72), (43, 88),
(88, 43)]:
(uvw,jd,(i,j)),d,f = uv.read(raw=True)
uv.rewind()
if opts.infile:
xfile = '%f.xtalk.rep.npz' % jd
if not os.path.exists(xfile): xfile = '%f.xtalk.npz' % jd
if not os.path.exists(xfile):
print xfile, 'does not exist. Skipping...'
continue
print ' using', xfile
xtalk = np.load(xfile)
else:
guess, cnt, xtalk = {}, {}, {}
if opts.omnimdl:
#if pass in omnical file, load in the mdl
omnifile = opts.omnimdl % '.'.join(filename.split('/')[-1].split('.')[0:4])
m,_,mdl,_ = omni.from_npz(omnifile)
#make info object so that we can get a mapping of mdl bls to all bls.
aa = a.cal.get_aa(opts.cal, np.array([.150]))
info = omni.aa_to_info(aa)
redmapping = {}
for k in mdl[a.miriad.pol2str[uv['pol']]].keys():
for gp in info.get_reds():
if k in gp:
for kk in gp : redmapping[kk] = k
for (uvw,t,(i,j)),d,f in uv.all(raw=True):
ti = np.where(m['jds']==t)[0]
bl = str(a.pol.ijp2blp(i,j,uv['pol']))
if not guess.has_key(bl): guess[bl], cnt[bl] = 0, 0
if (i,j) in [(97,80), (80,97), (72,96), (96,72), (43, 88), (88,43)]:
if opts.verbose: print 'No Model Visibility for {0}'.format((i,j))
ml = np.zeros_like(d)
def ij2grid(bl):
for sep in grid:
if bl in grid[sep].split(','):
return sep
def bltxt(bltuple):
return '%s_%s' % bltuple
print ij2grid('0_103')
seps = ['0,1']
data = {}
for filename in sys.argv[1:]:
meta, gains, vismdl, xtalk, jds, lsts, freqs = from_npz(filename)
#for select off my bls
for pol in vismdl:
try:
data[pol]
except (KeyError):
data[pol] = {}
for bl in vismdl[pol]:
blsep = ij2grid(bltxt(bl))
if blsep in seps:
try:
data[pol][blsep].append(vismdl[pol][bl])
except (KeyError):
data[pol][blsep] = [vismdl[pol][bl]]
datacount = 0
for pol in data:
a single npz file per decimal JD. This lets us use omni_apply.py
to calibrate xx,xy,yx,yy at once, even though we only used info
from the linear instrumental polarizations.
Assumes xx and yy npz files are in the same directory.
Returns
"""
o = optparse.OptionParser()
o.set_usage('merge_omni_npz.py [options] /path/to/*xx.npz')
opts,args = o.parse_args(sys.argv[1:])
def merge_dicts(*dict_args):
result = {}
for dictionary in dict_args:
result.update(dictionary)
return result
for xx_npz in args:
yy_npz = xx_npz.replace("xx","yy")
new_npz = xx_npz.replace("xx.","")
print "Reading %s %s"%(xx_npz,yy_npz)
metax,gainsx,vismdlx,xtalkx = omni.from_npz(xx_npz)
metay,gainsy,vismdly,xtalky = omni.from_npz(yy_npz)
meta = merge_dicts(metax,metay)
gains = merge_dicts(gainsx,gainsy)
vismdl = merge_dicts(vismdlx,vismdly)
xtalk = merge_dicts(xtalkx,xtalky)
print " => %s"%new_npz
omni.to_npz(new_npz,meta,gains,vismdl,xtalk)
#o.add_option('-C',dest='cal',default='psa6622_v003',type='string',
# help='Path and name of calfile.')
a.scripting.add_standard_options(o,chan=True)
opts,args = o.parse_args(sys.argv[1:])
chan = int(opts.chan)
### Save Options ###
for filename in args:
print filename,'-->',
outfile = filename[:-4]+'.slice.npz'
print outfile
if os.path.exists(outfile):
print " ... exists. skipping"
continue
meta,gains,vismdl,xtalk = from_npz(filename)
slice_gains = {}
slice_vismdl = {}
slice_xtalk = {}
for pol in gains:
slice_gains[pol] = {}
for ant,gain in gains[pol].iteritems():
slice_gains[pol][ant] = n.array([g[chan] for g in gain])
for pol in vismdl:
slice_vismdl[pol] = {}
for bl,vis in vismdl[pol].iteritems():
slice_vismdl[pol][bl] = n.array([v[chan] for v in vis])
slice_xtalk[pol] = {}
for bl,xt in xtalk[pol].iteritems():
slice_xtalk[pol][bl] = n.array(xt[chan])
to_npz(outfile,meta,slice_gains,slice_vismdl,slice_xtalk)
to calibrate xx,xy,yx,yy at once, even though we only used info
from the linear instrumental polarizations.
Assumes xx and yy npz files are in the same directory.
Returns
"""
o = optparse.OptionParser()
o.set_usage('merge_omni_npz.py [options] /path/to/*xx.npz')
opts, args = o.parse_args(sys.argv[1:])
def merge_dicts(*dict_args):
result = {}
for dictionary in dict_args:
result.update(dictionary)
return result
for xx_npz in args:
yy_npz = xx_npz.replace("xx", "yy")
new_npz = xx_npz.replace("xx.", "")
print "Reading %s %s" % (xx_npz, yy_npz)
metax, gainsx, vismdlx, xtalkx = omni.from_npz(xx_npz)
metay, gainsy, vismdly, xtalky = omni.from_npz(yy_npz)
meta = merge_dicts(metax, metay)
gains = merge_dicts(gainsx, gainsy)
vismdl = merge_dicts(vismdlx, vismdly)
xtalk = merge_dicts(xtalkx, xtalky)
print " => %s" % new_npz
omni.to_npz(new_npz, meta, gains, vismdl, xtalk)
#allus = np.arange(100,102,.1)
#pixelDecs = np.asarray([-25])/180.0*np.pi
#pixelRAs = np.asarray([90])/180.0*np.pi
#fluxes = np.asarray([100])
pixelDecs = np.asarray([-30, -35, -20, -10, 5, -30, -50, -40, -60, -20
]) / 180.0 * np.pi
pixelRAs = np.asarray([90, 60, 120, 76, 91, 44, 50, 70, 30, 10
]) / 180.0 * np.pi
fluxes = np.asarray([100, 49, 125, 60, 20, 99, 22, 102, 5, 30]) * 1.0
#spectralIndices = np.ones(len(fluxes)) * -2.55
spectralIndices = np.asarray(
[-2.45, -2.3, -2.7, -2.4, -2.55, -2.63, -2.51, -2.3, -2.9, -2.5]) + 2
for source in sourceFiles:
meta, _, _, _ = omni.from_npz(source)
# times = meta['jds']
times = [meta['jds'][0]]
meta['SimulationOptions'] = opts.__dict__
#vismdl = {opts.pol: {blIndex: np.zeros((len(times), opts.nchan), dtype=complex) for blIndex in blIndices}}
visibilities = np.zeros((len(allus), len(freqs)), dtype=complex)
allPixelTops, allPixelAzAlts = [], []
for time in times:
aa.set_jultime(time)
pixelEq = a.coord.radec2eq([pixelRAs, pixelDecs])
pixelTop = a.coord.eq2top_m(-aa.sidereal_time(), aa.lat).dot(pixelEq)
allPixelTops.append(pixelTop)
allPixelAzAlts.append(a.coord.top2azalt(pixelTop))
for chan in range(opts.nchan):
import capo.omni as omni
import numpy as n, pylab as p, capo.plot as plot
import sys
args = sys.argv()[1:]
m,g,v,x = omni.from_npz(args, verbose=True)
conjugate = [(0,101), (0,62), (0,100), (0,97), (12,43), (57,64)]
aa = a.cal.get_aa(opts.cal, opts.sdf, opts.sfreq, opts.nchan)
freqs = aa.get_afreqs()
#Fixed coordinate calculations
healpixCoords = np.transpose(np.asarray(hp.pix2ang(NSIDE,np.arange(12*NSIDE**2))))
pixelRAs = healpixCoords[:,1]
pixelDecs = -healpixCoords[:,0] + np.pi/2
galCoords = SkyCoord(frame="icrs", ra=pixelRAs*u.rad, dec=pixelDecs*u.rad).transform_to("galactic")
#I should probably make this command line parameterizable
allus = np.arange(10,160,.125)
for source in sourceFiles:
meta,_,_,_ = omni.from_npz(source)
# times = meta['jds']
times = [meta['jds'][0]]
meta['SimulationOptions'] = opts.__dict__
#vismdl = {opts.pol: {blIndex: np.zeros((len(times), opts.nchan), dtype=complex) for blIndex in blIndices}}
visibilities = np.zeros((len(allus), len(freqs)),dtype=complex)
allPixelTops, allPixelAzAlts = [],[]
for time in times:
aa.set_jultime(time)
pixelEq = a.coord.radec2eq([pixelRAs,pixelDecs])
pixelTop = a.coord.eq2top_m(-aa.sidereal_time(),aa.lat).dot(pixelEq)
allPixelTops.append(pixelTop)
allPixelAzAlts.append(a.coord.top2azalt(pixelTop))
for chan in range(opts.nchan):
import aipy as a
import numpy as np
import optparse, sys, os
import capo.omni as omni, capo.zsa as zsa, capo.arp as arp
import healpy as hp
import matplotlib.pyplot as plt
#%%
meta,_,mdl,_ = omni.from_npz('./SimulatedVisibilities/simple.2456679.35658.xx.npz')
meta2,_,mdl2,_ = omni.from_npz('../Data/zen.2456679.35658.xx.npz')
#%%
data = mdl['xx']
data2 = mdl2['xx']
bls = [(0, 103), (1, 4), (0, 101), (0, 62), (0, 100), (1, 13), (1, 70), (1, 56), (1, 71), (1, 59), (0, 97), (12, 43), (9, 71), (9, 59), (57, 64)]
conjugate = [(0,101), (0,62), (0,100), (0,97), (12,43), (57,64)]
aa = a.cal.get_aa('psa6622_v003', 0.1/203, 0.1, 203)
blLengths = [np.abs((np.round(aa.get_baseline(blIndex[0],blIndex[1]) * a.const.c / 1.5e12))[0]) for blIndex in bls]
freqs = aa.get_afreqs()
#! /usr/bin/env python -u
import numpy as n
import aipy as a
import optparse, sys
import capo.omni as omni, capo.zsa as zsa, capo.arp as arp
import pylab as p
o = optparse.OptionParser()
a.scripting.add_standard_options(o, cal=True)
opts, args = o.parse_args(sys.argv[1:])
pol = 'xx'
freqs = n.arange(.1, .2, .1 / 203)
aa = a.cal.get_aa('psa6622_v003', freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
_, _, mdl, _, jds, lsts, freqs = omni.from_npz(args[0])
seps = n.arange(1, 16) #* 15m = baseline lengths
bls = [(0, i) for i in seps]
sep2ij = {}
for ij in mdl['xx'].keys():
bl = a.miriad.ij2bl(ij[0], ij[1])
try:
s = tuple(map(int, bl2sep[bl].split(',')))
except (KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1] - 1] = ij
print bls
conjugate = [(0, 101), (0, 62), (0, 100), (0, 97), (12, 43), (57, 64)]
import numpy as n
import aipy as a
import optparse, sys
import capo.omni as omni, capo.zsa as zsa, capo.arp as arp
import pylab as p
o = optparse.OptionParser()
a.scripting.add_standard_options(o, cal=True)
opts, args = o.parse_args(sys.argv[1:])
pol = "xx"
freqs = n.arange(0.1, 0.2, 0.1 / 203)
aa = a.cal.get_aa("psa6622_v003", freqs)
blstr, _, bl2sep = zsa.grid2ij(aa.ant_layout)
_, _, mdl, _, jds, lsts, freqs = omni.from_npz(args[0])
seps = n.arange(1, 16) # * 15m = baseline lengths
bls = [(0, i) for i in seps]
sep2ij = {}
for ij in mdl["xx"].keys():
bl = a.miriad.ij2bl(ij[0], ij[1])
try:
s = tuple(map(int, bl2sep[bl].split(",")))
except (KeyError):
continue
if s in bls and s[0] == 0:
bls[s[-1] - 1] = ij
print bls
conjugate = [(0, 101), (0, 62), (0, 100), (0, 97), (12, 43), (57, 64)]
