https://github.com/JoeMcEwen/FAST-PT
Revision c56761670c0e4c154b7f2794c79c06c840ba5b0a authored by Jonathan Blazek on 01 March 2019, 22:19:49 UTC, committed by Jonathan Blazek on 01 March 2019, 22:19:49 UTC
1 parent 137418e
Tip revision: c56761670c0e4c154b7f2794c79c06c840ba5b0a authored by Jonathan Blazek on 01 March 2019, 22:19:49 UTC
merging changes from develop branch to prepare for merge with dev. develop will be deprecated.
merging changes from develop branch to prepare for merge with dev. develop will be deprecated.
Tip revision: c567616
IA_paper_EB_final.py
from __future__ import division
import numpy as np
from matter_power_spt import one_loop
import FASTPT
from time import time
# load the input power spectrum data
d=np.genfromtxt('inputs/P_IA.dat',skip_header=1)
## note: for non-trimmed data, genfromtxt is required to deal with NaN values.
k=d[:,0]
P=d[:,1]
d_extend=np.genfromtxt('inputs/P_lin.dat',skip_header=1)
k=d_extend[:-1,0]
P=d_extend[:-1,1]
# use if you want to interpolate data
#from scipy.interpolate import interp1d
#power=interp1d(k,P)
#k=np.logspace(np.log10(k[0]),np.log10(k[-1]),3000)
#P=power(k)
#print d[:,0]-k
P_window=np.array([.2,.2])
C_window=.65
n_pad=1000
# initialize the FASTPT class
fastpt=FASTPT.FASTPT(k,to_do=['IA'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
t1=time()
IA_E, IA_B=fastpt.IA_tt(P,C_window=C_window)
t2=time()
# print('execution time to make IA data'), t2-t1
print('To make a one-loop power spectrum for IA', k.size, ' grid points, using FAST-PT takes ', t2-t1, 'seconds.')
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
#import matplotlib as mpl
#mpl.use('Agg')
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'serif','serif':['Times','Palatino']})
# rc('text', usetex=True)
IA_E=IA_E[98:599]
IA_B=IA_B[98:599]
k=k[98:599]
fig=plt.figure(figsize=(16,10))
x1=10**(-2.5)
x2=10
ax1=fig.add_subplot(211)
ax1.set_ylim(8e-2,4e2)
ax1.set_xlim(x1,x2)
ax1.set_xscale('log')
ax1.set_yscale('log')
ax1.set_ylabel(r'$P_{\rm IA,quad}^{EE/BB}(k)$ [Mpc/$h$]$^3$', size=25)
ax1.tick_params(axis='both', which='major', labelsize=25)
ax1.tick_params(axis='both', width=2, length=10)
ax1.tick_params(axis='both', which='minor', width=1, length=5)
ax1.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
ax1.xaxis.labelpad = 20
ax1.set_xticklabels([])
ax1.plot(k,IA_E, lw=4, color='black', label = r'$P_{\rm IA,quad}^{EE}(k)$')
ax1.plot(k,IA_B, '--', lw=4, color='black', label = r'$P_{\rm IA,quad}^{BB}(k)$')
plt.legend(loc=3,fontsize=25)
plt.grid()
ax2=fig.add_subplot(212)
ax2.set_xscale('log')
ax2.set_xlabel(r'$k$ [$h$/Mpc]', size=25)
ax2.set_ylim(-3e-5,3e-5)
ax2.set_xlim(x1,x2)
# labels = [item.get_text() for item in ax2.get_yticklabels()]
# labels[1] = r'$-3\times 10^{-5}$'
# labels[2] = r'$-2\times 10^{-5}$'
# labels[3] = r'$-1\times 10^{-5}$'
# labels[4] = '0'
# labels[5] = r'$1\times 10^{-5}$'
# labels[6] = r'$2\times 10^{-5}$'
# labels[7] = r'$3\times 10^{-5}$'
# ax2.set_yticklabels(labels)
ax2.tick_params(axis='both', which='major', labelsize=25)
ax2.tick_params(axis='both', width=2, length=10)
ax2.tick_params(axis='both', which='minor', width=1, length=5)
ax2.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
ax2.xaxis.labelpad = 20
ax2.plot(k,IA_E/(d[:,2])-1,lw=2, color='black')
ax2.plot(k,IA_B/(d[:,4])-1,'--',lw=2, color='black')
ax2.text(0.02, 0.07, 'fractional difference',transform=ax2.transAxes,verticalalignment='bottom', horizontalalignment='left', fontsize=25, bbox=dict(facecolor='white',edgecolor='black', pad=8.0))
# plt.legend(loc=3,fontsize=30)
plt.grid()
#plt.tight_layout()
plt.show()
fig.savefig('IA_plot.pdf')
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