{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Short demo of interactive fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e09f787c7d94549a40efbc670e9eec7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(), VBox(children=(HBox(children=(Button(description='Fit', style=ButtonStyle()), ToggleB…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from iminuit import Minuit, cost\n",
    "\n",
    "truth = 100., 200., 0.3, 0.1, 0.7, 0.2\n",
    "\n",
    "def scaled_cdf(xe, n1, n2, mu1, sigma1, mu2, sigma2):\n",
    "    return n1 * norm.cdf(xe, mu1, sigma1) + n2 * norm.cdf(xe, mu2, sigma2)\n",
    "\n",
    "xe = np.linspace(0, 1)\n",
    "m = np.diff(scaled_cdf(xe, *truth))\n",
    "n = np.random.default_rng(1).poisson(m)  # generate random histogram\n",
    "\n",
    "c = cost.ExtendedBinnedNLL(n, xe, scaled_cdf)\n",
    "m = Minuit(c, *truth)\n",
    "\n",
    "m.interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "python3",
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  "vscode": {
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