.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_cookbook_adaptive_fitting.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_cookbook_adaptive_fitting.py:


Re-scan until fit achieves desired confidence
*********************************************

Problem
=======

Scan a peak and, in real time, fit Gaussian model to the data. Repeatedly
re-scan the same region until the uncertainty in the Gaussian width parameter,
sigma, is below some threshold.

Approach
========

Use :func:`bluesky.callbacks.LiveFit` and :func:`bluesky.callbacks.LiveFitPlot` to perform and
visualize a non-linear least-squared fit.

Normally we would use :func:`plans.scan` to perform the 1D scan. In this case,
we need something more sophisticated to incorporate adaptive logic that
continues the scan until the fit attains sufficient confidence in sigma. We
write our scan logic using the lower-level plans :func:`bluesky.plans.abs_set` and
:func:`bluesky.plans.trigger_and_read`.

Example Solution
================



.. image:: /cookbook/images/sphx_glr_adaptive_fitting_001.png
    :class: sphx-glr-single-img





.. code-block:: default

    import matplotlib.pyplot as plt
    import numpy as np
    import lmfit
    import bluesky.plans as bp
    import bluesky.preprocessors as bpp
    import bluesky.plan_stubs as bps
    import bluesky.callbacks as bc
    import bluesky.utils as bu
    from ophyd.sim import motor, SynGauss
    from bluesky import RunEngine

    # Do this if running the example interactively;
    # skip it when building the documentation.
    import os
    if 'BUILDING_DOCS' not in os.environ:
        from bluesky.utils import install_qt_kicker  # for notebooks, qt -> nb
        install_qt_kicker()
        plt.ion()

    noisy_det = SynGauss('noisy_det', motor, 'motor', center=0, Imax=100,
                         noise='poisson', sigma=1)

    RE = RunEngine({})


    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, *, ax=None,
                      err_thresh=0.03):

        if ax is None:
            ax = plt.gca()
        main_detector = detectors[0]
        main_motor_field, *_ = motor.describe()
        lf = bc.LiveFit(model, main_detector.name, {'x': main_motor_field}, guess)
        lfp = bc.LiveFitPlot(lf, color='r', ax=ax)
        lp = bc.LivePlot(main_detector, main_motor_field,
                         linestyle='none', marker='o', ax=ax)

        @bpp.subs_decorator([lfp, lp])
        @bpp.stage_decorator(list(detectors) + [motor])
        @bpp.run_decorator()
        def plan():
            while True:
                for step in np.linspace(start, stop, num):
                    yield from bps.abs_set(motor, step, wait=True)
                    yield from bps.trigger_and_read(list(detectors) + [motor])
                    yield from bps.checkpoint()

                err = errorbar(lf.result, 'sigma')
                if err < err_thresh:
                    break

        yield from plan()


    RE(scan_gaussian([noisy_det], motor, -5, 5, 100, ax=plt.gca()))


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  58.207 seconds)


.. _sphx_glr_download_cookbook_adaptive_fitting.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: adaptive_fitting.py <adaptive_fitting.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: adaptive_fitting.ipynb <adaptive_fitting.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_