Exemple #1
0
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
Exemple #2
0
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()