minRa = np.min(ra)
maxRa = np.max(ra)
minDec = np.min(dec)
maxDec = np.max(dec)
if (crval[0] < minRa) | (crval[0] > maxRa) | (crval[1] < minDec) | (crval[1] > maxDec):
print('WARNING: MAP CENTRE DOES NOT MATCH TELESCOPE POINTING CENTRE. CHECK COORDINATES')
print('MEAN RA: {:.2f}, MEAN DEC: {:.2f}'.format(np.mean(ra), np.mean(dec)))
wcs,_,_ = Mapping.DefineWCS(naxis, cdelt, crval)
maps, hits = Mapping.MakeMaps(tod, ra, dec, wcs)
dataout['hits'] = hits
dataout['maps'] = maps
dataout['naxis'] = np.array(naxis)
dataout['cdelt'] = np.array(cdelt)
dataout['crval'] = np.array(crval)
sbStr = ''.join(str(e) for e in sidebands)
hoStr = ''.join(str(e) for e in pixels)
FileTools.WriteH5Py('{}/{}_{}_Horns{}_Sidebands{}.h5'.format(Parameters.get('Inputs', 'outputDir'),
Parameters.get('Inputs', 'outputname'),
prefix,
hoStr,
sbStr), dataout)
dataout = {} # clear data
amps = np.zeros((len(filenames), 32 * 4))
rms = np.zeros((len(filenames), 32 * 4))
nu = np.zeros((32 * 4))
r = np.zeros((len(filenames)))
fwhm = np.zeros((4, 32, 2, 2, len(filenames)))
fwhmErrs = np.zeros((4, 32, 2, 2, len(filenames)))
outputs = np.zeros((4, 2, len(filenames)))
az, el = np.zeros(filenames.size), np.zeros(filenames.size)
horn = 1
hornID = [1, 12]
markers = ['x', 'o']
for i, filename in enumerate(filenames):
print(filename)
d = FileTools.ReadH5Py(filename)
P = d['TODFits']
for horn in [0, 1]:
amps[i, :] = d['TODFits'][horn, :, :, 0].flatten()
rms[i, :] = d['TODFits'][horn, :, :, -1].flatten()
# sideband, frequency, x/y, horn, date
fwhm[:, :, :, horn, i] = d['TODFits'][horn, :, :, 1:3]
fwhmErrs[:, :, :, horn, i] = d['TODFitsErrors'][horn, :, :, 1:3]
#print(d.keys())
r[i] = d['Jupiter'][2]
if i == 0:
#print(d['nu'])
def MeasureAmps(filename):
d = FileTools.ReadH5Py(filename)
naxis = d['naxis']
cdelt = d['cdelt']
crval = d['crval']
mAzEl = d['mAzEl']
print(crval)
wcs = DefineWCS(naxis, cdelt, crval)
ra, dec = RaDecGrid(naxis, cdelt, crval)
if crval[0] == 0:
ra[ra > 180] -= 360.
w = d['maps']
h = d['hits']
A = d['atmos']
nhorns = w.shape[0]
nsbs = w.shape[1]
nfreqs = w.shape[2]
amps = np.zeros((nhorns, nsbs * nfreqs))
stds = np.zeros((nhorns, nsbs * nfreqs))
fits = np.zeros((nhorns, nsbs * nfreqs, 7))
tfits = np.zeros((nhorns, nsbs, 7))
amaxs = np.zeros((nhorns, nsbs * nfreqs, 2))
for i in range(nhorns):
l = 0
for j in range(nsbs):
m = w[i, j, :, ...] / h[i, ...]
xpix, ypix = FindPeaks(
np.nanmean(m, axis=0)
) # first perform quick estimate of peak location per sideband
print ra[xpix, ypix], dec[xpix, ypix]
for k in range(nfreqs):
try:
fits[i, l, :] = AperFit(m[k], ra, dec, ra[xpix, ypix],
dec[xpix, ypix], 10. / 60.)
except TypeError:
fits[i, l, :] = np.nan
amps[i, l], stds[i, l] = AperPhot(m[k], ra, dec, crval[0],
crval[1], 6. / 60., 8. / 60.,
15. / 60.)
l += 1
if (j == 0) | (j == 2):
amps[i, j * nfreqs:(j + 1) *
nfreqs] = (amps[i, j * nfreqs:(j + 1) * nfreqs:1])[::-1]
stds[i, j * nfreqs:(j + 1) *
nfreqs] = (stds[i, j * nfreqs:(j + 1) * nfreqs:1])[::-1]
fits[i, j * nfreqs:(j + 1) *
nfreqs, :] = (fits[i, j * nfreqs:(j + 1) *
nfreqs:1, :])[::-1, :]
# Then plot the images
for i in range(nhorns):
fig = pyplot.figure(figsize=(15, 10))
for j in range(nsbs):
m = w[i, j, :, ...] / h[i, ...]
xpix, ypix = FindPeaks(
np.nanmean(m, axis=0)
) # first perform quick estimate of peak location per sideband
# weighted mean by amplitude of jupiter e.g., the gain
if (j == 0) | (j == 2):
signal = amps[0, (j + 1) * nfreqs:j * nfreqs:-1]
noise = stds[0, (j + 1) * nfreqs:j * nfreqs:-1]
weight = (signal / noise)**2
amax = np.nansum(signal * weight) / np.nansum(weight)
nmax = np.nansum(noise * weight) / np.nansum(weight)
else:
signal = amps[0, j * nfreqs:(j + 1) * nfreqs:1]
noise = stds[0, j * nfreqs:(j + 1) * nfreqs:1]
weight = (signal / noise)**2
weight[np.isnan(weight)] = 0.
amax = np.nansum(signal * weight) / np.nansum(weight)
nmax = np.nansum(noise * weight) / np.nansum(weight)
#print(np.sum(weight))
#pyplot.plot(weight)
#pyplot.show()
m = np.nanmean(m, axis=0)
#m = np.nansum(m*weight[:,np.newaxis,np.newaxis], axis=0)/np.nansum(np.ones(m.shape) * weight[:,np.newaxis,np.newaxis],axis=0)
if np.nansum(m) == 0:
print('Horn {} SB {} has no data'.format(i, j))
continue
tfits[i, j, :] = AperFit(m, ra, dec, ra[xpix, ypix],
dec[xpix, ypix], 10. / 60.)
ax = fig.add_subplot(2, 4, 2 * j + 1, projection=wcs)
pyplot.imshow(m,
origin='lower',
vmax=amax + nmax * 3,
vmin=-nmax * 3)
pyplot.plot([crval[0]], [crval[1]],
'kx',
transform=ax.get_transform('world'),
alpha=0.7)
pyplot.title('SB {}'.format(j))
if crval[0] == 0:
pyplot.xlabel(r'$\Delta \alpha$')
pyplot.ylabel(r'$\Delta \delta$')
else:
pyplot.xlabel(r'$\alpha$')
pyplot.ylabel(r'$\delta$')
if j < 2:
ax.coords[0].set_ticklabel_visible(False)
ax.coords[0].set_axislabel('')
if (j == 1) | (j == 3):
ax.coords[1].set_ticklabel_visible(False)
ax.coords[1].set_axislabel('')
ax.coords[0].set_ticks(size=10)
ax.coords[1].set_ticks(size=10)
ax = fig.add_subplot(2, 4, 2 * j + 2, projection=wcs)
mdl = Gauss2D(tfits[i, j, :], ra, dec, ra[xpix, ypix], dec[xpix,
ypix])
pyplot.imshow(m - mdl,
origin='lower',
vmax=amax + nmax * 3,
vmin=-nmax * 3)
pyplot.plot([crval[0]], [crval[1]],
'kx',
transform=ax.get_transform('world'),
alpha=0.7)
pyplot.title('SB {}'.format(j))
if crval[0] == 0:
pyplot.xlabel(r'$\Delta \alpha$')
pyplot.ylabel(r'$\Delta \delta$')
else:
pyplot.xlabel(r'$\alpha$')
pyplot.ylabel(r'$\delta$')
ax.coords[1].set_ticklabel_visible(False)
ax.coords[1].set_axislabel('')
if j < 2:
ax.coords[0].set_ticklabel_visible(False)
ax.coords[0].set_axislabel('')
if (j == 1) | (j == 3):
ax.coords[1].set_ticklabel_visible(False)
ax.coords[1].set_axislabel('')
ax.coords[0].set_ticks(size=10)
ax.coords[1].set_ticks(size=10)
pyplot.subplots_adjust(wspace=0.2, hspace=0.05)
outname = filename.split('.')[0] + 'horn{}_images.png'.format(i)
pyplot.savefig('Plots/{}'.format(outname), bbox_inches='tight')
pyplot.clf()
return amps, fits, mAzEl, tfits
self.bVec *= 1.
def Ax(self, r):
self.AxVec = self.A.dot(r[:,np.newaxis]).flatten()
if __name__ == "__main__":
# Read in parameter file
#inputParameters = sys.argv[1]
#Parameters = ConfigParser.ConfigParser()
#Parameters.read(inputParameters)
filename = 'Continuous_El37_4-2deg_Full_25Hz_1Channel_60Day_Fnoise_TOD-DATA_realisation0_FileNo0314.hdf5'
from pipeline.Tools import FileTools
d = FileTools.ReadH5Py(filename)
filename = 'Continuous_El37_4-2deg_Full_25Hz_1Channel_60Day_TOD-DATA_realisation0_FileNo0314.hdf5'
nonoise = FileTools.ReadH5Py(filename)['TOD'].flatten().astype(float) - 20.
ra = d['RA'].flatten()
dec = d['DEC'].flatten()
nside = 128
npix = 12*nside**2
import healpy as hp
pixels = hp.ang2pix(nside, (90-dec)*np.pi/180., ra*np.pi/180.).astype(int)
t = d['TOD'][0,0,:].flatten()-200# np.random.normal(size=1000)
pstod = np.abs(np.fft.fft(t[250000:500000]))
ftod = np.fft.fftfreq(pstod.size, d=1./25.)
offsetLength = 20
if (j == 1) | (j == 3):
ax.coords[1].set_ticklabel_visible(False)
ax.coords[1].set_axislabel('')
ax.coords[0].set_ticks(size=10)
ax.coords[1].set_ticks(size=10)
pyplot.subplots_adjust(wspace=0.2, hspace=0.05)
outname = filename.split('.')[0] + 'horn{}_images.png'.format(i)
pyplot.savefig('Plots/{}'.format(outname), bbox_inches='tight')
pyplot.clf()
return amps, fits, mAzEl, tfits
listname = sys.argv[1]
filelist = np.loadtxt(listname, dtype='string', ndmin=1)
for i, filename in enumerate(filelist):
amps, fits, mAzEl, tfits = MeasureAmps(filename)
outname = filename.split('.')[0] + '_amps.h5'
FileTools.WriteH5Py('amps/{}'.format(outname), {
'amps': amps,
'fits': fits,
'mAzEl': mAzEl,
'meanFits': tfits
})
#