`correctDrift` fails

After the 📞 call, I had another look into the function and the associated error, the following code snippets are direct copies from the code for illustration purposes:

Function _driftFit

def _driftFit(x, shift_target, cal_mean, driftModel):
    x_index = x.index - x.index[0]
    x_data = x_index.total_seconds().values
    x_data = x_data / x_data[-1]
    y_data = x.values
    origin_mean = np.mean(y_data[:cal_mean])
    target_mean = np.mean(y_data[-cal_mean:])

    dataFitFunc = functools.partial(driftModel, origin=origin_mean, target=target_mean)
    # if drift model has free parameters:
    try:
        # try fitting free parameters
        fit_paras, *_ = curve_fit(dataFitFunc, x_data, y_data)
        data_fit = dataFitFunc(x_data, *fit_paras)
        data_shift = driftModel(
            x_data, *fit_paras, origin=origin_mean, target=shift_target
        )
    except RuntimeError:
        # if fit fails -> make no correction
        data_fit = np.array([0] * len(x_data))
        data_shift = np.array([0] * len(x_data))
    # when there are no free parameters in the model:
    except ValueError:
        data_fit = dataFitFunc(x_data)
        data_shift = driftModel(x_data, origin=origin_mean, target=shift_target)

    return data_fit, data_shift

Depending on the input (I always triggered the failure with x of length 1) the call to curve_fit(dataFitFunc, x_data, y_data) fails with a ValueError and execution jumps to dataFitFunc(x_data). In my use case the argument to dataFitFunc is the function lib.ts_operators.expDriftModel shown below:

def expDriftModel(x, c, origin, target):
    c = abs(c)
    b = (target - origin) / (np.exp(c) - 1)
    return expModelFunc(x, origin, b, c)

This function however takes 4 arguments but only gets passed a single one, which finally raises a TypeError.

So I guess the main problem are not input series of length 1 but rather the faulty call to dataFitFunc. Could you please have a look into this @luenensc ?