Re-scan until fit achieves desired confidence

import matplotlib.pyplot as plt
import numpy as np
import lmfit
from bluesky.plans import (subs_decorator, abs_set, trigger_and_read,
                           run_decorator, stage_decorator)
from bluesky.callbacks import LiveFit, LiveFitPlot, LivePlot
from bluesky.examples import motor, noisy_det


def errorbar(lmfit_result, param_name):
    # width of 95% conf interfal:
    ci = lmfit_result.conf_interval()
    return ci[param_name][-2][1] - ci[param_name][1][1]


def gaussian(x, A, sigma, x0):
    return A * np.exp(-(x - x0)**2 / (2 * sigma**2))


model = lmfit.Model(gaussian)
guess = {'A': 10,
         'x0': 1,
         'sigma': lmfit.Parameter('sigma', 3, min=0)}


def scan_gaussian(detectors, motor, start, stop, num):
    global lf
    main_detector = detectors[0]
    main_motor_field, *_ = motor.describe()
    lf = LiveFit(model, main_detector.name, {'x': main_motor_field}, guess)
    lfp = LiveFitPlot(lf, color='r', ax=plt.gca())
    lp = LivePlot(main_detector, main_motor_field,
                  linestyle='none', marker='o', ax=plt.gca())

    @subs_decorator([lfp, lp])
    @stage_decorator(list(detectors) + [motor])
    @run_decorator()
    def plan():
        while True:
            for step in np.linspace(start, stop, num):
                yield from abs_set(motor, step, wait=True)
                yield from trigger_and_read(list(detectors) + [motor])
            err = errorbar(lf.result, 'sigma')
            if err < .03:
                break

    yield from plan()


# Example usage:

RE(scan_gaussian([noisy_det], motor, -1, 1, 200))