{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Live Nonlinear Fitting\n",
    "\n",
    "## Configuration\n",
    "\n",
    "This code would normally go in a script automatically run at startup. The user would not have to worry about this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import lmfit\n",
    "from bluesky import RunEngine\n",
    "from bluesky.plans import scan\n",
    "from ophyd.sim import motor, noisy_det\n",
    "from bluesky.callbacks import LiveFit, LivePlot, LiveFitPlot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "RE = RunEngine({})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Acquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots() # As before, create plot to be able to follow the progress of the fit during the scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define Gaussian function and save it as a model using lmfit\n",
    "def gaussian(x, A, sigma, x0):\n",
    "    return A*np.exp(-(x - x0)**2/(2 * sigma**2))\n",
    "\n",
    "model = lmfit.Model(gaussian)\n",
    "\n",
    "# The initial guess (gray on the plot) is just a starting point from which the fitting algorithm works.\n",
    "init_guess = {'A': 2,\n",
    "              'sigma': lmfit.Parameter('sigma', 3, min=0),\n",
    "              'x0': -0.2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LiveFit example\n",
    "lf = LiveFit(model, 'noisy_det', {'x': 'motor'}, init_guess)\n",
    "\n",
    "# now add the plot...\n",
    "lfp = LiveFitPlot(lf, color='r', ax=ax)\n",
    "\n",
    "RE(scan([noisy_det], motor, -1, 1, 100), lfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.gcf() # Unfortunately, since we do not see the motor positions relative to the fit,\n",
    "          # we cannot evaluate how well the fit worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show motor position vs. fit during the scan\n",
    "\n",
    "fig, ax = plt.subplots()  # explitly create figure to follow fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfp = LiveFitPlot(lf, ax=ax, color='r')\n",
    "lp = LivePlot('noisy_det', 'motor', ax=ax, marker='o', linestyle='none') # plotting the noisy_det scalar object\n",
    "\n",
    "RE(scan([noisy_det], motor, -1, 1, 100), [lfp, lp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that from imperfect initial conditions, the fit now matches the data well\n",
    "plt.gcf() # Display a snapshot of the current state of the figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the final fit parameters\n",
    "lf.result.best_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. Try a different user-defined peak-like function to use as a model."
   ]
  }
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