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testfnl.py
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testfnl.py
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from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
from math import *
def plotfnl(fnl,b):
fac = 10.e-8
pk = np.loadtxt('powerspectra/Challenge_matterpower.dat').transpose()
pl = []
pln = []
plm = []
kl = []
for i in range(0,len(pk[0])):
k = pk[0][i]
if k > 0.0005 and k < 0.1:
bk = (b+fnl*(b-1.)*fac/k**2.)**2.
pl.append(bk*pk[1][i])
pln.append(b*b*pk[1][i])
plm.append(pk[1][i])
kl.append(k)
kl = np.array(kl)
pl = np.array(pl)
plm = np.array(plm)
#plt.loglog(k,pl)
#plt.xlim(1.e-4,1.e-1)
plt.plot(kl*1000.,(pl-pln)/plm)
plt.xlim(.5,1.e2)
plt.xscale('log')
#plt.ylim(1.e-6,3e-5)
plt.xlabel(r'$k$ ($h$Gpc$^{-1}$)')
plt.ylabel(r'$[P(k)-P(k,f_{NL}=0)]/P_{\rm matter}(k)$')
#plt.yscale('log')
plt.show()
return True
def plotfnl_b(fnl,kmin=.0008,kmax=.1):
fac = 10.e-8
pk = np.loadtxt('powerspectra/Challenge_matterpower.dat').transpose()
bl = [.2,1.,1.5,2.]
#b = .2
#while b < 4:
for b in bl:
pl = []
pln = []
kl = []
plm = []
for i in range(0,len(pk[0])):
k = pk[0][i]
if k > kmin and k < kmax:
bk = (b+fnl*(b-1.)*fac/k**2.)**2.
pl.append(bk*pk[1][i])
pln.append(b*b*pk[1][i])
plm.append(pk[1][i])
kl.append(k)
kl = np.array(kl)
pl = np.array(pl)
plm = np.array(plm)
if b == 1:
plt.plot(kl*1000.,1.e-2*(kl/0.001)*(pl-pln),'k--')
#plt.plot(kl*1000.,(pl-pln),'k--')
else:
plt.plot(kl*1000.,1.e-2*(kl/0.001)*(pl-pln))
#plt.plot(kl*1000.,(pl-pln))
#b += .2
#plt.xlim(.5,1.e2)
plt.xscale('log')
#plt.ylim(1.e-6,3e-5)
plt.xlabel(r'$k$ ($h$Gpc$^{-1}$)')
#plt.ylabel(r'$[P(k)-P(k,f_{NL}=0)]/P_{\rm matter}(k)$')
plt.ylabel(r'$10^{-2}k[P(k)-P(k,f_{NL}=0)]$ ($h^{-2}$Gpc$^2$)')
plt.legend(labels=[r'$b=0.2$',r'$b=1$',r'$b=1.5$',r'$b=2$'])
plt.title(r'$f_{NL,local}=10$')
#plt.yscale('log')
plt.show()
return True
def plotfnlx(fnl,b1,b2):
fac = 5.e-8
pk = np.loadtxt('powerspectra/Challenge_matterpower.dat').transpose()
pl = []
for i in range(0,len(pk[0])):
k = pk[0][i]
bk = (b1+fnl*(b1-1.)*fac/k**2.)*(b2+fnl*(b2-1.)*fac/k**2.)
pl.append(bk*pk[1][i])
plt.loglog(pk[0],pl)
plt.xlim(1.e-4,1.e-1)
#plt.yscale('log')
plt.show()
return True
def plotfnlpsys(fnl,b1,b2):
fac = 5.e-8
pk = np.loadtxt('powerspectra/Challenge_matterpower.dat').transpose()
pl = []
for i in range(0,len(pk[0])):
k = pk[0][i]
b1t = (b1+fnl*(b1-1.)*fac/k**2.)
bk = (b2+fnl*(b2-1.)*fac/k**2.)
pl.append(bk*pk[1][i])
plt.loglog(pk[0],pl)
plt.xlim(1.e-4,1.e-1)
#plt.yscale('log')
plt.show()
return True
def plotGPC():
pp = PdfPages('PkMpc.pdf')
pk = np.loadtxt('powerspectra/Challenge_matterpower.dat').transpose()
plt.loglog(pk[0],pk[1])
plt.xlim(1.e-3,1.e-1)
plt.ylim(1.e3,3e4)
plt.xlabel(r'$k$ ($h$Mpc$^{-1}$)')
plt.ylabel(r'$P(k)$ ($h^{-3}$Mpc$^3$)')
pp.savefig()
pp.close()
pp = PdfPages('PkGpc.pdf')
plt.clf()
plt.loglog(pk[0]*1000.,pk[1]*1.e-9)
plt.xlim(1.,1.e2)
plt.ylim(1.e-6,3e-5)
plt.xlabel(r'$k$ ($h$Gpc$^{-1}$)')
plt.ylabel(r'$P(k)$ ($h^{-3}$Gpc$^3$)')
pp.savefig()
pp.close()
return True
def plotvolz():
pp = PdfPages('volz.pdf')
from Cosmo import distance
d = distance(.3,.7)
vl = []
zl = []
dz = .01
z = .01
while z < 10:
v = pi*(d.dc(z)/1000.)**3.
vl.append(v)
zl.append(z)
z += dz
#plt.loglog(zl,vl)
plt.plot(zl,vl)
plt.ylim(1.,1000)
plt.yscale('log')
plt.xlabel('Maximum Redshift')
plt.ylabel(r'Volume for 3/4 of sky (Gpc$h^{-1}$)$^3$')
pp.savefig()
pp.close()
return True
def pksnr(k,v):
kvol = 4.*pi*k**3.
return kvol*v/(2.*pi)**3.