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_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_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_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_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_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 plot1():
print "Comparing: " + flag1 + " and "+ flag2
minmax = "min"
#load temperature means
data1 = plot_1D.mean('temp_igm','data_'+flag1+'/',redshifts1)
data2 = plot_1D.mean('temp_igm','data_'+flag2+'/',redshifts2)
print data1
#load xfrac means
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(data1),len(data2))
if minmax=="max":
length = min(len(data1),len(data2))
#print redshifts2
meantemp = np.zeros(2*length).reshape(length,2)
meanxfrac = np.zeros(6*length).reshape(length,6)
if minmax=="max":
for i in range(len(data1)):
meantemp[i,0] = data1[i]
meanxfrac[i,3] = wstars_xfrac[i]
meanxfrac[i,4] = wstars_xfracHe1[i]
meanxfrac[i,5] = wstars_xfracHe2[i]
for i in range(len(data2)):
meanxfrac[i,0] = wpls_xfrac[i]
meanxfrac[i,1] = wpls_xfracHe1[i]
meanxfrac[i,2] = wpls_xfracHe2[i]
meantemp[i,1] = data2[i]
#print means[i,1]
#print redshifts_wstars[i], redshifts_wpls[i]
# plot_1D.plot_mean(means,"Temperature","Temperature (K)",'compare_'+flag1+'_'+flag2+'/',redshifts1,redshifts1,label1,label2)
elif minmax=="min":
for i in range(length):
meantemp[i,0] = data1[i]
meantemp[i,1] = data2[i]
meanxfrac[i,0] = wpls_xfrac[i]
meanxfrac[i,1] = wpls_xfracHe1[i]
meanxfrac[i,2] = wpls_xfracHe2[i]
meanxfrac[i,3] = wstars_xfrac[i]
meanxfrac[i,4] = wstars_xfracHe1[i]
meanxfrac[i,5] = wstars_xfracHe2[i]
meanxfrac[0,5]=meanxfrac[0,4]
meanxfrac[0,2]=meanxfrac[0,1]
#print len(redshifts2), len(meantemp[:,1])
print meantemp[:,1]
print meantemp[:,0]
T0=2.725
Tcmb = np.zeros(len(redshifts1))
for z in range(len(redshifts1)):
Tcmb[z] = T0*(1.0+redshifts1[z])
size=26
lw = 2.5
#print np.log10(meanxfrac[:,5])
f, (ax1, ax2) = plt.subplots(2, figsize=(14,14),sharex=True)
#ax1.set_xlim(redshifts2[0],redshifts2[len(redshifts2)-1])
ax1.plot(redshifts2[0:length],np.log10(meanxfrac[:,0]),color="Red",label="HII",linewidth=lw)
ax1.plot(redshifts2[0:length],np.log10(meanxfrac[:,1]),color="Red",label="HeII",linewidth=lw,linestyle='--')
ax1.plot(redshifts2[0:length],np.log10(meanxfrac[:,2]),color="Red",label="HeIII",linewidth=lw,linestyle=':')
ax1.plot(redshifts1[0:length],np.log10(meanxfrac[:,3]),color="Blue",label="HII",linewidth=lw)
ax1.plot(redshifts1[0:length],np.log10(meanxfrac[:,4]),color="Blue",label="HeII",linewidth=lw,linestyle='--')
ax1.plot(redshifts1[0:length],np.log10(meanxfrac[:,5]),color="Blue",label="HeIII",linewidth=lw,linestyle=':')
ax1.set_ylabel(r'Ionised Fraction',size=size)
ax1.set_ylim(-13.5,0)
ax2.set_ylim(0,800)
ax2.plot(redshifts1,Tcmb,label='$T_{cmb}$',color='black',linestyle='--',linewidth=lw)
ax2.plot(redshifts1[0:length],meantemp[:,0],color='Blue',label=label1,linewidth=lw)
ax2.plot(redshifts2[0:length],meantemp[:,1],color='Red',label=label2,linewidth=lw)
ax2.set_ylabel("Temperature [K]",size=size)
ax2.set_xlabel("Redshifts",size=size)
#ax2.set_ylim(-250,80)
ax1.tick_params(axis='both', which='major', labelsize=numberfontsize, width = tickwidth, length = 11, direction='in',pad=14.0,top='off',right='off')
ax1.tick_params(axis='both', which='minor', labelsize=numberfontsize, width = tickwidth, length = 5.5, direction='in',pad=14.0,top='off',right='off')
ax2.tick_params(axis='both', which='major', width = tickwidth, length = 11,direction='in',top='off',right='off')
ax2.tick_params(axis='both', which='minor', width = tickwidth, length = 5.5,direction='in',top='off',right='off')
# lg1 = ax1.legend(bbox_to_anchor=(0.04, 0.65), loc=2, borderaxespad=0.,prop={'size':size})
# lg1.draw_frame(False)
lg = ax2.legend(bbox_to_anchor=(0.04, 0.8), loc=2, borderaxespad=0.,prop={'size':size})
lg.draw_frame(False)
plt.gcf().subplots_adjust(left=0.2,bottom=0.15)
#plt.tight_layout()
f.subplots_adjust(hspace=0)
# fig = plt.figure(figsize=(12,10))
# ax = fig.add_subplot(111)
# ax.plot(redshifts2,meantemp[:,0],color='Blue',label=label1,linewidth=lw)
# ax.plot(redshifts2,meantemp[:,1],color='Red',label=label2,linewidth=lw)
# ax.plot(redshifts1,Tcmb,label='$T_{cmb}$',color='black',linestyle='--',linewidth=lw)
# ax.set_ylabel("Temperature [K]",fontsize=size)
# ax.set_xlabel("Redshift",fontsize=size)
# ax.xaxis.set_tick_params(labelsize=size)
# ax.yaxis.set_tick_params(labelsize=size)
#lg = plt.legend(bbox_to_anchor=(0.55, 0.97), loc=2,prop={'size':size-1})
#lg.draw_frame(False)
plt.xlim(redshifts2[0],redshifts1[len(redshifts1)-1])
# ax.text(22.7,1100,"(b)",fontsize=size)
#
# rect = [0.15,0.2,0.55,0.55]
# ax1 = add_subplot_axes(ax,rect)
# ax1.set_ylabel("log$_{10}$(xfrac)",fontsize=size-2)
## ax1.set_xlabel("z",fontsize=size-2)
# ax1.xaxis.set_tick_params(labelsize=size-2)
# ax1.yaxis.set_tick_params(labelsize=size-2)
# ax1.plot(redshifts2,np.log10(meanxfrac[:,0]),color="Red",label="HII",linewidth=lw)
# ax1.plot(redshifts2,np.log10(meanxfrac[:,1]),color="Red",label="HeII",linewidth=lw,linestyle='--')
# ax1.plot(redshifts2,np.log10(meanxfrac[:,2]),color="Red",label="HeIII",linewidth=lw,linestyle=':')
# ax1.plot(redshifts2,np.log10(meanxfrac[:,3]),color="Blue",label="HII",linewidth=lw)
# ax1.plot(redshifts2,np.log10(meanxfrac[:,4]),color="Blue",label="HeII",linewidth=lw,linestyle='--')
# ax1.plot(redshifts2,np.log10(meanxfrac[:,5]),color="Blue",label="HeIII",linewidth=lw,linestyle=':')
ax1.text(22,-2,"(a)",fontsize=size-2)
ax2.text(22,700,"(b)",fontsize=size-2)
# lg =plt.legend(loc=4,ncol=2,prop={'size':size-3})
# lg.draw_frame(False)
# plt.ylim(-16,0)
# plt.xlim(redshifts2[0],redshifts2[len(redshifts2)-1])
print "saving as paperplots/plot1.png"
#plt.tight_layout()
plt.savefig("paperplots/plot1.png")
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