def get_wf_ws(theta):
wf, e, rms = gws.gwf(azaz[0], azaz[1], azaz[2], azaz[3], nl2j[0], nl2j[1],
nl2j[2], theta[0], theta[1], theta[2], theta[3],
theta[4], theta[5], emin, emax)
wf = wf * math.sqrt(sf)
print(rms)
return wf, e
def get_wf_ws(theta):
wf, e, _ = gws.gwf(A, Z, a, z, nl2j[0], nl2j[1], nl2j[2], theta[0],
theta[1], theta[2], theta[3], theta[4], theta[5], emin,
emax)
wf = wf * math.sqrt(sf)
wf = wf[:50] # Fit only up to 5 fm
# norm = integrate.simps(np.square(wf_out[:, 1]) * np.square(wf_out[:, 0]), wf_out[:, 0])
# print(norm)
if np.isnan(e):
e = -500
# print("NaN e")
if np.isnan(wf).any():
# print("NaN in rad.dat")
return np.full((50, 1), -500), e
return wf, e
def get_wf_ws(theta):
wf, e = gws.gwf(6, 3, 1, 0, 0, 1, 1, theta[0], theta[1], theta[2],
theta[3], theta[4], theta[5], -10, -1)
wf = wf * math.sqrt(sf)
ll_wf = -0.5 * np.sum(((wf_exp - wf) / sigma_wf)**2) + 0.5 * 100 * np.log(
2 * np.pi * sigma_wf**2)
ll_e = -0.5 * np.sum((
(e_exp - e) / sigma_e)**2) + 0.5 * 100 * np.log(2 * np.pi * sigma_e**2)
if np.isnan(e):
e = -1000
# print("NaN e")
if np.isnan(wf).any():
# print("NaN in rad.dat")
return np.full((100, 1), -1000), e
return ll_wf + ll_e
import matplotlib.pyplot as plt
# Initialize physical parameters
import numpy as np
A = 6 # Lithium 6
Z = 3
a = 1 # Neutron is 1,0
z = 0
nl2j = [0, 1, 1] # Desired quantum numbers
theta = [-39.11, 3.30, 0.75, 4.55, 1.12, 0.88]
emin = -90
emax = -1
wf, e, rms = gws.gwf(A, Z, a, z, nl2j[0], nl2j[1], nl2j[2],
theta[0], theta[1], theta[2], theta[3], theta[4], theta[5], emin, emax)
# Calculate root-mean-squared
wf_exp_in = np.genfromtxt(r"/mnt/c/Users/noeld/OneDrive - Louisiana State University/PHYS "
r"4399/6Li_7Li_overlaps_NNLOopt_Nmax12.csv", skip_header=1,
delimiter=",")
sf = integrate.simps(wf_exp_in[:, 3] * np.square(np.linspace(0, 10, 100)) * wf_exp_in[:, 3], np.linspace(0, 10, 100))
# make a corner plot with the posterior distribution
fig1 = plt.figure(1)
plt.plot(np.linspace(0, 20, 200), np.log(wf * math.sqrt(sf)))
plt.errorbar(np.linspace(0, 10, 100), np.log(wf_exp_in[:, 3]), yerr=wf_exp_in[:, 4], label='SA-NCSM', ms=5,
c='gray', fmt='.', zorder=1)
plt.savefig(r'/mnt/c/Users/noeld/OneDrive - Louisiana State University/PHYS 4399/testplot.jpg')
print(e)
print(rms)
r"4399/6Li_7Li_overlaps_NNLOopt_Nmax12.csv",
skip_header=1,
delimiter=",")
wf_exp_p1 = wf_exp_in[:, 3]
wf_exp_p1_err = wf_exp_in[:, 4]
wf_exp_p3 = wf_exp_in[:, 1]
wf_exp_p3_err = wf_exp_in[:, 2]
sf_p1 = integrate.simps(
np.square(wf_exp_p1) * np.square(wf_exp_in[:, 0]), wf_exp_in[:, 0])
sf_p3 = integrate.simps(
np.square(wf_exp_p3) * np.square(wf_exp_in[:, 0]), wf_exp_in[:, 0])
wf_br_p1, e_1, _ = gws.gwf(A, Z, a, z, nl2j[0], nl2j[1], 1, theta_brida_p1[0],
theta_brida_p1[1], theta_brida_p1[2],
theta_brida_p1[3], theta_brida_p1[4],
theta_brida_p1[5], emin, emax)
wf_br_p3, e_3, _ = gws.gwf(A, Z, a, z, nl2j[0], nl2j[1], 3, theta_brida_p3[0],
theta_brida_p3[1], theta_brida_p3[2],
theta_brida_p3[3], theta_brida_p3[4],
theta_brida_p3[5], emin, emax)
wf_br_p1 = wf_br_p1 * math.sqrt(sf_p1)
wf_br_p3 = wf_br_p3 * math.sqrt(sf_p3)
plt.plot(np.linspace(0, 10, 100), wf_br_p1, label='Brida p=1/2', c='tab:blue')
plt.plot(np.linspace(0, 10, 100),
wf_br_p3,
label='Brida p=3/2',
c='tab:orange')
plt.errorbar(np.linspace(0, 10, 100),