/
sens_map.py
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/
sens_map.py
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import matplotlib.pyplot as plt
import numpy as np
import survey_sensitivity as ss
from survey import Survey
from radiometer import calcFlux
from cov import get_glgb
from plot_cov import get_colorlist
# Takes position arrays (alter to take arrays or individual points?) and returns corresponding Tsky
def tsky_glgb(gl,gb,freq):
tsky_freq = ss.tsky_test(freq) # Use i, j to get correct Tsky for a given position
tsky_glgb = []
for ll, bb in zip(gl,gb):
i, j = ss.get_ij(ll,bb)
tsky_glgb.append(tsky_freq[180*int(i)+int(j)])
return np.array(tsky_glgb)
# Generates an array of Smin given various things
# Can use duty == 0.0 to trigger pulse width calculator...
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
if __name__ == "__main__":
surv_list = ['AODRIFT','GBNCC','GBT350','HTRU-Nh','HTRU-Nl','HTRU-Nm','HTRU-Sh',
'HTRU-Sl','HTRU-Sm','PALFA-Mi','PALFA-Wi','PMPS','SMPS']
#"""
# Added to compare survey sensitivities
desired_frequency = 900.0
colorlist = get_colorlist(len(surv_list)) # assign color to each survey
surv_obj_list = [Survey(s) for s in surv_list] # list of survey objects
surv_cfreq_list = [s.freq for s in surv_obj_list] # get c_freq from each survey
seff_factor_list = [(f/desired_frequency)**1.4 for f in surv_cfreq_list] # convert S_xxx to desired frequency
surv_sens_list = []
for surv_str in surv_list:
smin = Smin(surv_str)
surv_sens_list.append(smin)
gl,gb = get_glgb()
smin_overall = np.zeros(len(gl))+999999.0 # Set index to lowest smin.
survey_color = ['black']*len(gl) # Set index to most sensitive survey color.
num_overall = np.zeros(len(gl)) # Set index to number of surveys that overlap.
# Determine most sensitive survey, redundancies, etc.
for ii in range(len(surv_list)):
print surv_list[ii],seff_factor_list[ii]
for jj in range(len(gl)):
if surv_sens_list[ii][jj] < 999999.0: # First count coverage/sky position
num_overall[jj] += 1.0
fac = seff_factor_list[ii] # Use to convert to "effective sensitivity" (1400 MHz)
if surv_sens_list[ii][jj]*fac < smin_overall[jj]:
smin_overall[jj] = surv_sens_list[ii][jj]*fac
survey_color[jj] = colorlist[ii]
file = open('all.dat','w')
file.write('# '+str(desired_frequency)+'\n') # Record effective frequency at which all sensitivities are quoted.
for ss,sc,sn in zip(smin_overall,survey_color,num_overall):
file.write(str(ss)+' '+str(sc)+' '+str(sn)+'\n')
file.close()
#"""
"""
# Will plot survey sensitivities at their nominal center frequencies.
....loop over surveys...
# Kind of janky just to get c_freq... (fix this)
s = Survey(surv_str)
freq_str = str(np.int(s.freq))
gl,gb = get_glgb()
deg2rad = np.pi/180.
cm = plt.cm.get_cmap('RdYlBu')
f = plt.figure()
ax = f.add_subplot(111,projection='mollweide')
# To avoid major outliers, find 10th and 90th percentiles of sensitivity distribution
smin_10 = np.percentile(smin[inds],10.0)
smin_90 = np.percentile(smin[inds],90.0)
# Plot covered grid positions (color corresponds to Smin)
plt.scatter(gl[inds]*deg2rad,gb[inds]*deg2rad,edgecolor='',s=1.5,c=smin[inds],cmap=cm,vmin=smin_10,vmax=smin_90)
cbar = plt.colorbar()
cbar.set_label(r"S$_{\rm "+freq_str+"}$ (mJy)")
plt.grid()
plt.xlabel('Galactic Longitude (deg)')
plt.ylabel('Galactic Latitude (deg)')
plt.title(surv_str+' ('+freq_str+' MHz)')
plt.savefig(surv_str+'_sens.pdf',format='pdf')
"""