smrt.microstructure_model package

Submodules

smrt.microstructure_model.autocorrelation module

This module contains the base classes for the microstructure classes. It is not used directly.

class AutocorrelationBase(params)

Bases: object

Low level base class for the Autocorrelation base class to handle optional and required arguments. It should not be used directly.

classmethod compute_all_arguments()

classmethod valid_arguments()

class Autocorrelation(params)

Bases: smrt.microstructure_model.autocorrelation.AutocorrelationBase

Base class for autocorrelation function classes. It should not be used directly but sub-classed. It provides generic handling of the numerical fft and invfft when required by the user or when necessary due to the lack of implementation of the real or ft autocorrelation functions. See the source of Exponential to see how to use this class.

args = []

optional_args = {'ft_numerical': False, 'real_numerical': False}

ft_autocorrelation_function_fft(k)

compute the fourier transform of the autocorrelation function via fft Args: k: array of wave vector magnitude values, ordered, and non-negative

autocorrelation_function_invfft(r)

Compute the autocorrelation function from an analytically known FT via fft Args: r: array of lag vector magnitude values, ordered, non-negative

inverted_medium()

return the same autocorrelation for the inverted medium. In general, it is only necessary to invert the fractional volume if the autocorrelation function is numerically symmetric as it should be. This needs to be reimplemented in the sub classes if this is not sufficient.

smrt.microstructure_model.exponential module

Exponential autocorrelation function model of the microstructure. This microstructure model is used by MEMLS when IBA is selected.

parameters: frac_volume, corr_length

class Exponential(params)

Bases: smrt.microstructure_model.autocorrelation.Autocorrelation

args = ['frac_volume', 'corr_length']

optional_args = {}

corr_func_at_origin

inv_slope_at_origin

basic_check()

check consistency between the parameters

compute_ssa()

compute the ssa for the exponential model according to Debye 1957. See also Maetzler 2002 Eq. 11

autocorrelation_function(r)

compute the real space autocorrelation function

ft_autocorrelation_function(k)

compute the fourier transform of the autocorrelation function analytically

smrt.microstructure_model.gaussian_random_field module

Gaussian Random field model of the microstructure.

parameters: frac_volume, corr_length, repeat_distance

class GaussianRandomField(params)

Bases: smrt.microstructure_model.autocorrelation.Autocorrelation

args = ['frac_volume', 'corr_length', 'repeat_distance']

optional_args = {}

corr_func_at_origin

inv_slope_at_origin

basic_check()

check consistency between the parameters

compute_ssa()

Compute the ssa for a sphere

autocorrelation_function(r)

compute the real space autocorrelation function for the Gaussian random field model

smrt.microstructure_model.homogeneous module

Homogeneous microstructure. This microstructure model is to be used with non-scattering emmodel.

parameters: none

class Homogeneous(params)

Bases: smrt.microstructure_model.autocorrelation.Autocorrelation

args = ['frac_volume']

optional_args = {}

corr_func_at_origin

inv_slope_at_origin

basic_check()

check consistency between the parameters

compute_ssa()

compute the ssa of an homogeneous medium

autocorrelation_function(r)

compute the real space autocorrelation function

ft_autocorrelation_function(k)

compute the fourier transform of the autocorrelation function analytically

smrt.microstructure_model.independent_sphere module

Independent sphere model of the microstructure.

parameters: frac_volume, radius

class IndependentSphere(params)

Bases: smrt.microstructure_model.autocorrelation.Autocorrelation

args = ['frac_volume', 'radius']

optional_args = {}

corr_func_at_origin

inv_slope_at_origin

basic_check()

check consistency between the parameters

compute_ssa()

Compute the ssa for a sphere

autocorrelation_function(r)

compute the real space autocorrelation function for an independent sphere

ft_autocorrelation_function(k)

Compute the 3D Fourier transform of the isotropic correlation function for an independent sphere for given magnitude k of the 3D wave vector (float).

smrt.microstructure_model.sampled_autocorrelation module

Sampled autocorrelation function model. To use when no analytical form of the autocorrelation function but the values of the autocorrelation function (acf) is known at a series of lag.

parameters: frac_volume, lag, acf

acf contains the values at different lag. These parameters must be lists or arrays.

class SampledAutocorrelation(params)

Bases: smrt.microstructure_model.autocorrelation.Autocorrelation

args = ['frac_volume', 'lag', 'acf']

optional_args = {}

corr_func_at_origin

basic_check()

check consistency between the parameters

compute_ssa()

compute the ssa according to Debye 1957. See also Maetzler 2002 Eq. 11

autocorrelation_function(r)

compute the real space autocorrelation function by interpolation of requested values from known values

smrt.microstructure_model.sticky_hard_spheres module

Monodisperse sticky hard sphere model of the microstructure.

parameters: frac_volume, radius, stickiness.

The stickiness is optional but it is recommended to use value around 0.2 as a first guess. Be aware that low values of stickiness are invalid, the limit depends on the fractional volume (see for instance Loewe and Picard, 2015). See the tau_min() method.

Currently the implementation is specific to ice / snow. It can not be used for other materials.

class StickyHardSpheres(params)

Bases: smrt.microstructure_model.autocorrelation.Autocorrelation

args = ['frac_volume', 'radius']

optional_args = {'stickiness': 1000}

corr_func_at_origin

inv_slope_at_origin

basic_check()

check consistency between the parameters

compute_ssa()

Compute the ssa of a sphere assembly

ft_autocorrelation_function(k)

Compute the 3D Fourier transform of the isotropic correlation function for sticky hard spheres in Percus–Yevick approximation for given magnitude k of the 3D wave vector (float).

compute_t()

compute the t parameter used in the stickiness

tau_min(frac_volume)

compute the minimum possible stickiness value for given ice volume fraction

smrt.microstructure_model.test_autocorrelation module

smrt.microstructure_model.test_exponential module

test_constructor()

smrt.microstructure_model.test_sticky_hard_spheres module

test_constructor()

test_autocorrelation()

smrt.microstructure_model.teubner_strey module

Teubner Strey model model of the microstructure.

parameters: frac_volume, corr_length, repeat_distance

class TeubnerStrey(params)

Bases: smrt.microstructure_model.autocorrelation.Autocorrelation

args = ['frac_volume', 'corr_length', 'repeat_distance']

optional_args = {}

corr_func_at_origin

basic_check()

check consistency between the parameters

compute_ssa()

Compute the ssa for a sphere

autocorrelation_function(r)

compute the real space autocorrelation function for the Teubner Strey model

ft_autocorrelation_function(k)

Compute the 3D Fourier transform of the isotropic correlation function for Teubner Strey for given magnitude k of the 3D wave vector (float).

Module contents

Microstructure models are different representations of the snow microstructure. Because these representations are different, the parameters to describe actual snow micro-structure depends on the model. For instance, the Sticky Hard Spheres medium is implemented in sticky_hard_spheres and its parameters are: the radius (required) and the stickiness (optional, default value is non-sticky, even though we do recommend to use a stickiness of ~0.1-0.3 in practice).

Because IBA is one of the important electromagnetic theories provided by SMRT, the first/main role of microstructure models is to provide the Fourier transform of the autocorrelation functions. Hence most microstructure models are named after the autocorrelation function. For instance, the exponential autocorrelation function is that used in MEMLS. Its only parameter is the corr_length.

To use microstructure models, it is only required to read the documentation of each model to determine the required and optional parameters. Selecting the microstructure model is usually done with make_snowpack which only requires the name of the module (the filename with .py). The import of the module is automatic. For instance:

from smrt import make_snowpack

sp = make_snowpack([1, 1000], "exponential", density=[200, 300], corr_length=[0.2e-3, 0.5e-3])

This snippet creates a snowpack with the exponential autocorrelation function for all (2) layers. Import of the exponential is automatic and creation of instance of the class Exponential is done by the model smrt.core.model.Model.run() method.