BGlib.gmode.analysis.utils.giv_utils.plot_bayesian_results

BGlib.gmode.analysis.utils.giv_utils.plot_bayesian_results(bias_sine, i_meas, i_corrected, bias_triang, resistance, r_variance, i_recon=None, pix_pos=[0, 0], broken_resistance=True, r_max=None, res_scatter=False, **kwargs)

Plots the basic Bayesian Inference results for a specific pixel :param bias_sine: Original bias vector used for experiment :type bias_sine: 1D float numpy array :param i_meas: Current measured from experiment :type i_meas: 1D float numpy array :param i_corrected: current with capacitance and R extra compensated :type i_corrected: 1D float numpy array :param i_recon: Reconstructed current :type i_recon: 1D float numpy array :param bias_triang: Interpolated bias :type bias_triang: 1D float numpy array :param resistance: Inferred resistance :type resistance: 1D float numpy array :param r_variance: Variance of the resistance :type r_variance: 1D float numpy array :param pix_pos: Pixel row and column positions or values :type pix_pos: list of two numbers :param broken_resistance: Whether or not to break the resistance plots into sections so as to avoid plotting areas with high variance :type broken_resistance: bool, Optional :param r_max: Maximum value of resistance to plot :type r_max: float, Optional :param res_scatter: Use scatter instead of line plots for resistance :type res_scatter: bool, Optional

Returns:

fig – Handle to figure

Return type:

matplotlib.pyplot figure handle