miriad.py
"""Class for reading and writing Miriad files."""
from astropy import constants as const
import os
import shutil
import numpy as np
import copy
import warnings
import aipy
from uvdata import UVData
import telescopes as uvtel
import utils as uvutils
class Miriad(UVData):
"""
Defines a Miriad-specific subclass of UVData for reading and writing Miriad files.
This class should not be interacted with directly, instead use the read_miriad
and write_miriad methods on the UVData class.
"""
def _pol_to_ind(self, pol):
if self.polarization_array is None:
raise(ValueError, "Can't index polarization {p} because "
"polarization_array is not set".format(p=pol))
pol_ind = np.argwhere(self.polarization_array == pol)
if len(pol_ind) != 1:
raise(ValueError, "multiple matches for pol={pol} in "
"polarization_array".format(pol=pol))
return pol_ind
def read_miriad(self, filepath, correct_lat_lon=True, run_check=True,
check_extra=True, run_check_acceptability=True, phase_type=None):
"""
Read in data from a miriad file.
Args:
filepath: The miriad file directory to read from.
correct_lat_lon: flag -- that only matters if altitude is missing --
to update the latitude and longitude from the known_telescopes list
run_check: Option to check for the existence and proper shapes of
parameters after reading in the file. Default is True.
check_extra: Option to check optional parameters as well as required
ones. Default is True.
run_check_acceptability: Option to check acceptable range of the values of
parameters after reading in the file. Default is True.
"""
if not os.path.exists(filepath):
raise(IOError, filepath + ' not found')
uv = aipy.miriad.UV(filepath)
# list of miriad variables always read
# NB: this includes variables in try/except (i.e. not all variables are
# necessarily present in the miriad file)
default_miriad_variables = ['nchan', 'npol', 'inttime', 'sdf',
'source', 'telescop', 'latitud', 'longitu',
'altitude', 'history', 'visunits',
'instrume', 'dut1', 'gst0', 'rdate',
'timesys', 'xorient', 'cnt', 'ra', 'dec',
'lst', 'pol', 'nants', 'antnames', 'nblts',
'ntimes', 'nbls', 'sfreq', 'epoch',
'antpos', 'antnums', 'degpdy', 'antdiam',
]
# list of miriad variables not read, but also not interesting
# NB: nspect (I think) is number of spectral windows, will want one day
# NB: xyphase & xyamp are "On-line X Y phase/amplitude measurements" which we may want in
# a calibration object some day
# NB: systemp, xtsys & ytsys are "System temperatures of the antenna/X/Y feed"
# which we may want in a calibration object some day
# NB: freqs, leakage and bandpass may be part of a calibration object some day
other_miriad_variables = ['nspect', 'obsdec', 'vsource', 'ischan',
'restfreq', 'nschan', 'corr', 'freq',
'freqs', 'leakage', 'bandpass',
'tscale', 'coord', 'veldop', 'time', 'obsra',
'operator', 'version', 'axismax', 'axisrms',
'xyphase', 'xyamp', 'systemp', 'xtsys', 'ytsys'
]
extra_miriad_variables = []
for variable in uv.vars():
if (variable not in default_miriad_variables and
variable not in other_miriad_variables):
extra_miriad_variables.append(variable)
miriad_header_data = {'Nfreqs': 'nchan',
'Npols': 'npol',
'integration_time': 'inttime',
'channel_width': 'sdf', # in Ghz!
'object_name': 'source',
'telescope_name': 'telescop'
}
for item in miriad_header_data:
if isinstance(uv[miriad_header_data[item]], str):
header_value = uv[miriad_header_data[item]].replace('\x00', '')
else:
header_value = uv[miriad_header_data[item]]
setattr(self, item, header_value)
latitude = uv['latitud'] # in units of radians
longitude = uv['longitu']
try:
altitude = uv['altitude']
self.telescope_location_lat_lon_alt = (latitude, longitude, altitude)
except(KeyError):
# get info from known telescopes. Check to make sure the lat/lon values match reasonably well
telescope_obj = uvtel.get_telescope(self.telescope_name)
if telescope_obj is not False:
tol = 2 * np.pi * 1e-3 / (60.0 * 60.0 * 24.0) # 1mas in radians
lat_close = np.isclose(telescope_obj.telescope_location_lat_lon_alt[0],
latitude, rtol=0, atol=tol)
lon_close = np.isclose(telescope_obj.telescope_location_lat_lon_alt[1],
longitude, rtol=0, atol=tol)
if correct_lat_lon:
self.telescope_location_lat_lon_alt = telescope_obj.telescope_location_lat_lon_alt
else:
self.telescope_location_lat_lon_alt = (latitude, longitude, telescope_obj.telescope_location_lat_lon_alt[2])
if lat_close and lon_close:
if correct_lat_lon:
warnings.warn('Altitude is not present in Miriad file, '
'using known location values for '
'{telescope_name}.'.format(telescope_name=telescope_obj.telescope_name))
else:
warnings.warn('Altitude is not present in Miriad file, '
'using known location altitude value '
'for {telescope_name} and lat/lon from '
'file.'.format(telescope_name=telescope_obj.telescope_name))
else:
warn_string = ('Altitude is not present in file ')
if not lat_close and not lon_close:
warn_string = warn_string + 'and latitude and longitude values do not match values '
else:
if not lat_close:
warn_string = warn_string + 'and latitude value does not match value '
else:
warn_string = warn_string + 'and longitude value does not match value '
if correct_lat_lon:
warn_string = (warn_string + 'for {telescope_name} in known '
'telescopes. Using values from known '
'telescopes.'.format(telescope_name=telescope_obj.telescope_name))
warnings.warn(warn_string)
else:
warn_string = (warn_string + 'for {telescope_name} in known '
'telescopes. Using altitude value from known '
'telescopes and lat/lon from '
'file.'.format(telescope_name=telescope_obj.telescope_name))
warnings.warn(warn_string)
else:
warnings.warn('Altitude is not present in Miriad file, and '
'telescope {telescope_name} is not in '
'known_telescopes. Telescope location will be '
'set using antenna positions.'
.format(telescope_name=self.telescope_name))
self.history = uv['history']
if not uvutils.check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
self.channel_width *= 1e9 # change from GHz to Hz
# check for pyuvdata variables that are not recognized miriad variables
if 'visunits' in uv.vartable.keys():
self.vis_units = uv['visunits'].replace('\x00', '')
else:
self.vis_units = 'UNCALIB' # assume no calibration
if 'instrume' in uv.vartable.keys():
self.instrument = uv['instrume'].replace('\x00', '')
else:
self.instrument = self.telescope_name # set instrument = telescope
if 'dut1' in uv.vartable.keys():
self.dut1 = uv['dut1']
if 'degpdy' in uv.vartable.keys():
self.earth_omega = uv['degpdy']
if 'gst0' in uv.vartable.keys():
self.gst0 = uv['gst0']
if 'rdate' in uv.vartable.keys():
self.rdate = uv['rdate'].replace('\x00', '')
if 'timesys' in uv.vartable.keys():
self.timesys = uv['timesys'].replace('\x00', '')
if 'xorient' in uv.vartable.keys():
self.x_orientation = uv['xorient'].replace('\x00', '')
# read through the file and get the data
_source = uv['source'] # check source of initial visibility
# dict of extra variables to see if they change
check_variables = {}
for extra_variable in extra_miriad_variables:
check_variables[extra_variable] = uv[extra_variable]
data_accumulator = {}
pol_list = []
for (uvw, t, (i, j)), d, f in uv.all(raw=True):
# control for the case of only a single spw not showing up in
# the dimension
# Note that the (i, j) tuple is calculated from a baseline number in
# aipy (see miriad_wrap.h). The i, j values are also adjusted by aipy
# to start at 0 rather than 1.
if len(d.shape) == 1:
d.shape = (1,) + d.shape
self.Nspws = d.shape[0]
self.spw_array = np.arange(self.Nspws)
else:
raise(ValueError, """Sorry. Files with more than one spectral
window (spw) are not yet supported. A great
project for the interested student!""")
try:
cnt = uv['cnt']
except(KeyError):
cnt = np.ones(d.shape, dtype=np.float)
ra = uv['ra']
dec = uv['dec']
lst = uv['lst']
source = uv['source']
if source != _source:
raise(ValueError, 'This appears to be a multi source file, which is not supported.')
else:
_source = source
# check extra variables for changes compared with initial value
for extra_variable in check_variables.keys():
if type(check_variables[extra_variable]) == str:
if uv[extra_variable] != check_variables[extra_variable]:
check_variables.pop(extra_variable)
else:
if not np.allclose(uv[extra_variable],
check_variables[extra_variable]):
check_variables.pop(extra_variable)
try:
data_accumulator[uv['pol']].append([uvw, t, i, j, d, f, cnt,
ra, dec])
except(KeyError):
data_accumulator[uv['pol']] = [[uvw, t, i, j, d, f, cnt,
ra, dec]]
pol_list.append(uv['pol'])
# NB: flag types in miriad are usually ints
# keep all single valued extra_variables as extra_keywords
for key in check_variables.keys():
if type(check_variables[key]) == str:
value = check_variables[key].replace('\x00', '')
# check for booleans encoded as strings
if value == 'True':
value = True
elif value == 'False':
value = False
self.extra_keywords[key] = value
else:
self.extra_keywords[key] = check_variables[key]
# Check for items in itemtable to put into extra_keywords
# These will end up as variables in written files, but is internally consistent.
for key in uv.items():
# A few items that are not needed, we read elsewhere, or is not supported
# when downselecting, so we don't read here.
if key not in ['vartable', 'history', 'obstype'] and key not in other_miriad_variables:
if type(uv[key]) == str:
value = uv[key].replace('\x00', '')
value = uv[key].replace('\x01', '')
if value == 'True':
value = True
elif value == 'False':
value = False
self.extra_keywords[key] = value
else:
self.extra_keywords[key] = uv[key]
for pol, data in data_accumulator.iteritems():
data_accumulator[pol] = np.array(data)
self.polarization_array = np.array(pol_list)
if len(self.polarization_array) != self.Npols:
warnings.warn('npols={npols} but found {n} pols in data file'.format(
npols=self.Npols, n=len(self.polarization_array)))
# makes a data_array (and flag_array) of zeroes to be filled in by
# data values
# any missing data will have zeros
# use set to get the unique list of all times ever listed in the file
# iterate over polarizations and all spectra (bls and times) in two
# nested loops, then flatten into a single vector, then set
# then list again.
times = list(set(
np.concatenate([[k[1] for k in d] for d in data_accumulator.values()])))
times = np.sort(times)
ant_i_unique = list(set(
np.concatenate([[k[2] for k in d] for d in data_accumulator.values()])))
ant_j_unique = list(set(
np.concatenate([[k[3] for k in d] for d in data_accumulator.values()])))
sorted_unique_ants = sorted(list(set(ant_i_unique + ant_j_unique)))
ant_i_unique = np.array(ant_i_unique)
ant_j_unique = np.array(ant_j_unique)
# Determine maximum digits needed to distinguish different values
ndig_ant = np.ceil(np.log10(sorted_unique_ants[-1])).astype(int) + 1
# Be excessive in precision because we use the floating point values as dictionary keys later
prec_t = - 2 * np.floor(np.log10(self._time_array.tols[-1])).astype(int)
ndig_t = (np.ceil(np.log10(times[-1])).astype(int) + prec_t + 2)
blts = []
for d in data_accumulator.values():
for k in d:
blt = ["{1:.{0}f}".format(prec_t, k[1]).zfill(ndig_t),
str(k[2]).zfill(ndig_ant), str(k[3]).zfill(ndig_ant)]
blt = "_".join(blt)
blts.append(blt)
unique_blts = np.unique(np.array(blts))
reverse_inds = dict(zip(unique_blts, range(len(unique_blts))))
self.Nants_data = len(sorted_unique_ants)
# Miriad has no way to keep track of antenna numbers, so the antenna
# numbers are simply the index for each antenna in any array that
# describes antenna attributes (e.g. antpos for the antenna_postions).
# Therefore on write, nants (which gives the size of the antpos array)
# needs to be increased to be the max value of antenna_numbers+1 and the
# antpos array needs to be inflated with zeros at locations where we
# don't have antenna information. These inflations need to be undone at
# read. If the file was written by pyuvdata, then the variable antnums
# will be present and we can use it, otherwise we need to test for zeros
# in the antpos array and/or antennas with no visibilities.
try:
# The antnums variable will only exist if the file was written with pyuvdata.
# For some reason Miriad doesn't handle an array of integers properly,
# so we convert to floats on write and back here
self.antenna_numbers = uv['antnums'].astype(int)
self.Nants_telescope = len(self.antenna_numbers)
except(KeyError):
self.antenna_numbers = None
self.Nants_telescope = None
nants = uv['nants']
try:
# Miriad stores antpos values in units of ns, pyuvdata uses meters.
antpos = uv['antpos'].reshape(3, nants).T * const.c.to('m/ns').value
# first figure out what are good antenna positions so we can only
# use the non-zero ones to evaluate position information
antpos_length = np.sqrt(np.sum(np.abs(antpos)**2, axis=1))
good_antpos = np.where(antpos_length > 0)[0]
mean_antpos_length = np.mean(antpos_length[good_antpos])
if mean_antpos_length > 6.35e6 and mean_antpos_length < 6.39e6:
absolute_positions = True
else:
absolute_positions = False
# Miriad stores antpos values in a rotated ECEF coordinate system
# where the x-axis goes through the local meridan. Need to convert
# these positions back to standard ECEF and if they are absolute positions,
# subtract off the telescope position to make them relative to the array center.
ecef_antpos = uvutils.ECEF_from_rotECEF(antpos, longitude)
if self.telescope_location is not None:
if absolute_positions:
rel_ecef_antpos = ecef_antpos - self.telescope_location
# maintain zeros because they mark missing data
rel_ecef_antpos[np.where(antpos_length == 0)[0]] = ecef_antpos[np.where(antpos_length == 0)[0]]
else:
rel_ecef_antpos = ecef_antpos
else:
self.telescope_location = np.mean(ecef_antpos[good_antpos, :], axis=0)
valid_location = self._telescope_location.check_acceptability()[0]
# check to see if this could be a valid telescope_location
if valid_location:
mean_lat, mean_lon, mean_alt = self.telescope_location_lat_lon_alt
tol = 2 * np.pi / (60.0 * 60.0 * 24.0) # 1 arcsecond in radians
mean_lat_close = np.isclose(mean_lat, latitude, rtol=0, atol=tol)
mean_lon_close = np.isclose(mean_lon, longitude, rtol=0, atol=tol)
if mean_lat_close and mean_lon_close:
# this looks like a valid telescope_location, and the
# mean antenna lat & lon values are close. Set the
# telescope_location using the file lat/lons and the mean alt.
# Then subtract it off of the antenna positions
warnings.warn('Telescope location is not set, but antenna '
'positions are present. Mean antenna latitude and '
'longitude values match file values, so '
'telescope_position will be set using the '
'mean of the antenna altitudes')
self.telescope_location_lat_lon_alt = (latitude, longitude, mean_alt)
rel_ecef_antpos = ecef_antpos - self.telescope_location
else:
# this looks like a valid telescope_location, but the
# mean antenna lat & lon values are not close. Set the
# telescope_location using the file lat/lons at sea level.
# Then subtract it off of the antenna positions
self.telescope_location_lat_lon_alt = (latitude, longitude, 0)
warn_string = ('Telescope location is set at sealevel at '
'the file lat/lon coordinates. Antenna '
'positions are present, but the mean '
'antenna ')
rel_ecef_antpos = ecef_antpos - self.telescope_location
if not mean_lat_close and not mean_lon_close:
warn_string += ('latitude and longitude values do not '
'match file values so they are not used '
'for altiude.')
elif not mean_lat_close:
warn_string += ('latitude value does not '
'match file values so they are not used '
'for altiude.')
else:
warn_string += ('longitude value does not '
'match file values so they are not used '
'for altiude.')
warnings.warn(warn_string)
else:
# This does not give a valid telescope_location. Instead
# calculate it from the file lat/lon and sea level for altiude
self.telescope_location_lat_lon_alt = (latitude, longitude, 0)
warn_string = ('Telescope location is set at sealevel at '
'the file lat/lon coordinates. Antenna '
'positions are present, but the mean '
'antenna ')
warn_string += ('position does not give a '
'telescope_location on the surface of the '
'earth.')
if absolute_positions:
rel_ecef_antpos = ecef_antpos - self.telescope_location
else:
warn_string += (' Antenna positions do not appear to be '
'on the surface of the earth and will be treated '
'as relative.')
rel_ecef_antpos = ecef_antpos
warnings.warn(warn_string)
if self.Nants_telescope is not None:
# in this case there is an antnums variable
# (meaning that the file was written with pyuvdata), so we'll use it
if nants == self.Nants_telescope:
# no inflation, so just use the positions
self.antenna_positions = rel_ecef_antpos
else:
# there is some inflation, just use the ones that appear in antnums
self.antenna_positions = np.zeros((self.Nants_telescope, 3), dtype=antpos.dtype)
for ai, num in enumerate(self.antenna_numbers):
self.antenna_positions[ai, :] = rel_ecef_antpos[num, :]
else:
# there is no antnums variable (meaning that this file was not
# written by pyuvdata), so we test for antennas with non-zero
# positions and/or that appear in the visibility data
# (meaning that they have entries in ant_1_array or ant_2_array)
antpos_length = np.sqrt(np.sum(np.abs(antpos)**2, axis=1))
good_antpos = np.where(antpos_length > 0)[0]
# take the union of the antennas with good positions (good_antpos)
# and the antennas that have visisbilities (sorted_unique_ants)
# if there are antennas with visibilities but zeroed positions we issue a warning below
ants_use = set(good_antpos).union(sorted_unique_ants)
# ants_use are the antennas we'll keep track of in the UVData
# object, so they dictate Nants_telescope
self.Nants_telescope = len(ants_use)
self.antenna_numbers = np.array(list(ants_use))
self.antenna_positions = np.zeros((self.Nants_telescope, 3), dtype=rel_ecef_antpos.dtype)
for ai, num in enumerate(self.antenna_numbers):
if antpos_length[num] == 0:
warnings.warn('antenna number {n} has visibilities '
'associated with it, but it has a position'
' of (0,0,0)'.format(n=num))
else:
# leave bad locations as zeros to make them obvious
self.antenna_positions[ai, :] = rel_ecef_antpos[num, :]
except(KeyError):
# there is no antpos variable
warnings.warn('Antenna positions are not present in the file.')
self.antenna_positions = None
if self.antenna_numbers is None:
# there are no antenna_numbers or antenna_positions, so just use
# the antennas present in the visibilities
# (Nants_data will therefore match Nants_telescope)
self.antenna_numbers = np.array(sorted_unique_ants)
self.Nants_telescope = len(self.antenna_numbers)
# antenna names is a foreign concept in miriad but required in other formats.
try:
# Here we deal with the way pyuvdata tacks it on to keep the
# name information if we have it:
# make it into one long comma-separated string
ant_name_var = uv['antnames']
if isinstance(ant_name_var, str):
ant_name_str = ant_name_var.replace('\x00', '')
ant_name_list = ant_name_str[1:-1].split(', ')
self.antenna_names = ant_name_list
else:
# Backwards compatibility for old way of storing antenna_names.
# This is a horrible hack to save & recover antenna_names array.
# Miriad can't handle arrays of strings and AIPY use to not handle
# long enough single strings to put them all into one string
# so we convert them into hex values and then into floats on
# write and convert back to strings here
warnings.warn('{file} was written with an old version of '
'pyuvdata, which has been deprecated. Rewrite this '
'file with write_miriad to ensure future '
'compatibility'.format(file=filepath))
ant_name_flt = uv['antnames']
ant_name_list = [('%x' % elem.astype(np.int64)).decode('hex') for elem in ant_name_flt]
self.antenna_names = ant_name_list
except(KeyError):
self.antenna_names = self.antenna_numbers.astype(str).tolist()
# check for antenna diameters
try:
self.antenna_diameters = uv['antdiam']
except(KeyError):
# backwards compatibility for when keyword was 'diameter'
try:
self.antenna_diameters = uv['diameter']
# if we find it, we need to remove it from extra_keywords to keep from writing it out
self.extra_keywords.pop('diameter')
except(KeyError):
pass
if self.antenna_diameters is not None:
self.antenna_diameters = (self.antenna_diameters *
np.ones(self.Nants_telescope, dtype=np.float))
# form up a grid which indexes time and baselines along the 'long'
# axis of the visdata array
tij_grid = np.array(map(lambda x: map(float, x.split("_")), unique_blts))
t_grid, ant_i_grid, ant_j_grid = tij_grid.T
# set the data sizes
try:
self.Nblts = uv['nblts']
if self.Nblts != len(t_grid):
warnings.warn('Nblts does not match the number of unique blts in the data')
self.Nblts = len(t_grid)
except(KeyError):
self.Nblts = len(t_grid)
try:
self.Ntimes = uv['ntimes']
if self.Ntimes != len(times):
warnings.warn('Ntimes does not match the number of unique times in the data')
self.Ntimes = len(times)
except(KeyError):
self.Ntimes = len(times)
self.time_array = t_grid
self.ant_1_array = ant_i_grid.astype(int)
self.ant_2_array = ant_j_grid.astype(int)
self.baseline_array = self.antnums_to_baseline(ant_i_grid.astype(int),
ant_j_grid.astype(int))
try:
self.Nbls = uv['nbls']
if self.Nbls != len(np.unique(self.baseline_array)):
warnings.warn('Nbls does not match the number of unique baselines in the data')
self.Nbls = len(np.unique(self.baseline_array))
except(KeyError):
self.Nbls = len(np.unique(self.baseline_array))
# slot the data into a grid
self.data_array = np.zeros((self.Nblts, self.Nspws, self.Nfreqs,
self.Npols), dtype=np.complex64)
self.flag_array = np.ones(self.data_array.shape, dtype=np.bool)
self.uvw_array = np.zeros((self.Nblts, 3))
# NOTE: Using our lst calculator, which uses astropy,
# instead of aipy values which come from pyephem.
# The differences are of order 5 seconds.
if self.telescope_location is not None:
self.set_lsts_from_time_array()
self.nsample_array = np.ones(self.data_array.shape, dtype=np.float)
self.freq_array = (np.arange(self.Nfreqs) * self.channel_width +
uv['sfreq'] * 1e9)
# Tile freq_array to shape (Nspws, Nfreqs).
# Currently does not actually support Nspws>1!
self.freq_array = np.tile(self.freq_array, (self.Nspws, 1))
# Temporary arrays to hold polarization axis, which will be collapsed
ra_pol_list = np.zeros((self.Nblts, self.Npols))
dec_pol_list = np.zeros((self.Nblts, self.Npols))
uvw_pol_list = np.zeros((self.Nblts, 3, self.Npols))
c_ns = const.c.to('m/ns').value
for pol, data in data_accumulator.iteritems():
pol_ind = self._pol_to_ind(pol)
for ind, d in enumerate(data):
blt = ["{1:.{0}f}".format(prec_t, d[1]).zfill(ndig_t),
str(d[2]).zfill(ndig_ant), str(d[3]).zfill(ndig_ant)]
blt = "_".join(blt)
blt_index = reverse_inds[blt]
self.data_array[blt_index, :, :, pol_ind] = d[4]
self.flag_array[blt_index, :, :, pol_ind] = d[5]
self.nsample_array[blt_index, :, :, pol_ind] = d[6]
# because there are uvws/ra/dec for each pol, and one pol may not
# have that visibility, we collapse along the polarization
# axis but avoid any missing visbilities
uvw = d[0] * c_ns
uvw.shape = (1, 3)
uvw_pol_list[blt_index, :, pol_ind] = uvw
ra_pol_list[blt_index, pol_ind] = d[7]
dec_pol_list[blt_index, pol_ind] = d[8]
# Collapse pol axis for ra_list, dec_list, and uvw_list
ra_list = np.zeros(self.Nblts)
dec_list = np.zeros(self.Nblts)
for blt_index in xrange(self.Nblts):
test = ~np.all(self.flag_array[blt_index, :, :, :], axis=(0, 1))
good_pol = np.where(test)[0]
if len(good_pol) == 1:
# Only one good pol, use it
self.uvw_array[blt_index, :] = uvw_pol_list[blt_index, :, good_pol]
ra_list[blt_index] = ra_pol_list[blt_index, good_pol]
dec_list[blt_index] = dec_pol_list[blt_index, good_pol]
elif len(good_pol) > 1:
# Multiple good pols, check for consistency. pyuvdata does not
# support pol-dependent uvw, ra, or dec.
if np.any(np.diff(uvw_pol_list[blt_index, :, good_pol], axis=0)):
raise ValueError('uvw values are different by polarization.')
else:
self.uvw_array[blt_index, :] = uvw_pol_list[blt_index, :, good_pol[0]]
if np.any(np.diff(ra_pol_list[blt_index, good_pol])):
raise ValueError('ra values are different by polarization.')
else:
ra_list[blt_index] = ra_pol_list[blt_index, good_pol[0]]
if np.any(np.diff(dec_pol_list[blt_index, good_pol])):
raise ValueError('dec values are different by polarization.')
else:
dec_list[blt_index] = dec_pol_list[blt_index, good_pol[0]]
else:
# No good pols for this blt. Fill with first one.
self.uvw_array[blt_index, :] = uvw_pol_list[blt_index, :, 0]
ra_list[blt_index] = ra_pol_list[blt_index, 0]
dec_list[blt_index] = dec_pol_list[blt_index, 0]
# first check to see if the phase_type was specified.
if phase_type is not None:
if phase_type is 'phased':
self.set_phased()
elif phase_type is 'drift':
self.set_drift()
else:
raise ValueError('The phase_type was not recognized. '
'Set the phase_type to "drift" or "phased" to '
'reflect the phasing status of the data')
else:
# check if ra is constant throughout file; if it is,
# file is tracking if not, file is drift scanning
if self.Ntimes > 1:
blt_good = np.where(~np.all(self.flag_array, axis=(1, 2, 3)))
if np.isclose(np.mean(np.diff(ra_list[blt_good])), 0.):
self.set_phased()
else:
self.set_drift()
else:
# if there's only one time, checking for consistent RAs doesn't work.
# instead check for the presence of an epoch variable, which isn't
# really a good option, but at least it prevents crashes.
if 'epoch' in uv.vartable.keys():
self.set_phased()
else:
self.set_drift()
if self.phase_type == 'phased':
# check that the RA values do not vary
blt_good = np.where(~np.all(self.flag_array, axis=(1, 2, 3)))
if not np.isclose(np.mean(np.diff(ra_list[blt_good])), 0.):
raise(ValueError, 'phase_type is "phased" but the RA values are varying.')
self.phase_center_ra = float(ra_list[0])
self.phase_center_dec = float(dec_list[0])
self.phase_center_epoch = uv['epoch']
else:
# check that the RA values are not constant (if more than one time present)
blt_good = np.where(~np.all(self.flag_array, axis=(1, 2, 3)))
if np.isclose(np.mean(np.diff(ra_list[blt_good])), 0.) and self.Ntimes > 1:
raise(ValueError, 'phase_type is "drift" but the RA values are constant.')
self.zenith_ra = ra_list
self.zenith_dec = dec_list
try:
self.set_telescope_params()
except ValueError, ve:
warnings.warn(str(ve))
# check if object has all required uv_properties set
if run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
def write_miriad(self, filepath, run_check=True, check_extra=True,
run_check_acceptability=True,
clobber=False, no_antnums=False):
"""
Write the data to a miriad file.
Args:
filename: The miriad file directory to write to.
run_check: Option to check for the existence and proper shapes of
parameters before writing the file. Default is True.
check_extra: Option to check optional parameters as well as required
ones. Default is True.
run_check_acceptability: Option to check acceptable range of the values of
parameters before writing the file. Default is True.
clobber: Option to overwrite the filename if the file already exists.
Default is False.
no_antnums: Option to not write the antnums variable to the file.
Should only be used for testing purposes.
"""
if run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
# check for multiple spws
if self.data_array.shape[1] > 1:
raise ValueError('write_miriad currently only handles single spw files.')
if os.path.exists(filepath):
if clobber:
print 'File exists: clobbering'
shutil.rmtree(filepath)
else:
raise ValueError('File exists: skipping')
if self.Nfreqs > 1:
freq_spacing = self.freq_array[0, 1:] - self.freq_array[0, :-1]
if not np.isclose(np.min(freq_spacing), np.max(freq_spacing),
rtol=self._freq_array.tols[0], atol=self._freq_array.tols[1]):
raise ValueError('The frequencies are not evenly spaced (probably '
'because of a select operation). The miriad format '
'does not support unevenly spaced frequencies.')
if not np.isclose(np.max(freq_spacing), self.channel_width,
rtol=self._freq_array.tols[0], atol=self._freq_array.tols[1]):
raise ValueError('The frequencies are separated by more than their '
'channel width (probably because of a select operation). '
'The miriad format does not support frequencies '
'that are spaced by more than their channel width.')
uv = aipy.miriad.UV(filepath, status='new')
# initialize header variables
uv._wrhd('obstype', 'mixed-auto-cross')
# avoid inserting extra \n.
if not self.history[-1] == '\n':
self.history += '\n'
uv._wrhd('history', self.history)
# recognized miriad variables
uv.add_var('nchan', 'i')
uv['nchan'] = self.Nfreqs
uv.add_var('npol', 'i')
uv['npol'] = self.Npols
uv.add_var('nspect', 'i')
uv['nspect'] = self.Nspws
uv.add_var('inttime', 'd')
uv['inttime'] = self.integration_time
uv.add_var('sdf', 'd')
uv['sdf'] = self.channel_width / 1e9 # in GHz
uv.add_var('source', 'a')
uv['source'] = self.object_name
uv.add_var('telescop', 'a')
uv['telescop'] = self.telescope_name
uv.add_var('latitud', 'd')
uv['latitud'] = self.telescope_location_lat_lon_alt[0]
uv.add_var('longitu', 'd')
uv['longitu'] = self.telescope_location_lat_lon_alt[1]
uv.add_var('nants', 'i')
if self.x_orientation is not None:
uv.add_var('xorient', 'a')
uv['xorient'] = self.x_orientation
if self.antenna_diameters is not None:
if not np.allclose(self.antenna_diameters, self.antenna_diameters[0]):
warnings.warn('Antenna diameters are not uniform, but miriad only'
'supports a single diameter. Skipping.')
else:
uv.add_var('antdiam', 'd')
uv['antdiam'] = float(self.antenna_diameters[0])
# These are added to make files written by pyuvdata more "miriad correct", and
# should be changed when support for more than one spectral window is added.
# 'nschan' is the number of channels per spectral window, and 'ischan' is the
# starting channel for each spectral window. Both should be arrays of size Nspws.
# Also note that indexing in Miriad is 1-based
uv.add_var('nschan', 'i')
uv['nschan'] = self.Nfreqs
uv.add_var('ischan', 'i')
uv['ischan'] = 1
# Miriad has no way to keep track of antenna numbers, so the antenna
# numbers are simply the index for each antenna in any array that
# describes antenna attributes (e.g. antpos for the antenna_postions).
# Therefore on write, nants (which gives the size of the antpos array)
# needs to be increased to be the max value of antenna_numbers+1 and the
# antpos array needs to be inflated with zeros at locations where we
# don't have antenna information. These inflations need to be undone at
# read. If the file was written by pyuvdata, then the variable antnums
# will be present and we can use it, otherwise we need to test for zeros
# in the antpos array and/or antennas with no visibilities.
nants = np.max(self.antenna_numbers) + 1
uv['nants'] = nants
if self.antenna_positions is not None:
# Miriad wants antenna_positions to be in absolute coordinates
# (not relative to array center) in a rotated ECEF frame where the
# x-axis goes through the local meridian.
rel_ecef_antpos = np.zeros((nants, 3), dtype=self.antenna_positions.dtype)
for ai, num in enumerate(self.antenna_numbers):
rel_ecef_antpos[num, :] = self.antenna_positions[ai, :]
# find zeros so antpos can be zeroed there too
antpos_length = np.sqrt(np.sum(np.abs(rel_ecef_antpos)**2, axis=1))
ecef_antpos = rel_ecef_antpos + self.telescope_location
longitude = self.telescope_location_lat_lon_alt[1]
antpos = uvutils.rotECEF_from_ECEF(ecef_antpos, longitude)
# zero out bad locations (these are checked on read)
antpos[np.where(antpos_length == 0), :] = [0, 0, 0]
uv.add_var('antpos', 'd')
# Miriad stores antpos values in units of ns, pyuvdata uses meters.
uv['antpos'] = antpos.T.flatten() / const.c.to('m/ns').value
uv.add_var('sfreq', 'd')
uv['sfreq'] = self.freq_array[0, 0] / 1e9 # first spw; in GHz
if self.phase_type == 'phased':
uv.add_var('epoch', 'r')
uv['epoch'] = self.phase_center_epoch
# required pyuvdata variables that are not recognized miriad variables
uv.add_var('ntimes', 'i')
uv['ntimes'] = self.Ntimes
uv.add_var('nbls', 'i')
uv['nbls'] = self.Nbls
uv.add_var('nblts', 'i')
uv['nblts'] = self.Nblts
uv.add_var('visunits', 'a')
uv['visunits'] = self.vis_units
uv.add_var('instrume', 'a')
uv['instrume'] = self.instrument
uv.add_var('altitude', 'd')
uv['altitude'] = self.telescope_location_lat_lon_alt[2]
# optional pyuvdata variables that are not recognized miriad variables
if self.dut1 is not None:
uv.add_var('dut1', 'd')
uv['dut1'] = self.dut1
if self.earth_omega is not None:
uv.add_var('degpdy', 'd')
uv['degpdy'] = self.earth_omega
if self.gst0 is not None:
uv.add_var('gst0', 'd')
uv['gst0'] = self.gst0
if self.rdate is not None:
uv.add_var('rdate', 'a')
uv['rdate'] = self.rdate
if self.timesys is not None:
uv.add_var('timesys', 'a')
uv['timesys'] = self.timesys
# other extra keywords
# set up dictionaries to map common python types to miriad types
# NB: arrays/lists/dicts could potentially be written as strings or 1D
# vectors. This is not supported at present!
# NB: complex numbers *should* be supportable, but are not currently
# supported due to unexplained errors in aipy.miriad and/or its underlying libraries
numpy_types = {np.int8: int,
np.int16: int,
np.int32: int,
np.int64: int,
np.uint8: int,
np.uint16: int,
np.uint32: int,
np.uint64: int,
np.float16: float,
np.float32: float,
np.float64: float,
np.float128: float,
}
types = {str: 'a',
int: 'i',
float: 'd',
bool: 'a', # booleans are stored as strings and changed back on read
}
for key, value in self.extra_keywords.iteritems():
if type(value) in numpy_types.keys():
if numpy_types[type(value)] == int:
value = int(value)
elif numpy_types[type(value)] == float:
value = float(value)
elif type(value) == bool:
value = str(value)
elif type(value) not in types.keys():
raise TypeError('Extra keyword {keyword} is of {keytype}. '
'Only strings and real numbers are '
'supported in miriad.'.format(keyword=key,
keytype=type(value)))
if len(str(key)) > 8:
warnings.warn('key {key} in extra_keywords is longer than 8 '
'characters. It will be truncated to 8 as required '
'by the miriad file format.'.format(key=key))
uvkeyname = str(key)[:8] # name must be string, max 8 letters
typestring = types[type(value)]
uv.add_var(uvkeyname, typestring)
uv[uvkeyname] = value
if not no_antnums:
# Add in the antenna_numbers so we have them if we read this file back in.
# For some reason Miriad doesn't handle an array of integers properly,
# so convert to floats here and integers on read.
uv.add_var('antnums', 'd')
uv['antnums'] = self.antenna_numbers.astype(np.float64)
# antenna names is a foreign concept in miriad but required in other formats.
# Miriad can't handle arrays of strings, so we make it into one long
# comma-separated string and convert back on read.
ant_name_str = '[' + ', '.join(self.antenna_names) + ']'
uv.add_var('antnames', 'a')
uv['antnames'] = ant_name_str
# variables that can get updated with every visibility
uv.add_var('pol', 'i')
uv.add_var('lst', 'd')
uv.add_var('cnt', 'd')
uv.add_var('ra', 'd')
uv.add_var('dec', 'd')
# write data
c_ns = const.c.to('m/ns').value
for viscnt, blt in enumerate(self.data_array):
uvw = (self.uvw_array[viscnt] / c_ns).astype(np.double) # NOTE issue 50 on conjugation
t = self.time_array[viscnt]
i = self.ant_1_array[viscnt]
j = self.ant_2_array[viscnt]
uv['lst'] = self.lst_array[viscnt]
if self.phase_type == 'phased':
uv['ra'] = self.phase_center_ra
uv['dec'] = self.phase_center_dec
elif self.phase_type == 'drift':
uv['ra'] = self.zenith_ra[viscnt]
uv['dec'] = self.zenith_dec[viscnt]
else:
raise ValueError('The phasing type of the data is unknown. '
'Set the phase_type to "drift" or "phased" to '
'reflect the phasing status of the data')
# NOTE only writing spw 0, not supporting multiple spws for write
for polcnt, pol in enumerate(self.polarization_array):
uv['pol'] = pol.astype(np.int)
uv['cnt'] = self.nsample_array[viscnt, 0, :, polcnt].astype(np.double)
data = self.data_array[viscnt, 0, :, polcnt]
flags = self.flag_array[viscnt, 0, :, polcnt]
if i > j:
i, j, data = j, i, np.conjugate(data)
preamble = (uvw, t, (i, j))
uv.write(preamble, data, flags)
