Esempio n. 1
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    def project_sky(self, sky, mlist=None, threshold=None, harmonic=False):

        # Set default list of m-modes (i.e. all of them), and partition
        if mlist is None:
            mlist = list(range(self.telescope.mmax + 1))
        mpart = mpiutil.partition_list_mpi(mlist)

        # Total number of sky modes.
        nmodes = self.beamtransfer.nfreq * self.beamtransfer.ntel

        # If sky is alm fine, if not perform spherical harmonic transform.
        alm = sky if harmonic else hputil.sphtrans_sky(sky, lmax=self.telescope.lmax)

        ## Routine to project sky onto eigenmodes
        def _proj(mi):
            p1 = self.project_sky_vector_forward(mi, alm[:, :, mi], threshold)
            p2 = np.zeros(nmodes, dtype=np.complex128)
            p2[-p1.size :] = p1
            return p2

        # Map over list of m's and project sky onto eigenbasis
        proj_sec = [(mi, _proj(mi)) for mi in mpart]

        # Gather projections onto the rank=0 node.
        proj_all = mpiutil.world.gather(proj_sec, root=0)

        proj_arr = None

        if mpiutil.rank0:
            # Create array to put projections into
            proj_arr = np.zeros(
                (2 * self.telescope.mmax + 1, nmodes), dtype=np.complex128
            )

            # Iterate over all gathered projections and insert into the array
            for proc_rank in proj_all:
                for pm in proc_rank:
                    proj_arr[pm[0]] = pm[1]

        # Return the projections (rank=0) or None elsewhere.
        return proj_arr
Esempio n. 2
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    def project_sky(self, sky, mlist = None, threshold=None, harmonic=False):

        # Set default list of m-modes (i.e. all of them), and partition
        if mlist is None:
            mlist = range(self.telescope.mmax + 1)
        mpart = mpiutil.partition_list_mpi(mlist)
        
        # Total number of sky modes.
        nmodes = self.beamtransfer.nfreq * self.beamtransfer.ntel

        # If sky is alm fine, if not perform spherical harmonic transform.
        alm = sky if harmonic else hputil.sphtrans_sky(sky, lmax=self.telescope.lmax)


        ## Routine to project sky onto eigenmodes
        def _proj(mi):
            p1 = self.project_sky_vector_forward(mi, alm[:, :, mi], threshold)
            p2 = np.zeros(nmodes, dtype=np.complex128)
            p2[-p1.size:] = p1
            return p2

        # Map over list of m's and project sky onto eigenbasis
        proj_sec = [(mi, _proj(mi)) for mi in mpart]

        # Gather projections onto the rank=0 node.
        proj_all = mpiutil.world.gather(proj_sec, root=0)

        proj_arr = None
        
        if mpiutil.rank0:
            # Create array to put projections into
            proj_arr = np.zeros((2*self.telescope.mmax + 1, nmodes), dtype=np.complex128)

            # Iterate over all gathered projections and insert into the array
            for proc_rank in proj_all:
                for pm in proc_rank:
                    proj_arr[pm[0]] = pm[1]

        # Return the projections (rank=0) or None elsewhere.
        return proj_arr
Esempio n. 3
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def generate_map(args):
    import numpy as np
    import h5py
    import healpy
    from cora.util import hputil

    with h5py.File(args.in_map, 'r') as f:
        in_map = f['map'][...]
    nside = healpy.pixelfunc.get_nside(in_map[0])
    in_alm = hputil.sphtrans_sky(in_map)
    # lmax = in_alm.shape[-2] - 1
    mmax = in_alm.shape[-1] - 1
    print 'mmax = %d' % mmax
    # l_cut = min(args.l_cut, lmax)
    # m_cut = min(args.m_cut, mmax)
    m_index = min(args.m_index, mmax)
    temp_alm = np.zeros_like(in_alm, dtype=in_alm.dtype)
    temp_alm[:, :, :, m_index] = in_alm[:, :, :, m_index]
    out_map = hputil.sphtrans_inv_sky(temp_alm, nside)
    out_file = args.out_file or ('m_slice_%d_' % m_index + args.in_map)
    with h5py.File(out_file, 'w') as f:
        f.create_dataset('map', data=out_map)
Esempio n. 4
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def plot_alm(args):
    import os
    import numpy as np
    import h5py
    from cora.util import hputil
    import matplotlib
    matplotlib.use('Agg')
    from matplotlib import pyplot as plt

    with h5py.File(args.in_map, 'r') as f:
        in_map = f['map'][...]
    alm = hputil.sphtrans_sky(in_map, lmax=args.maxl)

    # plot alm
    plt.figure(figsize=(args.figlength, args.figwidth))
    plt.subplot(121)
    plt.pcolor(alm[args.ifreq, args.pol].T.real, vmin=args.min, vmax=args.max)
    lmin = args.lmin or 0
    lmax = args.lmax or alm.shape[-2]
    mmin = args.mmin or 0
    mmax = args.mmax  or alm.shape[-1]
    plt.xlim(lmin, lmax)
    plt.ylim(mmin, mmax)
    plt.xlabel(r'$l$')
    plt.ylabel(r'$m$')
    plt.title(r'$\Re\left(a_{lm}\right)$')
    plt.colorbar()

    plt.subplot(122)
    plt.pcolor(alm[args.ifreq, args.pol].T.imag, vmin=args.min, vmax=args.max)
    plt.xlim(lmin, lmax)
    plt.ylim(mmin, mmax)
    plt.xlabel(r'$l$')
    plt.ylabel(r'$m$')
    plt.title(r'$\Im\left(a_{lm}\right)$')
    plt.colorbar()
    out_file = args.out_file or ('alm_of_' + os.path.basename(args.in_map).replace('.hdf5', '_%d_%s.%s' % (args.ifreq, ('{%d}' % args.pol).format('T', 'Q', 'U', 'V'), args.figfmt)))
    plt.savefig(out_file)
Esempio n. 5
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def generate_map(args):
    import os
    import numpy as np
    import h5py
    import healpy
    from cora.util import hputil

    with h5py.File(args.in_map, 'r') as f:
        in_map = f['map'][...]
    nside = healpy.pixelfunc.get_nside(in_map[0])
    in_alm = hputil.sphtrans_sky(in_map, lmax=args.maxl)
    lmax = in_alm.shape[-2] - 1
    mmax = in_alm.shape[-1] - 1
    print 'lmax = %d\nmmax = %d' % (lmax, mmax)
    lmin_cut = max(args.lmin, 0)
    lmax_cut = min(args.lmax, lmax) if args.lmax is not None else lmax
    mmin_cut = max(args.mmin, 0)
    mmax_cut = min(args.mmax, mmax) if args.mmax is not None else mmax
    temp_alm = np.zeros_like(in_alm, dtype=in_alm.dtype)
    temp_alm[:, :, lmin_cut:(lmax_cut+1), mmin_cut:(mmax_cut+1)] = in_alm[:, :, lmin_cut:(lmax_cut+1), mmin_cut:(mmax_cut+1)]
    out_map = hputil.sphtrans_inv_sky(temp_alm, nside)
    out_file = args.out_file or ('lm_cut_%d_%d_%d_%d_' % (lmin_cut, lmax_cut, mmin_cut, mmax_cut) + os.path.basename(args.in_map))
    with h5py.File(out_file, 'w') as f:
        f.create_dataset('map', data=out_map)
Esempio n. 6
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    with h5py.File(cm_name, 'r') as f:
        cm_map = f['map'][:]
else:
    map_dir = '../sky_map/'
    ps_name = map_dir + 'sim_pointsource_%d_700_800_256.hdf5' % nside
    ga_name = map_dir + 'sim_galaxy_%d_700_800_256.hdf5' % nside
    cm_name = map_dir + 'sim_21cm_%d_700_800_256.hdf5' % nside
    with h5py.File(ps_name, 'r') as f:
        ps_map = f['map'][:, 0, :]
    with h5py.File(ga_name, 'r') as f:
        ga_map = f['map'][:, 0, :]
    with h5py.File(cm_name, 'r') as f:
        cm_map = f['map'][:, 0, :]


ps_alm = hputil.sphtrans_sky(ps_map)
ga_alm = hputil.sphtrans_sky(ga_map)
cm_alm = hputil.sphtrans_sky(cm_map)

ps_alm_name = 'alm_pointsource_%d_700_800_256.hdf5' % nside
ga_alm_name = 'alm_galaxy_%d_700_800_256.hdf5' % nside
cm_alm_name = 'alm_21cm_%d_700_800_256.hdf5' % nside

# save alms
with h5py.File(out_dir+ps_alm_name, 'w') as f:
    ps = f.create_dataset('alm', data=ps_alm)
    ps.attrs['axes'] = '(freq, l, m)'
with h5py.File(out_dir+ga_alm_name, 'w') as f:
    ga = f.create_dataset('alm', data=ga_alm)
    ga.attrs['axes'] = '(freq, l, m)'
with h5py.File(out_dir+cm_alm_name, 'w') as f:
Esempio n. 7
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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
    ----------
    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
    """

    # 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
    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

    mpiutil.barrier()

    return tstream
Esempio n. 8
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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
Esempio n. 9
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    ## Useful output
    print("==================================")
    print("Projecting file:\n    %s\ninto:\n    %s" %
          (args.mapfile, args.outfile))
    print("Using beamtransfer: %s" % args.teldir)
    print("Truncating to modes with S/N > %f" % cut)
    print("==================================")

    # Calculate alm's and broadcast
    print("Read in skymap.")
    f = h5py.File(args.mapfile)
    skymap = f["map"][:]
    f.close()
    nside = healpy.get_nside(skymap[0])

    alm = hputil.sphtrans_sky(skymap, lmax=cyl.lmax)
# else:
#    almr = None

mpiutil.world.Bcast([alm, MPI.COMPLEX16], root=0)

cb = cyl.baselines + np.array([[cyl.u_width, 0.0]])

if cyl.positive_m_only:
    taumax = (cb**2).sum(axis=-1)**0.5 / 3e8
else:
    taumax = (np.concatenate((cb, cb))**2).sum(axis=-1)**0.5 / 3e8
tau = np.fft.fftfreq(cyl.nfreq,
                     (cyl.frequencies[1] - cyl.frequencies[0]) * 1e6)

# blmask = (np.abs(tau)[:, np.newaxis] > cut * (taumax[np.newaxis, :] + 10.0 / 3e8))
Esempio n. 10
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    def mapmake_full(self, nside, mapname, nbin=None, dirty=False, method='svd', normalize=True, threshold=1.0e3, eps=0.01, correct_order=0, prior_map_file=None):

        nfreq = self.telescope.nfreq
        if nbin is None:
            nbin = nfreq
        else:
            if (nbin < 1 or nbin > nfreq): # invalid nbin
                nbin = nfreq
            else:
                nbin = int(nbin)

        if prior_map_file is not None:
            # read in the prior sky map
            with h5py.File(prior_map_file, 'r') as f:
                prior_map = f['map'][:] # shape (nbin, npol, npix)

            # alm of the prior map
            alm0 = hputil.sphtrans_sky(prior_map, lmax=self.telescope.lmax).reshape(nbin, self.telescope.num_pol_sky, self.telescope.lmax+1, self.telescope.lmax+1) # shape (nbin, npol, lmax+1, lmax+1)
        else:
            alm0 = None

        def _make_alm(mi):

            print "Making %i" % mi

            mmode = self.mmode(mi)
            if dirty:
                sphmode = self.beamtransfer.project_vector_backward_dirty(mi, mmode, nbin, normalize, threshold)
            else:
                if method == 'svd':
                    sphmode = self.beamtransfer.project_vector_telescope_to_sky(mi, mmode, nbin)
                elif method == 'tk':
                    # sphmode = self.beamtransfer.project_vector_telescope_to_sky_tk(mi, mmode, nbin, eps=eps)
                    mmode0 = alm0[:, :, :, mi] if alm0 is not None else None
                    sphmode = self.beamtransfer.project_vector_telescope_to_sky_tk(mi, mmode, nbin, eps=eps, correct_order=correct_order, mmode0=mmode0)
                else:
                    raise ValueError('Unknown map-making method %s' % method)

            return sphmode

        alm_list = mpiutil.parallel_map(_make_alm, range(self.telescope.mmax + 1), root=0, method='rand')

        if mpiutil.rank0:

            # get center freq of each bin
            n, s, e = mpiutil.split_m(nfreq, nbin)
            cfreqs = np.array([ self.beamtransfer.telescope.frequencies[(s[i]+e[i])/2] for i in range(nbin) ])

            alm = np.zeros((nbin, self.telescope.num_pol_sky, self.telescope.lmax + 1,
                            self.telescope.lmax + 1), dtype=np.complex128)

            mlist = range(1 if self.no_m_zero else 0, self.telescope.mmax + 1)

            for mi in mlist:

                alm[..., mi] = alm_list[mi]

                alm[:, :, 100:, 1] = 0

            skymap = hputil.sphtrans_inv_sky(alm, nside)

            with h5py.File(self.output_directory + '/' + mapname, 'w') as f:
                f.create_dataset('/map', data=skymap)
                f.attrs['frequency'] = cfreqs
                f.attrs['polarization'] = np.array(['I', 'Q', 'U', 'V'])[:self.beamtransfer.telescope.num_pol_sky]

        mpiutil.barrier()
Esempio n. 11
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if mpiutil.rank0:
    ## Useful output
    print "=================================="
    print "Projecting file:\n    %s\ninto:\n    %s" % (args.mapfile, args.outfile)
    print "Using beamtransfer: %s" % args.teldir
    print "Truncating to modes with S/N > %f" % cut
    print "=================================="

    # Calculate alm's and broadcast
    print "Read in skymap."
    f = h5py.File(args.mapfile, 'r')
    skymap = f['map'][:]
    f.close()
    nside = healpy.get_nside(skymap[0])

    alm = hputil.sphtrans_sky(skymap, lmax=cyl.lmax)
#else:
#    almr = None
    
#mpiutil.world.Bcast([alm, MPI.COMPLEX16], root=0)




def projm(mi):
    ## Worker function for mapping over list and projecting onto signal modes.
    print "Projecting %i" % mi

    mvals, mvecs = klt.modes_m(mi, threshold=cut)
    
    if mvals is None:
Esempio n. 12
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    def generate(self):

        for mentry in self.maps:

            mfile = mentry['file']
            stem = mentry['stem']

            if mpiutil.rank0 and not os.path.exists(os.path.dirname(stem)):
                os.makedirs(os.path.dirname(stem))

            mpiutil.barrier()

            if self.copy_orig:
                shutil.copy(mfile, stem + 'orig.hdf5')

            print "============\nProjecting file %s\n============\n" % mfile

            ## Load map and perform spherical harmonic transform
            if mpiutil.rank0:
                # Calculate alm's and broadcast
                print "Read in skymap."
                f = h5py.File(mfile, 'r')
                skymap = f['map'][:]
                f.close()
                nside = healpy.get_nside(skymap[0])
                alm = hputil.sphtrans_sky(skymap, lmax=self.telescope.lmax)
            else:
                alm = None

            ## Function to write out a map from the collected array of alms
            def _write_map_from_almarray(almp, filename, attrs=None):
                if mpiutil.rank0:

                    almp = np.squeeze(np.transpose(almp, axes=(2, 1, 3, 0)))
                    almf = np.zeros(
                        (almp.shape[0], almp.shape[1], almp.shape[1]),
                        dtype=np.complex128)
                    almf[:, :, :almp.shape[2]] = almp

                    pmap = hputil.sphtrans_inv_sky(almf, self.nside)

                    f = h5py.File(filename, 'w')
                    if attrs is not None:
                        for key, val in attrs.items():
                            f.attrs[repr(key)] = val
                    f.create_dataset('/map', data=pmap)
                    f.close()

            mpiutil.barrier()

            ## Broadcast set of alms to the world
            alm = mpiutil.world.bcast(alm, root=0)
            mlist = range(self.kltransform.telescope.mmax + 1)
            nevals = self.beamtransfer.ntel * self.beamtransfer.nfreq

            ## Construct beam projection of map
            if self.beam_proj:

                def proj_beam(mi):
                    print "Projecting %i" % mi
                    bproj = self.beamtransfer.project_vector_forward(
                        mi, alm[:, :, mi]).flatten()
                    return self.beamtransfer.project_vector_backward(mi, bproj)

                shape = (self.telescope.nfreq, self.telescope.num_pol_sky,
                         self.telescope.lmax + 1)
                almp = kltransform.collect_m_array(mlist, proj_beam, shape,
                                                   np.complex128)
                _write_map_from_almarray(almp, stem + "beam.hdf5")

            mpiutil.barrier()

            ## Construct EV projection of map
            if self.evec_proj:

                def proj_evec(mi):
                    ## Worker function for mapping over list and projecting onto signal modes.
                    print "Projecting %i" % mi
                    p2 = np.zeros(nevals, dtype=np.complex128)
                    if self.kltransform.modes_m(mi)[0] is not None:
                        p1 = self.kltransform.project_sky_vector_forward(
                            mi, alm[:, :, mi])
                        p2[-p1.size:] = p1

                    return p2

                shape = (nevals, )
                evp = kltransform.collect_m_array(mlist, proj_evec, shape,
                                                  np.complex128)

                if mpiutil.rank0:
                    f = h5py.File(stem + "ev.hdf5", 'w')
                    f.create_dataset("/evec_proj", data=evp)
                    f.close()

            mpiutil.barrier()

            ## Iterate over noise cuts and filter out noise.
            for cut in self.thresholds:

                def filt_kl(mi):
                    ## Worker function for mapping over list and projecting onto signal modes.
                    print "Projecting %i" % mi

                    mvals, mvecs = self.kltransform.modes_m(mi, threshold=cut)

                    if mvals is None:
                        return None

                    ev_vec = self.kltransform.project_sky_vector_forward(
                        mi, alm[:, :, mi], threshold=cut)
                    tel_vec = self.kltransform.project_tel_vector_backward(
                        mi, ev_vec, threshold=cut)

                    alm2 = self.beamtransfer.project_vector_backward(
                        mi, tel_vec)

                    return alm2

                shape = (self.telescope.nfreq, self.telescope.num_pol_sky,
                         self.telescope.lmax + 1)
                almp = kltransform.collect_m_array(mlist, filt_kl, shape,
                                                   np.complex128)
                _write_map_from_almarray(almp, stem + ("kl_%g.hdf5" % cut),
                                         {'threshold': cut})

                mpiutil.barrier()
Esempio n. 13
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    def generate(self):

        for mentry in self.maps:

            mfile = mentry['file']
            stem = mentry['stem']

            if mpiutil.rank0 and not os.path.exists(os.path.dirname(stem)):
                os.makedirs(os.path.dirname(stem))

            mpiutil.barrier()

            if self.copy_orig:
                shutil.copy(mfile, stem + 'orig.hdf5')

            print "============\nProjecting file %s\n============\n" % mfile

            ## Load map and perform spherical harmonic transform
            if mpiutil.rank0:
                # Calculate alm's and broadcast
                print "Read in skymap."
                f = h5py.File(mfile, 'r')
                skymap = f['map'][:]
                f.close()
                nside = healpy.get_nside(skymap[0])
                alm = hputil.sphtrans_sky(skymap, lmax=self.telescope.lmax)
            else:
                alm = None

            ## Function to write out a map from the collected array of alms
            def _write_map_from_almarray(almp, filename, attrs=None):
                if mpiutil.rank0:

                    almp = np.squeeze(np.transpose(almp, axes=(2, 1, 3, 0)))
                    almf = np.zeros((almp.shape[0], almp.shape[1], almp.shape[1]), dtype=np.complex128)
                    almf[:, :, :almp.shape[2]] = almp

                    pmap = hputil.sphtrans_inv_sky(almf, self.nside)

                    f = h5py.File(filename, 'w')
                    if attrs is not None:
                        for key, val in attrs.items():
                            f.attrs[repr(key)] = val
                    f.create_dataset('/map', data=pmap)
                    f.close()

            mpiutil.barrier()

            ## Broadcast set of alms to the world
            alm = mpiutil.world.bcast(alm, root=0)
            mlist = range(self.kltransform.telescope.mmax+1)
            nevals = self.beamtransfer.ntel * self.beamtransfer.nfreq


            ## Construct beam projection of map
            if self.beam_proj:

                def proj_beam(mi):
                    print "Projecting %i" % mi
                    bproj = self.beamtransfer.project_vector_forward(mi, alm[:, :, mi]).flatten()
                    return self.beamtransfer.project_vector_backward(mi, bproj)

                shape = (self.telescope.nfreq, self.telescope.num_pol_sky, self.telescope.lmax+1)
                almp = kltransform.collect_m_array(mlist, proj_beam, shape, np.complex128)
                _write_map_from_almarray(almp, stem + "beam.hdf5")

            mpiutil.barrier()

            ## Construct EV projection of map
            if self.evec_proj:

                def proj_evec(mi):
                    ## Worker function for mapping over list and projecting onto signal modes.
                    print "Projecting %i" % mi
                    p2 = np.zeros(nevals, dtype=np.complex128)
                    if self.kltransform.modes_m(mi)[0] is not None:
                        p1 = self.kltransform.project_sky_vector_forward(mi, alm[:, :, mi])
                        p2[-p1.size:] = p1

                    return p2

                shape = (nevals,)
                evp = kltransform.collect_m_array(mlist, proj_evec, shape, np.complex128)

                if mpiutil.rank0:
                    f = h5py.File(stem + "ev.hdf5", 'w')
                    f.create_dataset("/evec_proj", data=evp)
                    f.close()

            mpiutil.barrier()

            ## Iterate over noise cuts and filter out noise.
            for cut in self.thresholds:

                def filt_kl(mi):
                    ## Worker function for mapping over list and projecting onto signal modes.
                    print "Projecting %i" % mi

                    mvals, mvecs = self.kltransform.modes_m(mi, threshold=cut)

                    if mvals is None:
                        return None

                    ev_vec = self.kltransform.project_sky_vector_forward(mi, alm[:, :, mi], threshold=cut)
                    tel_vec = self.kltransform.project_tel_vector_backward(mi, ev_vec, threshold=cut)

                    alm2 = self.beamtransfer.project_vector_backward(mi, tel_vec)

                    return alm2

                shape = (self.telescope.nfreq, self.telescope.num_pol_sky, self.telescope.lmax+1)
                almp = kltransform.collect_m_array(mlist, filt_kl, shape, np.complex128)
                _write_map_from_almarray(almp, stem + ("kl_%g.hdf5" % cut), {'threshold' : cut})

                mpiutil.barrier()
Esempio n. 14
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    def process(self, map_):
        """Simulate a SiderealStream

        Parameters
        ----------
        map : :class:`containers.Map`
            The sky map to process to into a sidereal stream. Frequencies in the
            map, must match the Beam Transfer matrices.

        Returns
        -------
        ss : SiderealStream
            Stacked sidereal day.
        feeds : list of CorrInput
            Description of the feeds simulated.
        """

        if self.done:
            raise pipeline.PipelineStopIteration

        # Read in telescope system
        bt = self.beamtransfer
        tel = self.telescope

        lmax = tel.lmax
        mmax = tel.mmax
        nfreq = tel.nfreq
        npol = tel.num_pol_sky

        lfreq, sfreq, efreq = mpiutil.split_local(nfreq)

        lm, sm, em = mpiutil.split_local(mmax + 1)

        # Set the minimum resolution required for the sky.
        ntime = 2 * mmax + 1

        freqmap = map_.index_map["freq"][:]
        row_map = map_.map[:]

        if (tel.frequencies != freqmap["centre"]).any():
            raise ValueError(
                "Frequencies in map do not match those in Beam Transfers.")

        # Calculate the alm's for the local sections
        row_alm = hputil.sphtrans_sky(row_map, lmax=lmax).reshape(
            (lfreq, npol * (lmax + 1), lmax + 1))

        # Trim off excess m's and wrap into MPIArray
        row_alm = row_alm[..., :(mmax + 1)]
        row_alm = mpiarray.MPIArray.wrap(row_alm, axis=0)

        # 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 = row_alm.redistribute(axis=2)

        # Transpose and reshape to shift m index first.
        col_alm = col_alm.transpose((2, 0, 1)).reshape(
            (None, nfreq, npol, lmax + 1))

        # Create storage for visibility data
        vis_data = mpiarray.MPIArray((mmax + 1, nfreq, bt.ntel),
                                     axis=0,
                                     dtype=np.complex128)
        vis_data[:] = 0.0

        # Iterate over m's local to this process and generate the corresponding
        # visibilities
        for mp, mi in vis_data.enumerate(axis=0):
            vis_data[mp] = bt.project_vector_sky_to_telescope(
                mi, col_alm[mp].view(np.ndarray))

        # Rearrange axes such that frequency is last (as we want to divide
        # frequencies across processors)
        row_vis = vis_data.transpose((0, 2, 1))

        # Parallel transpose to get all m's back onto the same processor
        col_vis_tmp = row_vis.redistribute(axis=2)
        col_vis_tmp = col_vis_tmp.reshape((mmax + 1, 2, tel.npairs, None))

        # 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 = mpiarray.MPIArray((tel.npairs, nfreq, ntime),
                                    axis=1,
                                    dtype=np.complex128)
        col_vis[:] = 0.0
        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

        # Fourier transform m-modes back to get final timestream.
        vis_stream = np.fft.ifft(col_vis, axis=-1) * ntime
        vis_stream = vis_stream.reshape((tel.npairs, lfreq, ntime))
        vis_stream = vis_stream.transpose((1, 0, 2)).copy()

        # Try and fetch out the feed index and info from the telescope object.
        try:
            feed_index = tel.input_index
        except AttributeError:
            feed_index = tel.nfeed

        # Construct a product map
        prod_map = np.zeros(tel.uniquepairs.shape[0],
                            dtype=[("input_a", int), ("input_b", int)])
        prod_map["input_a"] = tel.uniquepairs[:, 0]
        prod_map["input_b"] = tel.uniquepairs[:, 1]

        # Construct container and set visibility data
        sstream = containers.SiderealStream(
            freq=freqmap,
            ra=ntime,
            input=feed_index,
            prod=prod_map,
            distributed=True,
            comm=map_.comm,
        )
        sstream.vis[:] = mpiarray.MPIArray.wrap(vis_stream, axis=0)
        sstream.weight[:] = 1.0

        self.done = True

        return sstream