def phase_to_src(self, src='ZEN', generate_uvw=True):
""" Apply phase corrections to phase to source.
Generates new UVW coordinates, then applies geometric delay (W component)
to phase flux data to the new phase center.
Parameters
----------
src (str): Source to phase to. Sources are three capital letters:
ZEN: Zenith (RA will be computed from timestamps)
CYG: Cygnus A
CAS: Cassiopeia A
TAU: Taurus A
VIR: Virgo A
generate_uvw (bool): Skip regeneration of UVW coords?
"""
self.pp.h1("Phasing flux data to %s" % src)
current_tgs = self.d_uv_data["WW"]
if generate_uvw is True:
self.generateUVW(src, update_src=True)
freqs = self.formatFreqs()
w = 2 * np.pi * freqs # Angular freq
# Note WW *is* the geometric delay tg
new_tgs = self.d_uv_data["WW"]
try:
assert self.d_uv_data["FLUX"].dtype == 'float32'
except AssertionError:
raise RuntimeError("Unexpected data type for FLUX: %s" % str(self.d_uv_data["FLUX"].dtype))
flux = self.d_uv_data["FLUX"].view('complex64')
bls = set(self.d_uv_data["BASELINE"])
if not 257 in bls:
bls, ant_arr = coords.generateBaselineIds(self.n_ant, autocorrs=False)
else:
bls, ant_arr = coords.generateBaselineIds(self.n_ant, autocorrs=True)
n_int = len(flux) / len(bls)
for nn in range(n_int):
for ii in range(len(bls)):
# Compute phases for X and Y pol on antennas A and B
tg = new_tgs[nn * len(bls) + ii] - current_tgs[nn * len(bls) + ii]
#if ant1 < ant2:
# tg *= -1 # Compensate for geometry
p = np.exp(-1j * w * tg) # Needs to be -ve as compensating delay
phase_corrs = np.column_stack((p, p, p, p)).flatten()
flux[nn * len(bls) + ii] = flux[nn * len(bls) + ii] * phase_corrs
# Now we have applied geometric delays, we need to
# convert from viewing as complex to viewing as floats
assert flux.dtype == 'complex64'
self.d_uv_data["FLUX"] = flux.view('float32')
def apply_cable_delays(self, debug=True):
""" Apply antenna cable delays
Each cable introduces a phase shift of
phi = 2 pi f t
Visibility is VpVq*, so we need to apply
exp(-i (phip - phiq))
to compensate for cable delay
"""
self.pp.h1("Applying cable delays")
#t0 = time.time()
# Load antenna Electrical Lengths
sol = ledafits_config.SPEED_OF_LIGHT
try:
els = self.z_elength["EL"]
except:
raise RuntimeError("No cable delay data for telescope '%s'" % self.telescope)
els = np.array(els)
tdelts = els / sol
if debug:
print "X-POL (ns) \tY-POL (ns)"
for line in tdelts:
print "%2.2f \t%2.2f" % (line[0] * 1e9, line[1] * 1e9)
# Generate frequency array from metadata
freqs = self.formatFreqs()
# Compute phase delay for each antenna pair
try:
assert self.d_uv_data["FLUX"].dtype == 'float32'
except AssertionError:
raise RuntimeError("Unexpected data type for FLUX: %s" % str(self.d_uv_data["FLUX"].dtype))
# Convert the data to complex values
flux = self.d_uv_data["FLUX"].view('complex64')
# Pre-compute the phasing information
bls, ant_arr = coords.generateBaselineIds(self.n_ant)
w = 2 * np.pi * freqs # Angular freq
delay_corrs = np.zeros((4, len(bls), len(freqs)), dtype=flux.dtype)
for ii in range(len(bls)):
ant1, ant2 = ant_arr[ii]
bl = bls[ii]
td1, td2 = tdelts[ant1 - 1, :], tdelts[ant2 - 1, :]
# Compute phases for X and Y pol on antennas A and B
pxa, pya, pxb, pyb = w * td1[0], w * td1[1], w * td2[0], w * td2[1]
# Corrections require negative sign (otherwise reapplying delays)
delay_corrs[0, ii, :] = np.exp(1j * (pxa - pxb)) # XX
delay_corrs[1, ii, :] = np.exp(1j * (pya - pyb)) # YY
delay_corrs[2, ii, :] = np.exp(1j * (pxa - pyb)) # XY
delay_corrs[3, ii, :] = np.exp(1j * (pya - pxb)) # YX
n_int = len(flux) / len(bls)
for nn in range(n_int):
for ii in range(len(bls)):
e_xx = delay_corrs[0, ii, :].flatten()
e_yy = delay_corrs[1, ii, :].flatten()
e_xy = delay_corrs[2, ii, :].flatten()
e_yx = delay_corrs[3, ii, :].flatten()
phase_corrs = np.column_stack((e_xx, e_yy, e_xy, e_yx)).flatten()
flux[nn * len(bls) + ii] = flux[nn * len(bls) + ii] * phase_corrs
assert flux.dtype == 'complex64'
self.d_uv_data["FLUX"] = flux.view('float32')
def generateUVW(self, src='ZEN', update_src=True, conjugate=False, use_stored=False):
""" Generate UVW coordinates based on timestamps and array geometry
Updates UVW coordinates to phase to a given source. Uses pyEphem observer
along with methods is lib.uvw for computations
src (str): Source to phase to. Sources are three capital letters:
ZEN: Zenith (RA will be computed from timestamps)
CYG: Cygnus A
CAS: Cassiopeia A
TAU: Taurus A
VIR: Virgo A
use_stored (bool): If True, uses stored UVW coordinates (does not recompute).
this is faster than recomputing.
update_src (bool): Default True, update the SOURCE table.
conjugate (bool): Conjuagte UVW coordinates? Do this if things are flipped in map.
"""
self.pp.h1("Generating UVW coordinates")
ra_deg, dec_deg, lst_deg, ha_deg = self._compute_lst_ha(src)
H = np.deg2rad(ha_deg)
d = np.deg2rad(dec_deg)
self.pp.pp("LST: %2.3f deg" % lst_deg)
self.pp.pp("Source RA: %2.3f deg" % ra_deg)
self.pp.pp("Source DEC: %2.3f deg" % dec_deg)
self.pp.pp("HA: %2.3f deg" % np.rad2deg(H))
try:
assert H < 2 * np.pi and d < 2 * np.pi
except AssertionError:
raise ValueError("HA and DEC are too large (may not be in radians).")
# Recreate list of baselines
self.pp.h2("Computing UVW coordinates for %s" % src)
xyz = self.d_array_geometry['STABXYZ']
if 257 in set(self.d_uv_data["BASELINE"]):
bl_ids, ant_arr = coords.generateBaselineIds(self.n_ant)
bl_vecs = coords.computeBaselineVectors(xyz)
else:
bl_ids, ant_arr = coords.generateBaselineIds(self.n_ant, autocorrs=False)
bl_vecs = coords.computeBaselineVectors(xyz, autocorrs=False)
n_iters = int(len(self.d_uv_data["BASELINE"]) / len(bl_ids))
self.pp.h2("Generating timestamps")
dd, tt = [], []
for ii in range(n_iters):
jd, jt = coords.convertToJulianTuple(self.date_obs)
tdelta = self.t_int * ii / 86400.0 # In days
jds = [jd for jj in range(len(ant_arr))]
jts = [jt + tdelta for jj in range(len(ant_arr))]
dd.append(jds)
tt.append(jts)
self.d_uv_data["DATE"] = np.array(dd, dtype='float64').ravel()
self.d_uv_data["TIME"] = np.array(tt, dtype='float64').ravel()
if use_stored:
self.pp.h2("Loading stored values")
self.loadUVW()
else:
uvw = coords.computeUVW(bl_vecs, H, d, conjugate=conjugate)
# Fill with data
# TODO: update this so that it can lock to zenith or phase to src
uu, vv, ww = [], [], []
for ii in range(n_iters):
uu.append(uvw[:, 0])
vv.append(uvw[:, 1])
ww.append(uvw[:, 2])
self.d_uv_data["UU"] = np.array(uu).ravel()
self.d_uv_data["VV"] = np.array(vv).ravel()
self.d_uv_data["WW"] = np.array(ww).ravel()
if update_src:
self.pp.h2("Updating SOURCE table")
self.d_source["SOURCE"] = self.s2arr(src)
self.d_source["RAEPO"] = self.s2arr(ra_deg)
self.d_source["DECEPO"] = self.s2arr(dec_deg)
self.source = src
def _vis_matrix_to_flux(self, vis, remap=False):
"""Convert a visibility matrix to FITS-IDI flux standard
Notes
-----
Visibility matrix should have shape:
(n_int, ant1, ant2, chans, pola, polb)
FITS-IDI is a flattened row on a per-baseline basis:
(xx, yy, xy, yx)
where each xx is (re_chan0, im_chan0, ... re_chanX, im_chanX).
We only read one triangle of the visibility matrix, with ant1 >= ant2
"""
# h2("Generating baseline IDs")
n_int = vis.shape[0]
n_ant = vis.shape[1]
n_chans = vis.shape[3]
n_stk = 4
n_bls = n_ant * (n_ant - 1) / 2 + n_ant
bls, ant_arr = coords.generateBaselineIds(n_ant)
ant_arr0 = np.array(ant_arr) - 1 # Zero indexed
flux = np.zeros([n_bls * n_int, n_chans * n_stk * 2], dtype='float32')
try:
assert vis.dtype == 'complex64'
except AssertionError:
raise RuntimeError('Vis data is not complex64, but is instead %s' % vis.dtype)
for int_num in xrange(n_int):
idx = int_num * n_bls
vis_int = vis[int_num, ...]
for ii in xrange(n_bls):
ant1, ant2 = ant_arr0[ii]
vv = vis_int[ant1, ant2, ...]
xx = vv[:, 0, 0]
yy = vv[:, 1, 1]
xy = vv[:, 0, 1]
yx = vv[:, 1, 0]
flux[idx + ii] = np.column_stack((xx, yy, xy, yx)).flatten().view('float32')
if remap:
self.pp.h2("Remapping antennas")
mapping = {
"255A": "238A",
"255B": "240B",
"242B": "253B",
"240B": "252B",
"252A": "254A",
"252B": "244B",
"256B": "248B",
"248B": "256B",
"245B": "255B",
"253B": "245B",
"253A": "255A",
"244B": "254B",
"238A": "252A",
"250B": "242B",
"250A": "253A",
"254B": "250B",
"254A": "250A"
}
map_keys = set(mapping.keys())
bls_all, ants = coords.generateBaselineIds(self.n_ant, autocorrs=True)
for int_num in xrange(n_int):
idx = int_num * n_bls
vis_int = vis[int_num, ...]
# For every antenna remapping
for k in map_keys:
ant_old, pol_id = int(k[:3]), 0 if k[3] == 'B' else 1
ant_new = int(mapping[k][:3])
# Find affected baselines
bls_old = self.search_baselines(ant_old)
bls_new = self.search_baselines(ant_new)
# For every baseline affected
for bb in range(len(bls_old)):
# Find new antenna pair indexes
bl_old, bl_new = bls_old[bb], bls_new[bb]
bl_idx = bls_all.index(bl_old)
if bl_new >= 65536:
a1, a2 = (bl_new - 65536) / 2048 - 1, (bl_new - 65536) % 2048 - 1
else:
a1, a2 = bl_new / 256 - 1, bl_new % 256 - 1
# Grab all the visibility data for this baseline
xx = vis_int[a1, a2, :, 0, 0]
xy = vis_int[a1, a2, :, 0, 1]
yx = vis_int[a1, a2, :, 1, 0]
yy = vis_int[a1, a2, :, 1, 1]
# Now we need to figure out what we need to update
# Are we updating pol A or pol B? Ant1 or Ant2?
data = flux[idx + bl_idx]
sp = len(xx) * 2
if ant_new == a1:
if pol_id == 0:
data[0:sp] = xx.flatten().view('float32')
data[2 * sp:3 * sp] = xy.flatten().view('float32')
else:
data[1 * sp:2 * sp] = yy.flatten().view('float32')
data[3 * sp:] = yx.flatten().view('float32')
else:
if pol_id == 0:
data[0:sp] = xx.flatten().view('float32')
data[2 * sp:3 * sp] = yx.flatten().view('float32')
else:
data[1 * sp:2 * sp] = yy.flatten().view('float32')
data[3 * sp:] = xy.flatten().view('float32')
# Write this back into the right baseline
flux[idx + bl_idx] = data
return flux
def readDada(self, n_int=None, xmlbase=None, header_dict=None, data_arr=None, inspectOnly=False):
""" Read a LEDA DADA file.
header_dict (dict): psrdada header. Defaults to None. If a dict is passed, then instead of
loading data from file, data will be loaded from data_arr
data_arr (np.ndarray): data array. This should be a preformatted FLUX data array.
"""
self.pp.h1("Loading DADA data")
if type(header_dict) is dict:
self.pp.h2("Loading from shared memory")
d = HeaderDataUnit(header_dict, data_arr)
flux = data_arr
self.pp.h2("Generating baseline IDs")
bls, ant_arr = coords.generateBaselineIds(d.n_ant)
bl_lower = []
while len(bl_lower) < len(flux):
bl_lower += bls
else:
self.pp.h2("Loading visibility data")
d = dada.DadaReader(self.filename, n_int, inspectOnly=inspectOnly)
vis = d.data
self.dada_header = d.header
try:
n_ant = d.n_ant
n_int = d.n_int
self.n_ant = n_ant
except ValueError:
raise RuntimeError("Cannot load NCHAN / NPOL / NSTATION from dada file")
if not header_dict:
self.pp.h2("Converting visibilities to FLUX columns")
do_remap = False
if d.header["TELESCOPE"] in ('LEDA', 'LWAOVRO', 'LWA-OVRO', 'LEDAOVRO', 'LEDA512', 'LEDA-OVRO'):
do_remap = False
flux = self._vis_matrix_to_flux(vis, remap=do_remap)
bls, ant_arr = coords.generateBaselineIds(n_ant)
bl_lower = []
for dd in range(vis.shape[0] / n_int):
bl_lower += bls
self.d_uv_data["BASELINE"] = np.array([bl_lower for ii in range(n_int)]).flatten()
self.d_uv_data["FLUX"] = flux
self.pp.h1("Generating FITS-IDI schema from XML")
if xmlbase is None:
dirname, filename = os.path.split(os.path.abspath(__file__))
xmlbase = os.path.join(dirname, 'config/config.xml')
self.xmlData = etree.parse(xmlbase)
hdu_primary = make_primary(config=self.xmlData)
tbl_array_geometry = make_array_geometry(config=self.xmlData, num_rows=n_ant)
tbl_antenna = make_antenna(config=self.xmlData, num_rows=n_ant)
tbl_frequency = make_frequency(config=self.xmlData, num_rows=1)
tbl_source = make_source(config=self.xmlData, num_rows=1)
#h1('Creating HDU list')
hdulist = pf.HDUList(
[hdu_primary,
tbl_array_geometry,
tbl_frequency,
tbl_antenna,
tbl_source])
self.fits = hdulist
self.stokes_vals = [-5, -6, -7, -8]
self.readFitsidi(from_file=False, load_uv_data=False)
#hdulist.verify()
self.pp.h2("Populating interfits dictionaries")
self.setDefaults(n_uv_rows=len(bl_lower * n_int))
self.obs_code = ''
self.correlator = d.header["INSTRUMENT"]
self.instrument = d.header["INSTRUMENT"]
self.telescope = d.header["TELESCOPE"]
# Compute the integration time
tsamp = float(d.header["TSAMP"]) * 1e-6 # Sampling time per channel, in microseconds
navg = int(d.header["NAVG"]) # Number of averages per integration
int_tim = tsamp * navg # Integration time is tsamp * navg
self.t_int = d.t_int
# Compute time offset
self.pp.h2("Computing UTC offsets")
dt_obj = datetime.strptime(d.header["UTC_START"], "%Y-%m-%d-%H:%M:%S")
time_offset = d.t_offset # Time offset since observation began
dt_obj = dt_obj + timedelta(seconds=time_offset)
date_obs = dt_obj.strftime("%Y-%m-%dT%H:%M:%S")
dd_obs = dt_obj.strftime("%Y-%m-%d")
self.pp.pp("UTC START: %s" % d.header["UTC_START"])
self.pp.pp("TIME OFFSET: %s" % timedelta(seconds=time_offset))
self.pp.pp("NEW START: %s" % date_obs)
self.date_obs = date_obs
self.h_params["NSTOKES"] = 4
self.h_params["NBAND"] = 1
self.h_params["NCHAN"] = d.n_chans
self.h_common["NO_CHAN"] = d.n_chans
self.h_common["REF_FREQ"] = d.c_freq_mhz * 1e6
self.h_common["CHAN_BW"] = d.chan_bw_mhz * 1e6
self.h_common["REF_PIXL"] = d.n_chans / 2 + 1
self.h_common["RDATE"] = dd_obs # Ignore time component
self.h_common["STK_1"] = -5
self.d_frequency["CH_WIDTH"] = d.chan_bw_mhz * 1e6
self.d_frequency["TOTAL_BANDWIDTH"] = d.bandwidth_mhz * 1e6
self.stokes_axis = ['XX', 'YY', 'XY', 'YX']
self.stokes_vals = [-5, -6, -7, -8]
self.d_array_geometry["ANNAME"] = ["Stand%03d" % i for i in range(len(self.d_array_geometry["ANNAME"]))]
self.d_array_geometry["NOSTA"] = [i for i in range(len(self.d_array_geometry["NOSTA"]))]
self.d_uv_data["INTTIM"] = np.ones_like(self.d_uv_data["INTTIM"]) * d.t_int
# Recreate list of baselines
bl_ids, ant_arr = coords.generateBaselineIds(self.n_ant, autocorrs=True)
n_iters = int(len(self.d_uv_data["BASELINE"]) / len(bl_ids))
self.pp.h2("Generating timestamps")
dd, tt = [], []
for ii in range(n_iters):
jd, jt = coords.convertToJulianTuple(self.date_obs)
tdelta = int_tim * ii / 86400.0 # In days
jds = [jd for jj in range(len(ant_arr))]
jts = [jt + tdelta for jj in range(len(ant_arr))]
dd.append(jds)
tt.append(jts)
self.d_uv_data["DATE"] = np.array(dd, dtype='float64').ravel()
self.d_uv_data["TIME"] = np.array(tt, dtype='float64').ravel()
# Load array geometry from file, based on TELESCOP name
self.loadAntArr()
def readLfile(self, n_ant=32, n_chans=600, n_stk=4, config_xml=None):
""" Read a LEDA L-file
filename: str
name of L-file
n_ant: int
Number of antennas. Defaults to 256
n_pol: int
Number of polarizations. Defaults to 2 (dual-pol)
n_chans: int
Number of channels in file. Defaults to 109 (LEDA-512 default)
n_stk: int
Number of stokes parameters in file. Defaults to 4
config_xml: str
Filename of XML schema file. If None, will default to [filename].xml
Notes
-----
.LA and .LC are binary data streams.
.LA files store autocorrelations in the following way:
t0 |Ant1 109 chans XX | Ant1 109 chans YY| Ant2 ... | AntN ...
t1 |Ant1 109 chans XX | Ant1 109 chans YY| Ant2 ... | AntN ...
These are REAL VALUED (1x float)
.LC files store ant1 XY and are upper triangular, so
1x1y| 1x2x | 1x2y | ... | 1x32y |
2x2y | 2x3x | ... | ... |
3x3y | ... | ... |
These are COMPLEX VALUED (2xfloats)
"""
self.pp.h1("Opening L-file")
filename = self.filename.rstrip('.LA').rstrip('.LC')
if config_xml is None:
config_xml = filename + '.xml'
try:
self.xmlData = etree.parse(config_xml)
except IOError:
self.pp.err("ERROR: Cannot open %s" % config_xml)
exit()
# Load visibility data
self.pp.h2("Loading visibility data")
vis = self._readLfile(n_ant=n_ant, n_pol=2, n_chans=n_chans)
self.pp.h2("Generating baseline IDs")
# Create baseline IDs using MIRIAD >255 antenna format (which sucks)
bls, ant_arr = coords.generateBaselineIds(n_ant)
bl_lower = []
for dd in range(vis.shape[0]):
bl_lower += bls
self.pp.h2("Converting visibilities to FLUX columns")
flux = np.zeros([len(bl_lower), n_chans * n_stk * 2], dtype='float32')
for ii in range(len(bl_lower)):
ant1, ant2 = ant_arr[ii % len(ant_arr)]
idx1, idx2 = 2 * (ant1 - 1), 2 * (ant2 - 1)
xx = vis[0, idx1, idx2]
yy = vis[0, idx1 + 1, idx2 + 1]
xy = vis[0, idx1, idx2 + 1]
yx = vis[0, idx1 + 1, idx2]
flux[ii] = np.column_stack((xx, yy, xy, yx)).flatten()
self.d_uv_data["BASELINE"] = bl_lower
self.d_uv_data["FLUX"] = flux
self.pp.h1("Generating FITS-IDI schema from XML")
hdu_primary = make_primary(config=config_xml)
tbl_array_geometry = make_array_geometry(config=config_xml, num_rows=n_ant)
tbl_antenna = make_antenna(config=config_xml, num_rows=n_ant)
tbl_frequency = make_frequency(config=config_xml, num_rows=1)
tbl_source = make_source(config=config_xml, num_rows=1)
#h1('Creating HDU list')
hdulist = pf.HDUList(
[hdu_primary,
tbl_array_geometry,
tbl_frequency,
tbl_antenna,
tbl_source])
# We are now ready to back-fill Interfits dictionaries using readfitsidi
self.fits = hdulist
self.readFitsidi(from_file=False, load_uv_data=False)
self.pp.h2("Populating interfits dictionaries")
# Create interfits dictionary entries
self.setDefaultsLeda(n_uv_rows=len(bl_lower))
self.telescope = self.h_uv_data["TELESCOP"]
self.source = self.d_source["SOURCE"][0]
