def test_one_layer_direct():
ssa = 20
wavelength = 850e-9
alb = albedo(wavelength, ssa, dir_frac=1, theta_inc=30)
assert abs(alb - 0.8583967584060925) < 1e-10
def test_diffus():
ssa, density, thickness = get_multilayer()
wavelength = np.arange(400, 1000, 200) * 1e-9
alb_dir53 = albedo(wavelength, ssa, density, thickness, sza=53, dir_frac=1)
alb_diff = albedo(wavelength, ssa, density, thickness, dir_frac=0)
assert np.all(abs(alb_dir53 - alb_diff) < 1e-8)
alb_halfdir53 = albedo(wavelength,
ssa,
density,
thickness,
sza=53,
dir_frac=0.5)
assert np.all(abs(alb_halfdir53 - alb_diff) < 1e-8)
def test_one_layer_diffuse():
ssa = 20 # in m^2/kg
wavelength = [450e-9, 650e-9, 850e-9, 1030e-9, 1300e-9] # in m
alb = albedo(wavelength, ssa)
res = numpy.array(
(0.99614203926738309, 0.96047324904990949, 0.88128514422618554,
0.66596004345176796, 0.42830398442634676))
assert numpy.all(abs(alb - res) < 1e-10)
def test_impurities():
ssa = 20 # in m^2/kg
wavelength = [450e-9, 650e-9, 850e-9, 1030e-9, 1300e-9] # in m
alb = albedo(wavelength, ssa, impurities=100e-9)
print(alb)
res = np.array(
[0.95566775, 0.94667078, 0.87767193, 0.66526393, 0.42814038])
assert np.allclose(alb, res, atol=1e-8)
def test_multilayer():
ssa, density, thickness = get_multilayer()
wavelength = [450e-9, 650e-9, 850e-9, 1030e-9, 1300e-9] # in m
alb = albedo(wavelength, ssa, density, thickness)
res = np.array(
[0.98697191, 0.95453273, 0.87187588, 0.65985864, 0.42739316])
assert np.all(abs(alb - res) < 1e-8)
def test_multilayer():
ssa = [20, 15, 10]
density = [200, 250, 300]
thickness = [0.01, 0.10, 1000]
wavelength = 650e-9
alb = albedo(wavelength, ssa, density, thickness)
res = numpy.array(
(0.97861955812074553, 0.9545327263717367, 0.87187587848576675))
assert numpy.all(abs(alb - res)) < 1e-10
def test_w1995():
ssa = 20
wavelength = 550e-9
alb = albedo(wavelength, ssa, refrac_index="w1995")
assert abs(alb - 0.9798191182503498) < 1e-10
import tartes from pylab import * ssa = [20, 15, 10] # in m^2/kg density = [200, 250, 300] # in kg/m^3 thickness = [0.01, 0.10, 1000] # thickness of each layer in meter wavelengths = arange(300, 2500, 10) * 1e-9 # from 300 to 2500 nm albedo_3layers = tartes.albedo(wavelengths, ssa, density, thickness) ssa = 20 # in m^2/kg density = 300 # in kg/m^3 albedo_semiinfinite = tartes.albedo(wavelengths, ssa, density) # alpha controls the transparency of the curves plot(wavelengths * 1e9, albedo_semiinfinite, alpha=0.7) plot(wavelengths * 1e9, albedo_3layers, alpha=0.7) show()
resultfilepath="H:/2016-6-8/albedo.txt"
savefile=codecs.open(resultfilepath,"w","utf-8")
# ssa = 20 # in m^2/kg
density = 300 # in kg/m^3
# the result should be independent of the density for a semi-infinite medium
r_opt= 5.0059E-04
# in m optical radius (m)4.15:4.14E-04 4.16:1.14E-03 4.17:3.12E-04 4.18:5.17E-04
# wavelength = 850e-9 # in m
solar_zenith_angle= np.loadtxt('H:/2016-6-8/solar_zenith_angle.txt')
nameList = ['645e-9','858e-9','469e-9','1240e-9','2130e-9']
for m in range(len(nameList)):
waveleng=nameList[m]
print waveleng
wavelength =float(waveleng)
# solar_zenith_angle= np.loadtxt('H:/2016-6-8/solar_zenith_angle.txt')
# solar_zenith_angle= np.loadtxt('H:/2016-6-8/Solar_elevation_angle.txt')
print 'solar_zenith_angle=',solar_zenith_angle
# solar_zenith_angle1=np.arry(solar_zenith_angle1)
# solar_zenith_angle=90-solar_zenith_angle1
# solar_zenith_angle =68.02
for i in range(len(solar_zenith_angle)):
angle=solar_zenith_angle[i]
albedo = tartes.albedo(wavelength,ssa(r_opt),density,dir_frac=0.7,theta_inc=angle)
# albedo=[]
# albedo=np.arry(albedo)
print 'albedo=',albedo
# print(albedo)
savefile.writelines(str(albedo)+"\n")
# np.savetxt('H:/2016-6-8/albedo.txt',str(albedo),fmt='%1.4e')
# solar_zenith_angle.close()
print 'end'
import tartes from tartes import ssa r_opt = 100e-6 # in m #100e-6 density = 300 # in kg/m^3 # the result should be independent of the density for a semi-infinite medium wavelength = 850e-9 # in m albedo = tartes.albedo(wavelength, ssa(r_opt), density) print(albedo)
import tartes
import tartes.impurities
from pylab import *
ssa = 20 # in m^2/kg
density = 300 # in kg/m^3
wavelengths = arange(300, 1000, 10) * 1e-9 # from 300 to 2500nm
# pure snow
albedo_pure = tartes.albedo(wavelengths, ssa, density)
# 50ng/g of soot. Soot is the default impurity type
albedo_soot = tartes.albedo(wavelengths, ssa, density, impurities=50e-9)
# 200ng/g of Hulis.
albedo_hulis = tartes.albedo(wavelengths,
ssa,
density,
impurities=200e-9,
impurities_type=tartes.impurities.HULIS)
plot(wavelengths * 1e9, albedo_pure, label='pure snow')
plot(wavelengths * 1e9, albedo_soot, label='snow with 50 ng/g soot')
plot(wavelengths * 1e9, albedo_hulis, label='snow with 200 ng/g HULIS')
legend(loc='best')
show()
import tartes from pylab import * ssa = 20 # in m^2/kg density = 300 # in kg/m^3 wavelengths = arange(300, 2500, 10) * 1e-9 # from 300 to 2500nm albedo = tartes.albedo(wavelengths, ssa, density) plot(wavelengths * 1e9, albedo) show()
import tartes
import tartes.impurities
from pylab import *
ssa = [40, 30, 20] # in m^2/kg
density = [300, 300, 300] # in kg/m^3
thickness = [0.02, 0.05, 1000] # in m
wavelengths = arange(300, 1000, 10) * 1e-9 # from 300 to 2500nm
# pure snow
albedo_pure = tartes.albedo(wavelengths, ssa, density, thickness)
# with a profile of soot
albedo_soot = tartes.albedo(wavelengths,
ssa,
density,
thickness,
impurities=[10e-9, 50e-9, 0])
# A profile of mixture soot+Hulis
impurities_content = [
[10e-9, 50e-9], # soot and HULIS in the first layer
[30e-9, 100e-9], # soot and HULIS in the second layer
[0, 10e-9] # soot and HULIS in the last layer
]
albedo_mixture = tartes.albedo(
wavelengths,
ssa,
density,
import tartes
from pylab import *
ssa = 20 # in m^2/kg
density = 250 # in kg/m^3
thickness = arange(5, 30, 5) * 1e-2
wavelengths = arange(300, 1100, 10) * 1e-9 # from 300 to 1100nm
for th in thickness:
albedo = tartes.albedo(wavelengths, ssa, density, th, soilalbedo=0.2)
plot(wavelengths * 1e9, albedo, label='%g cm' % (th * 100))
legend(loc='best')
xlabel('wavelength (nm)')
ylabel('albedo')
show()
import tartes ssa = 20 # in m^2/kg density = 300 # in kg/m^3 # the result should be independent of the density for a semi-infinite medium wavelength = 850e-9 # in m albedo = tartes.albedo(wavelength, ssa, density, dir_frac=1, sza=30) print(albedo)
import tartes
from pylab import *
print(tartes)
nlayer = 200 # number of layers
ssa = [20] * nlayer # nlayer layers with the same SSA... (in m^2/kg)
density = [300] * nlayer # and the same density (in kg/m^3)
thickness = [
0.01
] * nlayer # all the layer are 0.01 m thick, the snowpack is nlayer*0.01m deep
wavelengths = [400e-9, 500e-9, 600e-9, 700e-9, 800e-9, 900e-9]
for wl in wavelengths:
z, absorption_profile = tartes.absorption_profile(wl,
ssa,
density,
thickness,
soilalbedo=0.50)
semilogx(absorption_profile, -z, label='%g nm' % (wl * 1e9))
albedo = tartes.albedo(wl, ssa, density, thickness, soilalbedo=0.50)
print(1 - sum(absorption_profile), " ", albedo)
ylabel('depth(m)')
xlabel('absorbed energy (for 1W/m2 incident)')
legend(loc='best')
show()
