def plot_rt_vs_theta_alpha(p, T, r, rl, alpha):
theta_l = thermo.theta_l(p, T, r, rl)
theta_e = thermo.theta_e(p, T, r, rl)
theta_alpha = (1. - alpha) * theta_l + alpha * theta_e
rt = r + rl
ax = subplot(1, 1, 1)
ax.plot(theta_alpha, rt * 1000)
yl = ax.get_ylim()
if yl[0] < yl[1]: ax.set_ylim([yl[1], yl[0]])
def plot_rt_vs_theta_alpha(p, T, r, rl, alpha):
theta_l = thermo.theta_l(p, T, r, rl)
theta_e = thermo.theta_e(p, T, r, rl)
theta_alpha = (1. - alpha)*theta_l + alpha*theta_e
rt = r + rl
ax = subplot(1,1,1)
ax.plot(theta_alpha, rt*1000)
yl = ax.get_ylim()
if yl[0] < yl[1]: ax.set_ylim([yl[1], yl[0]])
def Tfind(Tguess, p, theta_l, rt):
# due to the check in invert_theta_l, we can assume that r = r_star.
# However, we don't know the exact value of r_star because we don't know T.
r = thermo.r_star(p, Tguess)
rl = rt - r
return theta_l - thermo.theta_l(p, Tguess, r, rl)
def plot_rt_vs_theta_l(p, T, r, rl):
rt = r + rl
theta_l = thermo.theta_l(p, T, r, rl)
plot(theta_l, rt*1000)
def plot_rt_vs_theta_l(p, T, r, rl):
rt = r + rl
theta_l = thermo.theta_l(p, T, r, rl)
plot(theta_l, rt * 1000)
def Tfind(Tguess, p, theta_l, rt):
# due to the check in invert_theta_l, we can assume that r = r_star.
# However, we don't know the exact value of r_star because we don't know T.
r = thermo.r_star(p, Tguess)
rl = rt - r
return theta_l - thermo.theta_l(p, Tguess, r, rl)
