def _transfer_single(self, bl_index, f_index, lmax):
if self._nside != hputil.nside_for_lmax(lmax, accuracy_boost=self.accuracy_boost):
self._init_trans(hputil.nside_for_lmax(lmax, accuracy_boost=self.accuracy_boost))
cvis = self._beam_map_single(bl_index, f_index)
beam_cart = hpproj.cartesian_proj(cvis[0], self.cart_projector)
# Perform the inverse Fourier transform along phi direction to get the transfer matrix
btrans = np.fft.fft(beam_cart, axis=1) / self.phi_size # m = 0 is at left
return [ btrans ]
def _transfer_single(self, bl_index, f_index, lmax):
if self._nside != hputil.nside_for_lmax(
lmax, accuracy_boost=self.accuracy_boost):
self._init_trans(
hputil.nside_for_lmax(lmax,
accuracy_boost=self.accuracy_boost))
cvis = self._beam_map_single(bl_index, f_index)
beam_cart = hpproj.cartesian_proj(cvis[0], self.cart_projector)
# Perform the inverse Fourier transform along phi direction to get the transfer matrix
btrans = np.fft.fft(beam_cart,
axis=1) / self.phi_size # m = 0 is at left
return [btrans]
def simulate(beamtransfer, outdir, tsname, maps=[], ndays=None, resolution=0, add_noise=True, seed=None, **kwargs):
"""Create a simulated timestream and save it to disk.
Parameters
----------
beamtransfer : fmmode.core.beamtransfer.BeamTransfer
BeamTransfer object containing the analysis products.
outdir : directoryname
Directory that we will save the timestream into.
maps : list
List of map filenames. The sum of these form the simulated sky.
ndays : int, optional
Number of days of observation. Setting `ndays = None` (default) uses
the default stored in the telescope object; `ndays = 0`, assumes the
observation time is infinite so that the noise is zero.
resolution : scalar, optional
Approximate time resolution in seconds. Setting `resolution = 0`
(default) calculates the value from the mmax.
add_noise : bool, optional
Weather to add random noise to the simulated visibilities. Default True.
Returns
-------
timestream : Timestream
"""
# Create timestream object
tstream = Timestream(outdir, tsname, beamtransfer)
completed_file = tstream._tsdir + '/COMPLETED_TIMESTREAM'
if os.path.exists(completed_file):
if mpiutil.rank0:
print "******* timestream-files already generated ********"
mpiutil.barrier()
return tstream
# Make directory if required
try:
os.makedirs(tstream._tsdir)
except OSError:
# directory exists
pass
if mpiutil.rank0:
# if not os.path.exists(tstream._tsdir):
# os.makedirs(tstream._tsdir)
tstream.save()
## Read in telescope system
bt = beamtransfer
tel = bt.telescope
lmax = tel.lmax
mmax = tel.mmax
nfreq = tel.nfreq
nbl = tel.nbase
npol = tel.num_pol_sky
# If ndays is not set use the default value.
if ndays is None:
ndays = tel.ndays
# Calculate the number of timesamples from the resolution
if resolution == 0:
# Set the minimum resolution required for the sky.
ntime = 2*mmax+1
else:
# Set the cl
ntime = int(np.round(24 * 3600.0 / resolution))
indices = list(itertools.product(np.arange(nfreq), np.arange(npol)))
lind, sind, eind = mpiutil.split_local(nfreq * npol)
# local section of the Tm array
theta_size = tel.theta_size
phi_size = tel.phi_size
Tm = np.zeros((lind, theta_size, phi_size), dtype=np.complex128)
for ind, (f_ind, p_ind) in enumerate(indices[sind:eind]):
hp_map = None
for idx, mapfile in enumerate(maps):
with h5py.File(mapfile, 'r') as f:
if idx == 0:
hp_map = f['map'][f_ind, p_ind, :]
else:
hp_map += f['map'][f_ind, p_ind, :]
if hp_map is not None:
cart_map = hpproj.cartesian_proj(hp_map, tel.cart_projector)
# Calculate the Tm's for the local sections
Tm[ind] = np.fft.ifft(cart_map, axis=1) # / phi_size # m = 0 is at left
Tm = MPIArray.wrap(Tm, axis=0)
# redistribute along different m
Tm = Tm.redistribute(axis=2)
Tm = Tm.reshape((nfreq, npol, theta_size, None))
Tm = Tm.reshape((nfreq, npol*theta_size, None))
ms = np.concatenate([np.arange(0, mmax+1), np.arange(-mmax, 0)])
lm, sm, em = mpiutil.split_local(phi_size)
# local section of mmode
# mmode = np.zeros((lm, nbl, nfreq), dtype=np.complex128)
mmode = np.zeros((lm, nfreq, nbl), dtype=np.complex128)
for ind, mi in enumerate(ms[sm:em]):
mmode[ind] = bt.project_vector_sky_to_telescope(mi, Tm[:, :, ind].view(np.ndarray))
mmode = MPIArray.wrap(mmode, axis=0)
mmode = mmode.redistribute(axis=2) # distribute along bl
# add noise if required
if add_noise:
lbl, sbl, ebl = mpiutil.split_local(nbl)
# Fetch the noise powerspectrum
noise_ps = tel.noisepower(np.arange(sbl, ebl)[:, np.newaxis], np.arange(nfreq)[np.newaxis, :], ndays=ndays).reshape(lbl, nfreq).T[np.newaxis, :, :]
# Seed random number generator to give consistent noise
if seed is not None:
# Must include rank such that we don't have massive power deficit from correlated noise
np.random.seed(seed + mpiutil.rank)
# Create and weight complex noise coefficients
noise_mode = (np.array([1.0, 1.0J]) * np.random.standard_normal(mmode.shape + (2,))).sum(axis=-1)
noise_mode *= (noise_ps / 2.0)**0.5
mmode += noise_mode
del noise_mode
# Reset RNG
if seed is not None:
np.random.seed()
# The time samples the visibility is calculated at
tphi = np.linspace(0, 2*np.pi, ntime, endpoint=False)
# inverse FFT to get timestream
vis_stream = np.fft.ifft(mmode, axis=0) * ntime
vis_stream = MPIArray.wrap(vis_stream, axis=2)
# save vis_stream to file
vis_h5 = memh5.MemGroup(distributed=True)
vis_h5.create_dataset('/timestream', data=vis_stream)
vis_h5.create_dataset('/phi', data=tphi)
# Telescope layout data
vis_h5.create_dataset('/feedmap', data=tel.feedmap)
vis_h5.create_dataset('/feedconj', data=tel.feedconj)
vis_h5.create_dataset('/feedmask', data=tel.feedmask)
vis_h5.create_dataset('/uniquepairs', data=tel.uniquepairs)
vis_h5.create_dataset('/baselines', data=tel.baselines)
# Telescope frequencies
vis_h5.create_dataset('/frequencies', data=tel.frequencies)
# Write metadata
vis_h5.attrs['beamtransfer_path'] = os.path.abspath(bt.directory)
vis_h5.attrs['ntime'] = ntime
# save to file
vis_h5.to_hdf5(tstream._tsfile)
if mpiutil.rank0:
# Make file marker that the m's have been correctly generated:
open(completed_file, 'a').close()
mpiutil.barrier()
return tstream
def simulate(beamtransfer,
outdir,
tsname,
maps=[],
ndays=None,
resolution=0,
add_noise=True,
seed=None,
**kwargs):
"""Create a simulated timestream and save it to disk.
Parameters
----------
beamtransfer : fmmode.core.beamtransfer.BeamTransfer
BeamTransfer object containing the analysis products.
outdir : directoryname
Directory that we will save the timestream into.
maps : list
List of map filenames. The sum of these form the simulated sky.
ndays : int, optional
Number of days of observation. Setting `ndays = None` (default) uses
the default stored in the telescope object; `ndays = 0`, assumes the
observation time is infinite so that the noise is zero.
resolution : scalar, optional
Approximate time resolution in seconds. Setting `resolution = 0`
(default) calculates the value from the mmax.
add_noise : bool, optional
Weather to add random noise to the simulated visibilities. Default True.
Returns
-------
timestream : Timestream
"""
# Create timestream object
tstream = Timestream(outdir, tsname, beamtransfer)
completed_file = tstream._tsdir + '/COMPLETED_TIMESTREAM'
if os.path.exists(completed_file):
if mpiutil.rank0:
print "******* timestream-files already generated ********"
mpiutil.barrier()
return tstream
# Make directory if required
try:
os.makedirs(tstream._tsdir)
except OSError:
# directory exists
pass
if mpiutil.rank0:
# if not os.path.exists(tstream._tsdir):
# os.makedirs(tstream._tsdir)
tstream.save()
## Read in telescope system
bt = beamtransfer
tel = bt.telescope
lmax = tel.lmax
mmax = tel.mmax
nfreq = tel.nfreq
nbl = tel.nbase
npol = tel.num_pol_sky
# If ndays is not set use the default value.
if ndays is None:
ndays = tel.ndays
# Calculate the number of timesamples from the resolution
if resolution == 0:
# Set the minimum resolution required for the sky.
ntime = 2 * mmax + 1
else:
# Set the cl
ntime = int(np.round(24 * 3600.0 / resolution))
indices = list(itertools.product(np.arange(nfreq), np.arange(npol)))
lind, sind, eind = mpiutil.split_local(nfreq * npol)
# local section of the Tm array
theta_size = tel.theta_size
phi_size = tel.phi_size
Tm = np.zeros((lind, theta_size, phi_size), dtype=np.complex128)
for ind, (f_ind, p_ind) in enumerate(indices[sind:eind]):
hp_map = None
for idx, mapfile in enumerate(maps):
with h5py.File(mapfile, 'r') as f:
if idx == 0:
hp_map = f['map'][f_ind, p_ind, :]
else:
hp_map += f['map'][f_ind, p_ind, :]
if hp_map is not None:
cart_map = hpproj.cartesian_proj(hp_map, tel.cart_projector)
# Calculate the Tm's for the local sections
Tm[ind] = np.fft.ifft(cart_map,
axis=1) # / phi_size # m = 0 is at left
Tm = MPIArray.wrap(Tm, axis=0)
# redistribute along different m
Tm = Tm.redistribute(axis=2)
Tm = Tm.reshape((nfreq, npol, theta_size, None))
Tm = Tm.reshape((nfreq, npol * theta_size, None))
ms = np.concatenate([np.arange(0, mmax + 1), np.arange(-mmax, 0)])
lm, sm, em = mpiutil.split_local(phi_size)
# local section of mmode
# mmode = np.zeros((lm, nbl, nfreq), dtype=np.complex128)
mmode = np.zeros((lm, nfreq, nbl), dtype=np.complex128)
for ind, mi in enumerate(ms[sm:em]):
mmode[ind] = bt.project_vector_sky_to_telescope(
mi, Tm[:, :, ind].view(np.ndarray))
mmode = MPIArray.wrap(mmode, axis=0)
mmode = mmode.redistribute(axis=2) # distribute along bl
# add noise if required
if add_noise:
lbl, sbl, ebl = mpiutil.split_local(nbl)
# Fetch the noise powerspectrum
noise_ps = tel.noisepower(np.arange(sbl, ebl)[:, np.newaxis],
np.arange(nfreq)[np.newaxis, :],
ndays=ndays).reshape(
lbl, nfreq).T[np.newaxis, :, :]
# Seed random number generator to give consistent noise
if seed is not None:
# Must include rank such that we don't have massive power deficit from correlated noise
np.random.seed(seed + mpiutil.rank)
# Create and weight complex noise coefficients
noise_mode = (np.array([1.0, 1.0J]) *
np.random.standard_normal(mmode.shape +
(2, ))).sum(axis=-1)
noise_mode *= (noise_ps / 2.0)**0.5
mmode += noise_mode
del noise_mode
# Reset RNG
if seed is not None:
np.random.seed()
# The time samples the visibility is calculated at
tphi = np.linspace(0, 2 * np.pi, ntime, endpoint=False)
# inverse FFT to get timestream
vis_stream = np.fft.ifft(mmode, axis=0) * ntime
vis_stream = MPIArray.wrap(vis_stream, axis=2)
# save vis_stream to file
vis_h5 = memh5.MemGroup(distributed=True)
vis_h5.create_dataset('/timestream', data=vis_stream)
vis_h5.create_dataset('/phi', data=tphi)
# Telescope layout data
vis_h5.create_dataset('/feedmap', data=tel.feedmap)
vis_h5.create_dataset('/feedconj', data=tel.feedconj)
vis_h5.create_dataset('/feedmask', data=tel.feedmask)
vis_h5.create_dataset('/uniquepairs', data=tel.uniquepairs)
vis_h5.create_dataset('/baselines', data=tel.baselines)
# Telescope frequencies
vis_h5.create_dataset('/frequencies', data=tel.frequencies)
# Write metadata
vis_h5.attrs['beamtransfer_path'] = os.path.abspath(bt.directory)
vis_h5.attrs['ntime'] = ntime
# save to file
vis_h5.to_hdf5(tstream._tsfile)
if mpiutil.rank0:
# Make file marker that the m's have been correctly generated:
open(completed_file, 'a').close()
mpiutil.barrier()
return tstream
