def fnoise_list(mode = 'GFields'):
filelist = np.loadtxt(sys.argv[1],dtype=str)
obsid = np.array([int(f.split('-')[1]) for f in filelist])
filelist = filelist[np.argsort(obsid)]
obsid = np.sort(obsid)
fnoise = np.zeros(filelist.size)
enoise = np.zeros(filelist.size)
feed = None
ifeed = 0
isfg4 = np.zeros(filelist.size,dtype=bool)
dist = np.zeros(filelist.size)
pyplot.figure(figsize=(20,5))
for ifile, filename in enumerate(filelist):
print(filename)
try:
data = h5py.File(filename,'r')
except OSError:
print('{} cannot be opened (Resource unavailable)'.format(filename))
fnoise[ifile] = np.nan
if mode.lower() in data['level1/comap'].attrs['source'].decode('utf-8').lower():
isfg4[ifile] = True
try:
fits = data['level2/fnoise_fits'][ifeed,-1,1:-2,:]
fnoise[ifile] = np.median(fits[:,1])
enoise[ifile] = np.sqrt(np.median(np.abs(fits[:,1]-fnoise[ifile])**2))*1.4826
except:
print('{} not processed'.format(filename.split('/')[-1]))
fnoise[ifile] = np.nan
if isinstance(feed, type(None)):
feed = data['level1/spectrometer/feeds'][ifeed]
# Calculate sun distance
mjd = data['level1/spectrometer/MJD'][0:1]
lon=-118.2941
lat=37.2314
ra_sun, dec_sun, raddist = Coordinates.getPlanetPosition('SUN', lon, lat, mjd)
az_sun, el_sun = Coordinates.e2h(ra_sun, dec_sun, mjd, lon, lat)
ra = data['level1/spectrometer/pixel_pointing/pixel_ra'][0,0:1]
dec = data['level1/spectrometer/pixel_pointing/pixel_dec'][0,0:1]
dist[ifile] = el_sun[0]#angular_seperation(ra_sun, ra, dec_sun, dec)
data.close()
good = (fnoise > -1.2) & (fnoise < -0.5) & np.isfinite(fnoise) & (fnoise != -1)
with open('Plots/{}_good.list'.format(mode),'w') as f:
for line in filelist[good]:
f.write('{}\n'.format(line))
pyplot.errorbar(np.arange(fnoise.size),fnoise,fmt='.',yerr=enoise,capsize=3)
pyplot.errorbar(np.arange(fnoise.size)[good],fnoise[good],fmt='.',yerr=enoise[good],capsize=3)
pyplot.xticks(np.arange(fnoise.size),obsid, rotation=90,size=8)
pyplot.ylim(-2,-0.8)
pyplot.grid()
pyplot.savefig('Plots/Fnoise_feed{}_{}.png'.format(feed,mode),bbox_inches='tight')
pyplot.savefig('Plots/Fnoise_feed{}_{}.pdf'.format(feed,mode),bbox_inches='tight')
pyplot.clf()
def fnoise_plots(mode,ifeed):
filelist = np.loadtxt(sys.argv[1],dtype=str)
obsid = np.array([int(f.split('-')[1]) for f in filelist])
filelist = filelist[np.argsort(obsid)]
obsid = np.sort(obsid)
fnoise = np.zeros(filelist.size)
enoise = np.zeros(filelist.size)
feed = None
isfg4 = np.zeros(filelist.size,dtype=bool)
dist = np.zeros(filelist.size)
fnoise_power = np.zeros((filelist.size,64*4))
alphas = np.zeros((filelist.size,64*4))
for ifile, filename in enumerate(filelist):
try:
data = h5py.File(filename,'r')
except OSError:
print('{} cannot be opened (Resource unavailable)'.format(filename))
fnoise[ifile] = np.nan
if mode.lower() in data['level1/comap'].attrs['source'].decode('utf-8').lower():
isfg4[ifile] = True
try:
fits = data['level2/fnoise_fits'][ifeed,:,:,:]
fnoise[ifile] = np.median(fits[:,1])
enoise[ifile] = np.sqrt(np.median(np.abs(fits[:,1]-fnoise[ifile])**2))*1.4826
ps = data['level2/powerspectra'][ifeed,:,:,:]
rms = data['level2/wnoise_auto'][ifeed,:,:,:]
nu = data['level2/freqspectra'][ifeed,:,:,:]
freq = data['level1/spectrometer/frequency'][...]
bw = 16
freq = np.mean(np.reshape(freq, (freq.shape[0],freq.shape[1]//bw, bw)),axis=-1).flatten()
sfreq = np.argsort(freq)
fnoise_power[ifile,:] = (rms[:,:,0]**2 * (1/fits[:,:,0])**fits[:,:,1]).flatten()[sfreq]
alphas[ifile,:] = (fits[:,:,1]).flatten()[sfreq]
#print(nu.shape,ps.shape, rms.shape, fits.shape)
#pyplot.plot(freq[sfreq],fnoise_power[ifile,:])
except IOError:
print('{} not processed'.format(filename.split('/')[-1]))
fnoise[ifile] = np.nan
if isinstance(feed, type(None)):
feed = data['level1/spectrometer/feeds'][ifeed]
# Calculate sun distance
mjd = data['level1/spectrometer/MJD'][0:1]
lon=-118.2941
lat=37.2314
ra_sun, dec_sun, raddist = Coordinates.getPlanetPosition('SUN', lon, lat, mjd)
az_sun, el_sun = Coordinates.e2h(ra_sun, dec_sun, mjd, lon, lat)
ra = data['level1/spectrometer/pixel_pointing/pixel_ra'][0,0:1]
dec = data['level1/spectrometer/pixel_pointing/pixel_dec'][0,0:1]
dist[ifile] = el_sun[0]#angular_seperation(ra_sun, ra, dec_sun, dec)
data.close()
# Plot obs ID vs fnoise power
pyplot.imshow(np.log10(fnoise_power*1e3),aspect='auto',origin='lower',
extent=[np.min(freq),np.max(freq),-.5,fnoise_power.shape[0]-0.5])
pyplot.yticks(np.arange(fnoise_power.shape[0])-0.5, obsid, rotation=0,
ha='right',va='center',size=10)
ax = pyplot.gca()
fig = pyplot.gcf()
offset = ScaledTranslation(-0.08,0.02,fig.transFigure)
for label in ax.yaxis.get_majorticklabels():
label.set_transform(label.get_transform() + offset)
pyplot.grid()
pyplot.xlabel('Frequency (GHz)')
pyplot.ylabel('obs ID')
pyplot.colorbar(label=r'$\mathrm{log}_{10}$(mK)')
pyplot.title('Feed {}'.format(feed))
pyplot.savefig('Plots/fnoise_gfields_Feed{}.png'.format(feed),bbox_inches='tight')
pyplot.clf()
# Plot obs ID vs fnoise power
pyplot.imshow(alphas,aspect='auto',origin='lower',vmin=-1.5,vmax=-0.9,
extent=[np.min(freq),np.max(freq),-.5,fnoise_power.shape[0]-0.5])
pyplot.yticks(np.arange(fnoise_power.shape[0])-0.5, obsid, rotation=0,
ha='right',va='center',size=10)
ax = pyplot.gca()
fig = pyplot.gcf()
for label in ax.yaxis.get_majorticklabels():
label.set_transform(label.get_transform() + offset)
pyplot.grid()
pyplot.xlabel('Frequency (GHz)')
pyplot.ylabel('obs ID')
pyplot.colorbar(label=r'$\alpha$')
pyplot.title('Feed {}'.format(feed))
pyplot.savefig('Plots/alphas_gfields_Feed{}.png'.format(feed),bbox_inches='tight')
pyplot.clf()