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)
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