def plot_powerspectra(fr,data,title,i):
fig,axes=plt.subplots(1,1,figsize=(10,6))
print "plotting"
if i!='null':
plt.plot(np.log10(fr),np.log10(data))
fig.text(0.2,0.85,"z = "+str(redshifts[i]),fontsize=fontsize,color='red')
else:
print "plotting redshifts:"
for z in range(0,len(redshifts),10):
print redshifts[z]
plt.plot(np.log10(fr),np.log10(data[z,:]),label="z = " +str(redshifts[z]))
lg=plt.legend(loc=2,prop={'size':fontsize-1},ncol=2)
lg.draw_frame(False)
plt.ylim(0,2.0)
m0=MultipleLocator(0.5)
axes.xaxis.set_major_locator(m0)
m0=MultipleLocator(0.1)
axes.xaxis.set_minor_locator(m0)
#plt.ylim(0,1e6)
# plt.title(title)
plt.ylabel("log$_{10}$($\Delta_{21cm}$) [mK] ",size=fontsize)
plt.xlabel("log$_{10}$(k) [h Mpc$^{-1}$]",size=fontsize)
plt.tight_layout()
print "saing as: "+setup_dirs.plotsdir()+title+".png"
plt.savefig(setup_dirs.plotsdir()+title+".png")
plt.close()
def plot_3_log_histograms(dataHI,dataHeI,dataHeII,title,name,xlabel,nobins=5000,type='log'):
fig = plt.figure()
#print np.amin(data3)
dataHI = dataHI.flatten()
dataHeI = dataHeI.flatten()
dataHeII = dataHeII.flatten()
ax = fig.add_subplot(111)
ax.set_yscale('log',basey=10)
if (type=='log'):
weights = np.ones_like(dataHI)/len(dataHI)
ax.hist(np.log10(dataHI),weights=weights,bins=np.linspace(-16, 0, nobins),histtype='stepfilled',edgecolor='none',alpha=0.4,color='red',label='HII')
ax.hist(np.log10(dataHeI),weights=weights,bins=np.linspace(-16, 0, nobins),histtype='stepfilled',alpha=0.4,color='yellow',edgecolor='none',label='HeII')
ax.hist(np.log10(dataHeII),weights=weights,bins=np.linspace(-16, 0, nobins),histtype='stepfilled',alpha=0.4,color='blue',edgecolor='none',label='HeIII')
else:
ax.hist(data,nobins,color='red',alpha=0.01)
ax.hist(data,nobins,color='green',alpha=0.01)
plt.title(title)
plt.legend(loc=2)
if type=='log':
plt.xlabel('log10('+xlabel+')')
plt.yscale=('log')
else:
plt.xlabel(xlabel)
plt.ylabel("Frequency")
plt.savefig(setup_dirs.plotsdir()+name+".png")
plt.close()
print "histogram complete"
def plot_log_histogram(data,name,xlabel,label1='Stellar Sources',label2='Stellar sources and X-Rays',type='log',filedir='',nobins=90):
fig = plt.figure()
ax=plt.gca()
ax = fig.add_subplot(111)
ax.set_yscale('log',basey=10)
if(data.shape==(250**3,)):
weights = np.ones_like(data)/float(len(data))
ax.hist(np.log10(data),weights=weights,bins=10**np.linspace(-1, 0.6, nobins),histtype='stepfilled',color='red',edgecolor='red')
elif(data.shape==(250**3,2)):
print "Plotting 2 data sets..."+label1+" and "+label2
weights = np.ones_like(data[:,0])/float(len(data))
ax.hist(np.log10(data[:,0]),weights=weights,bins=10**np.linspace(-1, 0.6, nobins),histtype='stepfilled',color='red',edgecolor='none',alpha=0.5,label=label1)
ax.hist(np.log10(data[:,1]),weights=weights,bins=10**np.linspace(-1, 0.6, nobins),histtype='stepfilled',color='blue',edgecolor='none',alpha=0.5,label=label2)
lg = plt.legend(loc=2,prop={'size':fontsize})
lg.draw_frame(False)
# lg=plt.legend(loc=2,prop={'size':14})
# lg.draw_frame(False)
plt.xlabel('log$_{10}$('+xlabel+')',size=fontsize)
plt.ylabel("Probability",size=fontsize)
plt.tight_layout()
if filedir=='':filedir=setup_dirs.plotsdir()
plt.savefig(filedir+name+".png")
plt.close()
print "histogram complete, saving as "+filedir+name+".png"
def plot_dbt_histogram(data,title,name,xlabel,nobins=500,type='log'):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_yscale('log',basey=10)
weights = np.ones_like(data)/float(len(data))
#logbins = np.zeros(nobins)
#logbins[nobins/2:nobins] = np.logspace(-10,np.log10(100),nobins/2)
#temp = -1*np.logspace(-10,np.log10(100),nobins/2)
#logbins[0:nobins/2] = temp[::-1]
#print logbins
#print logbins
#if(data.shape==(len(redshifts1),))
ax.hist(data,weights=weights,bins=500,histtype='stepfilled',color='blue',edgecolor='blue')
# elif(data.shape==(len(redshifts1),2)):
# if legend1 == '' or legend2 =='':
# print "WARNING: Specify legend for plotting two data sets"
# ax.hist(data[:,0],weights=weights,bins=logbins,histtype='stepfilled',color='blue',edgecolor='red')
# ax.hist(data[:,1],weights=weights,bins=logbins,histtype='stepfilled',color='red',edgecolor='red')
#
plt.xlabel(xlabel,size=30)
plt.xlim(-457,83)
plt.ylabel("Probability",size=30)
plt.savefig(setup_dirs.plotsdir()+name+".png")
plt.close()
print "histogram complete"
def plot_scatter_contour(x,y,name,ylabel,xlabel,it,nbins=200):
#plt.figure()
fig=plt.figure(figsize=(9, 7.25), dpi= 300)
plt.subplot('111', axisbg='black')
ax = plt.gca()
#general tick parameters
ax.tick_params(axis='both', which='major', labelsize=numberfontsize, width = tickwidth, length = 11, direction='out',pad=14.0,top='off',right='off')
ax.tick_params(axis='both', which='minor', width = tickwidth, length = 5.5,direction='out',top='off',right='off')
#y-axis
#ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.ticklabel_format(axis='y', style='sci', scilimits=(-2,2),useOffset=False)
m0 = MultipleLocator(5000)
ax.yaxis.set_major_locator(m0)
m0 = MultipleLocator(1000)
ax.yaxis.set_minor_locator(m0)
#x-axis
m0 = MultipleLocator(1)
ax.xaxis.set_major_locator(m0)
m0 = MultipleLocator(0.2)
ax.xaxis.set_minor_locator(m0)
norm = 1.0
if name =="equation_of_state":
h =0.7
omega_m = 0.27
omega_delta = 0.73
z = redshifts[it]
G = 6.67408e-11
Mpc_in_metres=3.086e+22
H0 = h/Mpc_in_metres*1e5
pcrit = 3.0*H0**2/(8*np.pi*G)#*(omega_m*(0.0+z)**3+omega_delta)
pcrit = pcrit/1.0e4
norm = pcrit
#plot data
cax =plt.hist2d(x/norm, y, bins=nbins,norm=LogNorm(),normed=True,cmap='Blues_r')
cbar = plt.colorbar(orientation='vertical')
#cbar.set_ticks([maxi,maxi/10.0,maxi/100.0,maxi/1000.0,maxi/10000.0,maxi/1000000.0])
#cbar.set_ticklabels(['$10**0$','$10**{-1}$','$10**{-2}$','$10**{-3}$','$10**{-4}$','$10**{-5}$'])
plt.ylabel(ylabel,size=fontsize)
plt.ylim(0,25100)
plt.xlabel(xlabel,size=fontsize)
plt.tight_layout()
plt.savefig(setup_dirs.plotsdir()+name+"_"+str(it+10)+"_"+str(redshifts[it])+".png")
plt.close()
def plot(dataslice,it,label,name,mini,maxi,cmap='hot',type='lin'):
if name=='dbt':
cmapdbt = {'red': ((0.0, 0.0, 0.0),
(0.846, 0.0, 1.0),
(1.0, 0.0, 0.0)),
#'green': ((0.1, 0.1, 0.1),
# (0.846, 0.1, 0.0),
# (0.0, 0.0, 0.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(0.846, 1.0, 0.0),
(1.0, 0.0, 0.0))
#'white': ((0.0, 0.0, 0.0),
# (0.846, 1.0, 0.0),
# (1.0, 0.1, 1.0)),
}
plt.register_cmap(name='dbtmap',data=cmapdbt)
cmap = plt.get_cmap('dbtmap')
plt.figure()
print mini,maxi
# plt.title(str(title) + ", Redshift: " + str(redshifts[it]))
if (type=='lin'):
plt.imshow(dataslice,cmap=cmap,vmin=mini,vmax=maxi,origin='lower')
else:
plt.imshow(dataslice,cmap=cmap,norm=LogNorm(),vmin=mini,vmax=maxi,origin='lower')
cbar = plt.colorbar(orientation='vertical')
cbar.set_label(label,size=19)
cnv=250.0/244.0
tick_locs=[0,50*cnv,100*cnv,150*cnv,200*cnv,250*cnv]
tick_lbls=[0,50,100,150,200,250]
plt.xlabel("Distance (Mpc/h)",size=18)
plt.ylabel("Distance (Mpc/h)",size=18)
#plt.title(title+", Redshift: "+str(redshifts[it]))
plt.savefig(setup_dirs.plotsdir()+name+"_"+str(it+10)+"_"+str(redshifts[it])+".png")
plt.close()
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib.ticker import MultipleLocator
mpl.rc('xtick', labelsize=25)
mpl.rc('ytick', labelsize=25)
mpl.rc('font',family='serif')
fontsize=26
numberfontsize=25
tickwidth=1.5
start=0
#print plotsdir()
redshifts = setup_dirs.read_redshifts()
path_data = setup_dirs.resultsdir()
path_plots = setup_dirs.plotsdir()
def mean(name,pathd = path_data,redshifts1=redshifts):
mean = np.zeros(len(redshifts1))
print 'Reading mean from ../generate_data/'+pathd+'mean_'+name+'.dat'
file = open('../generate_data/'+pathd+'mean_'+name+'.dat', 'r')
i=0
for line in file:
mean[i] = float(line)
i=i+1
return mean
def plot_mean(data,title,ylabel,pathp = path_plots, redshifts1=redshifts, redshifts2=redshifts,legend1='',legend2='',legend3='',legend4=''):
#plt.figure()
fig=plt.figure(figsize=(10.25, 10.25), dpi= 300)
# plt.subplot('111')
def plot(dataslice,it,label,name,mini,maxi,cmap='hot',type='lin'):
if name=='dbt' or name=='dbt_lightcone_' or name=='smoothed_dbt':
print dataslice
print "using dbt colourmap"
cmapdbt = {'red': ((0.0, 0.0, 0.0),
(0.84629629629, 0.0, 1.0),
(1.0, 1.0, 1.0)),
#'green': ((0.1, 0.1, 0.1),
# (0.846, 0.1, 0.0),
# (0.0, 0.0, 0.0)),
'green': ((0.0, 0.0, 0.0),
(0.84629629629,0.0, 0.0),
(1.0, 1.0, 1.0)),
'blue': ((0.0, 0.0, 0.0),
(0.84629629629, 1.0, 0.0),
(1.0, 1.0, 1.0))
#'white': ((0.0, 0.0, 0.0),
# (0.846, 1.0, 0.0),
# (1.0, 0.1, 1.0)),
}
plt.register_cmap(name='dbtmap',data=cmapdbt)
cmap = plt.get_cmap('dbtmap')
#plt.figure()
plt.figure(figsize=(9,7.25),dpi=300)
#plt.subplots_adjust(right=0.3)
#plt.subplots_adjust(bottom=1.3)
print mini,maxi
# plt.title(str(title) + ", Redshift: " + str(redshifts[it]))
if (type=='lin'):
print "linear"
im = plt.imshow(dataslice,cmap=cmap,vmin=mini,vmax=maxi,origin='lower')
else:
im = plt.imshow(dataslice,cmap=cmap,norm=LogNorm(),vmin=mini,vmax=maxi,origin='lower')
font = FontProperties()
font.set_weight('bold')
if name == "xfrac" :
plt.text(20,220,"HII",fontsize=fontsize,color='white')
if name == "xfracHe1" :
plt.text(20,220,"HeII",fontsize=fontsize,color='white')
if name == "xfracHe2" :
plt.text(20,220,"HeIII",fontsize=fontsize,color='white')
plt.text(150,20,"z = "+str(redshifts[it]),fontsize=fontsize,color='red')
# if name == "Temperature":
#plt.text(20,220, "Temperature",fontsize=fontsize,color='white')
if name!='dbt':
print "not dbt..."
cbar = plt.colorbar(im,orientation='vertical',fraction=0.046,pad=0.04)
cbar.set_label(label,size=fontsize)
#cbar.ax.set_yticklabels
if name!="Temperature" :
cbar.set_ticks([1.,1./10.0,1./100.0,1./1000.0,1./10000.0,1./1000000.0])
cbar.set_ticklabels(['$ \ 10 ^0 $ ','$ \ 10 ^{-1} $ ','$ \ 10 ^{-2} $ ','$ \ 10 ^{-3} $ ','$ \ 10 ^{-4} $ ','$ \ 10 ^{-5} $ '])
else:
cbar.set_ticks([2000,4000,6000,8000,10000,12000,14000])
cbar.set_ticklabels([' 2 ',' 4 ',' 6 ',' 8 ',' 10 $ \ $',' 12 ' , ' 14 '])
if FLAG=='TEST':
print "Adding resolution to plot"
res=10./float(ss)
plt.text(0.08*ss,ss-0.15*ss,"Resolution: "+str('%.2f' % res)+ " Mpc/cell",fontsize=fontsize,color="white")
cnv=250.0/244.0
tick_locs=[0,50*cnv,100*cnv,150*cnv,200*cnv,250*cnv]
tick_lbls=[0,50,100,150,200,250]
#plt.xlabel("Distance (Mpc/h)",size=18)
#plt.ylabel("Distance (Mpc/h)",size=18)
#plt.title(title+", Redshift: "+str(redshifts[it]))i
#plt.tight_layout()
# if name == "Temperature":
#plt.gcf().subplots_adjust(left=0.5)
print "saving as..." + setup_dirs.plotsdir()+name+"_"+str(it+10)+"_"+str(redshifts[it])+".png"
plt.savefig(setup_dirs.plotsdir()+name+"_"+str(it+10)+"_"+str(redshifts[it])+".png")
plt.close()
def plot_lightcone(dataslice,it,label,name,mini,maxi,redshifts,cmap='hot',type='lin'):
if name=='dbt' or name=='dbt_lightcone' or name=='smoothed_dbt':
print "using dbt colourmap"
cmapdbt = {'red': ((0.0, 0.0, 0.0),
(0.846, 0.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.846,0.0, 0.0),
(1.0, 1.0, 1.0)),
'blue': ((0.0, 0.0, 0.0),
(0.846, 1.0, 0.0),
(1.0, 1.0, 1.0))
}
plt.register_cmap(name='dbtmap',data=cmapdbt)
cmap = plt.get_cmap('dbtmap')
if name=='temp_lightcone':
maxi =12000#400#13000
mini= 0.00
tcmap = {'green': ((0.0, 1.0, 1.0),
(0.00, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0),
(0.00,0.0, 0.0),
(1.0, 0.0, 0.0)),
'red': ((0.0, 1.0, 1.0),
(0.1, 0.1, 0.1),
# (0.05, 0.0, 0.0),
(1.0, 0.0, 0.0))
}
plt.register_cmap(name='cmapt',data=tcmap)
cmap = plt.get_cmap('cmapt')
if name=='xfrac_lightcone':
mini = 0
maxi = 1.0
#type='log'
fig,axes =plt.subplots(1,1,figsize=(26,6))
if (type=='lin'):
im = plt.imshow(dataslice,cmap=cmap,vmin=mini,vmax=maxi,origin='lower')
else:
im = plt.imshow(dataslice,cmap=cmap,norm=LogNorm(),vmin=mini,vmax=maxi,origin='lower')
font = FontProperties()
font.set_weight('bold')
cnv=250.0/244.0
ytick_locs=[0,50*cnv,100*cnv,150*cnv,200*cnv,250*cnv]
ytick_lbls=[0,50,100,150,200,250]
axes.tick_params(axis='both', which='major', labelsize=numberfontsize, width = tickwidth, length = 11, direction='out',pad=14.0,top='off',right='off')
m0=MultipleLocator(200)
axes.xaxis.set_major_locator(m0)
m0=MultipleLocator(100)
axes.xaxis.set_minor_locator(m0)
labels = np.zeros(len(redshifts)/200+2)
print len(labels-1)
for i in range(len(dataslice[1,:])):
if (i+1)%200==0:
if i<len(redshifts) and i/100<len(labels):
labels[i/100] = str('%.2f'%redshifts[i])
labels[len(labels)-2]=str('%.2f'%redshifts[len(redshifts)-2])
print labels
#axes.set_xticklabels(labels[::-1])
plt.xlabel("z",size=fontsize)
plt.ylabel("Distance (Mpc)",size=fontsize)
fig.subplots_adjust(right=0.85)
cbar_ax = fig.add_axes([0.87, 0.25, 0.02, 0.5])
cbar = fig.colorbar(im, cax=cbar_ax,ticks=[-400,-300,-200,-100,0,100,200,300])
cbar.ax.set_yticklabels
cbar.set_label("$\delta T_b \ (mK)$",size=fontsize)
# plt.tight_layout()
print "saving as..." + setup_dirs.plotsdir()+name+"_stepcell.png"
plt.savefig(setup_dirs.plotsdir()+name+"_stepcell.png")
plt.close()
