def setLevel1(self, datafile, source=''):
"""
"""
self.setSource(source)
self.teleLon = self.datafile['hk/antenna0/tracker/siteActual'][
0, 0] / (60.**2 * 1000.)
self.teleLat = self.datafile['hk/antenna0/tracker/siteActual'][
0, 1] / (60.**2 * 1000.)
self.datafile = datafile
self.attributes = self.datafile['comap'].attrs
self.tsamp = float(self.attributes['tsamp'].decode())
self.obsid = self.attributes['obsid'].decode()
self.source = self.attributes['source'].decode()
# load but do not read yet.
self.x = self.datafile['spectrometer/pixel_pointing/pixel_ra']
self.y = self.datafile['spectrometer/pixel_pointing/pixel_dec']
self.utc = self.datafile['spectrometer/MJD']
sunra, sundec, sundist = Coordinates.getPlanetPosition('Sun',
self.teleLon,
self.teleLat,
self.utc[:],
returnall=True)
sunra, sundec = Coordinates.precess(sunra, sundec, self.utc[:])
pa = Coordinates.pa(sunra, sundec, self.utc, self.teleLon,
self.teleLat)
for i in range(self.x.shape[0]):
self.x[i, :], self.y[i, :] = Coordinates.Rotate(
self.x[i, :], self.y[i, :], sunra, sundec, -pa)
self.xCoordinateName = r'$\Delta$A'
self.yCoordinateName = r'$\Delta$E'
self.el = self.datafile['spectrometer/pixel_pointing/pixel_el']
self.tod_bavg = self.datafile['spectrometer/band_average']
self.features = self.datafile['spectrometer/features'][:]
self.mask = np.ones(self.features.size).astype(int)
self.mask[self.featureBits(self.features.astype(float), 13)] = 0
self.mask[self.features == 0] = 0
self.mask = self.mask.astype(int)
# If we don't spe
self.setCrval()
self.setWCS(self.crval, self.cdelt, self.crpix, self.ctype)
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