def __init__(self,d,path='~/comm/catalogues/TBGAL_CONVL.FITS'):#d, nu, pol
""" First extract total power in each scan (both mean and standard deviation) """
T_skytemp = Sky_temp(inputfile=path)
T_skytemp.set_freq( d.freqs[0])
T_sky = T_skytemp.Tsky
self.units = d.data_unit
self.name = d.antenna.name
self.filename = args[0]
self.elevation = {}
self.Tsys = {}
self.sigma_Tsys = {}
self.Tsys_sky = {}
self.T_sky = []
# Sort data in the order of ascending elevation
elevation = np.array([np.average(scan_el) for scan_el in scape.extract_scan_data(d.scans,'el').data])
ra = np.array([np.average(scan_ra) for scan_ra in scape.extract_scan_data(d.scans,'ra').data])
dec = np.array([np.average(scan_dec) for scan_dec in scape.extract_scan_data(d.scans,'dec').data])
sort_ind = elevation.argsort()
elevation,ra,dec = elevation[sort_ind],ra[sort_ind],dec[sort_ind]
valid_el = (elevation >= 10)
self.elevation = elevation[valid_el]
self.ra = ra[valid_el]
self.dec = dec[valid_el]
self.surface_temperature = np.mean(d.enviro['temperature']['value'])# Extract surface temperature from weather data
self.freq = d.freqs[0] #MHz Centre frequency of observation
for pol in ['HH','VV']:
power_stats = [scape.stats.mu_sigma(s.pol(pol)[:, 0]) for s in d.scans]
tipping_mu, tipping_sigma = np.array([s[0] for s in power_stats]), np.array([s[1] for s in power_stats])
tipping_mu, tipping_sigma = tipping_mu[sort_ind], tipping_sigma[sort_ind]
self.Tsys[pol] = tipping_mu[valid_el]
self.sigma_Tsys[pol] = tipping_sigma[valid_el]
self.Tsys_sky[pol] = []
self.T_sky = []
for val_el,ra,dec,el in zip(sort_ind,self.ra,self.dec,self.elevation):
self.T_sky.append( T_sky(ra,dec))
self.Tsys_sky[pol].append(tipping_mu[val_el]-T_sky(ra,dec))
TmpSky = scape.fitting.PiecewisePolynomial1DFit()
TmpSky.fit(self.elevation, self.T_sky)
self.Tsky = TmpSky
T_skytemp.plot_sky(self.ra,self.dec)
aperture_efficiency_v = "%s/ant_eff_%s_V_AsBuilt.csv"%(opts.aperture_efficiency,str.upper(Band))
aperture_efficiency = aperture_efficiency_models(filenameH=aperture_efficiency_h,filenameV=aperture_efficiency_v)
spill_over_model_path = "%s/MK_%s_Tspill_AsBuilt_atm_mask.dat"%(spill_over_models,str.upper(Band))
SpillOver = Spill_Temp(filename=spill_over_model_path) #/var/kat/katconfig/user/spillover-models/mkat/MK_L_Tspill_AsBuilt_atm_mask.dat
num_channels = np.int(channel_bw/(h5.channel_width/1e6)) #number of channels per band
chunks=[h5.channels[x:x+num_channels] for x in range(0, len(h5.channels), num_channels)]
print("Selecting channel data to form %f MHz Channels"%(channel_bw) )
d = load_cal(filename, "%s" % (ant.name,), nd_models, chunks,channel_mask=channel_mask,n_chan=n_chans,channel_range=freq_chans,band_input=Band.lower())
if not d is None:
tsys = np.zeros((len(d.scans),len(d.freqs[:]),5))#*np.NaN
tant = np.zeros((len(d.scans),len(d.freqs[:]),5))#*np.NaN
receiver = Rec_Temp(receiver_model_H, receiver_model_V)
elevation = np.array([np.average(scan_el) for scan_el in scape.extract_scan_data(d.scans,'el').data])
ra = np.array([np.average(scan_ra) for scan_ra in scape.extract_scan_data(d.scans,'ra').data])
dec = np.array([np.average(scan_dec) for scan_dec in scape.extract_scan_data(d.scans,'dec').data])
sort_ind = elevation.argsort()
elevation,ra,dec = elevation[sort_ind],ra[sort_ind],dec[sort_ind]
surface_temperature = np.mean(d.enviro['temperature']['value'])
air_relative_humidity = h5.humidity.mean()/100. # Fractional
length = 0
#freq loop
for i,freq_val in enumerate(d.freqs):
if not d is None:
d.filename = [filename]
nu = d.freqs #MHz Centre frequency of observation
#print("PreLoad T_sysTemp = %.2f Seconds"%(time.time()-time_start))
T_SysTemp = System_Temp(d,d.freqs[i],freq_index=i,elevation=elevation,ra=ra,dec=dec,surface_temperature = surface_temperature,air_relative_humidity=air_relative_humidity)
#print("Load T_sysTemp = %.2f Seconds"%(time.time()-time_start))
else:
ant = opts.ant
#h5.select(ants=ant)
d = scape.DataSet(filename, baseline="%s,%s" % (ant, ant))
d = d.select(freqkeep=range(start_freq_channel, end_freq_channel + 1))
d = remove_rfi(d, width=21, sigma=5) # rfi flaging
#Leave the d dataset unchanged after this so that it can be examined interactively if necessary
antenna = d.antenna
d_uncal = d.select(copy=True)
d_uncal.average()
d_cal = d.select(copy=True)
print("do selects")
#extract timestamps from data
timestampfile = np.hstack([scan.timestamps for scan in d_cal.scans])
#get a user-friendly time axis that will plot in the same way as plot_xyz
time = scape.extract_scan_data(d_cal.scans, 'time')
tmin = np.min(np.hstack(time.data))
tmax = np.max(np.hstack(time.data))
#Get the gain from the noise diodes
g_hh, g_vv, delta_re_hv, delta_im_hv = scape.gaincal.estimate_gain(d_cal)
gain_hh = np.r_[gain_hh, g_hh(timestampfile, d.freqs).mean(axis=1)]
gain_vv = np.r_[gain_vv, g_vv(timestampfile, d.freqs).mean(axis=1)]
timestamps = np.r_[timestamps, timestampfile]
print("Applied gains")
print " gain_hh %i, gain_vv %i, timestamps %i" % (
gain_hh.shape[0], gain_vv.shape[0], timestamps.shape[0])
#Apply noise diode calibration
if False:
d_cal.convert_power_to_temperature(min_samples=opts.min_nd,
time_width=opts.time_width)
d_cal.average()