def compare_mean_xfrac():
wstars_xfrac = plot_1D.mean('xfrac','data_'+flag1+'/',redshifts1)
wpls_xfrac = plot_1D.mean('xfrac','data_'+flag2+'/',redshifts2)
wstars_xfracHe1 = plot_1D.mean('xfracHe1','data_'+flag1+'/',redshifts1)
wpls_xfracHe1 = plot_1D.mean('xfracHe1','data_'+flag2+'/',redshifts2)
wstars_xfracHe2 = plot_1D.mean('xfracHe2','data_'+flag1+'/',redshifts1)
wpls_xfracHe2 = plot_1D.mean('xfracHe2','data_'+flag2+'/',redshifts2)
length = min(len(wstars_xfrac),len(wpls_xfrac))
means = np.zeros(6*length).reshape(length,6)
for i in range(length):
if i<len(wpls_xfrac):
means[i,0] = wpls_xfrac[i]
means[i,1] = wpls_xfracHe1[i]
means[i,2] = wpls_xfracHe2[i]
means[i,3] = wstars_xfrac[i]
means[i,4] = wstars_xfracHe1[i]
means[i,5] = wstars_xfracHe2[i]
means[0,2] = means[1,2]
means[0,5] = means[1,5]
#print means[:,2], means[:,5]
#print redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"xfrac","log$_{10}$ (Ionised Fraction)",'compare_'+flag1+'_'+flag2+'/',redshifts1,redshifts2,"Stellar & X-ray binaries","Stellar only")
def compare_mean_temp():
minmax = "min"
data1 = plot_1D.mean('temp','tests/data_10/',redshifts)
data2 = plot_1D.mean('temp','tests/data_200/',redshifts)
# data3 = plot_1D.mean('temp','tests/data_121/',redshifts)
# data4 = plot_1D.mean('temp','tests/data_201/',redshifts)
length = max(len(data1),len(data2))
means = np.zeros(2*length).reshape(length,2)
for i in range(len(data1)):
means[i,0] = data1[i]
means[i,1] = data2[i]
# means[i,2] = data3[i]
# means[i,3] = data4[i]
print means[:,0]
print means[:,1]
# print data3
# print data4
#print means[i,1]
#print redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"Temperature","Temperature (K)",'tests/',redshifts,redshifts,"11","501")
def compare_rms():
rmsdata=numpy.zeros(len(wpls)**2).reshape(len(wpls),len(wpls))
for i in range(redshifts_wstars):
fr_wstars=np.loadtxt("../generate_data/data_wstars/powerSpectraFrequencies_dbt_"+str('%.3f' % redshifts[i]))
data_wstars = np.loadtxt("../generate_data/data_wstars/powerSpectra_"+str('%.3f' % redshifts[i]))*1.0e-3
rmsdata[0,i]=sci.simps(data_wstars,fr_wstars)/1.0e-3
fr_wpls=np.loadtxt("../generate_data/data_wstars/powerSpectraFrequencies_dbt_"+str('%.3f' % redshifts[i]))
data_wpls = np.loadtxt("../generate_data/data_wstars/powerSpectra_"+str('%.3f' % redshifts[i]))*1.0e-3
rmsdata[1,i]=sci.simps(data_pls,fr_wpls)/1.0e-3
plot_1D.plot_mean(rmsdata,"rms","rms (mK)",'compare_wstars_wpls/',redshifts_wpls,redshifts_wpls,"Stellar only","Stellar and X-ray")
wstars_temp = plot_1D.mean('temp','data_wstars/',redshifts_wstars)
wpls_temp = plot_1D.mean('temp','data_wpls/',redshifts_wpls)
length = min(len(wstars_temp),len(wpls_temp))
print length
means = np.zeros(2*length).reshape(length,2)
for i in range(length):
means[i,0] = wstars_temp[i]
for i in range(length):
means[i,1] = wpls_temp[i]
#print means[i,1]
#print redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"Temperature","Temperature (K)",'compare_wstars_wpls/',redshifts_wpls,redshifts_wpls,"Stellar only","Stellar and X-ray")
def compare_mean_dbt():
wstars_temp = plot_1D.mean('dbt','data_'+flag1+'/',redshifts1)
wpls_temp = plot_1D.mean('dbt','data_'+flag2+'/',redshifts2)
print wstars_temp
print wpls_temp
print "--------------"
length = min(len(wstars_temp),len(wpls_temp))/2
if (length%2!=0):
length=length-1
means = np.zeros(2*length).reshape(length,2)
redshifts3 = np.zeros(length)
redshifts4 = np.zeros(len(wpls_temp)/2)
for i in range(length*2):
if i%2==0:
means[i/2,0] = wstars_temp[i]
if (i<len(wpls_temp)):
means[i/2,1] = wpls_temp[i]
redshifts3[i/2]=redshifts1[i]
for i in range(length):
redshifts4[i/2]=redshifts2[i]
print means[:,1]
print means[:,0]
print "-----------"
#print redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"dbt","$\delta T_b$",'compare_'+flag1+'_'+flag2+'/',redshifts3,redshifts4,label1,label2)
def compare_rms():
length=min(len(redshifts1),len(redshifts2))
if length%2!=0:
length=length-1
rmsdata=np.zeros(length).reshape(length/2,2)
redshifts_short=np.zeros((length)/2)
for i in range(0,length,2):
redshifts_short[i/2]=redshifts2[i]
print "opening ../generate_data/data_"+flag1+"/map_dbt_"+str('%.3f' % redshifts1[i])+".bin and ../generate_data/data_"+flag2+"/map_dbt_"+str('%.3f' % redshifts2[i])+".bin"
data1=np.load("../generate_data/data_"+flag1+"/map_dbt_"+str('%.3f' % redshifts1[i])+".bin")
data2=np.load("../generate_data/data_"+flag2+"/map_dbt_"+str('%.3f' % redshifts2[i])+".bin")
mean1=np.mean(data1)
mean2=np.mean(data2)
# length=len(data)
sum=0.0
print "summing..."
for x in range(length):
for y in range(length):
for z in range(length):
rmsdata[i/2,0]=rmsdata[i/2,0]+(data1[x,y,z]-mean1)**2
rmsdata[i/2,1]=rmsdata[i/2,1]+(data2[x,y,z]-mean2)**2
rmsdata[i/2,:]=np.sqrt(rmsdata[i/2,:]/(length**3)/2.0)
# rmsdata[:,i]=np.sqrt(rmsdata[:,i]/(length**3)/2.0)
plot_1D.plot_mean(rmsdata,"rms_100b","rms (mK)","compare_"+flag1+"_"+flag2+"/",redshifts_short,redshifts_short,label1,label2)
def compare_mean_temp():
wstars_temp = plot_1D.mean('temp','data_wstars/',redshifts_wstars)
wpls_temp = plot_1D.mean('temp','data_wpls/',redshifts_wpls)
length = min(len(wstars_temp),len(wpls_temp))
means = np.zeros(2*length).reshape(length,2)
for i in range(length):
means[i,0] = wstars_temp[i]
for i in range(length):
means[i,1] = wpls_temp[i]
#print means[i,1]
#print redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"Temperature","Temperature (K)",'compare_wstars_wpls/',redshifts_wpls,"Stellar only","Stellar and X-ray")
def compare_mean_dbt():
wstars_temp = plot_1D.mean('dbt','data_wstars/',redshifts_wstars)
wpls_temp = plot_1D.mean('dbt','data_wpls/',redshifts_wpls)
length = max(len(wstars_temp),len(wpls_temp))/2
means = np.zeros(2*length).reshape(length,2)
redshifts2 = np.zeros(length)
for i in range(length*2):
if i%2==0:
means[i/2,0] = wstars_temp[i]
if (i<len(wpls_temp)):
means[i/2,1] = wpls_temp[i]
redshifts2[i/2]=redshifts_wstars[i]
#print means[i,1]
#print redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"dbt","$\delta T_b$",'compare_wstars_wpls/',redshifts2,"Stellar only","Stellar and X-ray")
def compare_mean_temp():
minmax = "min"
# print redshifts1
# print '-----------------------------'
# print redshifts2
data1 = plot_1D.mean('temp','data_'+flag1+'/',redshifts1)
data2 = plot_1D.mean('temp','data_'+flag2+'/',redshifts2)
#print redshifts1
#print '-----------------------------'
#print redshifts2
if minmax=="max":
length = max(len(data1),len(data2))
means = np.zeros(2*length).reshape(length,2)
for i in range(len(data1)):
means[i,0] = data1[i]
for i in range(len(data2)):
means[i,1] = data2[i]
print redshifts1
print '-----------------------------'
print redshifts2
#print means[i,1]
#print redshifts_wstars[i], redshifts_wpls[i]
print np.shape(means)
# print len(redshifts1), len(redshifts2)
# print redshifts2
# print len(redshifts2)
# plot_1D.plot_mean(means,"Temperature","Temperature (K)",'compare_'+flag1+'_'+flag2+'/',redshifts2,redshifts2,label1,label2)
elif minmax=="min":
length = min(len(data1),len(data2))
means = np.zeros(2*length).reshape(length,2)
for i in range(length):
means[i,0] = data1[i]
for i in range(length):
means[i,1] = data2[i]
print means[i,1]
print means[i,0]
#prin redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"Temperature","Temperature (K)",'compare_'+flag1+'_'+flag2+'/',redshifts2,redshifts2,label1,label2)
def compare_mean_test():
wstars_temp = plot_1D.mean('temp','data_wstars/',redshifts_wstars)
wpls_temp = plot_1D.mean('temp','data_wpls/',redshifts_wpls)
length = max(len(wstars_temp),len(wpls_temp))
wstars_tcmb = 2.725*(np.ones(len(redshifts_wstars))+redshifts_wstars)
wpls_tcmb = 2.725*(np.ones(len(redshifts_wpls))+redshifts_wpls)
wstars_test = (wstars_temp-wstars_tcmb)/wstars_temp
wpls_test = (wpls_temp-wpls_tcmb)/wpls_temp
means = np.zeros(2*length).reshape(length,2)
for i in range(length):
means[i,0] = wstars_test[i]
for i in range(length):
if (i<len(wpls_temp)):
means[i,1] = wpls_test[i]
#print means[i,1]
#print redshifts_wstars[i], redshifts_wpls[i]
plot_1D.plot_mean(means,"Test","$(T_S - T_{CMB}) / T_S$",'compare_wstars_wpls/',redshifts_wstars,redshifts_wpls,"Stellar only","Stellar and X-ray")
def compare_rms():
length=min(len(redshifts1),len(redshifts2))
if length%2!=0:
length=length-1
#rmsdata=np.zeros(length).reshape(length/2,2)
rmsdata=np.zeros((length/2,2))#length**2).reshape(length,2)
#redshifts_short=np.zeros((length)/2)
redshifts_short=np.zeros(length/2)
# for i in range(0,length,2):
# redshifts_short[i/2]=redshifts2[i]
# print "opening ../generate_data/data_"+flag1+"/map_dbt_"+str('%.3f' % redshifts1[i])+".bin and ../generate_data/data_"+flag2+"/map_dbt_"+str('%.3f' % redshifts2[i])+".bin"
data1=IO.readoned("rms",path="../generate_data/data_"+flag1+"/")#"../generate_data/data_"+flag1+"/rms.dat")
data2=IO.readoned("rms",path="../generate_data/data_"+flag2+"/")#"../generate_data/data_"+flag2+"/rms.da")
for i in range(0,length/2):
print i
rmsdata[i,0]=data1[i*2]
rmsdata[i,1]=data2[i*2]
# for i in range(length/2):
redshifts_short[i] = redshifts1[i*2]
# mean1=np.mean(data1)
# mean2=np.mean(data2)
# # length=len(data)
# sum=0.0
# print "summing..."
# for x in range(length):
# for y in range(length):
# for z in range(length):
# rmsdata[i/2,0]=rmsdata[i/2,0]+(data1[x,y,z]-mean1)**2
# rmsdata[i/2,1]=rmsdata[i/2,1]+(data2[x,y,z]-mean2)**2
# rmsdata[i/2,:]=np.sqrt(rmsdata[i/2,:]/(length**3)/2.0)
# rmsdata[:,i]=np.sqrt(rmsdata[:,i]/(length**3)/2.0)
print rmsdata[:,0]
print '-----------------------------------'
print rmsdata[:,1]
plot_1D.plot_mean(rmsdata,"rms_100b","rms (mK)","compare_"+flag1+"_"+flag2+"/",redshifts_short,redshifts_short,label1,label2)
plot_2D.plottemp()
plot_2D.plotxfrac('')
plot_2D.plotxfrac('He1')
plot_2D.plotxfrac('He2')
plot_2D.equation_of_state()
##these need information from generate_data so leave until last
plot_2D.plotdbt()
plot_2D.plot_smoothed_dbt()
#
plot_2D.lightcone_temp()
plot_2D.lightcone_xfrac()
plot_2D.lightcone_xfrac(rsd='rsd')
plot_2D.lightcone_temp('rsd')
#
plot_1D.plot_mean(plot_1D.mean("temp"),"Tempererature","Temperature (K)")
means = np.zeros(len(redshifts)*3).reshape(len(redshifts),3)
means[:,0] = plot_1D.mean('xfrac')
means[:,1] = plot_1D.mean('xfracHe1')
means[:,2] = plot_1D.mean('xfracHe2')
plot_1D.plot_mean(np.log10(means),"x-frac","Log (Ionised Fraction)")
##
meantemp=plot_1D.mean('temp')
plot_1D.plot_mean(meantemp,"Temper","Temperature (K)")
#meantemp_igm=plot_1D.mean('temp_igm')
#plot_1D.plot_mean(meantemp_igm,"igm_temper","Temperature (K)")
###CMBtemp=2.725*(np.ones(len(redshifts))+redshifts)
###meantest=(meantemp-CMBtemp)/meantemp