lib_dd.models package

Submodules

lib_dd.models.ccd_cond module

Cole-Cole decomposition in conductivity formulation

class lib_dd.models.ccd_cond.decomposition_conductivity(settings)[source]

Bases: lib_dd.plot_stats._plot_stats, lib_dd.base_class.integrated_parameters, lib_dd.starting_parameters.starting_parameters, NDimInv.model_template.model_template

Jacobian(pars_dec)[source]
Parameters

pars_dec (numpy.ndarray) – array containing (log10(sigma_infty), log10(m_i)

Returns

J – containing derivatives.

Return type

(2N) X K numpy.ndarray

compute_par_stats(pars)[source]

For a given parameter set (i.e. a fit result), compute relevant statistical values such as m_{tot}, m_{tot}^n, \tau_{50}, \tau_{mean}, \tau_{peak}

This is the way to compute any secondary results based on the fit results.

Store in self.stat_pars = dict()

convert_parameters(pars)[source]

Convert parameters given as (\sigma_\infty, m_i) to the parameterization used by this class.

convert_pars_back(pars)[source]

Convert parameters given in this parameterization back to the linear state Here: From log_{10}(\sigma_\infty), log_{10}(m_i)

forward(pars_dec)[source]

Forward response of this model

Parameters

pars_dec (or numpy.ndarray) – [log10(sigma_infty), log10(m_i)]

Returns

remim – with N the nr of frequencies, and the real and the negative imaginary parts on the second axis

Return type

Nx2 numpy.ndarray

get_data_base_dimensions()[source]

Return a dict with a description of the data base dimensions. In this case we have frequencies and re/im data

get_data_base_size()[source]

Return size of flattened base dimensions

get_model_base_dimensions()[source]

Return a dict with a description of the model base dimensions. In this case we have one dimension: the DD parameters (rho0, mi) where m_i denotes all chargeability values corresponding to the relaxation times.

set_settings(settings)[source]

Set the settings and call necessary functions

Parameters

settings (dict) – contains settings of the decomposition kernel

lib_dd.models.ccd_em_res module

Cole-Cole decomposition in resistivity formulation, including a high-frequency EM Cole-Cole term

class lib_dd.models.ccd_em_res.decomposition_em_resistivity(settings)[source]

Bases: lib_dd.plot_stats._plot_stats, lib_dd.base_class.integrated_parameters, lib_dd.starting_parameters.starting_parameters, NDimInv.model_template.model_template

Jacobian(pars_dec)[source]
Parameters

pars_dec (numpy.ndarray) – array containing (log10(rho0), log10(m_i)

Returns

J

Return type

(2N) X K array with derivatives.

convert_parameters(pars)[source]

Convert parameters given as (\rho_0, m_i) to the parameterization used by this class.

convert_pars_back(pars)[source]

Convert parameters given in this parameterization back to the linear state

Here: From log_{10}(\rho_0), log_{10}(m_i)

forward(pars_dec)[source]

Forward response of this model

Parameters

pars_dec (list) – [log10(rho0), log10(m_i), m_m, tau_em, c_em], with m_i a vector of the chargabilities

Returns

remim – array with N the nr of frequencies, and the real and the negative imaginary parts on the second axis

Return type

Nx2 numpy.ndarray

get_data_base_dimensions()[source]

Return a dict with a description of the data base dimensions. In this case we have frequencies and re/im data

get_data_base_size()[source]

Return size of flattened base dimensions

get_model_base_dimensions()[source]

Return a dict with a description of the model base dimensions. In this case we have one dimension: the DD parameters (rho0, mi) where m_i denotes all chargeability values corresponding to the relaxation times.

set_settings(settings)[source]

Set the settings and call necessary functions

lib_dd.models.ccd_res module

Cole-Cole decomposition in resistivity formulation

class lib_dd.models.ccd_res.decomposition_resistivity(settings)[source]

Bases: lib_dd.plot_stats._plot_stats, lib_dd.base_class.integrated_parameters, lib_dd.starting_parameters.starting_parameters, NDimInv.model_template.model_template

Jacobian(pars_dec)[source]

Compute the Jacobian of the CCD kernel :param pars_dec: array containing (log10(rho0), log10(m_i)) :type pars_dec: numpy.ndarray

Returns

J – (2N) X K array with derivatives.

Return type

numpy.ndarray

convert_parameters(pars)[source]

Convert parameters given as (\rho_0, m_i) to the parameterisation used by this class.

convert_pars_back(pars)[source]

Convert parameters given in this parameterisation back to the linear state Here: From log_{10}(\rho_0), log_{10}(m_i)

forward(pars_dec)[source]

Forward response of this model

Parameters

pars_dec (list) – [log10(rho0), log10(m_i)], with m_i a vector of the chargabilities

Returns

remim – array with N the nr of frequencies, and the real and the negative imaginary parts on the second axis

Return type

Nx2 numpy.ndarray

get_data_base_dimensions()[source]

Return a dict with a description of the data base dimensions. In this case we have frequencies and re/im data

get_data_base_size()[source]

Return size of flattened base dimensions

get_model_base_dimensions()[source]

Return a dict with a description of the model base dimensions. In this case we have one dimension: the DD parameters (rho0, mi) where m_i denotes all chargeability values corresponding to the relaxation times.

set_settings(settings)[source]

Set the settings and call necessary functions

Module contents