Circuit fitting

def default_parameters(self) -> Parameters:
    """Get default parameters. Use this to modify parameter values.

    Returns
    -------
    parameters : Parameters
        Default parameters for CDC.
    """
    return Parameters(self.cdc)

def fit(
    self, data: EISData, *, parameters: None | Sequence[float] | Parameters = None
) -> FitResult:
    """Fit circuit model.

    Parameters
    ----------
    data : EISData
        Input data.
    parameters : Optional[Sequence[float] | Parameters]
        Optional initial parameters for fit. Can be passed as
        `Parameters` object or list of values.

    Returns
    -------
    result : FitResult
        Returns dataclass with fit results. Can also be accessed via `.last_result`.
    """
    if not data.frequency_type == 'scan':
        raise ValueError(
            f'Fit only supports EIS scans at a fixed potential, got {data.frequency_type=}.'
        )
    if not data.scan_type == 'fixed':
        raise ValueError(
            f'Fit only supports EIS scans at a fixed potential, got {data.scan_type=}.'
        )

    opts = self._psfitoptions(data=data, parameters=parameters)

    fitter = PalmSens.Fitting.FitAlgorithm.FromAlgorithm(opts)
    fitter.ApplyFitCircuit()
    self._last_psfitter = fitter
    if self.algorithm == 'nelder-mead':
        self._last_result = FitResult.from_psfitresult_nelder_mead(
            fitter.FitResult, cdc=self.cdc
        )
    else:
        self._last_result = FitResult.from_psfitresult(fitter.FitResult, cdc=self.cdc)

    return self._last_result

@classmethod
def from_eisdata(cls, data: EISData):
    """Construct fitresulf from EISData."""
    return cls(
        cdc=data.cdc,
        parameters=data.cdc_values,
        chisq=0,
        error=[0.0 for _ in data.cdc_values],
        exit_code='',
        n_iter=0,
    )

@classmethod
def from_psfitresult(cls, result: PalmSens.Fitting.FitResult, cdc: str):
    """Construct fitresult from SDK FitResult."""
    if result.ParameterSDs:
        error = list(result.ParameterSDs)
    else:
        error = None

    return cls(
        cdc=cdc,
        chisq=result.ChiSq,
        exit_code=result.ExitCode.ToString(),
        n_iter=result.NIterations - 1,
        parameters=list(result.FinalParameters),
        error=error,
    )

@classmethod
def from_psfitresult_nelder_mead(cls, result: PalmSens.Fitting.FitResult, cdc: str):
    """Construct fitresult from SDK FitResult."""
    exit_codes = {
        0: 'None',
        1: 'RelativeGradient',
        2: 'LackOfProgress',
        3: 'AbsoluteGradient',
        4: 'WeakWolfeCriteria',
        5: 'BoundTolerance',
        6: 'StrongWolfeCriteria',
        7: 'Converged',
    }

    return cls(
        cdc=cdc,
        chisq=result.ChiSq,
        exit_code=exit_codes.get(int(result.ExitCode), 'Unknown'),
        n_iter=result.NIterations - 1,
        parameters=list(result.FinalParameters),
        error=None,
    )

def get_bode_phase(self, data: EISData) -> tuple[Curve, Curve]:
    """Calculate observed and calculated Bode curve phase vs Frequency.

    Parameters
    ----------
    data : EISData
        Input EIS data.

    Returns
    -------
    calc, meas : tuple[Curve, Curve]
        Returns the nyquist curve calculated from the model parameters
        and the measured curve from the EIS data.
    """
    psmodel = self.get_psmodel(data=data)
    curves = psmodel.GetCurvePhaseOverFrequency(False)
    calc, meas = (Curve(pscurve=pscurve) for pscurve in curves)
    return calc, meas

def get_bode_z(self, data: EISData) -> tuple[Curve, Curve]:
    """Calculate observed and calculated Bode curve Z vs Frequency.

    Parameters
    ----------
    data : EISData
        Input EIS data.

    Returns
    -------
    calc, meas : tuple[Curve, Curve]
        Returns the nyquist curve calculated from the model parameters
        and the measured curve from the EIS data.
    """
    psmodel = self.get_psmodel(data=data)
    curves = psmodel.GetCurveZabsOverFrequency(False)
    calc, meas = (Curve(pscurve=pscurve) for pscurve in curves)
    return calc, meas

def get_nyquist(self, data: EISData) -> tuple[Curve, Curve]:
    """Calculate observed and calculated nyquist curves.

    Parameters
    ----------
    data : EISData
        Input EIS data.

    Returns
    -------
    calc, meas : tuple[Curve, Curve]
        Returns the nyquist curve calculated from the model parameters
        and the measured curve from the EIS data.
    """
    psmodel = self.get_psmodel(data=data)
    curves = psmodel.GetNyquist()
    calc, meas = (Curve(pscurve=pscurve) for pscurve in curves)
    return calc, meas

def get_psmodel(self, data: EISData) -> PalmSens.Fitting.Models.CircuitModel:
    """Get SDK Circuit model object."""
    psmodel = PalmSens.Fitting.Models.CircuitModel()
    psmodel.SetEISdata(data._pseis)
    psmodel.SetCircuit(self.cdc)
    psmodel.SetInitialParameters(self.parameters)
    return psmodel

def plot_bode(self, data: EISData) -> figure.Figure:
    """Make bode plot using matplotlib.

    Parameters
    ----------
    data : EISData
        Input EIS data.

    Returns
    -------
    fig : fig.Figure
        Returns matplotlib figure object. use `fig.show()` to render plot.
    """
    import matplotlib.pyplot as plt

    calc_z, meas_z = self.get_bode_z(data=data)
    calc_ph, meas_ph = self.get_bode_phase(data=data)
    fig, ax1 = plt.subplots()
    _ = ax1.set_title('Bode plot')
    _ = ax1.set_xscale('log')

    _ = calc_z.plot(ax=ax1, legend=False, color='C0')
    _ = meas_z.plot(ax=ax1, marker='^', linestyle='None', color='C0', legend=False)

    ax2 = ax1.twinx()
    _ = calc_ph.plot(ax=ax2, legend=False, color='C1')
    _ = meas_ph.plot(ax=ax2, marker='^', linestyle='None', color='C1', legend=False)

    h1, l1 = ax1.get_legend_handles_labels()
    h2, l2 = ax2.get_legend_handles_labels()

    _ = ax2.legend(h1 + h2, l1 + l2, loc='upper right')

    return fig

def plot_nyquist(self, data: EISData) -> figure.Figure:
    """Make nyquist plot using matplotlib.

    Parameters
    ----------
    data : EISData
        Input EIS data.

    Returns
    -------
    fig : fig.Figure
        Returns matplotlib figure object. use `fig.show()` to render plot.
    """
    import matplotlib.pyplot as plt

    calc, meas = self.get_nyquist(data=data)
    fig, ax = plt.subplots()
    _ = ax.set_title('Nyquist plot')

    _ = calc.plot(ax=ax)
    _ = meas.plot(ax=ax, marker='^', linestyle='None')

    return fig