Beispiel #1
0
def summary_plot(out,ant_str='ant1-13',ptype='phase',pol='XX-YY'):
    ''' Makes a summary amplitude or phase plot for all baselines from ants in ant_str
        in out dictionary.
    '''
    import matplotlib.pyplot as plt
    
    ant_list = p.ant_str2list(ant_str)
    nant = len(ant_list)
    if ptype != 'amp' and ptype != 'phase':
        print "Invalid plot type.  Must be 'amp' or 'phase'."
        return
    poloff = 0
    if pol != 'XX-YY':
        poloff = 2
    f, ax = plt.subplots(nant,nant)
    f.subplots_adjust(hspace=0,wspace=0)
    bl2ord = p.bl_list()
    for axrow in ax:
        for a in axrow:
            a.xaxis.set_visible(False)
            a.yaxis.set_visible(False)
    for i in range(nant-1):
        ai = ant_list[i]
        for j in range(i+1,nant):
            aj = ant_list[j]
            if ptype == 'phase':
                ax[i,j].imshow(np.angle(out['x'][bl2ord[ai,aj],0+poloff]))
                ax[j,i].imshow(np.angle(out['x'][bl2ord[ai,aj],1+poloff]))
            elif ptype == 'amp':
                ax[i,j].imshow(np.abs(out['x'][bl2ord[ai,aj],0+poloff]))
                ax[j,i].imshow(np.abs(out['x'][bl2ord[ai,aj],1+poloff]))
    for i in range(nant):
        ai = ant_list[i]
        ax[i,i].text(0.5,0.5,str(ai+1),ha='center',va='center',transform=ax[i,i].transAxes,fontsize=14)
Beispiel #2
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def flag_sk(out):
    bl2ord = p.bl_list()
    m = out['m']
    sk = (m + 1.) / (m - 1.) * (m * out['p2'] / (out['p']**2) - 1)
    u_lim = 1.25
    l_lim = 0.75
    sk_flag = np.logical_or(sk > u_lim, sk < l_lim)
    nant, npol, nf, nt = m.shape
    for i in range(nant - 1):
        for j in range(i + 1, nant):
            flags = np.logical_or(sk_flag[i], sk_flag[j])
            out['x'][bl2ord[i, j], flags] = np.nan
    for i in range(nant):
        if 'meanp' in out:
            out['meanp'][i, sk_flag[i]] = np.nan
        out['a'][i, sk_flag[i]] = np.nan
    return out
Beispiel #3
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def flag_sk(out):
    bl2ord = p.bl_list()
    m = out['m']
    sk = (m+1.)/(m-1.)*(m*out['p2']/(out['p']**2) - 1)
    u_lim = 1.25
    l_lim = 0.75
    sk_flag = np.logical_or(sk > u_lim,sk < l_lim)
    nant,npol,nf,nt = m.shape
    for i in range(nant-1):
        for j in range(i+1,nant):
            flags = np.logical_or(sk_flag[i],sk_flag[j])
            out['x'][bl2ord[i,j],flags] = np.nan
    for i in range(nant):
        if 'meanp' in out:
            out['meanp'][i,sk_flag[i]] = np.nan
        out['a'][i,sk_flag[i]] = np.nan
    return out
Beispiel #4
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def summary_plot(out, ant_str='ant1-13', ptype='phase', pol='XX-YY'):
    ''' Makes a summary amplitude or phase plot for all baselines from ants in ant_str
        in out dictionary.
    '''
    import matplotlib.pyplot as plt

    ant_list = p.ant_str2list(ant_str)
    nant = len(ant_list)
    if ptype != 'amp' and ptype != 'phase':
        print "Invalid plot type.  Must be 'amp' or 'phase'."
        return
    poloff = 0
    if pol != 'XX-YY':
        poloff = 2
    f, ax = plt.subplots(nant, nant)
    f.subplots_adjust(hspace=0, wspace=0)
    bl2ord = p.bl_list()
    for axrow in ax:
        for a in axrow:
            a.xaxis.set_visible(False)
            a.yaxis.set_visible(False)
    for i in range(nant - 1):
        ai = ant_list[i]
        for j in range(i + 1, nant):
            aj = ant_list[j]
            if ptype == 'phase':
                ax[i, j].imshow(np.angle(out['x'][bl2ord[ai, aj], 0 + poloff]))
                ax[j, i].imshow(np.angle(out['x'][bl2ord[ai, aj], 1 + poloff]))
            elif ptype == 'amp':
                ax[i, j].imshow(np.abs(out['x'][bl2ord[ai, aj], 0 + poloff]))
                ax[j, i].imshow(np.abs(out['x'][bl2ord[ai, aj], 1 + poloff]))
    for i in range(nant):
        ai = ant_list[i]
        ax[i, i].text(0.5,
                      0.5,
                      str(ai + 1),
                      ha='center',
                      va='center',
                      transform=ax[i, i].transAxes,
                      fontsize=14)
Beispiel #5
0
def getphasecor(data,
                ant_str='ant1-14',
                polist=[0, 1],
                crange=[0, 4095],
                pplot=False):
    ''' Routine adapted from Maria Loukitcheva's code, to find phase slopes in
        correlated data on a geosynchronous satellite.
    '''
    def movingaverage(interval, window_size):
        # Range of ones, scaled for unit area
        window = np.ones(int(window_size)) / np.float(window_size)
        return np.convolve(interval, window, 'same')

    antlist = p.ant_str2list(ant_str)
    print antlist + 1
    ndon = False
    if len(data[:, 0, 0, 0]) == 16:
        ndon = True
    if ndon:
        chlist = antlist
    else:
        chlist = antlist[1:] - 1
    bl2ord = p.bl_list()
    if not ndon:
        data = data[bl2ord[0, 1:]]  # Consider only baselines with antenna 1
    print data.shape
    pcor = np.linspace(0, np.pi, 4096)
    istep = np.arange(-50, 50)  # Range of steps to search
    chan = np.arange(4096)
    phasecor = np.zeros((14, len(polist)))

    start = crange[0]
    end = crange[1]
    #antennalist=[2]

    if pplot:
        import matplotlib.pylab as plt
        f, ax = plt.subplots(len(antlist), len(polist))

    for i, iant in enumerate(chlist):
        for j, jpol in enumerate(polist):
            phaseall = np.zeros(len(istep))
            for k in range(len(istep)):
                #if movaver:
                #    aver = movingaverage(data[bl2ord[0, antennalist[i]], j, :, 0],201)
                #    aver = cos(angle(aver) + istep[k] * pcor)
                #else:
                aver = np.cos(
                    2 * (np.angle(data[iant, jpol, :, 0]) + istep[k] * pcor))
                phaseall[k] = np.polyfit(chan[start:end],
                                         aver[start:end],
                                         0,
                                         full=True)[1]
            #print phaseall
            #print argmin(drange)
            #print corind[argmin(drange)]
            ioff = iant + 1
            if ndon: ioff = iant
            phasecor[ioff, j] = -istep[np.argmin(
                phaseall)]  # Correct delay is negative of the phase correction
            phas0 = np.angle(data[iant, jpol, :, 0])
            phas = phas0 + istep[np.argmin(phaseall)] * pcor
            if pplot:
                if ndon:
                    iax = i
                else:
                    iax = i + 1
                if len(polist) > 1:
                    ax[iax, j].plot(phas0 % (2 * np.pi), '.')
                    ax[iax, j].plot(phas % (2 * np.pi), '.')
                    ax[iax, j].plot(chan[start:end],
                                    phas[start:end] % (2 * np.pi),
                                    color='white')
                    ax[iax, j].set_ylim(-1, 7)
                else:
                    ax[iax].plot(phas0 % (2 * np.pi), '.')
                    ax[iax].plot(phas % (2 * np.pi), '.')
                    ax[iax].plot(chan[start:end],
                                 phas[start:end] % (2 * np.pi),
                                 color='white')
                    ax[iax].set_ylim(-1, 7)
    if len(polist) == 1:
        # Reduce two-dimensional array with second dimension = 1 to a single dimension
        return phasecor[:, 0]
    return phasecor
Beispiel #6
0
def delay_centers(filename, satname, ants='ant1-13', band=23, doplot=False):
    ''' Reads specified 1-s capture file (specified as a filename string) 
        and analyzes data in either the 3.5-4 GHz band (band 6) or the 
        12-12.5 GHz band (band 23).  First finds the peak amplitude of the
        vector-summed measurements for delays, from -15 to 15 ns, on each 
        baseline, and then applies the optimum delay needed to correct 
        for the phase slope, optionally plotting the results.

        The keyword nants can be used to limit the solution to a smaller
        number of baselines.
        
        If doplot is True, plots an overview plot of the corrected phases,
        including the standard deviation of the fit as text in each box.
        
        TODO: It then optionally reads the delay_centers.txt file from 
        the ACC and updates it.
    '''

    # First see if we can read delay_centers.txt from the ACC (return if failure)
    userpass = '******'
    try:
        # Read delay center file from ACC
        dlafile = urllib2.urlopen('ftp://' + userpass +
                                  'acc.solar.pvt/parm/delay_centers.txt',
                                  timeout=1)
        lines = dlafile.readlines()
        dcenx = []
        dceny = []
        for line in lines:
            if line[0] != '#':
                # Skip comment lines and takes next 2 numbers as delay
                # centers [ns] in X & Y
                dcenx.append(float(line.strip().split()[1]))
                dceny.append(float(line.strip().split()[2]))
    except:
        print Time().iso, 'ACC connection for delay centers timed out.'
        return [None] * 3

    print 'Successfully read delay_centers.txt from ACC.'
    # Get satellite frequencies and polarizations (this is to use channel centers for fitting,
    # but is not yet completed)
    sat, = gs.get_sat_info(names=[satname])
    if band == 23:
        freq = np.linspace(0, 4095, 4096) * 0.4 / 4096 + 12.15
    elif band == 6:
        freq = np.linspace(0, 4095, 4096) * 0.4 / 4096 + 3.65
    else:
        print 'Band MUST be either 23 (12-12.5 GHz) or 6 (3.5-4 GHz)'
        return None, None, None
    freqmhz = (freq * 10000. + 0.5).astype('int') / 10.
    pol = sat['pollist']
    if band == 23:
        ridx, = np.where(pol == 'R')
    else:
        ridx, = np.where(pol == 'H')
    rfrq = sat['freqlist'][ridx]
    ridx = []
    for f in rfrq:
        try:
            ridx.append(np.where(f == freqmhz)[0][0])
        except:
            pass
    idxmin = ridx[0]
    if band == 23:
        lidx, = np.where(pol == 'L')
    else:
        lidx, = np.where(pol == 'V')
    lfrq = sat['freqlist'][lidx]
    lidx = []
    for f in lfrq:
        try:
            lidx.append(np.where(f == freqmhz)[0][0])
        except:
            pass
    idxmin = min(idxmin, lidx[0])
    freq = freq[idxmin:]

    ant_list = ant_str2list(ants)
    nants = len(ant_list)
    nbl = nants * (nants - 1) / 2
    # Create M arrays for nants antennas, and c arrays for nbl baselines
    Mx = np.zeros((nbl, nants))
    My = np.zeros((nbl, nants))
    cx = np.zeros(nbl, dtype='float')
    cy = np.zeros(nbl, dtype='float')
    # Read 1-s capture file of raw packets
    out = p.rd_jspec(filename)
    # Eliminate data on channels less than minimum, and perform sum over times
    out['x'] = out['x'][:, :, idxmin:, :].sum(3)
    out['a'] = out['a'][:, :, idxmin:, :].sum(3)
    print 'Successfully read file', filename
    # Get conversion from antenna pair (bl) to "data source" index
    bl2ord = p.bl_list()
    # Declare storage for delays in ns
    tau_xx = np.zeros((nants, nants), dtype='float')
    tau_yy = np.zeros((nants, nants), dtype='float')
    tau_xy = np.zeros((nants, nants), dtype='float')
    tau_yx = np.zeros((nants, nants), dtype='float')
    tau_ns = np.zeros((nants, nants), dtype='float')
    sigma = np.zeros((nants, nants), dtype='float')
    iblx = 0
    ibly = 0
    if doplot:
        f, ax = plt.subplots(nants, nants)
        f.subplots_adjust(hspace=0, wspace=0)
        for axrow in ax:
            for a in axrow:
                a.xaxis.set_visible(False)
                a.yaxis.set_visible(False)

    # Declare storage for auto-correlation (X vs. Y) delays in ns
    tau_a = np.zeros(nants, dtype='float')
    for i in range(nants):
        ai = ant_list[i]
        dat = out['a'][ai, 2]
        tau_a[i] = auto_delay_search(dat, freq)
    #plt.figure()
    for i in range(0, nants - 1):
        ai = ant_list[i]
        for j in range(i + 1, nants):
            aj = ant_list[j]
            dat = out['x'][bl2ord[ai, aj], 0]
            tau_0 = auto_delay_search(dat, freq)
            tau_xx[i] = delay_search(dat, freq, tau_0)
            dat = out['x'][bl2ord[ai, aj], 1]
            tau_0 = auto_delay_search(dat, freq)
            tau_yy[i] = delay_search(dat, freq, tau_0)

            if doplot:
                phi0 = tau_xx[i, j] * 2 * np.pi * freq
                phix = np.angle(out['x'][bl2ord[ai, aj], 0]) - phi0
                phix -= phix[2048]
                ax[i, j].plot(freq, (phix + np.pi) % (2 * np.pi) - np.pi, '.')
                phi0 = tau_yy[i, j] * 2 * np.pi * freq
                phiy = np.angle(out['x'][bl2ord[ai, aj], 1]) - phi0
                phiy -= phiy[2048]
                ax[j, i].plot(freq, (phiy + np.pi) % (2 * np.pi) - np.pi, '.')
            tau_ns[i, j] = tau_xx[i, j]  # delay in nsec
            tau_ns[j, i] = tau_yy[i, j]  # delay in nsec
            Mx[iblx, np.array((i, j))] = -1, 1
            cx[iblx] = dcenx[aj] - dcenx[ai] - tau_ns[i, j]
            iblx += 1
            My[ibly, np.array((i, j))] = -1, 1
            cy[ibly] = dceny[aj] - dceny[ai] - tau_ns[j, i]
            ibly += 1

    if doplot:
        # Set axis scales and label the plots
        for i in range(nants):
            ai = ant_list[i]
            ax[i, i].text(0.5,
                          0.5,
                          str(ai + 1),
                          ha='center',
                          va='center',
                          transform=ax[i, i].transAxes,
                          fontsize=14)
            for j in range(nants):
                ax[i, j].set_xlim(freq[0], freq[-1])
                ax[i, j].set_ylim(-np.pi, np.pi)

    print 'Successfully calculated delays from data.'
    # Obtain solution, only for those baselines with sigma < 1.0
    xdla, xr, _, _ = np.linalg.lstsq(Mx[:iblx], cx[:iblx])  # For X channel
    ydla, yr, _, _ = np.linalg.lstsq(My[:ibly], cy[:ibly])  # For Y channel
    print 'Successfully analyzed delays for mutual consistency.'
    # Calculate new delays for ants based on Ant 1 as reference
    newdlax = xdla - xdla[0] + dcenx[0]
    newdlay = ydla - ydla[0] + dceny[0] - tau_a

    f = open('/tmp/delay_centers_tmp.txt', 'w')
    for line in lines:
        if line[0] == '#':
            print line,
            f.write(line)
        else:
            ant = int(line[:6])
            idx, = np.where((ant - 1) == ant_list)
            if len(idx) == 0:
                # No update for this antenna
                fmt = '{:4d}*{:12.3f} {:12.3f} {:12.3f} {:12.3f}'
                print fmt.format(ant, dcenx[ant - 1], dceny[ant - 1],
                                 dcenx[ant - 1], dceny[ant - 1])
                fmt = '{:4d} {:12.3f} {:12.3f}\n'
                f.write(fmt.format(ant, dcenx[ant - 1], dceny[ant - 1]))
            else:
                idx = idx[0]
                fmt = '{:4d} {:12.3f} {:12.3f} {:12.3f} {:12.3f}'
                print fmt.format(ant, dcenx[ant - 1], dceny[ant - 1],
                                 newdlax[idx], newdlay[idx])
                fmt = '{:4d} {:12.3f} {:12.3f}\n'
                f.write(fmt.format(ant, newdlax[idx], newdlay[idx]))
    f.close()
    fmt = '{:6.2f} ' * nants
    for i in range(nants):
        print fmt.format(*tau_ns[i])
    return tau_ns, xr, yr
Beispiel #7
0
def readXdata(filename, filter=False, tp_only=False):
    ''' This routine reads the data from a single IDBfile.
        
        Optiona Keywords:
        filter   boolean--if True, returns only non-zero frequencies 
                    if False (default), returns uniform set of 500 frequencies
        tp_only  boolean--if True, returns only TP information
                    if False (default), returns everything (including 
                    auto & cross correlations)
    '''

    # Open uv file for reading
    uv = aipy.miriad.UV(filename)
    good_idx = np.arange(len(uv['sfreq']))
    if filter:
        good_idx = []
        # Read a bunch of records to get number of good frequencies, i.e. those with at least
        # some non-zero data.  Read 20 records for baseline 1-2, XX pol
        uv.select('antennae', 0, 2, include=True)
        uv.select('polarization', -5, -5, include=True)
        for i in range(20):
            preamble, data = uv.read()
            idx, = data.nonzero()
            if len(idx) > len(good_idx):
                good_idx = copy.copy(idx)
        uv.select('clear', 0, 0)
        uv.rewind()

    if 'source' in uv.vartable:
        src = uv['source']
    nf = len(good_idx)
    freq = uv['sfreq'][good_idx]
    npol = uv['npol']
    nants = uv['nants']
    nbl = nants * (nants - 1) / 2
    bl2ord = p.bl_list(nants)
    if not tp_only:
        outa = np.zeros((nants, npol, nf, 600),
                        dtype=np.complex64)  # Auto-correlations
        outx = np.zeros((nbl, npol, nf, 600),
                        dtype=np.complex64)  # Cross-correlations
    outp = np.zeros((nants, 2, nf, 600), dtype=np.float)
    outp2 = np.zeros((nants, 2, nf, 600), dtype=np.float)
    outm = np.zeros((nants, 2, nf, 600), dtype=np.int)
    uvwarray = np.zeros((nbl, 600, 3), dtype=np.float)
    timearray = []
    l = -1
    tprev = 0
    tsav = 0
    # Use antennalist if available
    if 'antlist' in uv.vartable:
        ants = uv['antlist']
        antlist = map(int, ants.split())
    else:
        antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]

    for preamble, data in uv.all():
        uvw, t, (i0, j0) = preamble
        i = antlist.index(i0 + 1)
        j = antlist.index(j0 + 1)
        if i > j:
            # Reverse order of indices
            j = antlist.index(i0 + 1)
            i = antlist.index(j0 + 1)
        # Assumes uv['pol'] is one of -5, -6, -7, -8
        k = -5 - uv['pol']
        if filter:
            if len(data.nonzero()[0]) == nf:
                if t != tprev:
                    #                    print preamble
                    # New time
                    l += 1
                    if l == 600:
                        break
                    tprev = t
                    timearray.append(t)
                    xdata = uv['xsampler'].reshape(500, nants, 3)
                    ydata = uv['ysampler'].reshape(500, nants, 3)
                    outp[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 0], 0, 1)
                    outp[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 0], 0, 1)
                    outp2[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 1], 0,
                                                    1)
                    outp2[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 1], 0,
                                                    1)
                    outm[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 2], 0, 1)
                    outm[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 2], 0, 1)

                if tp_only:
                    outa = None
                    outx = None
                else:
                    if i0 == j0:
                        # This is an auto-correlation
                        outa[i0, k, :, l] = data[data.nonzero()]
                    else:
                        outx[bl2ord[i, j], k, :, l] = data[data.nonzero()]
                        if k == 3:
                            uvwarray[bl2ord[i, j], l] = uvw[data.nonzero()]
        else:
            if t != tprev:
                # New time
                l += 1
                if l == 600:
                    break
                tprev = t
                timearray.append(t)
                xdata = uv['xsampler'].reshape(500, nants, 3)
                ydata = uv['ysampler'].reshape(500, nants, 3)
                outp[:, 0, :, l] = np.swapaxes(xdata[:, :, 0], 0, 1)
                outp[:, 1, :, l] = np.swapaxes(ydata[:, :, 0], 0, 1)
                outp2[:, 0, :, l] = np.swapaxes(xdata[:, :, 1], 0, 1)
                outp2[:, 1, :, l] = np.swapaxes(ydata[:, :, 1], 0, 1)
                outm[:, 0, :, l] = np.swapaxes(xdata[:, :, 2], 0, 1)
                outm[:, 1, :, l] = np.swapaxes(ydata[:, :, 2], 0, 1)

            if tp_only:
                outa = None
                outx = None
            else:
                if i0 == j0:
                    # This is an auto-correlation
                    outa[i0, k, :, l] = data
                    if k < 2 and np.sum(data != np.real(data)) > 0:
                        print preamble, uv['pol'], 'has imaginary data!'
                else:
                    outx[bl2ord[i, j], k, :, l] = data
                    if k == 3: uvwarray[bl2ord[i, j], l] = uvw

    # Truncate in case of early end of data
    nt = len(timearray)
    outp = outp[:, :, :, :nt]
    outp2 = outp2[:, :, :, :nt]
    outm = outm[:, :, :, :nt]
    outa = outa[:, :, :, :nt]
    outx = outx[:, :, :, :nt]
    out = {
        'a': outa,
        'x': outx,
        'uvw': uvwarray,
        'fghz': freq,
        'time': np.array(timearray),
        'source': src,
        'p': outp,
        'p2': outp2,
        'm': outm
    }
    return out
Beispiel #8
0
def readXdatmp(filename):
    # This temporary routine reads the data from a single IDBfile where the
    # polarization was written incorrectly. (-1,-2,0,0) instead of (-5,-6,-7,-8)

    # the IDB array sorts basline pairs into correct order for an output
    # array that just has 18 baselines (2 sets of 4 antennas correlated separately
    # now just need to convert i,j into slot in 136-slot array: 15*16/2 corrs +16 auto
    # this array corresponds to the first index (auto corr) for each antenna
    # 16*(iant-1)-(iant-1)(iant-2)/2
    ibl = np.array([
        0, 16, 31, 45, 58, 70, 81, 91, 100, 108, 115, 121, 126, 130, 133, 135
    ])

    # Open uv file for reading
    uv = aipy.miriad.UV(filename)
    good_idx = []
    # Read a bunch of records to get number of good frequencies, i.e. those with at least
    # some non-zero data.  Read 20 records for baseline 1-2, XX pol
    uv.select('antennae', 0, 2, include=True)
    uv.select('polarization', -1, -1, include=True)
    for i in range(20):
        preamble, data = uv.read()
        idx, = data.nonzero()
        if len(idx) > len(good_idx):
            good_idx = copy.copy(idx)
    if 'source' in uv.vartable:
        src = uv['source']
    uv.select('clear', 0, 0)
    uv.rewind()
    nf = len(good_idx)
    freq = uv['sfreq'][good_idx]
    npol = uv['npol']
    nants = uv['nants']
    nbl = nants * (nants - 1) / 2
    bl2ord = p.bl_list(nants)
    outa = np.zeros((nants, npol, nf, 600),
                    dtype=np.complex64)  # Auto-correlations
    outx = np.zeros((nbl, npol, nf, 600),
                    dtype=np.complex64)  # Cross-correlations
    outp = np.zeros((nants, 2, nf, 600), dtype=np.float)
    outp2 = np.zeros((nants, 2, nf, 600), dtype=np.float)
    outm = np.zeros((nants, 2, nf, 600), dtype=np.int)
    uvwarray = []
    timearray = []
    l = -1
    tprev = 0
    # Use antennalist if available
    if 'antlist' in uv.vartable:
        ants = uv['antlist']
        antlist = map(int, ants.split())
    else:
        antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
    kp = 0
    for preamble, data in uv.all():
        uvw, t, (i0, j0) = preamble
        i = antlist.index(i0 + 1)
        j = antlist.index(j0 + 1)
        if i > j:
            # Reverse order of indices
            j = antlist.index(i0 + 1)
            i = antlist.index(j0 + 1)
        # Assumes uv['pol'] is -1, -2, 0, 0
        if i == 0 and j == 0:
            if uv['pol'] == -1:
                k = 0
            elif uv['pol'] == -2:
                k = 1
            elif uv['pol'] == 0:
                if kp == 1:
                    k = 2
                    kp = 0
                else:
                    k = 3
                    kp = 1
        if k == 3:
            uvwarray.append(uvw)
        if len(data.nonzero()[0]) == nf:
            if t != tprev:
                # New time
                l += 1
                if l == 600:
                    break
                tprev = t
                timearray.append(t)
                xdata = uv['xsampler'].reshape(500, nants, 3)
                ydata = uv['ysampler'].reshape(500, nants, 3)
                outp[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 0], 0, 1)
                outp[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 0], 0, 1)
                outp2[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 1], 0, 1)
                outp2[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 1], 0, 1)
                outm[:, 0, :, l] = np.swapaxes(xdata[good_idx, :, 2], 0, 1)
                outm[:, 1, :, l] = np.swapaxes(ydata[good_idx, :, 2], 0, 1)

            if i0 == j0:
                # This is an auto-correlation
                outa[i0, k, :, l] = data[data.nonzero()]
            else:
                outx[bl2ord[i, j], k, :, l] = data[data.nonzero()]
    out = {
        'a': outa,
        'x': outx,
        'uvw': np.array(uvwarray),
        'fghz': freq,
        'time': np.array(timearray),
        'source': src,
        'p': outp,
        'p2': outp2,
        'm': outm
    }
    return out
Beispiel #9
0
def delay_centers(filename,satname,ants='ant1-13',band=23,doplot=False):
    ''' Reads specified 1-s capture file (specified as a filename string) 
        and analyzes data in either the 3.5-4 GHz band (band 6) or the 
        12-12.5 GHz band (band 23).  First finds the peak amplitude of the
        vector-summed measurements for delays, from -15 to 15 ns, on each 
        baseline, and then applies the optimum delay needed to correct 
        for the phase slope, optionally plotting the results.

        The keyword nants can be used to limit the solution to a smaller
        number of baselines.
        
        If doplot is True, plots an overview plot of the corrected phases,
        including the standard deviation of the fit as text in each box.
        
        TODO: It then optionally reads the delay_centers.txt file from 
        the ACC and updates it.
    '''
    
    # First see if we can read delay_centers.txt from the ACC (return if failure)
    userpass = '******'
    try:
        # Read delay center file from ACC
        dlafile = urllib2.urlopen('ftp://'+userpass+'acc.solar.pvt/parm/delay_centers.txt',timeout=1)
        lines = dlafile.readlines()
        dcenx = []
        dceny = []
        for line in lines:
            if line[0] != '#':
                # Skip comment lines and takes next 2 numbers as delay 
                # centers [ns] in X & Y
                dcenx.append(float(line.strip().split()[1]))
                dceny.append(float(line.strip().split()[2]))
    except:
        print Time().iso,'ACC connection for delay centers timed out.'
        return [None]*3
        
    print 'Successfully read delay_centers.txt from ACC.'
    # Get satellite frequencies and polarizations (this is to use channel centers for fitting,
    # but is not yet completed)
    sat, = gs.get_sat_info(names=[satname])
    if band == 23:
        freq = np.linspace(0,4095,4096)*0.4/4096 + 12.15
    elif band ==6:
        freq = np.linspace(0,4095,4096)*0.4/4096 + 3.65
    else:
        print 'Band MUST be either 23 (12-12.5 GHz) or 6 (3.5-4 GHz)'
        return None, None, None
    freqmhz = (freq*10000. + 0.5).astype('int')/10.
    pol = sat['pollist']
    if band == 23:
        ridx, = np.where(pol == 'R')
    else:
        ridx, = np.where(pol == 'H')
    rfrq = sat['freqlist'][ridx]
    ridx = []
    for f in rfrq:
        try:
            ridx.append(np.where(f == freqmhz)[0][0])
        except:
            pass
    idxmin = ridx[0]
    if band == 23:
        lidx, = np.where(pol == 'L')
    else:
        lidx, = np.where(pol == 'V')
    lfrq = sat['freqlist'][lidx]
    lidx = []
    for f in lfrq:
        try:
            lidx.append(np.where(f == freqmhz)[0][0])
        except:
            pass
    idxmin = min(idxmin,lidx[0])
    freq = freq[idxmin:]

    ant_list = ant_str2list(ants)
    nants = len(ant_list)
    nbl = nants*(nants-1)/2
    # Create M arrays for nants antennas, and c arrays for nbl baselines
    Mx = np.zeros((nbl,nants))
    My = np.zeros((nbl,nants))
    cx = np.zeros(nbl,dtype='float')
    cy = np.zeros(nbl,dtype='float')
    # Read 1-s capture file of raw packets
    out = p.rd_jspec(filename)
    # Eliminate data on channels less than minimum, and perform sum over times
    out['x'] = out['x'][:,:,idxmin:,:].sum(3)
    out['a'] = out['a'][:,:,idxmin:,:].sum(3)
    print 'Successfully read file',filename
    # Get conversion from antenna pair (bl) to "data source" index
    bl2ord = p.bl_list()
    # Declare storage for delays in ns
    tau_xx = np.zeros((nants,nants),dtype='float')
    tau_yy = np.zeros((nants,nants),dtype='float')
    tau_xy = np.zeros((nants,nants),dtype='float')
    tau_yx = np.zeros((nants,nants),dtype='float')
    tau_ns = np.zeros((nants,nants),dtype='float')
    sigma = np.zeros((nants,nants),dtype='float')
    iblx = 0
    ibly = 0
    if doplot:
        f, ax = plt.subplots(nants,nants)
        f.subplots_adjust(hspace=0,wspace=0)
        for axrow in ax:
            for a in axrow:
                a.xaxis.set_visible(False)
                a.yaxis.set_visible(False)

    # Declare storage for auto-correlation (X vs. Y) delays in ns
    tau_a = np.zeros(nants,dtype='float')
    for i in range(nants):
        ai = ant_list[i]
        dat = out['a'][ai,2]
        tau_a[i] = auto_delay_search(dat,freq)
    #plt.figure()
    for i in range(0,nants-1):
        ai = ant_list[i]
        for j in range(i+1,nants):
            aj = ant_list[j]
            dat = out['x'][bl2ord[ai,aj],0]
            tau_0 = auto_delay_search(dat,freq)
            tau_xx[i] = delay_search(dat,freq,tau_0)
            dat = out['x'][bl2ord[ai,aj],1]
            tau_0 = auto_delay_search(dat,freq)
            tau_yy[i] = delay_search(dat,freq,tau_0)

            if doplot:
                phi0 = tau_xx[i,j]*2*np.pi*freq
                phix = np.angle(out['x'][bl2ord[ai,aj],0]) - phi0
                phix -= phix[2048]
                ax[i,j].plot(freq, (phix + np.pi) % (2*np.pi) - np.pi , '.')
                phi0 = tau_yy[i,j]*2*np.pi*freq
                phiy = np.angle(out['x'][bl2ord[ai,aj],1]) - phi0
                phiy -= phiy[2048]
                ax[j,i].plot(freq, (phiy + np.pi) % (2*np.pi) - np.pi, '.')
            tau_ns[i,j] = tau_xx[i,j]             # delay in nsec
            tau_ns[j,i] = tau_yy[i,j]             # delay in nsec
            Mx[iblx,np.array((i,j))] = -1,1
            cx[iblx] = dcenx[aj] - dcenx[ai] - tau_ns[i,j]
            iblx += 1
            My[ibly,np.array((i,j))] = -1,1
            cy[ibly] = dceny[aj] - dceny[ai] - tau_ns[j,i]
            ibly += 1

    if doplot:
        # Set axis scales and label the plots
        for i in range(nants):
            ai = ant_list[i]
            ax[i,i].text(0.5,0.5,str(ai+1),ha='center',va='center',transform=ax[i,i].transAxes,fontsize=14)
            for j in range(nants):
                ax[i,j].set_xlim(freq[0],freq[-1])
                ax[i,j].set_ylim(-np.pi,np.pi)

    print 'Successfully calculated delays from data.'
    # Obtain solution, only for those baselines with sigma < 1.0
    xdla,xr,_,_ = np.linalg.lstsq(Mx[:iblx],cx[:iblx])   # For X channel
    ydla,yr,_,_ = np.linalg.lstsq(My[:ibly],cy[:ibly])   # For Y channel
    print 'Successfully analyzed delays for mutual consistency.'
    # Calculate new delays for ants based on Ant 1 as reference
    newdlax = xdla - xdla[0] + dcenx[0]
    newdlay = ydla - ydla[0] + dceny[0] - tau_a

    f = open('/tmp/delay_centers_tmp.txt','w')
    for line in lines:
        if line[0] == '#':
            print line,
            f.write(line)
        else:
            ant = int(line[:6])
            idx, = np.where((ant-1) == ant_list)
            if len(idx) == 0:
                # No update for this antenna
                fmt = '{:4d}*{:12.3f} {:12.3f} {:12.3f} {:12.3f}'
                print fmt.format(ant,dcenx[ant-1],dceny[ant-1],dcenx[ant-1],dceny[ant-1])
                fmt = '{:4d} {:12.3f} {:12.3f}\n'
                f.write(fmt.format(ant,dcenx[ant-1],dceny[ant-1]))
            else:
                idx = idx[0]
                fmt = '{:4d} {:12.3f} {:12.3f} {:12.3f} {:12.3f}'
                print fmt.format(ant,dcenx[ant-1],dceny[ant-1],newdlax[idx],newdlay[idx])
                fmt = '{:4d} {:12.3f} {:12.3f}\n'
                f.write(fmt.format(ant,newdlax[idx],newdlay[idx]))
    f.close()
    fmt = '{:6.2f} '*nants
    for i in range(nants): print fmt.format(*tau_ns[i])
    return tau_ns, xr, yr
Beispiel #10
0
import numpy as np
import matplotlib.pylab as plt
from pcapture2 import bl_list
bl2ord = bl_list()

def rot_sim(indict):
    ''' Simulates effect of non-ideal feed behavior on input data for a single baseline.
        If 'unrot' is true, it takes the data as output data and applies the inverse
        of the corresponding Mueller matrix to it.
    
        Input is a dictionary (indict) with the following keys (and their defaults if 
        omitted)
          'data' : 4 x ntimes array of data, corresponding to a set of XX, XY, YX, YY, 
                    in that order, for each time.  No default (error return if omitted)
          'chi1' : Parallactic (or rotation) angle of first antenna (default 0) (scalar or ntimes array)
          'chi2' : Parallactic (or rotation) angle of second antenna (default 0) (scalar or ntimes array)
          'a1'   : Relative amplitude of Y wrt X for first antenna (default 1)
          'a2'   : Relative amplitude of Y wrt X for second antenna (default 1)
          'd1'   : Relative cross-talk between X and Y for first antenna (default 0)
          'd2'   : Relative cross-talk between X and Y for second antenna (default 0)
          'unrot': Whether to rotate or unrotate the input data (default False)
          'verbose': Print some diagnostic messages
          'doplot': Create some plots of the before and after amplitudes and phases
          
        Result is a plot of input and output.  Returns the rotated or unrotated data in 
        the same form as the input data.
    '''
    # Input is a dictionary, contained needed keys.  Any missing
    # keys are filled in with defaults:
    data = indict.get('data')    # No default
    if data is None:
Beispiel #11
0
def idb_readXdata(filename, filter=False):
    '''This routine reads the data from a single IDBfile.  Optional
       Keywords: filter boolean--if True, returns only non-zero
       frequencies if False (default), returns all frequencies. This
       differs from Dale's version in that it includes all
       correlations, drops the tp_only option, and the outputs that
       are not in the UDB files.
    '''

    # Open uv file for reading
    uv = aipy.miriad.UV(filename)
    nf_orig = len(uv['sfreq'])
    good_idx = np.arange(nf_orig)
    if filter:
        good_idx = []
        # Read a bunch of records to get number of good frequencies,
        # i.e. those with at least some non-zero data.  Read 20
        # records for baseline 1-2, XX pol
        uv.select('antennae',0,2,include=True)
        uv.select('polarization',-5,-5,include=True)
        for i in range(20):
            preamble, data = uv.read()
            idx, = data.nonzero()
            if len(idx) > len(good_idx):
                good_idx = copy.copy(idx)
        uv.select('clear',0,0)
        uv.rewind()
    #endif
    #set up outputs
    nf = len(good_idx)
    print 'NF: ', nf
    freq = uv['sfreq'][good_idx]
    npol = uv['npol']
    polarr = np.array([-5, -6, -7, -8])
    nants = uv['nants']
    nbl = nants*(nants-1)/2
    nblc = nbl+nants
    # all-correlations, add a mask for the output vis array
    outx0 = np.zeros((nf, nblc, npol, 600),dtype=np.complex64)
    omask = np.zeros((nf, nblc, npol, 600),dtype=np.int32)
    outx = ma.masked_array(outx0, mask = omask)
    i0array = np.zeros((nblc, 600), dtype = np.int32)
    j0array = np.zeros((nblc, 600), dtype = np.int32)
    outpx = np.zeros((nf*nants, 600), dtype=np.float)
    outpy = np.zeros((nf*nants, 600), dtype=np.float)
    uvwarray = np.zeros((3, nblc, 600), dtype=np.float)
    delayarray = np.zeros((nants, 600), dtype=np.float)
    #lists for time arrays
    utarray = []
    timearray = []
    lstarray = []
    l = -1
    tprev = 0
    tsav = 0
    # Use antennalist if available
    if 'antlist' in uv.vartable:
        ants = strip_non_printable(uv['antlist'])
        antlist = map(int, ants.split())
    else:
        antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
    #endelse
    #keep autocorrelations in the array
    bl2ord = p.bl_list()
    for ij in range(len(antlist)):
        bl2ord[ij, ij] = nbl+ij
    #endfor
    for preamble, data in uv.all():
        uvw, t, (i0,j0) = preamble
        i = antlist.index(i0+1)
        j = antlist.index(j0+1)
        if i > j:
            # Reverse order of indices
            j = antlist.index(i0+1)
            i = antlist.index(j0+1)
        #endif
        # Assumes uv['pol'] is one of -5, -6, -7, -8
        k = -5 - uv['pol']
        if filter:
            if len(data.nonzero()[0]) == nf:
                if t != tprev:
                    # New time 
                    l += 1
                    if l == 600:
                        break
                    #endif
                    tprev = t
                    timearray.append(t)
                    utarray.append(uv['ut'])
                    try:
                        lstarray.append(uv['lst'])
                    except:
                        pass
                    #endexcept
                    xdata0 = uv['xsampler'].reshape(nf_orig,nants,3)
                    xdata = xdata0[good_idx, :, 0]
                    outpx[:,l] = xdata.reshape(nf*nants)
                    ydata0 = uv['ysampler'].reshape(nf_orig,nants,3)
                    ydata = ydata0[good_idx, :, 0]
                    outpy[:,l] = ydata.reshape(nf*nants)
                    delayarray[:,l] = uv['delay']
                #endif
                outx[:, bl2ord[i,j],k,l] = data[data.nonzero()]
                if k == 3:
                    uvwarray[:, bl2ord[i,j],l] = uvw
                    i0array[bl2ord[i,j],l] = i0
                    j0array[bl2ord[i,j],l] = j0
                #endif
            #endif, for len(data.nonzero()) == nf
        else:
            if t != tprev:
                # New time 
                l += 1
                if l == 600:
                    break
                tprev = t
                timearray.append(t)
                utarray.append(uv['ut'])
                try:
                    lstarray.append(uv['lst'])
                except:
                    pass
                #endexcept
                xdata = uv['xsampler'].reshape(nf,nants,3)
                outpx[:,l] = xdata[:, :, 0].reshape(nf*nants)
                ydata = uv['ysampler'].reshape(nf,nants,3)
                outpy[:,l] = ydata[:, :, 0].reshape(nf*nants)
                delayarray[:,l] = uv['delay']
            #endif
            outx[:,bl2ord[i,j],k,l] = data
            if k == 3:
                uvwarray[:,bl2ord[i,j],l] = uvw
                i0array[bl2ord[i,j],l] = i0
                j0array[bl2ord[i,j],l] = j0
            #endif
        #endelse (not filter)
    #endfor
    # Truncate in case of early end of data
    nt = len(timearray)
    outpx = outpx[:,:nt]
    outpy = outpy[:,:nt]
    outx = outx[:,:,:,:nt]
    uvwarray = uvwarray[:, :, :nt]
    delayarray = delayarray[:, :nt]
    if len(lstarray) != 0:
        pass
    else:
        tarray = Time(timearray,format='jd')
        for t in tarray:
            lstarray.append(el.eovsa_lst(t))
        #endfor
    #endelse
    #i0 and j0 should always be the same
    i0array = i0array[:,0]
    j0array = j0array[:,0]
    #timearray, lstarray and utarray are lists
    out = {'x':outx,'uvw':uvwarray,'time':np.array(timearray),'px':outpx,'py':outpy,'i0':i0array,'j0':j0array,'lst':np.array(lstarray),'pol':polarr,'delay':delayarray,'ut':np.array(utarray),'file0':filename}
    return out
Beispiel #12
0
def readXdata(filename):
    '''This routine reads the data from a single UDB file. '''

    # Open uv file for reading
    uv = aipy.miriad.UV(filename)
    # Read all to get a time array
    utcount = 0
    ut = 0.0
    for preamble, data in uv.all():
        # Look for time change
        if preamble[1] != ut:
            ut = preamble[1]
            utcount = utcount+1
        #endif
    #endfor
    uv.rewind()
    #set up outputs
    src = uv['source']
    sfreq = uv['sfreq']
    nf = len(sfreq)
    sdf = uv['sdf']
    npol = uv['npol']
    polarr = np.array([-5, -6, -7, -8])
    nants = uv['nants']
    nbl = nants*(nants-1)/2
    nblc = nants+nbl
    # all-correlations, add a mask for the output vis array
    outx0 = np.zeros((nf, nblc, npol, utcount),dtype=np.complex64)
    omask = np.zeros((nf, nblc, npol, utcount),dtype=np.int32)
    outx = ma.masked_array(outx0, mask = omask)
    i0array = np.zeros((nblc, utcount), dtype = np.int32)
    j0array = np.zeros((nblc, utcount), dtype = np.int32)
    outpx = np.zeros((nants*nf, utcount), dtype=np.float)
    outpy = np.zeros((nants*nf, utcount), dtype=np.float)
    uvwarray = np.zeros((3, nblc, utcount), dtype=np.float)
    delayarray = np.zeros((nants, utcount), dtype=np.float)
    utarray = []
    timearray = []
    lstarray = []
    l = -1
    tprev = 0
    tsav = 0
    # Use antennalist if available
    if 'antlist' in uv.vartable:
        ants = strip_non_printable(uv['antlist'])
        antlist = map(int, ants.split())
    else:
        antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
    #endelse
    #keep autocorrelations in the array
    bl2ord = p.bl_list()
    for ij in range(len(antlist)):
        bl2ord[ij, ij] = nbl+ij
    #endfor
    xxx = 0
    for preamble, data in uv.all():
        uvw, t, (i0,j0) = preamble
        i = antlist.index(i0+1)
        j = antlist.index(j0+1)
        if i > j: #this should not happen post-2016
            # Reverse order of indices
            j = antlist.index(i0+1)
            i = antlist.index(j0+1)
        # Assumes uv['pol'] is one of -5, -6, -7, -8
        # Endif
        k = -5 - uv['pol']
        if t != tprev:
            # New time 
            l += 1
            if l == utcount:
                break
            #endif
            tprev = t
            try:
                lstarray.append(uv['lst'])
            except:
                pass
            timearray.append(t)
            utarray.append(uv['ut'])
            xdata = uv['xtsys']
            ydata = uv['ytsys']
            outpx[:, l] = xdata
            outpy[:, l] = ydata
            delayarray[:, l] = uv['delay']
        #endif
        outx[:,bl2ord[i,j],k,l] = data
        #apply mask
        outx[:,bl2ord[i,j],k,l].mask = data.mask
        if k == 3:
            uvwarray[:, bl2ord[i,j],l] = uvw
            i0array[bl2ord[i,j],l] = i0
            j0array[bl2ord[i,j],l] = j0
        #endif
    #endfor
            
    if len(lstarray) != 0:
        pass
    else:
        tarray = Time(timearray,format='jd')
        for t in tarray:
            lstarray.append(el.eovsa_lst(t))
        #endfor
    #endelse
    #i0 and j0 should always be the same
    i0array = i0array[:,0]
    j0array = j0array[:,0]
    #timearray, lstarray and utarray are lists
    out = {'x':outx,'uvw':uvwarray,'time':np.array(timearray),'px':outpx,'py':outpy,'i0':i0array,'j0':j0array,'lst':np.array(lstarray),'pol':polarr,'delay':delayarray,'ut':np.array(utarray),'file0':filename}
    return out
Beispiel #13
0
def readXdata(filename, filter=False, tp_only=False):
    ''' This routine reads the data from a single IDBfile.
        
        Optiona Keywords:
        filter   boolean--if True, returns only non-zero frequencies 
                    if False (default), returns uniform set of 500 frequencies
        tp_only  boolean--if True, returns only TP information
                    if False (default), returns everything (including 
                    auto & cross correlations)
    '''

    # Open uv file for reading
    uv = aipy.miriad.UV(filename)
    good_idx = np.arange(len(uv['sfreq']))
    if filter:
        good_idx = []
        # Read a bunch of records to get number of good frequencies, i.e. those with at least 
        # some non-zero data.  Read 20 records for baseline 1-2, XX pol
        uv.select('antennae',0,2,include=True)
        uv.select('polarization',-5,-5,include=True)
        for i in range(20):
            preamble, data = uv.read()
            idx, = data.nonzero()
            if len(idx) > len(good_idx):
                good_idx = copy.copy(idx)
        uv.select('clear',0,0)
        uv.rewind()

    if 'source' in uv.vartable:
        src = uv['source']
    nf = len(good_idx)
    freq = uv['sfreq'][good_idx]
    npol = uv['npol']
    nants = uv['nants']
    nbl = nants*(nants-1)/2
    bl2ord = p.bl_list(nants)
    if not tp_only:
        outa = np.zeros((nants,npol,nf,600),dtype=np.complex64)  # Auto-correlations
        outx = np.zeros((nbl,npol,nf,600),dtype=np.complex64)  # Cross-correlations
    outp = np.zeros((nants,2,nf,600),dtype=np.float)
    outp2 = np.zeros((nants,2,nf,600),dtype=np.float)
    outm = np.zeros((nants,2,nf,600),dtype=np.int)
    uvwarray = np.zeros((nbl,600,3),dtype=np.float)
    timearray = []
    l = -1
    tprev = 0
    tsav = 0
    # Use antennalist if available
    if 'antlist' in uv.vartable:
        ants = uv['antlist']
        antlist = map(int, ants.split())
    else:
        antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]

    for preamble, data in uv.all():
        uvw, t, (i0,j0) = preamble
        i = antlist.index(i0+1)
        j = antlist.index(j0+1)
        if i > j:
            # Reverse order of indices
            j = antlist.index(i0+1)
            i = antlist.index(j0+1)
        # Assumes uv['pol'] is one of -5, -6, -7, -8
        k = -5 - uv['pol']
        if filter:
            if len(data.nonzero()[0]) == nf:
                if t != tprev:
#                    print preamble
                    # New time 
                    l += 1
                    if l == 600:
                        break
                    tprev = t
                    timearray.append(t)
                    xdata = uv['xsampler'].reshape(500,nants,3)
                    ydata = uv['ysampler'].reshape(500,nants,3)
                    outp[:,0,:,l] = np.swapaxes(xdata[good_idx,:,0],0,1)
                    outp[:,1,:,l] = np.swapaxes(ydata[good_idx,:,0],0,1)
                    outp2[:,0,:,l] = np.swapaxes(xdata[good_idx,:,1],0,1)
                    outp2[:,1,:,l] = np.swapaxes(ydata[good_idx,:,1],0,1)
                    outm[:,0,:,l] = np.swapaxes(xdata[good_idx,:,2],0,1)
                    outm[:,1,:,l] = np.swapaxes(ydata[good_idx,:,2],0,1)
    
                if tp_only:
                    outa = None
                    outx = None
                else:
                    if i0 == j0:
                        # This is an auto-correlation
                        outa[i0,k,:,l] = data[data.nonzero()]
                    else:
                        outx[bl2ord[i,j],k,:,l] = data[data.nonzero()]
                        if k == 3: uvwarray[bl2ord[i,j],l] = uvw[data.nonzero()]
        else:
            if t != tprev:
                # New time 
                l += 1
                if l == 600:
                    break
                tprev = t
                timearray.append(t)
                xdata = uv['xsampler'].reshape(500,nants,3)
                ydata = uv['ysampler'].reshape(500,nants,3)
                outp[:,0,:,l] = np.swapaxes(xdata[:,:,0],0,1)
                outp[:,1,:,l] = np.swapaxes(ydata[:,:,0],0,1)
                outp2[:,0,:,l] = np.swapaxes(xdata[:,:,1],0,1)
                outp2[:,1,:,l] = np.swapaxes(ydata[:,:,1],0,1)
                outm[:,0,:,l] = np.swapaxes(xdata[:,:,2],0,1)
                outm[:,1,:,l] = np.swapaxes(ydata[:,:,2],0,1)

            if tp_only:
                outa = None
                outx = None
            else:
                if i0 == j0:
                    # This is an auto-correlation
                    outa[i0,k,:,l] = data
                    if k < 2 and np.sum(data != np.real(data)) > 0:
                        print preamble,uv['pol'], 'has imaginary data!'
                else:
                    outx[bl2ord[i,j],k,:,l] = data
                    if k == 3: uvwarray[bl2ord[i,j],l] = uvw

    # Truncate in case of early end of data
    nt = len(timearray)
    outp = outp[:,:,:,:nt]
    outp2 = outp2[:,:,:,:nt]
    outm = outm[:,:,:,:nt]
    outa = outa[:,:,:,:nt]
    outx = outx[:,:,:,:nt]
    out = {'a':outa, 'x':outx, 'uvw':uvwarray, 'fghz':freq, 'time':np.array(timearray),'source':src,'p':outp,'p2':outp2,'m':outm}
    return out
Beispiel #14
0
def readXdatmp(filename):
    # This temporary routine reads the data from a single IDBfile where the
    # polarization was written incorrectly. (-1,-2,0,0) instead of (-5,-6,-7,-8)
    
    # the IDB array sorts basline pairs into correct order for an output
    # array that just has 18 baselines (2 sets of 4 antennas correlated separately
    # now just need to convert i,j into slot in 136-slot array: 15*16/2 corrs +16 auto
    # this array corresponds to the first index (auto corr) for each antenna
    # 16*(iant-1)-(iant-1)(iant-2)/2
    ibl = np.array( [ 0,16,31,45,58,70,81,91,100,108,115,121,126,130,133,135 ])

    # Open uv file for reading
    uv = aipy.miriad.UV(filename)
    good_idx = []
    # Read a bunch of records to get number of good frequencies, i.e. those with at least 
    # some non-zero data.  Read 20 records for baseline 1-2, XX pol
    uv.select('antennae',0,2,include=True)
    uv.select('polarization',-1,-1,include=True)
    for i in range(20):
        preamble, data = uv.read()
        idx, = data.nonzero()
        if len(idx) > len(good_idx):
            good_idx = copy.copy(idx)
    if 'source' in uv.vartable:
        src = uv['source']
    uv.select('clear',0,0)
    uv.rewind()
    nf = len(good_idx)
    freq = uv['sfreq'][good_idx]
    npol = uv['npol']
    nants = uv['nants']
    nbl = nants*(nants-1)/2
    bl2ord = p.bl_list(nants)
    outa = np.zeros((nants,npol,nf,600),dtype=np.complex64)  # Auto-correlations
    outx = np.zeros((nbl,npol,nf,600),dtype=np.complex64)  # Cross-correlations
    outp = np.zeros((nants,2,nf,600),dtype=np.float)
    outp2 = np.zeros((nants,2,nf,600),dtype=np.float)
    outm = np.zeros((nants,2,nf,600),dtype=np.int)
    uvwarray = []
    timearray = []
    l = -1
    tprev = 0
    # Use antennalist if available
    if 'antlist' in uv.vartable:
        ants = uv['antlist']
        antlist = map(int, ants.split())
    else:
        antlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
    kp = 0
    for preamble, data in uv.all():
        uvw, t, (i0,j0) = preamble
        i = antlist.index(i0+1)
        j = antlist.index(j0+1)
        if i > j:
            # Reverse order of indices
            j = antlist.index(i0+1)
            i = antlist.index(j0+1)
        # Assumes uv['pol'] is -1, -2, 0, 0
        if i == 0 and j == 0:
            if uv['pol'] == -1:
                k = 0
            elif uv['pol'] == -2:
                k = 1
            elif uv['pol'] == 0:
                if kp == 1: 
                    k = 2
                    kp = 0
                else:
                    k = 3
                    kp = 1
        if k == 3:
            uvwarray.append(uvw)
        if len(data.nonzero()[0]) == nf:
            if t != tprev:
                # New time 
                l += 1
                if l == 600:
                    break
                tprev = t
                timearray.append(t)
                xdata = uv['xsampler'].reshape(500,nants,3)
                ydata = uv['ysampler'].reshape(500,nants,3)
                outp[:,0,:,l] = np.swapaxes(xdata[good_idx,:,0],0,1)
                outp[:,1,:,l] = np.swapaxes(ydata[good_idx,:,0],0,1)
                outp2[:,0,:,l] = np.swapaxes(xdata[good_idx,:,1],0,1)
                outp2[:,1,:,l] = np.swapaxes(ydata[good_idx,:,1],0,1)
                outm[:,0,:,l] = np.swapaxes(xdata[good_idx,:,2],0,1)
                outm[:,1,:,l] = np.swapaxes(ydata[good_idx,:,2],0,1)

            if i0 == j0:
                # This is an auto-correlation
                outa[i0,k,:,l] = data[data.nonzero()]
            else:
                outx[bl2ord[i,j],k,:,l] = data[data.nonzero()]
    out = {'a':outa, 'x':outx, 'uvw':np.array(uvwarray), 'fghz':freq, 'time':np.array(timearray),'source':src,'p':outp,'p2':outp2,'m':outm}
    return out