def __generateMieEffective(self,
n_particle,
n_host,
particle_mu,
particle_sigma,
effective_model=True,
wavelen_n=1000,
wavelen_max=1100.0,
wavelen_min=100.0,
particle_n=20,
particle_max=20.0,
particle_min=1.0):
'''
Private function.
Generates new effective mie-data file for the database.
'''
if particle_n < 10:
print(
"Too few particles to calculate \
effective model: particle_n < 10")
exit()
n_x_rv = 10000
n_tht = 91
wavelengths = np.linspace(wavelen_min, wavelen_max, wavelen_n) / 1000.0
p_diameters = np.linspace(particle_min, particle_max, particle_n)
o_f = ("mie_eff_p-%dum-%dum-%d_" % (particle_min,
particle_max,
particle_n) +
'np-%s_nh-%.2f_' % (n_particle.__format__('.2f'),
n_host) +
'wave-%.1fnm-%.1fnm-%d' % (wavelen_min,
wavelen_max,
wavelen_n) +
'.hdf5')
o_f = baseDir + '/' + o_f
# Calculate particle distribution
N = lognorm(particle_sigma, scale=np.exp(particle_mu))
# Weight factors of each particle size
pdf = N.pdf(p_diameters)
pdf /= pdf.sum()
weight = dict(zip(p_diameters, pdf))
df = 0
df = mie.generateMieDataEffective(wavelengths,
p_normed_weights_dict=weight,
number_of_rvs=n_x_rv,
number_of_theta_angles=n_tht,
n_particle=n_particle,
n_silicone=n_host,
p_diameters=p_diameters)
print(df.info())
mie.saveMieDataToHDF5([df],
particle_diameters=[p_diameters.mean()],
out_fname=o_f,
wavelengths=wavelengths * 1000.0)
return(o_f)
def __generateMie(self,
n_particle,
n_host,
particle_mu,
particle_sigma,
effective_model=True,
wavelen_n=1000,
wavelen_max=1100.0,
wavelen_min=100.0,
particle_n=20,
particle_max=20.0,
particle_min=1.0):
'''
Private function.
Generates new mie-data file for the database.
'''
n_x_rv = 1000
n_tht = 90
wavelengths = np.linspace(wavelen_min, wavelen_max, wavelen_n) / 1000.0
p_diameters = np.linspace(particle_min, particle_max, particle_n)
o_f = ("mie_p-%dum-%dum-%d_" % (particle_min,
particle_max,
particle_n) +
'np-%s_nh-%.2f_' % (n_particle.__format__('.2f'),
n_host) +
'wave-%.1fnm-%.1fnm-%d' % (wavelen_min,
wavelen_max,
wavelen_n) +
'.hdf5')
o_f = baseDir + '/' + o_f
df = mie.generateMieData(wavelengths,
number_of_rvs=n_x_rv,
number_of_theta_angles=n_tht,
n_particle=n_particle,
n_silicone=n_host,
p_diameters=p_diameters)
mie.saveMieDataToHDF5([df],
particle_diameters=p_diameters,
out_fname=o_f,
wavelengths=wavelengths * 1000.0)
return(o_f)
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy as np
from pyraytracer import mieGenerator as mie
if __name__ == '__main__':
w = np.linspace(100, 1100, 501)
p = np.linspace(3, 35, 50)
p_weights = dict(zip(p, np.ones(50) / 50.0))
df = mie.generateMieDataEffective(wavelengths=w,
p_normed_weights_dict=p_weights,
number_of_rvs=1000,
number_of_theta_angles=90,
n_particle=1.83,
n_silicone=1.55,
p_diameters=p)
mie.saveMieDataToHDF5(df_list=[df],
particle_diameters=[1],
wavelengths=w,
out_fname="mieData.hdf5")