def Smin(surv_str,duty=0.06):
s = Survey(surv_str)
gl,gb = get_glgb()
Tsky = tsky_glgb(gl,gb,s.freq)
cov = np.loadtxt(surv_str+'.cov',dtype='float')
cov_inds = [cov > 0.0]
# Return an array based on tsky map.
Smin = calcFlux(s.SNRlimit,
s.beta,
s.tsys,
Tsky,
s.gain,
s.npol,
s.tobs,
s.bw,
duty)
# Print some pertinent information:
print ''
print 'Survey: '+s.surveyName
print 'Center frequency (MHz): '+str(s.freq)
print 'Tsky mean, median (K): '+str(np.mean(Tsky[cov_inds]))+', '+str(np.median(Tsky[cov_inds]))
print 'Smin mean, median (mJy): '+str(np.mean(Smin[cov_inds]))+', '+str(np.median(Smin[cov_inds]))
print ''
# Set positions without coverage == 999999.0
not_cov_inds = [cov == 0.0]
Smin[not_cov_inds] = 999999.0
return Smin
import matplotlib.pyplot as plt
import numpy as np
from cov import get_glgb
from plot_cov import get_colorlist
surv_list = ['AODRIFT','GBNCC','GBT350','HTRU-Nh','HTRU-Nl','HTRU-Nm','HTRU-Sh',
'HTRU-Sl','HTRU-Sm','PALFA-Mi','PALFA-Wi','PMPS','SMPS']
col = np.loadtxt('all.dat', usecols=[1],dtype='str',unpack=True)
smin,ncov = np.loadtxt('all.dat',dtype='float',usecols=[0,2],unpack=True)
# What kind of plot??
# 0: smin, 1: most sensitive survey, 2: redundancy
plot_type = 2
gl,gb = get_glgb()
deg2rad = np.pi/180.
f = plt.figure()
ax = f.add_subplot(111,projection='mollweide')
if plot_type == 0:
outfile = 'lowest_smin.pdf'
cm = plt.cm.get_cmap('RdYlBu')
# To avoid major outliers, find 10th and 90th percentiles of sensitivity distribution
smin_lo = np.percentile(smin,10.0)
smin_hi = np.percentile(smin,90.0)
plt.scatter(gl*deg2rad,gb*deg2rad,edgecolor='',s=1.5,c=smin,cmap=cm,vmin=smin_lo,vmax=smin_hi)
cbar = plt.colorbar()
