Gives the electronic part of the thermal conductivity in either a
        full 3x3 tensor form,
        as 3 eigenvalues, or as the average value (trace/3.0) If
        doping_levels=True, the results are given at
        different p and n doping levels (given by self.doping), otherwise it
        is given as a series of
        electron chemical potential values

        Args:
            output (string): the type of output. 'tensor' give the full 3x3
            tensor, 'eigs' its 3 eigenvalues and
            'average' the average of the three eigenvalues
            doping_levels (boolean): True for the results to be given at
            different doping levels, False for results
            at different electron chemical potentials
            k_el (boolean): True for k_0-PF*T, False for k_0
            relaxation_time (float): constant relaxation time in secs

        Returns:
            If doping_levels=True, a dictionary {temp:{'p':[],'n':[]}}. The
            'p' links to thermal conductivity
            at p-type doping and 'n' to the thermal conductivity at n-type
            doping. Otherwise,
            returns a {temp:[]} dictionary. The result contains either the
            sorted three eigenvalues of the symmetric
            conductivity tensor (format='eigs') or a full tensor (3x3 array) (
            output='tensor') or as an average
            (output='average').
            The result includes a given constant relaxation time

            units are W/mK

def get_thermal_conductivity(self, output='eigs', doping_levels=True,
                                 k_el=True, relaxation_time=1e-14):
        """
        Gives the electronic part of the thermal conductivity in either a
        full 3x3 tensor form,
        as 3 eigenvalues, or as the average value (trace/3.0) If
        doping_levels=True, the results are given at
        different p and n doping levels (given by self.doping), otherwise it
        is given as a series of
        electron chemical potential values

        Args:
            output (string): the type of output. 'tensor' give the full 3x3
            tensor, 'eigs' its 3 eigenvalues and
            'average' the average of the three eigenvalues
            doping_levels (boolean): True for the results to be given at
            different doping levels, False for results
            at different electron chemical potentials
            k_el (boolean): True for k_0-PF*T, False for k_0
            relaxation_time (float): constant relaxation time in secs

        Returns:
            If doping_levels=True, a dictionary {temp:{'p':[],'n':[]}}. The
            'p' links to thermal conductivity
            at p-type doping and 'n' to the thermal conductivity at n-type
            doping. Otherwise,
            returns a {temp:[]} dictionary. The result contains either the
            sorted three eigenvalues of the symmetric
            conductivity tensor (format='eigs') or a full tensor (3x3 array) (
            output='tensor') or as an average
            (output='average').
            The result includes a given constant relaxation time

            units are W/mK
        """
        result = None
        result_doping = None
        if doping_levels:
            result_doping = {doping: {t: [] for t in
                                      self._seebeck_doping[doping]} for
                             doping in self._seebeck_doping}
            for doping in result_doping:
                for t in result_doping[doping]:
                    for i in range(len(self.doping[doping])):
                        if k_el:
                            pf_tensor = np.dot(self._cond_doping[doping][t][i],
                                               np.dot(
                                                   self._seebeck_doping[doping][
                                                       t][i],
                                                   self._seebeck_doping[doping][
                                                       t][i]))
                            result_doping[doping][t].append((
                                self._kappa_doping[doping][t][
                                    i] - pf_tensor * t))
                        else:
                            result_doping[doping][t].append((
                                self._kappa_doping[doping][t][i]))
        else:
            result = {t: [] for t in self._seebeck}
            for t in result:
                for i in range(len(self.mu_steps)):
                    if k_el:
                        pf_tensor = np.dot(self._cond[t][i],
                                           np.dot(self._seebeck[t][i],
                                                  self._seebeck[t][i]))
                        result[t].append((self._kappa[t][i] - pf_tensor * t))
                    else:
                        result[t].append((self._kappa[t][i]))

        return BoltztrapAnalyzer._format_to_output(result, result_doping,
                                                   output, doping_levels,
                                                   multi=relaxation_time)