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_ta.py
from __future__ import division
import numpy as np
from J_table import J_table
import sys
from time import time
from numpy import log, sqrt, exp, pi
from scipy.signal import fftconvolve as convolve
def P_IA_deltaE2(k,P):
N=k.size
n= np.arange(-N+1,N )
dL=log(k[1])-log(k[0])
s=n*dL
cut=3
high_s=s[s > cut]
low_s=s[s < -cut]
mid_high_s=s[ (s <= cut) & (s > 0)]
mid_low_s=s[ (s >= -cut) & (s < 0)]
# For Zbar
Z1=lambda r : 30. + 146*r**2 - 110*r**4 + 30*r**6 + log(np.absolute(r-1.)/(r+1.))*(15./r - 60.*r + 90*r**3 - 60*r**5 + 15*r**7)
Z1_high=lambda r : 256*r**2 - 256*r**4 + (768*r**6)/7. - (256*r**8)/21. - (256*r**10)/231. - (256*r**12)/1001. - (256*r**14)/3003.
Z1_low=lambda r: 768./7 - 256/(7293.*r**10) - 256/(3003.*r**8) - 256/(1001.*r**6) - 256/(231.*r**4) - 256/(21.*r**2)
f_mid_low=Z1(exp(-mid_low_s))*exp(-mid_low_s)
f_mid_high=Z1(exp(-mid_high_s))*exp(-mid_high_s)
f_high = Z1_high(exp(-high_s))*exp(-high_s)
f_low = Z1_low(exp(-low_s))*exp(-low_s)
f=np.hstack((f_low,f_mid_low,96.,f_mid_high,f_high))
# print(f)
g= convolve(P, f) * dL
g_k=g[N-1:2*N-1]
deltaE2= k**3/(896.*pi**2) * P*g_k
return deltaE2
def IA_deltaE1():
# Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
l_mat_deltaE1= np.array([[0,0,0,2,0,17./21],\
[0,0,0,2,2,4./21],\
[1,-1,0,2,1,1./2],\
[-1,1,0,2,1,1./2]], dtype=float)
table=np.zeros(10,dtype=float)
for i in range(l_mat_deltaE1.shape[0]):
x=J_table(l_mat_deltaE1[i])
table=np.row_stack((table,x))
return table[1:,:]
def IA_0E0E():
# Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
l_mat_0E0E= np.array([[0,0,0,0,0,29./90],\
[0,0,2,0,0,5./63],\
[0,0,2,2,0,19./18],\
[0,0,0,4,0,19./35]], dtype=float)
table=np.zeros(10,dtype=float)
for i in range(l_mat_0E0E.shape[0]):
x=J_table(l_mat_0E0E[i])
table=np.row_stack((table,x))
return table[1:,:]
def IA_0B0B():
# Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
l_mat_0B0B= np.array([[0,0,0,0,0,2./45],\
[0,0,2,0,0,-44./63],\
[0,0,2,2,0,-8./9],\
[0,0,0,4,0,-16./35],\
[0,0,1,1,1,2.]], dtype=float)
table=np.zeros(10,dtype=float)
for i in range(l_mat_0B0B.shape[0]):
x=J_table(l_mat_0B0B[i])
table=np.row_stack((table,x))
return table[1:,:]
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