lib_dd.conductivity package

Submodules

lib_dd.conductivity.model module

Template class for models

class lib_dd.conductivity.model.dd_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)[source]

Return the Jacobian corresponding to the forward response. The Jacobian has the dimensions B \times D \times M

TODO: Check the return dimensions

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 from linear to the actually used scale

convert_pars_back(pars)[source]

Convert from log10 to linear

forward(pars)[source]

Return the forward response in base dimensions

Parameters

pars ([log10(sigma_infty), log10(m_i)]) –

Returns

response – imaginary parts

Return type

Nx2 array, first axis denotes frequencies, seconds real and

get_data_base_dimensions()[source]
Returns

  • 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]

Usually you do not need to modify this.

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