Example #1
0
def make_beam(lats,lons,alts,spec_raw,lat0=0.0,lon0=0.0,
              nsides=8,volts=False,normalize=False,freq_chan=0):
    # Convert lat/lon to x/y
    x,y = latlon2xy(lats,lons,lat0,lon0)
    # Obtain spherical coordinates for x, y, and alt
    rs,thetas,phis = to_spherical(x,y,alts)

    #freq_chan = np.argmax(spec_raw[0,:])
    # Only extract information from appropriate column (index = 10)
    if len(spec_raw.shape)>1:
        z = spec_raw[:,freq_chan].copy()
    else:
        z=spec_raw.copy()
    if volts:
        # Distance normalization
        r0 = 100 # reference position for distance normalization (unit: meters)
        z = 10*np.log10((2*z**2)*(rs/r0)**2)
    if normalize:
        z -= z.max() # Scaled on [-infty,0]

    # Set binsize (used in function grid_data)
    # Affects the apparent size of the pixels on the plot created below.
    binsize=5
    # Obtain gridded data
    grid,bins,rmsBins,binloc,xg,yg,gcounts,grms = grid_data(x,y,z,binsize=binsize)

    # Healpix things
    nPixels = hp.nside2npix(nsides)
    hpx_beam = np.zeros(nPixels)
    hpx_counts = np.zeros(nPixels)
    hpx_rms = np.zeros(nPixels)
    # Find pixel # for a given theta and phi
    pixInd = hp.ang2pix(nsides,thetas,phis,nest=False)
    # Set pixel values at pixInd to power values
    hpx_beam[pixInd] = z
    hpx_counts[pixInd] = gcounts
    hpx_rms[pixInd] = grms
    # Grey out pixels with no measurements
    hpx_beam[hpx_beam == 0] = np.nan
    hpx_counts[hpx_counts == 0] = np.nan
    hpx_rms[hpx_rms == 0] = np.nan

    return np.ma.masked_invalid(hpx_beam),\
           np.ma.masked_invalid(hpx_counts),\
           np.ma.masked_invalid(hpx_rms)
Example #2
0
def grid_to_healpix(lats,lons,alts,rx,lat0,lon0,nside=8):
    """
    input:
        lats (deg)
        lons (deg)
        alts (m) (relative)
        rx: power in dB
        lat0: lat of rx ant (deg)
        lon0: lon of rx ant
        nside: of healpix map
    """
    # Convert lat/lon to x/y
    x,y = latlon2xy(lats,lons,lat0,lon0)
    # Obtain spherical coordinates for x, y, and alt
    rs,thetas,phis = to_spherical(x,y,alts)
    pixes = hp.ang2pix(nside,thetas,phis)
    beam = np.zeros(hp.nside2npix(nside))
    rms = np.zeros(hp.nside2npix(nside))
    counts = np.zeros(hp.nside2npix(nside))
    for i,pix in enumerate(pixes):
        beam[pix] += rx[i]
        rms[pix] += rx[i]**2
        counts[pix] += 1
    beam[counts>0] /= counts[counts>0]
    rms[counts>0] /= counts[counts>0]
    rms -= beam**2
    rms = np.sqrt(rms)
    beam[counts==0] = hp.UNSEEN
    counts[counts==0] = hp.UNSEEN
    rms[counts==0] = hp.UNSEEN
    #the standard deviation is uncertain for small counts
    #the the error in the standard deviation goes as 1/sqrt(2(N-1))
    #lets inflate the reported error to report the worst case upper limit
    #print 'Inflating error bars for low number counts'
    rms *= (1+1./np.sqrt(2*(counts-1)))
    return beam,rms,counts