def generate_mmodes(self):
"""Calculate the m-modes corresponding to the Timestream.
Perform an MPI transpose for efficiency.
"""
if os.path.exists(self.output_directory + "/mmodes/COMPLETED_M"):
if mpiutil.rank0:
print "******* m-files already generated ********"
return
tel = self.telescope
mmax = tel.mmax
nfreq = tel.nfreq
lfreq, sfreq, efreq = mpiutil.split_local(nfreq)
lm, sm, em = mpiutil.split_local(mmax + 1)
# Load in the local frequencies of the time stream
tstream = np.zeros((lfreq, tel.npairs, self.ntime), dtype=np.complex128)
for lfi, fi in enumerate(range(sfreq, efreq)):
tstream[lfi] = self.timestream_f(fi)
# FFT to calculate the m-modes for the timestream
row_mmodes = np.fft.fft(tstream, axis=-1) / self.ntime
## Combine positive and negative m parts.
row_mpairs = np.zeros((lfreq, 2, tel.npairs, mmax+1), dtype=np.complex128)
row_mpairs[:, 0, ..., 0] = row_mmodes[..., 0]
for mi in range(1, mmax+1):
row_mpairs[:, 0, ..., mi] = row_mmodes[..., mi]
row_mpairs[:, 1, ..., mi] = row_mmodes[..., -mi].conj()
# Transpose to get the entirety of an m-mode on each process (i.e. all frequencies)
col_mmodes = mpiutil.transpose_blocks(row_mpairs, (nfreq, 2, tel.npairs, mmax + 1))
# Transpose the local section to make the m's first
col_mmodes = np.transpose(col_mmodes, (3, 0, 1, 2))
for lmi, mi in enumerate(range(sm, em)):
# Make directory for each m-mode
if not os.path.exists(self._mdir(mi)):
os.makedirs(self._mdir(mi))
# Create the m-file and save the result.
with h5py.File(self._mfile(mi), 'w') as f:
f.create_dataset('/mmode', data=col_mmodes[lmi])
f.attrs['m'] = mi
if mpiutil.rank0:
# Make file marker that the m's have been correctly generated:
open(self.output_directory + "/mmodes/COMPLETED_M", 'a').close()
mpiutil.barrier()
def load(self, filename, freq_split=True):
"""Load a large dataset from a single file on disk.
Parameters
----------
filename : string
File to load.
freq_split : boolean
Split file across nodes by frequency (default) or by time.
Returns
-------
mpi_dset : MPIDataset
"""
mpi_dset = MPIDataset()
with h5py.File(filename, 'r') as f:
mpi_dset.global_timestamp = f['timestamp'][:]
mpi_dset.global_nfreq = f['vis'].shape[0]
if freq_split:
st = 0
et = mpi_dset.global_timestamp.shape[0]
nf, sf, ef = mpiutil.split_local(mpi_dset.global_nfreq)
else:
# Reset flags of which split we are in
mpi_dset.freq_split = False
mpi_dset.time_split = True
sf = 0
ef = mpi_dset.global_nfreq
nt, st, et = mpiutil.split_local(mpi_dset.global_timestamp.shape[0])
# Copy correct section of data from file.
mpi_dset.data = f['vis'][sf:ef, ..., st:et][:]
# Load mask if required
if 'mask' in f:
mpi_dset.mask = f['mask'][sf:ef, ..., st:et][:]
else:
mpi_dset.mask = np.ones_like(mpi_dset.data, dtype=np.int8)
mpi_dset.time_start = st
mpi_dset.time_end = et
mpi_dset.freq_start = sf
mpi_dset.freq_end = ef
return mpi_dset
def make_clzz_array(self):
p_bands, s_bands, e_bands = mpiutil.split_all(self.nbands)
p, s, e = mpiutil.split_local(self.nbands)
self.clarray = np.zeros((self.nbands, self.telescope.lmax + 1,
self.telescope.nfreq, self.telescope.nfreq), dtype=np.float64)
for bi in range(s, e):
self.clarray[bi] = self.make_clzz(self.band_pk[bi])
bandsize = (self.telescope.lmax + 1) * self.telescope.nfreq * self.telescope.nfreq
sizes = p_bands * bandsize
displ = s_bands * bandsize
MPI.COMM_WORLD.Allgatherv(MPI.IN_PLACE, [self.clarray, sizes, displ, MPI.DOUBLE])
def to_time_split(self):
"""Transform from a frequency split dataset to a time split one.
Returns
-------
mpi_dset : MPIDataset
A dataset which is now distributed along the time axis.
"""
if self.time_split:
return self
if not self.freq_split:
raise Exception("Can't transform from mixed splitting.")
tsplit_dset = MPIDataset()
# Set global properties
tsplit_dset.global_timestamp = self.global_timestamp
tsplit_dset.global_nfreq = self.global_nfreq
# Set local frequency properties
tsplit_dset.freq_start = 0
tsplit_dset.freq_end = self.global_nfreq
# Determine local time properties
gtime = self.global_timestamp.size
lt, st, et = mpiutil.split_local(gtime)
tsplit_dset.time_start = st
tsplit_dset.time_end = et
# MPI transpose the data
ts_data = mpiutil.transpose_blocks(self.data, self.global_shape)
ts_mask = mpiutil.transpose_blocks(self.mask, self.global_shape)
tsplit_dset.data = ts_data
tsplit_dset.mask = ts_mask
tsplit_dset.freq_split = False
tsplit_dset.time_split = True
return tsplit_dset
def to_freq_split(self):
"""Transform from a time split dataset to a frequency split one.
Returns
-------
mpi_dset : MPIDataset
A dataset which is now distributed along the freq axis.
"""
if self.freq_split:
return self
if not self.time_split:
raise Exception("Can't transform from mixed splitting.")
fsplit_dset = MPIDataset()
# Set global properties
fsplit_dset.global_timestamp = self.global_timestamp
fsplit_dset.global_nfreq = self.global_nfreq
# Set local frequency properties
fsplit_dset.time_start = 0
fsplit_dset.time_end = self.global_timestamp.size
# Determine local time properties
lf, sf, ef = mpiutil.split_local(self.global_nfreq)
fsplit_dset.freq_start = sf
fsplit_dset.freq_end = ef
fs_data = mpiutil.transpose_blocks(self.data.T, self.global_shape[::-1])
fs_mask = mpiutil.transpose_blocks(self.mask.T, self.global_shape[::-1])
fsplit_dset.data = fs_data.T
fsplit_dset.mask = fs_mask.T
return fsplit_dset
def simulate(m, outdir, maps=[], ndays=None, resolution=0, seed=None, **kwargs):
"""Create a simulated timestream and save it to disk.
Parameters
----------
m : ProductManager object
Products of telescope to simulate.
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.
Returns
-------
timestream : Timestream
"""
## Read in telescope system
bt = m.beamtransfer
tel = bt.telescope
lmax = tel.lmax
mmax = tel.mmax
nfreq = tel.nfreq
npol = tel.num_pol_sky
projmaps = (len(maps) > 0)
lfreq, sfreq, efreq = mpiutil.split_local(nfreq)
local_freq = range(sfreq, efreq)
lm, sm, em = mpiutil.split_local(mmax + 1)
# 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))
col_vis = np.zeros((tel.npairs, lfreq, ntime), dtype=np.complex128)
## If we want to add maps use the m-mode formalism to project a skymap
## into visibility space.
if projmaps:
# Load file to find out the map shapes.
with h5py.File(maps[0], 'r') as f:
mapshape = f['map'].shape
if lfreq > 0:
# Allocate array to store the local frequencies
row_map = np.zeros((lfreq,) + mapshape[1:], dtype=np.float64)
# Read in and sum up the local frequencies of the supplied maps.
for mapfile in maps:
with h5py.File(mapfile, 'r') as f:
row_map += f['map'][sfreq:efreq]
# Calculate the alm's for the local sections
row_alm = hputil.sphtrans_sky(row_map, lmax=lmax).reshape((lfreq, npol * (lmax+1), lmax+1))
else:
row_alm = np.zeros((lfreq, npol * (lmax+1), lmax+1), dtype=np.complex128)
# Perform the transposition to distribute different m's across processes. Neat
# tip, putting a shorter value for the number of columns, trims the array at
# the same time
col_alm = mpiutil.transpose_blocks(row_alm, (nfreq, npol * (lmax+1), mmax+1))
# Transpose and reshape to shift m index first.
col_alm = np.transpose(col_alm, (2, 0, 1)).reshape(lm, nfreq, npol, lmax+1)
# Create storage for visibility data
vis_data = np.zeros((lm, nfreq, bt.ntel), dtype=np.complex128)
# Iterate over m's local to this process and generate the corresponding
# visibilities
for mp, mi in enumerate(range(sm, em)):
vis_data[mp] = bt.project_vector_sky_to_telescope(mi, col_alm[mp])
# Rearrange axes such that frequency is last (as we want to divide
# frequencies across processors)
row_vis = vis_data.transpose((0, 2, 1))#.reshape((lm * bt.ntel, nfreq))
# Parallel transpose to get all m's back onto the same processor
col_vis_tmp = mpiutil.transpose_blocks(row_vis, ((mmax+1), bt.ntel, nfreq))
col_vis_tmp = col_vis_tmp.reshape(mmax + 1, 2, tel.npairs, lfreq)
# Transpose the local section to make the m's the last axis and unwrap the
# positive and negative m at the same time.
col_vis[..., 0] = col_vis_tmp[0, 0]
for mi in range(1, mmax+1):
col_vis[..., mi] = col_vis_tmp[mi, 0]
col_vis[..., -mi] = col_vis_tmp[mi, 1].conj() # Conjugate only (not (-1)**m - see paper)
del col_vis_tmp
## If we're simulating noise, create a realisation and add it to col_vis
if ndays > 0:
# Fetch the noise powerspectrum
noise_ps = tel.noisepower(np.arange(tel.npairs)[:, np.newaxis], np.array(local_freq)[np.newaxis, :], ndays=ndays).reshape(tel.npairs, lfreq)[:, :, 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_vis = (np.array([1.0, 1.0J]) * np.random.standard_normal(col_vis.shape + (2,))).sum(axis=-1)
noise_vis *= (noise_ps / 2.0)**0.5
# Reset RNG
if seed is not None:
np.random.seed()
# Add into main noise sims
col_vis += noise_vis
del noise_vis
# Fourier transform m-modes back to get timestream.
vis_stream = np.fft.ifft(col_vis, axis=-1) * ntime
vis_stream = vis_stream.reshape(tel.npairs, lfreq, ntime)
# The time samples the visibility is calculated at
tphi = np.linspace(0, 2*np.pi, ntime, endpoint=False)
# Create timestream object
tstream = Timestream(outdir, m)
## Iterate over the local frequencies and write them to disk.
for lfi, fi in enumerate(local_freq):
# Make directory if required
if not os.path.exists(tstream._fdir(fi)):
os.makedirs(tstream._fdir(fi))
# Write file contents
with h5py.File(tstream._ffile(fi), 'w') as f:
# Timestream data
f.create_dataset('/timestream', data=vis_stream[:, lfi])
f.create_dataset('/phi', data=tphi)
# Telescope layout data
f.create_dataset('/feedmap', data=tel.feedmap)
f.create_dataset('/feedconj', data=tel.feedconj)
f.create_dataset('/feedmask', data=tel.feedmask)
f.create_dataset('/uniquepairs', data=tel.uniquepairs)
f.create_dataset('/baselines', data=tel.baselines)
# Write metadata
f.attrs['beamtransfer_path'] = os.path.abspath(bt.directory)
f.attrs['ntime'] = ntime
tstream.save()
mpiutil.barrier()
return tstream
def from_files(cls, filelist, acq=True):
"""Load from a set of CHIME data files.
Parameters
----------
filelist : list
List of filenames to load.
acq : boolean, optional
Are the files analysis files or acquisition files (default)?
Returns
-------
mpi_dset : MPIDataset
Distributed dataset type.
"""
mpi_dset = cls()
filelist.sort()
# Global number of files
ngfiles = len(filelist)
# Split into local set of files.
lf, sf, ef = mpiutil.split_local(ngfiles)
local_files = filelist[sf:ef]
fshape = None
# Set file loading routine depending on whether files are acq or
# analysis.
_load_file = andata.AnData.from_acq_h5 if acq else andata.AnData.from_file
# Rank 0 should open file and check the shape.
if mpiutil.rank0:
d0 = _load_file(local_files[0])
fshape = d0.datasets['vis'].shape
# Broadcast the shape to all other ranks
fshape = mpiutil.world.bcast(fshape, root=0)
# Unpack to get the individual lengths
nfreq, nprod, ntime = fshape
# This will be the local shape, file ordered.
lshape = (lf, ntime, nprod, nfreq)
local_array = np.zeros(lshape, dtype=np.complex128)
# Timestamps
timestamps = []
for li, lfile in enumerate(local_files):
print "Rank %i reading %s" % (mpiutil.rank, lfile)
# Load file
df = _load_file(lfile)
# Copy data into local dataset
dset = df.datasets['vis']
if dset.shape != fshape:
raise Exception("Data from %s is not the right shape" % lfile)
local_array[li] = dset.T
# Get timestamps
timestamps.append((li + sf, df.timestamp))
## Merge timestamps
tslist = mpiutil.world.allgather(timestamps)
tsflat = [ts for proclist in tslist for ts in proclist] # Flatten list
# Add timestamps into array
timestamp_array = np.zeros((ngfiles, ntime), dtype=np.float64)
for ind, tstamps in tsflat:
timestamp_array[ind] = tstamps
timestamp_array = timestamp_array.reshape(ngfiles * ntime)
if mpiutil.rank0:
print "Starting transpose...",
data_by_freq = mpiutil.transpose_blocks(local_array, (ngfiles, ntime, nprod, nfreq))
if mpiutil.rank0:
print " done."
# Get local frequencies
lfreq, sfreq, efreq = mpiutil.split_local(nfreq)
data_by_freq = data_by_freq.reshape((ngfiles * ntime, nprod, lfreq)).T
# Set dataset
mpi_dset.data = data_by_freq
mpi_dset.mask = np.ones_like(mpi_dset.data, dtype=np.int8)
# Set time properties
mpi_dset.global_timestamp = timestamp_array
mpi_dset.time_start = 0
mpi_dset.time_end = mpi_dset.timestamp.shape[0]
# Set frequency properties
mpi_dset.freq_start = sfreq
mpi_dset.freq_end = efreq
mpi_dset.global_nfreq = nfreq
return mpi_dset