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read_intp_funcs.py
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from scipy.interpolate import interp1d, RectBivariateSpline
import numpy as np
from math import log10, floor
def read_F_eta_nu(fdir, fname): # for F2 and F3
f = open(fdir + fname + '.txt', 'r')
# print('read data from ' + fdir + fname + '.txt')
f.seek(0) # go to the beginning
data = f.read().split('\n') # all rows
Neta_nu = len(data)
logF_arr = np.zeros(Neta_nu)
for i in range(Neta_nu):
logF_arr[i] = data[i]
f.close()
return logF_arr
def read_IK_logeta_logthe(fdir, fname): # include I1-I8, K1-K6
f = open(fdir + fname + '.txt', 'r')
f.seek(0) # go to the beginning
data = f.read().split('\n') # all rows
N1, N2 = len(data), len(data[0].split('\t'))
logIK_arr = np.zeros((N1, N2))
for i in range(N1):
row = data[i].split('\t')
logIK_arr[i, :] = row[:]
f.close()
return logIK_arr
def intp_F_logeta_nu(n, eta_nu_arr, Fdata):
return interp1d(eta_nu_arr, Fdata[n-2][:])
def intp_I_logeta_logthe(n, logeta_arr, logthe_arr, Idata):
return RectBivariateSpline(logeta_arr, logthe_arr, Idata[n-1][:],
kx=3, ky=3, s=0) # s is smoothing parameter
def intp_K_logeta_logthe(n, logeta_arr, logthe_arr, Kdata):
return RectBivariateSpline(logeta_arr, logthe_arr, Kdata[n-1][:],
kx=3, ky=3, s=0) # s is smoothing parameter
# read only 2 tables
def read_data_K14(Ye, logYe_arr, logeta_arr, logthe_arr, Kdata, fdir):
n_list = [1, 4] # only these four tables are loaded
# new prescription (linear interpolation)
logYe = log10(Ye)
dlogYe = logYe_arr[1] - logYe_arr[0]
iYe1 = int(floor((logYe-logYe_arr[0])/dlogYe))
iYe2 = int(iYe1 + 1)
Ntab = len(n_list)
Nlogeta, Nlogthe = len(logeta_arr), len(logthe_arr)
Kdata1 = np.zeros((Ntab, Nlogeta, Nlogthe)) # for iYe1
Kdata2 = np.zeros((Ntab, Nlogeta, Nlogthe)) # for iYe2
logYe1 = logYe_arr[iYe1]
for i in range(Ntab):
n = n_list[i]
fname = 'K%d_logYe_m%dp%04d'\
% (n, floor(-logYe1), round((-logYe1) % 1*1e4))
Kdata1[i, :] = read_IK_logeta_logthe(fdir, fname)
logYe2 = logYe_arr[iYe2]
for i in range(Ntab):
n = n_list[i]
fname = 'K%d_logYe_m%dp%04d'\
% (n, floor(-logYe2), round((-logYe2) % 1*1e4))
Kdata2[i, :] = read_IK_logeta_logthe(fdir, fname)
# linear interpolation between Kdata1 and Kdata2
for i in range(Ntab):
n = n_list[i]
slope = (Kdata2[i, :] - Kdata1[i, :])/dlogYe
Kdata[n-1][:] = Kdata1[i, :] + slope*(logYe - logYe1)
# del Kdata1, Kdata2, slope
return None
# read only 4 tables
def read_data_K1245(Ye, logYe_arr, logeta_arr, logthe_arr, Kdata, fdir):
n_list = [1, 2, 4, 5] # only these four tables are loaded
# new prescription (linear interpolation)
logYe = log10(Ye)
dlogYe = logYe_arr[1] - logYe_arr[0]
iYe1 = int(floor((logYe-logYe_arr[0])/dlogYe))
iYe2 = int(iYe1 + 1)
Ntab = len(n_list)
Nlogeta, Nlogthe = len(logeta_arr), len(logthe_arr)
Kdata1 = np.zeros((Ntab, Nlogeta, Nlogthe)) # for iYe1
Kdata2 = np.zeros((Ntab, Nlogeta, Nlogthe)) # for iYe2
logYe1 = logYe_arr[iYe1]
for i in range(Ntab):
n = n_list[i]
fname = 'K%d_logYe_m%dp%04d'\
% (n, floor(-logYe1), round((-logYe1) % 1*1e4))
Kdata1[i, :] = read_IK_logeta_logthe(fdir, fname)
logYe2 = logYe_arr[iYe2]
for i in range(Ntab):
n = n_list[i]
fname = 'K%d_logYe_m%dp%04d'\
% (n, floor(-logYe2), round((-logYe2) % 1*1e4))
Kdata2[i, :] = read_IK_logeta_logthe(fdir, fname)
# linear interpolation between Kdata1 and Kdata2
for i in range(Ntab):
n = n_list[i]
slope = (Kdata2[i, :] - Kdata1[i, :])/dlogYe
Kdata[n-1][:] = Kdata1[i, :] + slope*(logYe - logYe1)
# del Kdata1, Kdata2, slope
return None
# read all 6 tables
def read_data_K123456(Ye, logYe_arr, logeta_arr, logthe_arr, Kdata, fdir):
n_list = [1, 2, 3, 4, 5, 6]
# new prescription (linear interpolation)
logYe = log10(Ye)
dlogYe = logYe_arr[1] - logYe_arr[0]
iYe1 = int(floor((logYe-logYe_arr[0])/dlogYe))
iYe2 = int(iYe1 + 1)
Ntab = len(n_list)
Nlogeta, Nlogthe = len(logeta_arr), len(logthe_arr)
Kdata1 = np.zeros((Ntab, Nlogeta, Nlogthe)) # for iYe1
Kdata2 = np.zeros((Ntab, Nlogeta, Nlogthe)) # for iYe2
logYe1 = logYe_arr[iYe1]
for i in range(Ntab):
n = n_list[i]
fname = 'K%d_logYe_m%dp%04d'\
% (n, floor(-logYe1), round((-logYe1) % 1*1e4))
Kdata1[i, :] = read_IK_logeta_logthe(fdir, fname)
logYe2 = logYe_arr[iYe2]
for i in range(Ntab):
n = n_list[i]
fname = 'K%d_logYe_m%dp%04d'\
% (n, floor(-logYe2), round((-logYe2) % 1*1e4))
Kdata2[i, :] = read_IK_logeta_logthe(fdir, fname)
# linear interpolation between Kdata1 and Kdata2
for i in range(Ntab):
n = n_list[i]
slope = (Kdata2[i, :] - Kdata1[i, :])/dlogYe
Kdata[n-1][:] = Kdata1[i, :] + slope*(logYe - logYe1)
# del Kdata1, Kdata2, slope
return None