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')