def get_rh(Mtot):
H = calculate_r200.get_H(z)
rs = calculate_r200.get_r200(Mtot, H)
concentration = 10.**(1.025-0.097*np.log(Mtot/((10.**12)/little_h)))
rh = rs*(0.6082 - 0.1843*np.log(concentration) - 0.1011*(np.log(concentration)**2.) + \
0.03918*(np.log(concentration)**3.))
return rh
def run():
tend = 13.722672e3 #Myr
gal = '1'
r200_list = []
m200_list = []
c_list = []
R_half_mass_list = []
R_H_list = []
rho_c_list = []
t_s = np.arange(t0, tend, 100.)
for t in t_s: #increment is 100 Myr
z = get_z(t)
H = get_H(z)
NFW_params = get_NFW_parameters(t, H, gal)
m200, r200, c, R_S, rho_crit, rho_0 = NFW_params
r200_list.append(r200)
m200_list.append(m200)
c_list.append(c)
# R = np.linspace(0.01, r200, num=100000)
# rho_NFW = get_rho_NFW(R, rho_0, R_S)
R_half_mass = get_R_half_mass(c, r200=r200)
R_half_mass_list.append(R_half_mass)
R_H = R_half_mass
R_H_list.append(R_H)
R_C = Constants_NFW.R_C
rho_c = get_rho_c(m200, R_C, R_H)
rho_c_list.append(rho_c)
r200_array = np.array(r200_list)
m200_array = np.array(m200_list)
c_array = np.array(c_list)
R_half_mass_array = np.array(R_half_mass_list)
R_H_array = np.array(R_H_list)
rho_c_array = np.array(rho_c_list)
# plot_c_vs_t(t_s, c_array)
'''
plt.figure()
plt.loglog(t_s, rho_c_array)
plt.xlabel('Log time (Myr)')
plt.ylabel('Log rho_c of OS profile (Msun/pc**3)')
plt.savefig(''.join([plots_folder, 'rho_c.png']))
plt.close()
'''
R_H_index = 0
# R = np.linspace(0.1, R_H_array[R_H_index], num=10000)
'''
rho_rhoc = ((1+R**2/R_C**2)*(1+R**2/R_H_array[R_H_index]**2))**(-1)
plt.figure()
plt.loglog(R, rho_rhoc)
plt.axvline(x=R_H_array[R_H_index], color='r')
plt.xlabel('Log R')
plt.ylabel('Log rho/rho_c')
plt.title('rho/rho_c as function of R for r_h=%.f' % (R_H_array[R_H_index]))
plt.savefig(''.join([plots_folder, 'rho_rhoc_as_func_of_r.png']))
'''
m200_index = R_H_index
rho_c_index = R_H_index
##############################
# TEMPORARY, JUST FOR NICK'S TEST CASE FROM THE PAPER
R_H = 1.
m200 = 1.e5
R_C = [.002, .01, .05, .1]
# colors = ['k', 'b', 'm', 'r']
R = np.linspace(1.e-3, 100., num=100000)
R_near = [R[R<=0.065*1.15], R[R<=0.12*1.15], R[R<=0.2*1.15], R[R<=0.25*1.15]]
R_far = [R[R>=0.075*.85], R[R>=0.15*.85], R[R>=0.25*.85], R[R>=0.3*.85]]
###############################
fig, ax = plt.subplots(2, 2)
for i, a in enumerate(ax.flatten()):
rho_c = get_rho_c(m200, R_C[i], R_H)
sigma_near = get_sigma_near(R, R_H, R_C[i], rho_c)
##############################
# sigma_near = get_sigma_near(R, R_H_array[R_H_index], R_C, rho_c_array[rho_c_index])
# a.loglog(R_near[i], sigma_near[:len(R_near[i])], c='k', label='sigma near')
a.loglog(R, sigma_near, c='k', label='sigma near')
##############################
# TEMPORARY, JUST FOR NICK'S TEST CASE FROM THE PAPER
sigma_far = get_sigma_far(R, R_H, R_C[i], m200)
##############################
# sigma_far = get_sigma_far(R, R_H_array[R_H_index], R_C, m200_array[m200_index])
# a.loglog(R_far[i], sigma_far[len(sigma_far)-len(R_far[i]):], c='r', label='sigma far')
a.loglog(R, sigma_far, c='r', label='sigma far')
##############################
# TEMPORARY, JUST FOR NICK'S TEST CASE FROM THE PAPER
a.axvline(x=R_C[i], color='b', linestyle='--')
a.set_ylim([8., 25.])
a.set_xlim([.001, 10.])
##############################
a.set_xlabel('Log r[pc]')
a.set_ylabel('Log sigma[km/s]')
# plt.legend(loc='best')
a.grid(b=True, which='major', color='r', linestyle='-')
##############################
# TEMPORARY, JUST FOR NICK'S TEST CASE FROM THE PAPER
a.set_title('r_c=%.3f' % (R_C[i]), fontsize=10)
##############################
# plt.title('sigma as function of R for r_h=%.f' % (R_H_array[R_H_index]))
fig.suptitle(''.join(['sigma as f\'n of r for r_h=%.1f, m200=%.f' % (R_H, m200)]), fontsize=12, y=1.)
fig.tight_layout()
fig.savefig(os.path.join(plots_folder, 'sigma_as_func_of_r.png'))
print(plots_folder)
