p_dict = {}
p_dict['g'] = 9.81
p_dict['m'] = 3.0
p_dict['b'] = 1.5
tao_perm = 29.0
avg_hgt = 10.0 #m
mei = 0.05
kn = 1.0
s = 0.012
rho = 1000.0
gamma = p_dict['g'] * rho
p_dict['y'] = tao_perm / (gamma * s)
rh = cp.r_trap(p_dict)
tao = gamma * rh * s
print tao
u_c_star = 0.028 + 6.33 * mei**2
if u_c_star > 0.23 * mei**0.106:
u_c_start = 0.23 * mei**0.106
k = avg_hgt * 0.14 * (((mei / tao)**0.25) / avg_hgt)**1.59
u_star = (p_dict['g'] * rh * s)**0.5
ratio = u_star / u_c_star
if ratio <= 1.0:
a, b = 0.15, 1.85
elif ratio > 1.0 and ratio <= 1.5:
a, b = 0.2, 2.7
elif ratio > 1.5 and ratio <= 2.5:
s = 0.004
rho = 1000.0
gamma = p_dict['g'] * rho
u_c_star = 0.028 + 6.33*mei**2
if u_c_star > 0.23*mei**0.106:
u_c_start = 0.23*mei**0.106
Y = np.arange(0.001,10.0,0.001)
r = 1.0e+20
correct_y1 = -999
correct_n = -999
correct_tao = -999
for y in Y:
p_dict['y'] = y
rh = cp.r_trap(p_dict)
tao = gamma * y * s
u_star = (p_dict['g'] * rh * s)**0.5
ratio = u_star/u_c_star
if ratio <= 1.0:
a,b = 0.15,1.85
elif ratio > 1.0 and ratio <= 1.5:
a,b = 0.2,2.7
elif ratio > 1.5 and ratio <=2.5:
a,b = 0.28,3.08
else:
a,b = 0.29,3.5
k = avg_hgt * 0.14 * (((mei/tao)**0.25)/avg_hgt)**1.59
n_term1 = kn /((8.0*p_dict['g'])**0.5)
n_term2 = ((rh/k)**(1.0/6.0)/(a + (b * math.log10(rh/k))))