def make_ammonium_sulfate(epsilon=0.3, N=200.):
ammonium_sulfate = { 'Ms': 0.13214, # Molecular weight, kg/mol
'rho_s': 1.769*1e-3*1e6,
'rho_u': 1.769*1e-3*1e6,
'nu': 3.0, # number of ions into which a solute dissolve
'epsilon': epsilon # mass fraction of soluble material in the dry particle
}
ammonium_sulfate['rho_p'] = 1.769*1e-3*1e6
mu, sigma, bins, N = 0.05, 2.0, 50, N
l = 0
r = bins
aerosol_dist = Lognorm(mu=mu, sigma=sigma, N=N)
rs = np.logspace(np.log10(mu/(sigma*10.)), np.log10(mu*sigma*10), num=bins+1)[:]
mids = np.array([np.sqrt(a*b) for a, b in zip(rs[:-1], rs[1:])])[l:r]
Nis = np.array([0.5*(b-a)*(aerosol_dist.pdf(a) + aerosol_dist.pdf(b)) for a, b in zip(rs[:-1], rs[1:])])[l:r]
r_drys = mids*1e-6
ammonium_sulfate['distribution'] = aerosol_dist
ammonium_sulfate['r_drys'] = r_drys
ammonium_sulfate['rs'] = rs
ammonium_sulfate['Nis'] = Nis*1e6
ammonium_sulfate['species'] = '(NH4)2SO4'
ammonium_sulfate['nr'] = len(r_drys)
return AerosolSpecies(**ammonium_sulfate)
from chi_square import Chi_square
from exponential import Exponential
from f_dist import F_dist
from gamma import Gamma
from inv_gamma import Inverse_gamma
from student_t import Student_t
from uniform import Uniform
from numpy import array
x = array([1.2, 1.5, 2.1, 5.4])
x2 = array([.1, .3, .4, .7])
mu, sigma, alpha, beta, k, gamma, d1, d2, kk, theta = [1.2, 2.3, .4, .5, 4,
3, 4, 6, 1.6, .7]
lnorm = Lognorm(mu, sigma)
norm = Normal(mu, sigma)
beta = Beta(alpha, beta)
chi = Chi(k)
chi2 = Chi_square(k)
exp = Exponential(gamma)
f = F_dist(d1, d2)
gamma = Gamma(kk, theta)
invgamma = Inverse_gamma(mu, sigma)
t = Student_t(d2)
uniform = Uniform(alpha, d2)
print 'lnorm.cdf(x): ', lnorm.cdf(x)
print 'norm.cdf(x): ', norm.cdf(x)
print 'beta.cdf(x2): ', beta.cdf(x2)
print 'chi.cdf(x): ', chi.cdf(x)
from exponential import Exponential
from f_dist import F_dist
from gamma import Gamma
from inv_gamma import Inverse_gamma
from student_t import Student_t
from uniform import Uniform
from numpy import array
x = array([1.2, 1.5, 2.1, 5.4])
x2 = array([.1, .3, .4, .7])
mu, sigma, alpha, beta, k, gamma, d1, d2, kk, theta = [
1.2, 2.3, .4, .5, 4, 3, 4, 6, 1.6, .7
]
lnorm = Lognorm(mu, sigma)
norm = Normal(mu, sigma)
beta = Beta(alpha, beta)
chi = Chi(k)
chi2 = Chi_square(k)
exp = Exponential(gamma)
f = F_dist(d1, d2)
gamma = Gamma(kk, theta)
invgamma = Inverse_gamma(mu, sigma)
t = Student_t(d2)
uniform = Uniform(alpha, d2)
print 'lnorm.cdf(x): ', lnorm.cdf(x)
print 'norm.cdf(x): ', norm.cdf(x)
print 'beta.cdf(x2): ', beta.cdf(x2)
print 'chi.cdf(x): ', chi.cdf(x)
