smrt.utils package

Contains various utilities that work with/for SMRT.

The wrappers to legacy snow radiative transfer models can be used to run DMRT-QMS (passive mode), HUT and MEMLS (passive mode). Other tools are listed below.

dB(x)

Computes the ratio x in dB. Any small value is converted to -200dB.

Parameters:

x – Input value or array.

Returns:

The value(s) in dB.

invdB(x)

Computes the dB value x in natural value.

Parameters:

x – Value(s) in dB.

Returns:

The value(s) in natural units.

smrt.utils.dmrt_qms_legacy module

Wraps the original DMRT_QMS matlab code using the SMRT framework.

To use this module, extra installations are needed:

  • Gets DMRT_QMS from http://web.eecs.umich.edu/~leutsang/Available%20Resources.html and extracts the model somewhere.

  • Installs the oct2py module using pip install oct2py or easy_install install oct2py.

  • Installs Octave version 3.6 or above.

  • For convenience, sets the DMRT_QMS_DIR environment variable to point to DMRT-QMS path. This path can also be programmatically set with and use set_dmrt_qms_path() function.

In case of problem, checks the instructions given in http://blink1073.github.io/oct2py/source/installation.html.

You may also want to increase the number of streams in passive/DMRT_QMS_passive.m

set_dmrt_qms_path(path)

Sets the path where dmrt_qms archive has been uncompressed, i.e. where the file dmrt_qmsmain.m is located.

Parameters:

path – Path to the DMRT_QMS directory.

run(sensor, snowpack, dmrt_qms_path=None, snowpack_dimension=None, full_output=False)

Calls DMRT-QMS for the snowpack and sensor configuration given as argument. The sticky_hard_spheres microstructure model must be used.

Parameters:

  • sensor – Sensor configuration.

  • snowpack – Snowpack description.

  • dmrt_qms_path – Optional path to DMRT_QMS.

  • snowpack_dimension – Optional dimension for results when a list of snowpacks is provided.

  • full_output – If True, returns ks, ka, and effective permittivity in addition to the result object.

Returns:

PassiveResult or tuple with additional outputs if full_output is True.

dmrt_qms_emmodel(sensor, layer, dmrt_qms_path=None)

Computes scattering and absorption coefficients using DMRT QMS.

Parameters:

  • sensor – Sensor configuration.

  • layer – Layer description.

  • dmrt_qms_path – Optional path to DMRT_QMS.

Returns:

namedtuple with ks and ka.

smrt.utils.hut_legacy module

Wraps the original HUT matlab using SMRT framework.

To use this module, extra installations are needed:

  • Gets HUT. Decompresses the archive somewhere on your disk.

  • In the file snowemis_nlayers, changes the 6 occurrences of the “do” variable into “dos” because it causes a syntax error in Octave.

  • Installs the oct2py module using pip install oct2py or easy_install install oct2py.

  • Installs Octave version 3.6 or above.

  • For convenience, sets the HUT_DIR environment variable to point to HUT path. This path can also be programmatically set with set_hut_path().

In case of problem, checks the instructions given in http://blink1073.github.io/oct2py/source/installation.html.

set_hut_path(path)

Sets the path where HUT archive has been uncompressed, i.e. where the file memlsmain.m is located.

Parameters:

path – Path to the HUT directory.

run(sensor, snowpack, ke_option=0, grainsize_option=1, hut_path=None)

Calls HUT for the snowpack and sensor configuration given as argument. Any microstructure model that defines the “radius” parameter is valid.

Parameters:

  • sensor – Sensor configuration.

  • snowpack – Snowpack description.

  • ke_option – Option for HUT snowemis_nlayers.m code.

  • grainsize_option – Option for HUT snowemis_nlayers.m code.

  • hut_path – Optional path to HUT.

Returns:

Result object.

smrt.utils.memls_legacy module

Wraps the original MEMLS matlab code using the SMRT framework.

To use this module, extra installations are needed:

  • Downloads MEMLS from http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html. Decompresses the archive somewhere on your disk.

  • Installs the oct2py module using pip install oct2py or easy_install install oct2py.

  • Installs Octave version 3.6 or above.

  • For convenience, sets the MEMLS_DIR environment variable to point to MEMLS path. This path can also be programmatically set with set_memls_path().

In case of problem, checks the instructions given in http://blink1073.github.io/oct2py/source/installation.html.

set_memls_path(path)

Sets the path where MEMLS archive has been uncompressed, i.e. where the file memlsmain.m is located.

Parameters:

path – Path to the MEMLS directory.

run(sensor, snowpack, scattering_choice=12, atmosphere=None, memls_path=None, memls_driver=None, snowpack_dimension=None)

Calls MEMLS for the snowpack and sensor configuration given as argument. Any microstructure model that defines the “corr_length” parameter is valid, but it must be clear that MEMLS only considers exponential autocorrelation.

param snowpack:

describe the snowpack.

param sensor:

describe the sensor configuration.

param scattering_choice:

MEMLS proposes several formulation to compute scattering_function. scattering_choice=ABORN (equals 12) is the default here and is recommended choice to compare with IBA. Note that some comments in memlsmain.m suggest to use scattering_choice=MEMLS_RECOMMENDED (equals 11). Note also that the default grain type in memlsmain is graintype=1 corresponding to oblate spheroidal calculation of effective permittivity from the empirical representation of depolarization factors. To use a Polder-Van Santen representation of effective permittivity for small spheres, graintype=2 must be set in your local copy of MEMLS.

param atmosphere:

describe the atmosphere. Only tbdown is used for the Tsky argument of memlsmain.

param memls_path:

directory path to the memls Matlab scripts

param memls_driver:

matlab function to call to run memls. memlsmain.m is the default driver in the original MEMLS distribution for the passive case and amemlsmain.m for the active case.

param snowpack_dimension:

name and values (as a tuple) of the dimension to create for the results when a list of snowpack is provided. E.g. time, point, longitude, latitude. By default the dimension is called ‘snowpack’ and the values are from 1 to the number of snowpacks.

memls_emmodel(sensor, layer, scattering_choice=12, graintype=2)

Compute scattering (gs6) and absorption coefficients (gai) using MEMLS

Parameters:

  • layer – describe the layer.

  • sensor – describe the sensor configuration.

  • scattering_choice – MEMLS proposes several formulation to compute scattering_function. scattering_choice=ABORN (equals 12) is the defaut here and is recommended choice to compare with IBA.

smrt.utils.mpl_plots module

class ReciprocalScale(axis)

Bases: LinearScale

set_default_locators_and_formatters(axis)

Set the locators and formatters of axis to instances suitable for this scale.

get_transform()

Return the transform for linear scaling, which is just the ~matplotlib.transforms.IdentityTransform.

class ReciprocalTransform(shorthand_name=None)

Bases: Transform

input_dims = 1

The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

output_dims = 1

The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

is_separable = True

True if this transform is separable in the x- and y- dimensions.

transform_non_affine(a)

Apply only the non-affine part of this transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Parameters:

values (array) – The input values as an array of length input_dims or shape (N, input_dims).

Returns:

The output values as an array of length output_dims or shape (N, output_dims), depending on the input.

Return type:

array

inverted()

Return the corresponding inverse transformation.

It holds x == self.inverted().transform(self.transform(x)).

The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

has_inverse = True

True if this transform has a corresponding inverse transform.

class InvertedReciprocalTransform(shorthand_name=None)

Bases: Transform

input_dims = 1

The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.

output_dims = 1

The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.

is_separable = True

True if this transform is separable in the x- and y- dimensions.

transform_non_affine(a)

Apply only the non-affine part of this transformation.

transform(values) is always equivalent to transform_affine(transform_non_affine(values)).

In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.

Parameters:

values (array) – The input values as an array of length input_dims or shape (N, input_dims).

Returns:

The output values as an array of length output_dims or shape (N, output_dims), depending on the input.

Return type:

array

inverted()

Return the corresponding inverse transformation.

It holds x == self.inverted().transform(self.transform(x)).

The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.

has_inverse = True

True if this transform has a corresponding inverse transform.