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))))
n = n_term1 * n_term2 * k**(1.0/6.0)
#print n
rhs = (n * p_dict['Q']) / (kn * (s**0.5))
lhs = (cp.area_trap(p_dict)**(5.0/3.0)) / (cp.p_trap(p_dict)**(2.0/3.0))
this_r = abs(lhs-rhs)
if this_r < r:
r = this_r
correct_y1 = y
correct_n = n
correct_tao = tao
print 'y',correct_y1
print 'n',correct_n
tao_bot_max = gamma * correct_y1 * s
print 'tao_perm,tao_max',tao_perm,correct_tao
p_dict['m'] = 1.0 / (3.0)**0.5
p_dict['Q'] = 1500.0
#nm_2_lbft = 47.9
n = 0.015
kn = 1.49
s = 0.0005
Y = np.arange(0.001, 20.0, 0.001)
r = 1.0e+20
correct_y1 = -999
correct_n = -999
correct_b = -999
for y in Y:
p_dict['y'] = y
p_dict['b'] = (2.0 / 3.0) * y * (3.0)**0.5
rh = cp.r_trap(p_dict)
rhs = (n * p_dict['Q']) / (kn * (s**0.5))
lhs = (cp.area_trap(p_dict)**(5.0 / 3.0)) / (cp.p_trap(p_dict)
**(2.0 / 3.0))
this_r = abs(lhs - rhs)
if this_r < r:
r = this_r
correct_y1 = y
correct_n = n
correct_b = p_dict['b']
print 'y', correct_y1
p_dict['y'] = correct_y1
print 'f', cp.f_trap(p_dict)
print 'n', correct_n
print 'b', correct_b