BGlib.gmode.analysis.utils.giv_utils.bayesian_inference_on_period

BGlib.gmode.analysis.utils.giv_utils.bayesian_inference_on_period(i_meas, excit_wfm, ex_freq, r_extra=110, num_x_steps=500, show_plots=False, r_max=None, **kwargs)

Performs Bayesian Inference on a single I-V curve. The excitation waveform must be a single period of a sine wave. This algorithm splits the curve into the forward and reverse sections, performs inference on each of the sections, stitches the results back again, and corrects the resistance which is not handled in the main bayesian function. :param i_meas: Current corresponding to a single period of sinusoidal excitation bias :type i_meas: array-like :param excit_wfm: Single period of the sinusoidal excitation waveform :type excit_wfm: array-like :param ex_freq: Frequency of the excitation waveform :type ex_freq: float :param r_extra: Extra resistance in the RC circuit that will provide correct current and resistance values :type r_extra: float (Optional, default = 220 [Ohms]) :param num_x_steps: Number of steps for the inferred results. Note: this may be different from what is specified. :type num_x_steps: uint (Optional, default = 500) :param show_plots: Whether or not to show plots :type show_plots: Boolean (Optional, Default = False) :param r_max: Maximum limit of the resistance plots. :type r_max: float (Optional, Default = None) :param kwargs: Other parameters that will be passed on to the do_bayesian_inference function :type kwargs: dict

Returns:

results – Dictionary iterms are ‘cValue’ : array-like - 2 elements

Capacitance on the forward and reverse sections

’x’array-like

Interpolated bias from bayesian inference of length num_x_steps

’mR’array-like

Resistance of sample infered by Bayesian Inference of length num_x_steps

’vR’array-like

Variance of the inferred resistance of length num_x_steps

’IcorrSine’array-like

Measured current with the capacitance correctly subtracted.

’Irec’array-like

Current reconstructed via Bayesian Inference

Return type:

dictionary