def initialPeak(self, tod, x, y):
rms = Filtering.calcRMS(tod)
r = np.sqrt((x)**2 + (y)**2)
close = (r < 10)
tod -= Filtering.estimateBackground(tod, rms, close)
dx, dy = 1. / 60., 1. / 60.
Dx, Dy = 1., 1.
npix = int(Dx / dx)
xpix, ypix = np.arange(npix + 1), np.arange(npix + 1)
xpix = xpix * dx - Dx / 2.
ypix = ypix * dy - Dy / 2.
m = np.histogram2d(x, y, xpix, weights=tod)[0] / np.histogram2d(
x, y, xpix)[0]
m = median_filter(m, 3)
xmax, ymax = np.unravel_index(np.nanargmax(m), m.shape)
return xpix[xmax], ypix[ymax]
def MakeMap(self, tod, ra, dec, mjd, el):
#takes a 1D tod array and makes a simple map
#produce arrays for mapping
npix = self.naxis[0] * self.naxis[1]
pixbins = np.arange(0, npix + 1).astype(int)
nHorns, nSBs, nChans, nSamples = tod.shape
rms = Filtering.calcRMS(tod)
maps = np.zeros((nHorns, nSBs, nChans, self.naxis[0], self.naxis[1]))
for i in range(nHorns):
good = (np.isnan(ra[i, :]) == False) & (np.isnan(tod[i, 0, 0])
== False)
pa = Coordinates.pa(ra[i, good], dec[i, good], mjd[good], self.lon,
self.lat)
x, y = Coordinates.Rotate(ra[i, good], dec[i, good], self.x0,
self.y0, -pa)
nbins = 10
xbins = np.linspace(np.min(x), np.max(x), nbins + 1)
xmids = (xbins[1:] + xbins[:-1]) / 2.
xbw, _ = np.histogram(x, xbins)
ybw, _ = np.histogram(y, xbins)
todAvg = np.nanmean(np.nanmean(tod[i, ...], axis=0), axis=0)
fitx0, fity0 = self.initialPeak(todAvg[good], x, y)
r = np.sqrt((x - fitx0)**2 + (y - fity0)**2)
close = (r < 6. / 60.)
pix = ang2pixWCS(self.wcs, x, y).astype('int')
mask = np.where((pix != -1))[0]
h, b = np.histogram(pix,
pixbins,
weights=(pix != -1).astype(float))
self.hits = np.reshape(h, (self.naxis[0], self.naxis[1]))
for j in range(nSBs):
for k in range(1): #nChans):
todmap = tod[i, j, k, good]
if self.filtertod:
txbw, _ = np.histogram(x, xbins, weights=todmap)
tybw, _ = np.histogram(y, xbins, weights=todmap)
fb = txbw / xbw
gd = np.isfinite(fb)
pmdl = np.poly1d(np.polyfit(xmids[gd], fb[gd], 1))
todmap -= pmdl(x)
fb = tybw / ybw
gd = np.isfinite(fb)
pmdl = np.poly1d(np.polyfit(xmids[gd], fb[gd], 1))
todmap -= pmdl(y)
w, b = np.histogram(pix[mask],
pixbins,
weights=todmap[mask])
# w, b = np.histogram(pix[:], pixbins, weights=tod[i,j,k,:])
m = np.reshape(w, (self.naxis[0], self.naxis[1]))
maps[i, j, k, ...] = m / self.hits
return maps