def print_timestamp(fn):
src_dict = {'CasA': eph.CasA, 'TauA': eph.TauA, 'CygA': eph.CygA, 'VirA': eph.VirA,
'1929': 292.0, '0329': 54.0}
try:
f = h5py.File(fn, 'r')
except IOError:
print "File couldn't be opened"
return
times = f['index_map']['time'].value['ctime'][:]
print "-------------------------------------"
print "start time %s in PT\n" % (eph.unix_to_datetime(times[0]))
print "RA range %f : %f" % (np.round(eph.transit_RA(times[0]), 1),
np.round(eph.transit_RA(times[-1]),1))
print "-------------------------------------"
srcz = []
for src in src_dict:
if eph.transit_times(src_dict[src], times[0])[0] < times[-1]:
print "%s is in this file" % src
srcz.append(src)
return srcz
def fs_from_file(filename, frq, src,
del_t=900, transposed=True, subtract_avg=False):
f = h5py.File(filename, 'r')
times = f['index_map']['time'].value['ctime'] + 10.6
src_trans = eph.transit_times(src, times[0])
# try to account for differential arrival time from cylinder rotation.
del_phi = (src._dec - np.radians(eph.CHIMELATITUDE)) * np.sin(np.radians(1.988))
del_phi *= (24 * 3600.0) / (2 * np.pi)
# Adjust the transit time accordingly
src_trans += del_phi
# Select +- del_t of transit, accounting for the mispointing
t_range = np.where((times < src_trans + del_t) & (times > src_trans - del_t))[0]
times = times[t_range[0]:t_range[-1]]#[offp::2] test
print "Time range:", times[0], times[-1]
print "\n...... This data is from %s starting at RA: %f ...... \n" \
% (eph.unix_to_datetime(times[0]), eph.transit_RA(times[0]))
if transposed is True:
v = f['vis'][frq[0]:frq[-1]+1, :]
v = v[..., t_range[0]:t_range[-1]]
vis = v['r'] + 1j * v['i']
del v
# Read in time and freq slice if data has not yet been transposed
if transposed is False:
v = f['vis'][t_range[0]:t_range[-1], frq[0]:frq[-1]+1, :]
vis = v['r'][:] + 1j * v['i'][:]
del v
vis = np.transpose(vis, (1, 2, 0))
inp = gen_inp()[0]
# Remove offset from galaxy
if subtract_avg is True:
vis -= 0.5 * (vis[..., 0] + vis[..., -1])[..., np.newaxis]
freq_MHZ = 800.0 - np.array(frq) / 1024.0 * 400.
print len(inp)
baddies = np.where(np.isnan(tools.get_feed_positions(inp)[:, 0]))[0]
# Fringestop to location of "src"
data_fs = tools.fringestop_pathfinder(vis, eph.transit_RA(times), freq_MHZ, inp, src)
# data_fs = fringestop_pathfinder(vis, eph.transit_RA(times), freq_MHZ, inp, src)
return data_fs
def print_timestamp(fn):
src_dict = {
'CasA': eph.CasA,
'TauA': eph.TauA,
'CygA': eph.CygA,
'VirA': eph.VirA,
'1929': 292.0,
'0329': 54.0
}
try:
f = h5py.File(fn, 'r')
except IOError:
print "File couldn't be opened"
return
times = f['index_map']['time'].value['ctime'][:]
print "-------------------------------------"
print "start time %s in PT\n" % (eph.unix_to_datetime(times[0]))
print "RA range %f : %f" % (np.round(eph.transit_RA(
times[0]), 1), np.round(eph.transit_RA(times[-1]), 1))
print "-------------------------------------"
srcz = []
for src in src_dict:
if eph.transit_times(src_dict[src], times[0])[0] < times[-1]:
print "%s is in this file" % src
srcz.append(src)
return srcz
def drao_aro_RA(times):
""" Go from DRAO transit RA to ARO transit RA
"""
RA_trans = eph.transit_RA(times)
long_diff = AROLONGITUDE - eph.CHIMELONGITUDE
return RA_trans + long_diff
def test_drao_aro_RA(self):
times = self.times
diff_true = np.round(np.mod(eph.CHIMELONGITUDE - (-78.0730), 360))
DRAO_RA_trans = eph.transit_RA(times)
ARO_RA_trans = pv.drao_aro_RA(times)
diff = np.round(np.mod(DRAO_RA_trans - ARO_RA_trans, 360).mean())
assert diff == diff_true
assert np.round(np.std(diff), 2)==0.0
def get_data(file, start=False, stop=False):
if not start and not stop:
data = chand.from_acq_h5(file)
else:
data = chand.from_acq_h5(file, start=start, stop=stop)
Vis = data.vis
print "vis shape:", Vis.shape
ctime = data.timestamp
if np.isnan(ctime).sum() > 0:
print "Removing NaNs"
Vis[:, :, ctime != ctime] = 0.0
ctime[ctime != ctime] = 0.0
RA = eph.transit_RA(ctime)
return data, Vis, ctime, RA
def get_data(file, start=False, stop=False):
if not start and not stop:
data = chand.from_acq_h5(file)
else:
data = chand.from_acq_h5(file, start=start, stop=stop)
Vis = data.vis
print "vis shape:", Vis.shape
ctime = data.timestamp
if np.isnan(ctime).sum() > 0:
print "Removing NaNs"
Vis[:, :, ctime != ctime] = 0.0
ctime[ctime != ctime] = 0.0
RA = eph.transit_RA(ctime)
fpga_count = data.datasets['timestamp_fpga_count'][:]
return data, Vis, ctime, RA, fpga_count
def fringestop_and_sum(fn, feeds, freq, src, transposed=True,
return_unfs=True, meridian=False, del_t=1500, frick=None):
""" Take an input file fn and a set of feeds and return
a formed beam on src.
"""
if transposed is True:
r = andata.Reader(fn)
r.freq_sel = freq
X = r.read()
times = r.time
else:
f = h5py.File(fn, 'r')
times = f['index_map']['time'].value['ctime']
print "Read in data"
# Get transit time for source
src_trans = eph.transit_times(src, times[0])
del_phi = 1.30 * (src._dec - np.radians(eph.CHIMELATITUDE)) * np.sin(np.radians(1.988))
del_phi *= (24 * 3600.0) / (2 * np.pi)
# Adjust the transit time accordingly
src_trans += del_phi
# Select +- del_t of transit, accounting for the mispointing
t_range = np.where((times < src_trans + del_t) & (times > src_trans - del_t))[0]
times = times[t_range[0]:t_range[-1]]#[offp::2] test
print "Time range:", times[0], times[-1]
# Generate correctly ordered corrinputs
corrinput_real = gen_inp()[0]
inp = np.array(corrinput_real)
# Ensure vis array is in correct order (freq, prod, time)
if transposed is True:
data = X.vis[:, :, t_range[0]:t_range[-1]]
freq = X.freq
else:
v = f['vis'][t_range[0]:t_range[-1], freq, :]
vis = v['r'] + 1j * v['i']
data = vis.transpose()[np.newaxis]
del vis
freq = 800 - 400.0 / 1024 * freq
freq = np.array([freq])
# autos = auto_corrs(256)
# offp = (abs(data[:, autos, 0::2]).mean() > (abs(data[:, autos, 1::2]).mean())).astype(int)
# data = data[..., offp::2] test
data_unfs = sum_corrs(data.copy(), feeds)
ra_ = eph.transit_RA(times)
ra_2 = nolan_ra(times)
#ra_ = ra_2.copy()
if meridian is True:
ra = np.ones_like(ra_) * np.degrees(src._ra)
else:
ra = ra_
print len(inp)
dfs = tools.fringestop_pathfinder(data.copy(), ra, freq, inp, src)
#dfs = fringestop_pathfinder(data.copy(), ra_2, freq, inp, src, frick=frick)
# dfs = fringestop_pathfinder(data.copy(), ra_1, freq, inp, src, frick=frick)
# fp = np.loadtxt('/home/connor/feed_layout_decrease.txt')
# PH = fill_nolan(times, src._ra * 180.0 / np.pi, src._dec * 180.0 / np.pi, fp)
dfs_sum = sum_corrs(dfs, feeds)
if return_unfs is True:
return dfs_sum, ra_, dfs, data
else:
return dfs_sum, ra_
def solve_ps_transit(filename, corrs, feeds, inp,
src, nfreq=1024, transposed=False, nfeed=128):
""" Function that fringestops time slice
where point source is in the beam, takes
all correlations for a given polarization, and then
eigendecomposes the correlation matrix freq by freq
after removing the fpga phases. It will also
plot intermediate steps to verify the phase solution.
Parameters
----------
filename : np.str
Full-path filename
corrs : list
List of correlations to use in solver
feeds : list
List of feeds to use
inp :
Correlator inputs (output of ch_util.tools.get_correlator_inputs)
src : ephem.FixedBody
Source to calibrate off of. e.g. ch_util.ephemeris.TauA
Returns
-------
Gains : np.array
Complex gain array (nfreq, nfeed)
"""
nsplit = 32 # Number of freq chunks to divide nfreq into
del_t = 800
f = h5py.File(filename, 'r')
# Add half an integration time to each. Hack.
times = f['index_map']['time'].value['ctime'] + 10.50
src_trans = eph.transit_times(src, times[0])
# try to account for differential arrival time from
# cylinder rotation.
del_phi = (src._dec - np.radians(eph.CHIMELATITUDE)) \
* np.sin(np.radians(1.988))
del_phi *= (24 * 3600.0) / (2 * np.pi)
# Adjust the transit time accordingly
src_trans += del_phi
# Select +- del_t of transit, accounting for the mispointing
t_range = np.where((times < src_trans +
del_t) & (times > src_trans - del_t))[0]
print "\n...... This data is from %s starting at RA: %f ...... \n" \
% (eph.unix_to_datetime(times[0]), eph.transit_RA(times[0]))
assert (len(t_range) > 0), "Source is not in this acq"
# Create gains array to fill in solution
Gains = np.zeros([nfreq, nfeed], np.complex128)
print "Starting the solver"
times = times[t_range[0]:t_range[-1]]
k=0
# Start at a strong freq channel that can be plotted
# and from which we can find the noise source on-sample
for i in range(12, nsplit) + range(0, 12):
k+=1
# Divides the arrays up into nfreq / nsplit freq chunks and solves those
frq = range(i * nfreq // nsplit, (i+1) * nfreq // nsplit)
print " %d:%d \n" % (frq[0], frq[-1])
# Read in time and freq slice if data has already been transposed
if transposed is True:
v = f['vis'][frq[0]:frq[-1]+1, corrs, :]
v = v[..., t_range[0]:t_range[-1]]
vis = v['r'] + 1j * v['i']
if k==1:
autos = auto_corrs(nfeed)
offp = (abs(vis[:, autos, 0::2]).mean() > \
(abs(vis[:, autos, 1::2]).mean())).astype(int)
times = times[offp::2]
vis = vis[..., offp::2]
gg = f['gain_coeff'][frq[0]:frq[-1]+1,
feeds, t_range[0]:t_range[-1]][..., offp::2]
gain_coeff = gg['r'] + 1j * gg['i']
del gg
# Read in time and freq slice if data has not yet been transposed
if transposed is False:
print "TRANSPOSED V OF CODE DOESN'T WORK YET!"
v = f['vis'][t_range[0]:t_range[-1]:2, frq[0]:frq[-1]+1, corrs]
vis = v['r'][:] + 1j * v['i'][:]
del v
gg = f['gain_coeff'][0, frq[0]:frq[-1]+1, feeds]
gain_coeff = gg['r'][:] + 1j * gg['i'][:]
vis = vis[..., offp::2]
vis = np.transpose(vis, (1, 2, 0))
# Remove fpga gains from data
vis = remove_fpga_gains(vis, gain_coeff, nfeed=nfeed, triu=False)
# Remove offset from galaxy
vis -= 0.5 * (vis[..., 0] + vis[..., -1])[..., np.newaxis]
# Get physical freq for fringestopper
freq_MHZ = 800.0 - np.array(frq) / 1024.0 * 400.
baddies = np.where(np.isnan(tools.get_feed_positions(inp)[:, 0]))[0]
a, b, c = select_corrs(baddies, nfeed=128)
vis[:, a + b] = 0.0
# Fringestop to location of "src"
data_fs = tools.fringestop_pathfinder(vis, eph.transit_RA(times), freq_MHZ, inp, src)
del vis
dr, sol_arr = solve_gain(data_fs)
# Find index of point source transit
drlist = np.argmax(dr, axis=-1)
# If multiple freq channels are zerod, the trans_pix
# will end up being 0. This is bad, so ensure that
# you are only looking for non-zero transit pixels.
drlist = [x for x in drlist if x != 0]
trans_pix = np.argmax(np.bincount(drlist))
assert trans_pix != 0.0
Gains[frq] = sol_arr[..., trans_pix-3:trans_pix+4].mean(-1)
zz = h5py.File('data' + str(i) + '.hdf5','w')
zz.create_dataset('data', data=dr)
zz.close()
print "%f, %d Nans out of %d" % (np.isnan(sol_arr).sum(), np.isnan(Gains[frq]).sum(), np.isnan(Gains[frq]).sum())
print trans_pix, sol_arr[..., trans_pix-3:trans_pix+4].mean(-1).sum(), sol_arr.mean(-1).sum()
# Plot up post-fs phases to see if everything has been fixed
if frq[0] == 12 * nsplit:
print "======================"
print " Plotting up freq: %d" % frq[0]
print "======================"
img_nm = './phs_plots/dfs' + np.str(frq[17]) + np.str(np.int(time.time())) +'.png'
img_nmcorr = './phs_plots/dfs' + np.str(frq[17]) + np.str(np.int(time.time())) +'corr.png'
plt_gains(data_fs, 0, img_name=img_nm, bad_chans=baddies)
dfs_corr = correct_dfs(data_fs, np.angle(Gains[frq])[..., np.newaxis], nfeed=128)
plt_gains(dfs_corr, 0, img_name=img_nmcorr, bad_chans=baddies)
del dfs_corr
del data_fs, a
return Gains
def test_transit_RA():
# transit RA is deprecated and should just throw an exception
with pytest.raises(NotImplementedError):
ephemeris.transit_RA(0.0)
elif on_gate > 9:
on_vis = 0.80 * arr_corr[..., on_gate] + \
0.10 * (arr_corr[..., on_gate+1] + arr_corr[..., on_gate-1])
off_vis = 0.5 * (arr_corr[..., on_gate-3] + \
arr_corr[..., on_gate+3])
psr_vis = on_vis - off_vis
x = 7
y = 3
print psr_vis.shape, time.shape
RC = chp.PulsarPipeline(psr_vis[:, np.newaxis], time)
RC.RA_src, RC.dec = 53.51337, 54.6248916
RC.ln = args.ln
RC.RA = eph.transit_RA(time)
RC.corrs = corrs[rank]
RC.fringestop()
psr_vis = RC.data[:, 0]
print "0"
psr_vis = misc.correct_delay(psr_vis, nfreq=nfreq)
print "1"
if args.svd == 1:
psr_vis_cal = misc.svd_model(psr_vis, phase_only=True)
print "Performing SVD on dynamic spectrum, rank %d" % rank
else:
print "Skipping SVD"
psr_vis_cal = psr_vis[:]
its.")
parser.add_argument("Data", help="Directory containing acquisition files.")
parser.add_argument("--Objects", help="Celestial objects to fit", default='All')
parser.add_argument("--minfile", help="Minfile number e.g. 0051", default="")
parser.add_argument("--maxfile", help="Maxfile number e.g. 0051", default="")
args = parser.parse_args()
files = np.str(args.Data) + '*h5.' + args.maxfile + '*'
print ""
print "Reading in Data:", files
Data, vis, utime, RA = misc.get_data(files)
print "RA range of data:", RA.min(),":",RA.max()
RA_sun = eph.transit_RA(eph.solar_transit(utime[0]))
sun_RA_low = RA_sun - 6
sun_RA_high = RA_sun + 6
print ""
print "Das sun was at: %f" % RA_sun
# Create a dictionary with each fitting object's information in the form: {"Obj": [RA_min, RA_max, Declination]}.
celestial_object = { "CasA": [344, 358, 58.83], "TauA": [77, 87, 83.6], "CygA": [297, 302, 40.73], "Sun": [sun_RA_low, sun_RA_high, 0]}
if args.Objects != "All":
srcs2fit = [args.Objects]
else:
srcs2fit = celestial_object.keys()
for src in srcs2fit:
vis_obj = vis[:, :, ( RA > celestial_object[src][0] ) & ( RA < celestial_object[src][1])]
def fringestop_and_sum(fn,
feeds,
freq,
src,
transposed=True,
return_unfs=True,
meridian=False,
del_t=1500,
frick=None):
""" Take an input file fn and a set of feeds and return
a formed beam on src.
"""
if transposed is True:
r = andata.Reader(fn)
r.freq_sel = freq
X = r.read()
times = r.time
else:
f = h5py.File(fn, 'r')
times = f['index_map']['time'].value['ctime']
print "Read in data"
# Get transit time for source
src_trans = eph.transit_times(src, times[0])
del_phi = 1.30 * (src._dec - np.radians(eph.CHIMELATITUDE)) * np.sin(
np.radians(1.988))
del_phi *= (24 * 3600.0) / (2 * np.pi)
# Adjust the transit time accordingly
src_trans += del_phi
# Select +- del_t of transit, accounting for the mispointing
t_range = np.where((times < src_trans + del_t)
& (times > src_trans - del_t))[0]
times = times[t_range[0]:t_range[-1]] #[offp::2] test
print "Time range:", times[0], times[-1]
# Generate correctly ordered corrinputs
corrinput_real = gen_inp()[0]
inp = np.array(corrinput_real)
# Ensure vis array is in correct order (freq, prod, time)
if transposed is True:
data = X.vis[:, :, t_range[0]:t_range[-1]]
freq = X.freq
else:
v = f['vis'][t_range[0]:t_range[-1], freq, :]
vis = v['r'] + 1j * v['i']
data = vis.transpose()[np.newaxis]
del vis
freq = 800 - 400.0 / 1024 * freq
freq = np.array([freq])
# autos = auto_corrs(256)
# offp = (abs(data[:, autos, 0::2]).mean() > (abs(data[:, autos, 1::2]).mean())).astype(int)
# data = data[..., offp::2] test
data_unfs = sum_corrs(data.copy(), feeds)
ra_ = eph.transit_RA(times)
ra_2 = nolan_ra(times)
#ra_ = ra_2.copy()
if meridian is True:
ra = np.ones_like(ra_) * np.degrees(src._ra)
else:
ra = ra_
print len(inp)
dfs = tools.fringestop_pathfinder(data.copy(), ra, freq, inp, src)
#dfs = fringestop_pathfinder(data.copy(), ra_2, freq, inp, src, frick=frick)
# dfs = fringestop_pathfinder(data.copy(), ra_1, freq, inp, src, frick=frick)
# fp = np.loadtxt('/home/connor/feed_layout_decrease.txt')
# PH = fill_nolan(times, src._ra * 180.0 / np.pi, src._dec * 180.0 / np.pi, fp)
dfs_sum = sum_corrs(dfs, feeds)
if return_unfs is True:
return dfs_sum, ra_, dfs, data
else:
return dfs_sum, ra_
def fs_from_file(filename,
frq,
src,
del_t=900,
transposed=True,
subtract_avg=False):
f = h5py.File(filename, 'r')
times = f['index_map']['time'].value['ctime'] + 10.6
src_trans = eph.transit_times(src, times[0])
# try to account for differential arrival time from cylinder rotation.
del_phi = (src._dec - np.radians(eph.CHIMELATITUDE)) * np.sin(
np.radians(1.988))
del_phi *= (24 * 3600.0) / (2 * np.pi)
# Adjust the transit time accordingly
src_trans += del_phi
# Select +- del_t of transit, accounting for the mispointing
t_range = np.where((times < src_trans + del_t)
& (times > src_trans - del_t))[0]
times = times[t_range[0]:t_range[-1]] #[offp::2] test
print "Time range:", times[0], times[-1]
print "\n...... This data is from %s starting at RA: %f ...... \n" \
% (eph.unix_to_datetime(times[0]), eph.transit_RA(times[0]))
if transposed is True:
v = f['vis'][frq[0]:frq[-1] + 1, :]
v = v[..., t_range[0]:t_range[-1]]
vis = v['r'] + 1j * v['i']
del v
# Read in time and freq slice if data has not yet been transposed
if transposed is False:
v = f['vis'][t_range[0]:t_range[-1], frq[0]:frq[-1] + 1, :]
vis = v['r'][:] + 1j * v['i'][:]
del v
vis = np.transpose(vis, (1, 2, 0))
inp = gen_inp()[0]
# Remove offset from galaxy
if subtract_avg is True:
vis -= 0.5 * (vis[..., 0] + vis[..., -1])[..., np.newaxis]
freq_MHZ = 800.0 - np.array(frq) / 1024.0 * 400.
print len(inp)
baddies = np.where(np.isnan(tools.get_feed_positions(inp)[:, 0]))[0]
# Fringestop to location of "src"
data_fs = tools.fringestop_pathfinder(vis, eph.transit_RA(times), freq_MHZ,
inp, src)
# data_fs = fringestop_pathfinder(vis, eph.transit_RA(times), freq_MHZ, inp, src)
return data_fs
def solve_ps_transit(filename,
corrs,
feeds,
inp,
src,
nfreq=1024,
transposed=False,
nfeed=128):
""" Function that fringestops time slice
where point source is in the beam, takes
all correlations for a given polarization, and then
eigendecomposes the correlation matrix freq by freq
after removing the fpga phases. It will also
plot intermediate steps to verify the phase solution.
Parameters
----------
filename : np.str
Full-path filename
corrs : list
List of correlations to use in solver
feeds : list
List of feeds to use
inp :
Correlator inputs (output of ch_util.tools.get_correlator_inputs)
src : ephem.FixedBody
Source to calibrate off of. e.g. ch_util.ephemeris.TauA
Returns
-------
Gains : np.array
Complex gain array (nfreq, nfeed)
"""
nsplit = 32 # Number of freq chunks to divide nfreq into
del_t = 800
f = h5py.File(filename, 'r')
# Add half an integration time to each. Hack.
times = f['index_map']['time'].value['ctime'] + 10.50
src_trans = eph.transit_times(src, times[0])
# try to account for differential arrival time from
# cylinder rotation.
del_phi = (src._dec - np.radians(eph.CHIMELATITUDE)) \
* np.sin(np.radians(1.988))
del_phi *= (24 * 3600.0) / (2 * np.pi)
# Adjust the transit time accordingly
src_trans += del_phi
# Select +- del_t of transit, accounting for the mispointing
t_range = np.where((times < src_trans + del_t)
& (times > src_trans - del_t))[0]
print "\n...... This data is from %s starting at RA: %f ...... \n" \
% (eph.unix_to_datetime(times[0]), eph.transit_RA(times[0]))
assert (len(t_range) > 0), "Source is not in this acq"
# Create gains array to fill in solution
Gains = np.zeros([nfreq, nfeed], np.complex128)
print "Starting the solver"
times = times[t_range[0]:t_range[-1]]
k = 0
# Start at a strong freq channel that can be plotted
# and from which we can find the noise source on-sample
for i in range(12, nsplit) + range(0, 12):
k += 1
# Divides the arrays up into nfreq / nsplit freq chunks and solves those
frq = range(i * nfreq // nsplit, (i + 1) * nfreq // nsplit)
print " %d:%d \n" % (frq[0], frq[-1])
# Read in time and freq slice if data has already been transposed
if transposed is True:
v = f['vis'][frq[0]:frq[-1] + 1, corrs, :]
v = v[..., t_range[0]:t_range[-1]]
vis = v['r'] + 1j * v['i']
if k == 1:
autos = auto_corrs(nfeed)
offp = (abs(vis[:, autos, 0::2]).mean() > \
(abs(vis[:, autos, 1::2]).mean())).astype(int)
times = times[offp::2]
vis = vis[..., offp::2]
gg = f['gain_coeff'][frq[0]:frq[-1] + 1, feeds,
t_range[0]:t_range[-1]][..., offp::2]
gain_coeff = gg['r'] + 1j * gg['i']
del gg
# Read in time and freq slice if data has not yet been transposed
if transposed is False:
print "TRANSPOSED V OF CODE DOESN'T WORK YET!"
v = f['vis'][t_range[0]:t_range[-1]:2, frq[0]:frq[-1] + 1, corrs]
vis = v['r'][:] + 1j * v['i'][:]
del v
gg = f['gain_coeff'][0, frq[0]:frq[-1] + 1, feeds]
gain_coeff = gg['r'][:] + 1j * gg['i'][:]
vis = vis[..., offp::2]
vis = np.transpose(vis, (1, 2, 0))
# Remove fpga gains from data
vis = remove_fpga_gains(vis, gain_coeff, nfeed=nfeed, triu=False)
# Remove offset from galaxy
vis -= 0.5 * (vis[..., 0] + vis[..., -1])[..., np.newaxis]
# Get physical freq for fringestopper
freq_MHZ = 800.0 - np.array(frq) / 1024.0 * 400.
baddies = np.where(np.isnan(tools.get_feed_positions(inp)[:, 0]))[0]
a, b, c = select_corrs(baddies, nfeed=128)
vis[:, a + b] = 0.0
# Fringestop to location of "src"
data_fs = tools.fringestop_pathfinder(vis, eph.transit_RA(times),
freq_MHZ, inp, src)
del vis
dr, sol_arr = solve_gain(data_fs)
# Find index of point source transit
drlist = np.argmax(dr, axis=-1)
# If multiple freq channels are zerod, the trans_pix
# will end up being 0. This is bad, so ensure that
# you are only looking for non-zero transit pixels.
drlist = [x for x in drlist if x != 0]
trans_pix = np.argmax(np.bincount(drlist))
assert trans_pix != 0.0
Gains[frq] = sol_arr[..., trans_pix - 3:trans_pix + 4].mean(-1)
zz = h5py.File('data' + str(i) + '.hdf5', 'w')
zz.create_dataset('data', data=dr)
zz.close()
print "%f, %d Nans out of %d" % (np.isnan(sol_arr).sum(),
np.isnan(Gains[frq]).sum(),
np.isnan(Gains[frq]).sum())
print trans_pix, sol_arr[..., trans_pix - 3:trans_pix +
4].mean(-1).sum(), sol_arr.mean(-1).sum()
# Plot up post-fs phases to see if everything has been fixed
if frq[0] == 12 * nsplit:
print "======================"
print " Plotting up freq: %d" % frq[0]
print "======================"
img_nm = './phs_plots/dfs' + np.str(frq[17]) + np.str(
np.int(time.time())) + '.png'
img_nmcorr = './phs_plots/dfs' + np.str(frq[17]) + np.str(
np.int(time.time())) + 'corr.png'
plt_gains(data_fs, 0, img_name=img_nm, bad_chans=baddies)
dfs_corr = correct_dfs(data_fs,
np.angle(Gains[frq])[..., np.newaxis],
nfeed=128)
plt_gains(dfs_corr, 0, img_name=img_nmcorr, bad_chans=baddies)
del dfs_corr
del data_fs, a
return Gains
def process(self, sstream, inputmap, inputmask):
"""Determine calibration from a timestream.
Parameters
----------
sstream : andata.CorrData or containers.SiderealStream
Timestream collected during the day.
inputmap : list of :class:`CorrInput`
A list describing the inputs as they are in the file.
inputmask : containers.CorrInputMask
Mask indicating which correlator inputs to use in the
eigenvalue decomposition.
Returns
-------
suntrans : containers.SunTransit
Response to the sun.
"""
from operator import itemgetter
from itertools import groupby
from .calibration import _extract_diagonal, solve_gain
# Ensure that we are distributed over frequency
sstream.redistribute("freq")
# Find the local frequencies
nfreq = sstream.vis.local_shape[0]
sfreq = sstream.vis.local_offset[0]
efreq = sfreq + nfreq
# Get the local frequency axis
freq = sstream.freq["centre"][sfreq:efreq]
wv = 3e2 / freq
# Get times
if hasattr(sstream, "time"):
time = sstream.time
ra = ephemeris.transit_RA(time)
else:
ra = sstream.index_map["ra"][:]
csd = (sstream.attrs["lsd"]
if "lsd" in sstream.attrs else sstream.attrs["csd"])
csd = csd + ra / 360.0
time = ephemeris.csd_to_unix(csd)
# Only examine data between sunrise and sunset
time_flag = np.zeros(len(time), dtype=np.bool)
rise = ephemeris.solar_rising(time[0] - 24.0 * 3600.0,
end_time=time[-1])
for rr in rise:
ss = ephemeris.solar_setting(rr)[0]
time_flag |= (time >= rr) & (time <= ss)
if not np.any(time_flag):
self.log.debug(
"No daytime data between %s and %s.",
ephemeris.unix_to_datetime(time[0]).strftime("%b %d %H:%M"),
ephemeris.unix_to_datetime(time[-1]).strftime("%b %d %H:%M"),
)
return None
# Convert boolean flag to slices
time_index = np.where(time_flag)[0]
time_slice = []
ntime = 0
for key, group in groupby(
enumerate(time_index),
lambda index_item: index_item[0] - index_item[1]):
group = list(map(itemgetter(1), group))
ngroup = len(group)
time_slice.append(
(slice(group[0], group[-1] + 1), slice(ntime, ntime + ngroup)))
ntime += ngroup
time = np.concatenate([time[slc[0]] for slc in time_slice])
ra = np.concatenate([ra[slc[0]] for slc in time_slice])
# Get ra, dec, alt of sun
sun_pos = np.array([
ra_dec_of(ephemeris.skyfield_wrapper.ephemeris["sun"], t)
for t in time
])
# Convert from ra to hour angle
sun_pos[:, 0] = np.radians(ra) - sun_pos[:, 0]
# Determine good inputs
nfeed = len(inputmap)
good_input = np.arange(
nfeed, dtype=np.int)[inputmask.datasets["input_mask"][:]]
# Use input map to figure out which are the X and Y feeds
xfeeds = np.array([
idx for idx, inp in enumerate(inputmap)
if tools.is_chime_x(inp) and (idx in good_input)
])
yfeeds = np.array([
idx for idx, inp in enumerate(inputmap)
if tools.is_chime_y(inp) and (idx in good_input)
])
self.log.debug(
"Performing sun calibration with %d/%d good feeds (%d xpol, %d ypol).",
len(good_input),
nfeed,
len(xfeeds),
len(yfeeds),
)
# Construct baseline vector for each visibility
feed_pos = tools.get_feed_positions(inputmap)
vis_pos = np.array([
feed_pos[ii] - feed_pos[ij]
for ii, ij in sstream.index_map["prod"][:]
])
vis_pos = np.where(np.isnan(vis_pos), np.zeros_like(vis_pos), vis_pos)
u = (vis_pos[np.newaxis, :, 0] / wv[:, np.newaxis])[:, :, np.newaxis]
v = (vis_pos[np.newaxis, :, 1] / wv[:, np.newaxis])[:, :, np.newaxis]
# Create container to hold results of fit
suntrans = containers.SunTransit(time=time,
pol_x=xfeeds,
pol_y=yfeeds,
axes_from=sstream)
for key in suntrans.datasets.keys():
suntrans.datasets[key][:] = 0.0
# Set coordinates
suntrans.coord[:] = sun_pos
# Loop over time slices
for slc_in, slc_out in time_slice:
# Extract visibility slice
vis_slice = sstream.vis[..., slc_in].copy()
ha = (sun_pos[slc_out, 0])[np.newaxis, np.newaxis, :]
dec = (sun_pos[slc_out, 1])[np.newaxis, np.newaxis, :]
# Extract the diagonal (to be used for weighting)
norm = (_extract_diagonal(vis_slice, axis=1).real)**0.5
norm = tools.invert_no_zero(norm)
# Fringestop
if self.fringestop:
vis_slice *= tools.fringestop_phase(
ha, np.radians(ephemeris.CHIMELATITUDE), dec, u, v)
# Solve for the point source response of each set of polarisations
ev_x, resp_x, err_resp_x = solve_gain(vis_slice,
feeds=xfeeds,
norm=norm[:, xfeeds])
ev_y, resp_y, err_resp_y = solve_gain(vis_slice,
feeds=yfeeds,
norm=norm[:, yfeeds])
# Save to container
suntrans.evalue_x[..., slc_out] = ev_x
suntrans.evalue_y[..., slc_out] = ev_y
suntrans.response[:, xfeeds, slc_out] = resp_x
suntrans.response[:, yfeeds, slc_out] = resp_y
suntrans.response_error[:, xfeeds, slc_out] = err_resp_x
suntrans.response_error[:, yfeeds, slc_out] = err_resp_y
# If requested, fit a model to the primary beam of the sun transit
if self.model_fit:
# Estimate peak RA
i_transit = np.argmin(np.abs(sun_pos[:, 0]))
body = ephemeris.skyfield_wrapper.ephemeris["sun"]
obs = ephemeris._get_chime()
obs.date = ephemeris.unix_to_ephem_time(time[i_transit])
body.compute(obs)
peak_ra = ephemeris.peak_RA(body)
dra = ra - peak_ra
dra = np.abs(dra - (dra > np.pi) * 2.0 * np.pi)[np.newaxis,
np.newaxis, :]
# Estimate FWHM
sig_x = cal_utils.guess_fwhm(freq,
pol="X",
dec=body.dec,
sigma=True)[:, np.newaxis, np.newaxis]
sig_y = cal_utils.guess_fwhm(freq,
pol="Y",
dec=body.dec,
sigma=True)[:, np.newaxis, np.newaxis]
# Only fit ra values above the specified dynamic range threshold
fit_flag = np.zeros([nfreq, nfeed, ntime], dtype=np.bool)
fit_flag[:, xfeeds, :] = dra < (self.nsig * sig_x)
fit_flag[:, yfeeds, :] = dra < (self.nsig * sig_y)
# Fit model for the complex response of each feed to the point source
param, param_cov = cal_utils.fit_point_source_transit(
ra,
suntrans.response[:],
suntrans.response_error[:],
flag=fit_flag)
# Save to container
suntrans.add_dataset("flag")
suntrans.flag[:] = fit_flag
suntrans.add_dataset("parameter")
suntrans.parameter[:] = param
suntrans.add_dataset("parameter_cov")
suntrans.parameter_cov[:] = param_cov
# Update attributes
units = "sqrt(" + sstream.vis.attrs.get("units",
"correlator-units") + ")"
suntrans.response.attrs["units"] = units
suntrans.response_error.attrs["units"] = units
suntrans.attrs["source"] = "Sun"
# Return sun transit
return suntrans
