docs/examples/notebooks/toys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pyhf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "plt.rcParams.update({\"font.size\": 14})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running Monte Carlo simulations (toys)\n",
    "\n",
    "Finding the (expected) significance can involve costly Monte Carlo calculations (\"toys\"). The asymptotic approximation described in the paper by Cowan, Cranmer, Gross, Vitells: *Asymptotic formulae for likelihood-based tests of new physics* [[arXiv:1007.1727](https://arxiv.org/abs/1007.1727)] provides an alternative to these computationally expensive toy calculations.\n",
    "\n",
    "This notebook demonstrates a reproduction of one of the key plots in the paper using `pyhf`.\n",
    "\n",
    "![Figure 5 from arXiv:1007.1727 for background-only ($mu=0$) and background+signal ($mu=1$) are shown for two different test statistics, comparing the asymptotic and toy calculations.](img/1007.1727.fig5.png)\n",
    "\n",
    "## Counting Experiment\n",
    "\n",
    "Consider a counting experiment where one observes $n$ events, following a Poisson distribution with expectation value\n",
    "\n",
    "$$\n",
    "E[n] = \\mu s + b \n",
    "$$\n",
    "\n",
    "with $s$ expected signal events and $b$ expected background events, and signal strength parameter $\\mu$. Follow up in the paper to understand more of the math behind this as the notation is being introduced here. What we will show is the distribution of the (alternative) test statistic $q_1$ ($\\tilde{q}_1$) calculated under the assumption of the nominal signal model $(\\mu=1)$ for data corresponding to the strength parameter of the background-only $(\\mu' = 0)$ and signal+background $(\\mu' = 1)$ model hypotheses. For the rest of this notebook, we'll refer to the background-like model $\\mu'=0$ and the signal-like model $\\mu'=1$.\n",
    "\n",
    "The first thing we will do is set up the `pyhf` model with $s=6$ signal events and $b=9$ background events (adding a Poisson uncertainty on the background)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels: ['singlechannel']\n",
      "Samples: ['background', 'signal']\n",
      "Parameters: ['mu', 'uncorr_bkguncrt']\n"
     ]
    }
   ],
   "source": [
    "signal = 6\n",
    "background = 9\n",
    "background_uncertainty = 3\n",
    "model = pyhf.simplemodels.uncorrelated_background(\n",
    "    [signal], [background], [background_uncertainty]\n",
    ")\n",
    "print(f\"Channels: {model.config.channels}\")\n",
    "print(f\"Samples: {model.config.samples}\")\n",
    "print(f\"Parameters: {model.config.parameters}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a single channel with two samples: `signal` and `background`. `mu` here is the signal strength. Next, we need to define the background-like and signal-like p.d.f.s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background parameters: [('mu', 0.0), ('uncorr_bkguncrt', 1.0)]\n",
      "Signal parameters: [('mu', 1.0), ('uncorr_bkguncrt', 1.0)]\n"
     ]
    }
   ],
   "source": [
    "# mu' = 0: background-like\n",
    "pars_bkg = model.config.suggested_init()\n",
    "pars_bkg[model.config.poi_index] = 0.0\n",
    "print(f\"Background parameters: {list(zip(model.config.parameters, pars_bkg))}\")\n",
    "\n",
    "# mu' = 1: signal-like\n",
    "pars_sig = model.config.suggested_init()\n",
    "pars_sig[model.config.poi_index] = 1.0\n",
    "print(f\"Signal parameters: {list(zip(model.config.parameters, pars_sig))}\")\n",
    "\n",
    "# make the pdfs\n",
    "pdf_bkg = model.make_pdf(pyhf.tensorlib.astensor(pars_bkg))\n",
    "pdf_sig = model.make_pdf(pyhf.tensorlib.astensor(pars_sig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the parameter of interest, $\\mu'$ is set to zero for background-like models and to one for signal-like models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Toys by Hand\n",
    "\n",
    "Now that we've built our pdfs, we can go ahead and randomly (Monte Carlo) sample them. In this case, we want to \"run 10,000 pseudo-experiments\" (or \"throw toys\" as particle physicists would say). This means to draw $n=10000$ samples from the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 2)\n",
      "(10000, 2)\n"
     ]
    }
   ],
   "source": [
    "# note: pdf.sample takes in a \"shape\" N=(10000,) given the number of samples\n",
    "n_samples = 10000\n",
    "\n",
    "# mu' = 0\n",
    "mc_bkg = pdf_bkg.sample((n_samples,))\n",
    "# mu' = 1\n",
    "mc_sig = pdf_sig.sample((n_samples,))\n",
    "\n",
    "print(mc_bkg.shape)\n",
    "print(mc_sig.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that the shape for `mc_bkg` and `mc_sig` is not the input shape we passed in `(10000,)` but rather `(10000,2)`! Why is that? The HistFactory model is a product of many separate pdfs: Poissons representing the main model, and Gaussians representing the auxiliary measurements. In `pyhf`, this is represented under the hood as a [`Simultaneous`](https://scikit-hep.org/pyhf/_generated/pyhf.probability.Simultaneous.html) pdf of the main model and the auxiliary model &mdash; hence the second dimension.\n",
    "\n",
    "We can now calculate the test statistic distributions for $\\tilde{q}_1$ given the background-like and signal-like models. This inference step (running the toys) will take some time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_pars = model.config.suggested_init()\n",
    "par_bounds = model.config.suggested_bounds()\n",
    "fixed_params = model.config.suggested_fixed()\n",
    "\n",
    "qtilde_bkg = pyhf.tensorlib.astensor(\n",
    "    [\n",
    "        pyhf.infer.test_statistics.qmu_tilde(\n",
    "            1.0, mc, model, init_pars, par_bounds, fixed_params\n",
    "        )\n",
    "        for mc in mc_bkg\n",
    "    ]\n",
    ")\n",
    "qtilde_sig = pyhf.tensorlib.astensor(\n",
    "    [\n",
    "        pyhf.infer.test_statistics.qmu_tilde(\n",
    "            1.0, mc, model, init_pars, par_bounds, fixed_params\n",
    "        )\n",
    "        for mc in mc_sig\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Toys using Calculators\n",
    "\n",
    "However, as you can see, a lot of this is somewhat cumbersome as you need to carry around two pieces of information: one for background-like and one for signal-like. Instead, `pyhf` provides a statistics calculator API that both simplifies and harmonizes some of this work for you.\n",
    "\n",
    "This calculator API allows you to:\n",
    "\n",
    "- compute a test statistic for the observed data\n",
    "- provide distributions of that test statistic under various hypotheses\n",
    "\n",
    "These provided distributions additionally have extra functionality to compute a *p*-value for the observed test statistic.\n",
    "\n",
    "We will create a toy-based calculator and evaluate the model $(\\mu=1)$ for data simulated under background-like hypothesis $(\\mu'=0)$ and under the signal-like hypothesis $(\\mu'=1)$. This will compute $\\tilde{q}_1$ for both values of $\\mu'$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                       \r"
     ]
    }
   ],
   "source": [
    "toy_calculator_qtilde = pyhf.infer.utils.create_calculator(\n",
    "    \"toybased\",\n",
    "    model.expected_data(pars_sig),\n",
    "    model,\n",
    "    ntoys=n_samples,\n",
    "    test_stat=\"qtilde\",\n",
    ")\n",
    "qtilde_sig, qtilde_bkg = toy_calculator_qtilde.distributions(1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute $q_1$, we just need to alleviate the bounds to allow for $\\mu$ (the parameter of interest) to go below zero. Right now, it is set to the default for `normfactor` which is `[0,10]` &mdash; a very sensible default most of the time. But if the $\\hat{\\mu}$ (the maximum likelihood estimator for $\\mu$) for our model is truly negative, then we should allow the fit to scan negative $\\mu$ values as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old bounds: [(0, 10), (1e-10, 10.0)]\n",
      "New bounds: [(-10, 10), (1e-10, 10.0)]\n"
     ]
    }
   ],
   "source": [
    "qmu_bounds = model.config.suggested_bounds()\n",
    "print(f\"Old bounds: {qmu_bounds}\")\n",
    "qmu_bounds[model.config.poi_index] = (-10, 10)\n",
    "print(f\"New bounds: {qmu_bounds}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run the toys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Signal-like:   0%|          | 0/10000 [00:00<?, ?toy/s]/Users/jovyan/pyhf/src/pyhf/tensor/numpy_backend.py:253: RuntimeWarning: invalid value encountered in log\n",
      "  return n * np.log(lam) - lam - gammaln(n + 1.0)\n",
      "                                                                       \r"
     ]
    }
   ],
   "source": [
    "toy_calculator_qmu = pyhf.infer.utils.create_calculator(\n",
    "    \"toybased\",\n",
    "    model.expected_data(model.config.suggested_init()),\n",
    "    model,\n",
    "    par_bounds=qmu_bounds,\n",
    "    ntoys=n_samples,\n",
    "    test_stat=\"q\",\n",
    ")\n",
    "qmu_sig, qmu_bkg = toy_calculator_qmu.distributions(1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've ran the toys, we can make the key plots 🙂."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "for ax in axes:\n",
    "    ax.set_xticks(np.arange(0, 10))\n",
    "ax0, ax1 = axes.flatten()\n",
    "\n",
    "bins = np.linspace(0, 10, 26)\n",
    "\n",
    "ax0.hist(\n",
    "    qmu_sig.samples,\n",
    "    bins=bins,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    "    label=\"$f(q_1|1)$ signal-like\",\n",
    "    linewidth=2,\n",
    ")\n",
    "ax0.hist(\n",
    "    qmu_bkg.samples,\n",
    "    bins=bins,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    "    label=\"$f(q_1|0)$ background-like\",\n",
    "    linewidth=2,\n",
    ")\n",
    "ax0.set_xlabel(r\"(a) $q_1$\", fontsize=18)\n",
    "ax0.set_ylabel(r\"$f\\,(q_1|\\mu')$\", fontsize=18)\n",
    "ax0.set_title(r\"Test statistic $(q_1)$ distributions\")\n",
    "ax0.legend()\n",
    "\n",
    "ax1.hist(\n",
    "    qtilde_sig.samples,\n",
    "    bins=bins,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    "    label=r\"$f(\\tilde{q}_1|1)$ signal-like\",\n",
    "    linewidth=2,\n",
    ")\n",
    "ax1.hist(\n",
    "    qtilde_bkg.samples,\n",
    "    bins=bins,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    "    label=r\"$f(\\tilde{q}_1|0)$ background-like\",\n",
    "    linewidth=2,\n",
    ")\n",
    "ax1.set_xlabel(r\"(b) $\\tilde{q}_1$\", fontsize=18)\n",
    "ax1.set_ylabel(r\"$f\\,(\\tilde{q}_1|\\mu')$\", fontsize=18)\n",
    "ax1.set_title(r\"Alternative test statistic $(\\tilde{q}_1)$ distributions\")\n",
    "ax1.legend()\n",
    "\n",
    "\n",
    "plt.setp(axes, xlim=(0, 9), ylim=(1e-3, 2), yscale=\"log\")\n",
    "fig.set_size_inches(14, 6)\n",
    "fig.tight_layout(pad=2.0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "00734572c9b14d4bb05151197307ec05": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
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     "0119a8a68a6c43919c0a9beeffe5e61f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
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     "01e50bc763ed4f94af8f7601237f5788": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
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     "02287564b426420a8937b34cc0c109a2": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9c2066ef6f9d434c8c64580d114587e6",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "0257c302e4944299bbd2e0da679bd840": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "02ac8de7054048b2bbf9197d69c9b5bd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc1_weight_var1",
       "layout": "IPY_MODEL_af501f32925b4ec7b3ccaf7bf2db3429",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_63acd75c082b44b091f13e37eec44ec8"
      }
     },
     "035df264797f42a8842b6cc2e3709423": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_4e50593f384745e18426450c6fd936fd",
        "IPY_MODEL_43ca77bdfa34414e948229a09a2c556d",
        "IPY_MODEL_8e509a8016894a93a89cd2ffcc3b54fe",
        "IPY_MODEL_f7a468370d6a414a9f72493adae47927",
        "IPY_MODEL_7611ddd19e2043a69c6ee991792f37fe",
        "IPY_MODEL_37a404df72f24f2c86928ade8fc26fe0"
       ],
       "layout": "IPY_MODEL_b029a789d30145408162dda832c1b8a1"
      }
     },
     "036d67f8ba724f87a4fe6f025de79bec": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "0394bef00682412284aea20f10601cdd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "03ec3889ec1c483abec6dcc8562a5d43": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "048a5d709ba84138a32c889441fdcc80": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_fb2ea65a42124f89999c4cc42c7ff596",
       "outputs": [
        {
         "data": {
          "image/png": 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       "outputs": [
        {
         "data": {
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5aa/W3nhPzfVYIbaOYWRs+bEmCwCQafVKJMR1021/Kkla8fhdNbeOWXFpr2667U81HPP5FlMQNG7pgaTXb1VGxirBrTIyVukTwmAkCwCQaXFLJMTVyLRdWtvlJL11TDNtqtxKCFloOq2yRxiAeIrForq6uua0LVQ0OI6403YhFsjHlfQeglzZmA6mC9FUGtkj7EKXaLPXINA84pZIaEScabs0t8tJeuuYlZeurzlFmsVNlVuJedXGqmnZvHmz79mzJ+1uoAnkcrmaew0ODAxoeHh49v78MCaVP/lWqrkTstoDdbKyKa3NpBtdQzXy8C2Sav2NNA088EIifVou89dkSeWRsYUW3jez4e0fD/4aZrbX3TfXO47pQjSVuAtg07pEG0DrSHpdVJoaubIRyWG6EE2lv7+/5kjW/AWwSV2NBKB99Vx3R83Rn8Wui0pbs2yq3EoIWWgqcfcIixvG0D5enJzUI8eP6cjMjN7Z0aH71vfq5u7utLvVluLWokprWrEi6XVRaD9MF6Kp5PN5DQ0NaWBgQGamgYGB2XVW1ZK+GgnN6faHOnT7Qx366G+d0gNvHNHhmRm5pMMzM3rgjSP66G+dOm8fxBcnJ7Xl5wd1zYGfacvPD+rFycl0Oo9FSfrq44uvuV4bP/MNDTzwgjZ+5hsELDSEkSw0nXw+X/fKnhBXI6F5je8cl5+du4DZz7rGd46r59qe2bYXJyf1xfEjOh1dEHR4ZkZfHD8iSYx6ZVhlZGz++RsZGdF/+fSn9e9f+O/l8xcVIwWWCyELLWX+VYOXfOUSXaNrJEnbZ7Zr+47taXQLKZs+MR2r/ZHjx2b/QFecdtcjx48RspoA5w9ZQ8gC0PI613XWDFqd6zrn3D8yU7vkw0LtCCvu2q3KdjmHH76l5uOHZ96aPQZYTqzJAtDy+rb2yVbZnDZbZerb2jen7Z0dtT93LtSObGmlkgtoDbxzoClQPBRLUVl3Nb5zXNMnptW5rlN9W/vmrMeSpPvW985Z0yNJa8x03/reZe0vFqfVSi6g+RGyALSFnmt7zgtV81XW7VDqIay4U3d/+9dfaOh5KbmArCFkAUCVm7u7CVUZsZh1VBTcRJYQsgCgzSVdqJXCr0AZIQsA2ljStcGoNQacQ8gC0PbmV31fyLN/3HqlHJKuLUWtKuAcQhYAtLGka4M1+nynXtvNQnW0LOpkAcAitMoeh0nXBmvk+U69tltvfO9reuvkMUmut04e0xvf+5pOvbZ7Ua8NZA0jWQiuVCotuIcg9a/QjNJed5TkwvKkaoNd/cl/lyT9yb4OFV6Y0VRVgf2uTulPbunQ1ZvKx+h0+dvEy0/NqWklST5zRhMvP8VoFloCIQtBlUolFQoFTU1NSSpv2FooFCSJzZrRtNJcd5R0wKtXGyzuerU393+zfOMK6aL/tFtnqqYAL7ruDg1ecb0GT8/9mbdOHq/5XAu1A82GkIWgBgcHZwNWxdTUlAYHBwlZaFpp7nEYIuBdqDbYvn8bjfUcuarbcWtVrbx0fTRVeH470AoIWQhqdLT2G/RC7UCWVUZ1Ou6vveF0x7rO2WOqr0RMcnpvuQNe7vQ3gzyvxDY4aH2ELATV39+vkZGRmu3zTbwyUXdvOSAL+rb2aezJMfnZcyNKtTaclpKf3ntnR4cO1whUzbiJNdvgoNU1328lmkqxWJyzJkuSurq6VCwW5xw38crEnD9a0yemNfbkmCQRtJA5cTeclpKf3mu1TazZBgetjJCFIKqvGlz7B2s1vXN69o/R2q1rtX1mu7bv2D57zPjO8TmjApLkZ13jO8cJWcikehtOV6YND2+rPY13eGYm9qJy6dz0I5tYA82j7m+4mV0u6SlJfZJc0pC7P2ZmayU9o/J6x2FJt7v7L83MJD0m6WOSpiRtc/cfhek+mkG9P0aSaq5vqW6Pu/h207vOn4YE0tS5rvb6rc51nYt+zribWLOHIJCuOB+jZiTd7+4/MrNLJO01s5ckbZO0y923m9mDkh6U9ICkmyRdEX39hqTHo+/AgkL8IQKyoJH1W0mqtxYs9ija/nM3qc4ONKZuxXd3P1wZiXL3N1X+ldsg6VZJO6LDdki6Lbp9q6SnvOwHknrM7LLEe46W0re1T7bK5rQtxx8iILSea3u0YduG2Q8Mnes6tWHbhpqjuxOvTOjA/Qf06rZXdeD+A5p4ZWLRr3uhtWCLQXV2oHENrckys5yk90v6oaQ+dz8cPXRE5elEqRzAXq/6sUNR22Gh6SVdoX12GvAyqXTzag3uOqPRSVd/t6m4ZbXyl52U/u1koq8JLLc4U+b1Lv5oZP2WVH8tWKP1r6jODjQu9m+tmV0saaekz7n7yfLSqzJ3dzPzBX+49vMVJBWk2pfzo/3kN61SftOqtLsBpCLpiz/qTcE3Wv+K6uxA42KFLDPrVDlgldz921HzuJld5u6Ho+nAo1H7mKTLq358Y9Q2h7sPSRqSpM2bNzcU0JANF6prFfdTctJYII9mVe/ij0YlvRaM6uxA4+JcXWiSnpC0393/rOqh5yXdKWl79P25qvZ7zOxplRe8T1ZNKyKDFjMF2C51rRoJiwQ3LEXSF380Ussr1vNRnR1oWJyRrA9J+rSkfWb2k6jtj1QOV8+a2d2SRiTdHj32XZXLNxxUuYTDXYn2GKmqhI7cM2/WnNpY+cyY9l2W/TVUIUbaGEXDUiQ18jTn/8PLJN1zkaSLooZzaxxzDfaP6uxA4+qGLHf/B0m2wMNbahzvkj67xH5hGS0mcIxO1p7hXagdQG1xL/6IG85D7jVIdXagMVR8x6L0d5tGagSq/u6F8jiAerj4A2gthCwsSnHLahVeOK2pqiUkXZ3ldgDJa7TkAoD0EbKwKJVP2+dNbfApvC7WbqFa3Om94TWfCtwTAEkjZGHRmNoAlk/ItVYAwqi7rQ4AAAAaR8gCAAAIgOlCnKe07yxrrZoIa7yyiYXqAAhZmKO07+ycqwZHJl2FF05LEkELAIAGELIwx+CuM3PKMkjS1HS5nZC1vNLa/xEAkAzWZGEOKrkDAJAMRrJaWNyNn/dV3aaSO5AMSi4AIGS1sMVMN1HJHVhYI79TuXDdANAkCFltIu4Vg1Ryb11Jr/HiakUAuDBCVkaVSiUNDg5qdHRU/f39KhaLyufzi3uuBq8YpJI72g0XGQAIgZCVQaVSSYVCQVNTU5KkkZERFQoFSVI+n5+z1mrilQmN7xzX9Ilpda7rVN/WPvVc2yPp3ForrhhE2tKq5UV4ApAmQlYGDQ4OzgasiqmpKQ0ODiqfz8/+4aiMUE1HAWr6xLTe+MYhbT9+fE544opBhJBmgCE8AWgGhKwMGh2t/QdkfnvcESquGASSwRWDABpBnawM6u+vPWUyvz3uCFVxy2p1dc49hisGAQAIi5CVQcViUV1dXXPaurq6VCwW57QtNBI1vz2/aZWGblmjgW6TSRroNg3dsob1WAAABMR0YQZVriKsd3VhIzWtuGIQAIDlRcjKqHw+X7dkAzWtAADILkJWk2OEClg6FrQDCIGQBSAzKM0AoJUQsjJkMRs6AwCAbCJkZQif4oFkMQ0IIE2UcAAAAAiAkAUAABAAIQsAACAA1mQBaDqstQLQDBjJAgAACICQBQAAEAAhCwAAIADWZC2HL3en3QMAALDMGMkCAAAIgJEsAMHFvRpweM2nAvcEAJYPI1nLrLTvrHKPvqkVXzmp3KNvqrTvbNpdAgAAATCStYxK+86q8MJpTU2X749MugovnJYk5TetSrFnAAAgaYxkLaPBXWdmA1bF1HS5HQAAtBZGspaiwasGRye9oXag3VDJHUArYSRrGfV3W0PtAACgeTGStYyKW1bPWZMlSV2d5Xag2TDqBAAXxkjWMspvWqWhW9ZooNtkkga6TUO3rGHROwAALYiQVUepVFIul9OKFSuUy+VUKpWW9Hz5Tas0/LlL9PaXLtXw5y4hYAEA0KLqThea2dcl3SzpqLu/N2pbK+kZSTlJw5Jud/dfmplJekzSxyRNSdrm7j8K0/XwSqWSCoWCpqamJEkjIyMqFAqSpHw+P/fYfWc1uOuMRidd/d2m4pbVBCg0JaYBASAZcUaynpR047y2ByXtcvcrJO2K7kvSTZKuiL4Kkh5PppvpGBwcnA1YFVNTUxocHJzTVql/NTLpcp2rf0WhUQAA2lfdkOXuL0t6Y17zrZJ2RLd3SLqtqv0pL/uBpB4zuyypzi6bL3dLX+7W6MhIzYdHR0bmlG+g/hUAAJhvsWuy+tz9cHT7iKS+6PYGSa9XHXcoajuPmRXMbI+Z7Tl27NgiuxFW3JIL1L8CAADzLXnhu7u7pIbThLsPuftmd9/c29u71G4EUdyyWl2dc9tqlVyg/hUAAJhvsSFrvDINGH0/GrWPSbq86riNUVtTiltyIW4YAwAA7WOxxUifl3SnpO3R9+eq2u8xs6cl/YakyappxaaU37Sq7lWClce5uhAAAFTEKeHwV5I+Imm9mR2S9CWVw9WzZna3pBFJt0eHf1fl8g0HVS7hcFeAPmdSnDAGhEDJBQDIprohy91/f4GHttQ41iV9dqmdAgAAaHZUfAcAAAiAkAUAABDAYhe+N5+q4qEAAAChMZIFAAAQACELAAAggPaZLgSaDKUZAKC5EbKAZUZ4AoD2wHQhAABAAIQsAACAAAhZAAAAAbAmC0gIa60AANUYyQIAAAigbUNWad9Z5R59Uyu+clK5R99Uad/ZtLsEAABaSFtOF5b2nVXhhdOami7fH5l0FV44LUnKb1qVYs+QNUwBAgAWqy1HsgZ3nZkNWBVT0+V2AACAJLRlyBqd9IbaAQAAGtWWIau/2xpqBwAAaFRbhqziltXq6pzb1tVZbgcAAEhCW4as/KZVGrpljQa6TSZpoNs0dMsaFr0DAIDEtOXVhVI5aBGq2hdXDQIAQmvbkIXmEjcUDa/5VOCeAAAQDyELLYURKgBAVrTlmiwAAIDQGMlCqhh5AgC0KkayAAAAAmAkC0EwQgUAaHeMZAE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      }
     },
     "11a2f3da851b4ba9b8e2056b4eddac23": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "11adecc1eea040f2bdbf60c2792ce783": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "1236bfc27c3f448ebf7542762a89d8ba": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "128e733d0f7a42db87290bdea538362f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ef3efa606e4049a9b26ba2023bc8c038",
       "outputs": [
        {
         "data": {
          "image/png": 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      "model_name": "LayoutModel",
      "state": {}
     },
     "1c4f9a9155f447debf935ca671cd04e5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "1cce1d6817be435a8f0497ba55f3ddb7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "1ce6a9beb61343648222ff4288193562": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "1d4d01e6953443f3906dfcac001a036b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9e1308c73fce44ebb6115cb22740cd80",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 720x360 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "1d785d0aa60d4d2fb523d513196574ea": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "SigXsecOverSM",
       "layout": "IPY_MODEL_1c4f9a9155f447debf935ca671cd04e5",
       "max": 10,
       "style": "IPY_MODEL_bbc2ed6abbaa43649b976e824f78230d",
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      }
     },
     "1d7a1943ffeb409f915218a17372c876": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "1dcb9d23d8ae49958bc7dacd5e40c2a9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
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      }
     },
     "1e92efacfbb64ab4a743a5029acb2097": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc2_shape_conv",
       "layout": "IPY_MODEL_d0ebb7a95b2e4e90bc12bb104e168098",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_d771b11888b24d89a05b2341566d3d17"
      }
     },
     "1ec2b3e4557c4736b95459febd57c2b8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "1ed4ca66f4e84483b04571d19aa33bcd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc1_shape_conv",
       "layout": "IPY_MODEL_b6e330c2f1a94ce0b1f50af636732edb",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_0e749886a1394ded9fc70edfd555225d"
      }
     },
     "1fb94012b1b1429e90ef43eca09de1fe": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "206d520a53a74b808d04650746b31e19": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "20ee9667b2124585b3db54b301f976aa": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "213e31b65836408b9a2755096627b60d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_490ad5047c0a4c8a8f108e969ff6e79e",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
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        {
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\n",
          "text/plain": "<Figure size 720x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "2ead0ec39df249ba94e2cf3cf299b0d1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "2ec94cf60fcc4967b89a5ec9cfbe9d08": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc1_weight_var1",
       "layout": "IPY_MODEL_57e85869b01442e29e02bb809bd60aff",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_c0be84354368415ba32520d8ccebd0c5"
      }
     },
     "2fee4766c9ec42d9a54dbc830da3b015": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "301ed8eefd6f42ba9d8aea9b73150cc3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "304e2ba55304457fa4ddb38730b4e773": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3714c80fb3864acb96e0de2eaf6041c2",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 720x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "38c49877fde5420cbfa6f60a739c2c92": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc2_shape_conv",
       "layout": "IPY_MODEL_1a37fd50f9ca402bac9904add7a3a48b",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_0394bef00682412284aea20f10601cdd"
      }
     },
     "3947ca77bf664c81a81ed49083cb8e5f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "398ef30934fd4fb4b418951b9657e0ef": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3b3f93ba8c2f471eaddaed08b29195e6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3b5571ab8e3c4a52b350c67d32f7fa46": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3b91806b944b4e82a605cdebbcd7887b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e42c063910d64995bb9a11fde034af61",
       "outputs": [
        {
         "data": {
          "image/png": 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       "outputs": [
        {
         "data": {
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "5104e65ea9784a58b846fa426e3734b5": {
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      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc2_weight_var1",
       "layout": "IPY_MODEL_fc6affd680b94fd48e7440dcef8cc4fd",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_86df9cdf283b41e8bad874b3057a92d3"
      }
     },
     "5111dd2107c444e78707e22a04aa640c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "5233d42254a9464295681ea859284751": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
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       "layout": "IPY_MODEL_8e1754f0bd2f41bb81bec6449d35edca",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_910254b4cfa44dad9a81268677cfe99e"
      }
     },
     "52b0c6e2e4464d5589059e4866ee5da5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
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      "model_name": "IntSliderModel",
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     "63043379d0ce48a5820e6ff37f38c97f": {
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     "634599e8dead4727b7a953c97ffba925": {
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8d404d9c0b104cf4b6c6c786b01c02da",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
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     "635bbd3044f846e89943a3a9df39e0e2": {
      "model_module": "@jupyter-widgets/base",
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      "model_name": "LayoutModel",
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     "63acd75c082b44b091f13e37eec44ec8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
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      }
     },
     "63af36ecb2ed462bafea39a2a82e426e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "63ef3083daca434dbc676113321e7bbb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
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     },
     "646dd1095c094e018d1abb5d2b20494c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
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      }
     },
     "64c30f7d101341eb9a09480a671e808c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
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      }
     },
     "65302216142c4c16bb2125b8cb54ae6b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
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     "67103f9ba3174395b9c2e35104bfcd40": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_01e50bc763ed4f94af8f7601237f5788",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "67532340a66f4ac9bb375ed7a9c7b184": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_985180a860ad4d8dab3c4c0eb05ba202",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "75845cc8ca2d4308b9d8849cfe33fb86": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "mc1_shape_conv",
       "layout": "IPY_MODEL_4fcf2bbe3339417f856b8a7c32ade0d3",
       "max": 5,
       "min": -5,
       "step": 0.1,
       "style": "IPY_MODEL_5c5a4605a7e64530bf76a36646f52b7b"
      }
     },
     "7589dfcb2d494aed94a83c2827c317fb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "75c72c9620dd4645a324fb0e9fbf6054": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "75e421a2ab744297a179b889e8b9fca2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "7611ddd19e2043a69c6ee991792f37fe": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc2_shape_conv",
       "layout": "IPY_MODEL_52c1039261174439b55b0d55b62bb6f7",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_3219c6471ac24102aa35c494680d449a"
      }
     },
     "767947bb25bf4a01a61b85ee6cd44d19": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc2_weight_var1",
       "layout": "IPY_MODEL_2301dca9a16148809453509afea3c5af",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_1888089cd3424f958d6aeca209b04bc9"
      }
     },
     "769dce3ada684269985c1018d72949bc": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9fe036dd2a82497aa747aa2f23972511",
       "outputs": [
        {
         "data": {
          "image/png": 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5aa/W3nhPzfVYIbaOYWRs+bEmCwCQafVKJMR1021/Kkla8fhdNbeOWXFpr2667U81HPP5FlMQNG7pgaTXb1VGxirBrTIyVukTwmAkCwCQaXFLJMTVyLRdWtvlJL11TDNtqtxKCFloOq2yRxiAeIrForq6uua0LVQ0OI6403YhFsjHlfQeglzZmA6mC9FUGtkj7EKXaLPXINA84pZIaEScabs0t8tJeuuYlZeurzlFmsVNlVuJedXGqmnZvHmz79mzJ+1uoAnkcrmaew0ODAxoeHh49v78MCaVP/lWqrkTstoDdbKyKa3NpBtdQzXy8C2Sav2NNA088EIifVou89dkSeWRsYUW3jez4e0fD/4aZrbX3TfXO47pQjSVuAtg07pEG0DrSHpdVJoaubIRyWG6EE2lv7+/5kjW/AWwSV2NBKB99Vx3R83Rn8Wui0pbs2yq3EoIWWgqcfcIixvG0D5enJzUI8eP6cjMjN7Z0aH71vfq5u7utLvVluLWokprWrEi6XVRaD9MF6Kp5PN5DQ0NaWBgQGamgYGB2XVW1ZK+GgnN6faHOnT7Qx366G+d0gNvHNHhmRm5pMMzM3rgjSP66G+dOm8fxBcnJ7Xl5wd1zYGfacvPD+rFycl0Oo9FSfrq44uvuV4bP/MNDTzwgjZ+5hsELDSEkSw0nXw+X/fKnhBXI6F5je8cl5+du4DZz7rGd46r59qe2bYXJyf1xfEjOh1dEHR4ZkZfHD8iSYx6ZVhlZGz++RsZGdF/+fSn9e9f+O/l8xcVIwWWCyELLWX+VYOXfOUSXaNrJEnbZ7Zr+47taXQLKZs+MR2r/ZHjx2b/QFecdtcjx48RspoA5w9ZQ8gC0PI613XWDFqd6zrn3D8yU7vkw0LtCCvu2q3KdjmHH76l5uOHZ96aPQZYTqzJAtDy+rb2yVbZnDZbZerb2jen7Z0dtT93LtSObGmlkgtoDbxzoClQPBRLUVl3Nb5zXNMnptW5rlN9W/vmrMeSpPvW985Z0yNJa8x03/reZe0vFqfVSi6g+RGyALSFnmt7zgtV81XW7VDqIay4U3d/+9dfaOh5KbmArCFkAUCVm7u7CVUZsZh1VBTcRJYQsgCgzSVdqJXCr0AZIQsA2ljStcGoNQacQ8gC0PbmV31fyLN/3HqlHJKuLUWtKuAcQhYAtLGka4M1+nynXtvNQnW0LOpkAcAitMoeh0nXBmvk+U69tltvfO9reuvkMUmut04e0xvf+5pOvbZ7Ua8NZA0jWQiuVCotuIcg9a/QjNJed5TkwvKkaoNd/cl/lyT9yb4OFV6Y0VRVgf2uTulPbunQ1ZvKx+h0+dvEy0/NqWklST5zRhMvP8VoFloCIQtBlUolFQoFTU1NSSpv2FooFCSJzZrRtNJcd5R0wKtXGyzuerU393+zfOMK6aL/tFtnqqYAL7ruDg1ecb0GT8/9mbdOHq/5XAu1A82GkIWgBgcHZwNWxdTUlAYHBwlZaFpp7nEYIuBdqDbYvn8bjfUcuarbcWtVrbx0fTRVeH470AoIWQhqdLT2G/RC7UCWVUZ1Ou6vveF0x7rO2WOqr0RMcnpvuQNe7vQ3gzyvxDY4aH2ELATV39+vkZGRmu3zTbwyUXdvOSAL+rb2aezJMfnZcyNKtTaclpKf3ntnR4cO1whUzbiJNdvgoNU1328lmkqxWJyzJkuSurq6VCwW5xw38crEnD9a0yemNfbkmCQRtJA5cTeclpKf3mu1TazZBgetjJCFIKqvGlz7B2s1vXN69o/R2q1rtX1mu7bv2D57zPjO8TmjApLkZ13jO8cJWcikehtOV6YND2+rPY13eGYm9qJy6dz0I5tYA82j7m+4mV0u6SlJfZJc0pC7P2ZmayU9o/J6x2FJt7v7L83MJD0m6WOSpiRtc/cfhek+mkG9P0aSaq5vqW6Pu/h207vOn4YE0tS5rvb6rc51nYt+zribWLOHIJCuOB+jZiTd7+4/MrNLJO01s5ckbZO0y923m9mDkh6U9ICkmyRdEX39hqTHo+/AgkL8IQKyoJH1W0mqtxYs9ija/nM3qc4ONKZuxXd3P1wZiXL3N1X+ldsg6VZJO6LDdki6Lbp9q6SnvOwHknrM7LLEe46W0re1T7bK5rQtxx8iILSea3u0YduG2Q8Mnes6tWHbhpqjuxOvTOjA/Qf06rZXdeD+A5p4ZWLRr3uhtWCLQXV2oHENrckys5yk90v6oaQ+dz8cPXRE5elEqRzAXq/6sUNR22Gh6SVdoX12GvAyqXTzag3uOqPRSVd/t6m4ZbXyl52U/u1koq8JLLc4U+b1Lv5oZP2WVH8tWKP1r6jODjQu9m+tmV0saaekz7n7yfLSqzJ3dzPzBX+49vMVJBWk2pfzo/3kN61SftOqtLsBpCLpiz/qTcE3Wv+K6uxA42KFLDPrVDlgldz921HzuJld5u6Ho+nAo1H7mKTLq358Y9Q2h7sPSRqSpM2bNzcU0JANF6prFfdTctJYII9mVe/ij0YlvRaM6uxA4+JcXWiSnpC0393/rOqh5yXdKWl79P25qvZ7zOxplRe8T1ZNKyKDFjMF2C51rRoJiwQ3LEXSF380Ussr1vNRnR1oWJyRrA9J+rSkfWb2k6jtj1QOV8+a2d2SRiTdHj32XZXLNxxUuYTDXYn2GKmqhI7cM2/WnNpY+cyY9l2W/TVUIUbaGEXDUiQ18jTn/8PLJN1zkaSLooZzaxxzDfaP6uxA4+qGLHf/B0m2wMNbahzvkj67xH5hGS0mcIxO1p7hXagdQG1xL/6IG85D7jVIdXagMVR8x6L0d5tGagSq/u6F8jiAerj4A2gthCwsSnHLahVeOK2pqiUkXZ3ldgDJa7TkAoD0EbKwKJVP2+dNbfApvC7WbqFa3Om94TWfCtwTAEkjZGHRmNoAlk/ItVYAwqi7rQ4AAAAaR8gCAAAIgOlCnKe07yxrrZoIa7yyiYXqAAhZmKO07+ycqwZHJl2FF05LEkELAIAGELIwx+CuM3PKMkjS1HS5nZC1vNLa/xEAkAzWZGEOKrkDAJAMRrJaWNyNn/dV3aaSO5AMSi4AIGS1sMVMN1HJHVhYI79TuXDdANAkCFltIu4Vg1Ryb11Jr/HiakUAuDBCVkaVSiUNDg5qdHRU/f39KhaLyufzi3uuBq8YpJI72g0XGQAIgZCVQaVSSYVCQVNTU5KkkZERFQoFSVI+n5+z1mrilQmN7xzX9Ilpda7rVN/WPvVc2yPp3ForrhhE2tKq5UV4ApAmQlYGDQ4OzgasiqmpKQ0ODiqfz8/+4aiMUE1HAWr6xLTe+MYhbT9+fE544opBhJBmgCE8AWgGhKwMGh2t/QdkfnvcESquGASSwRWDABpBnawM6u+vPWUyvz3uCFVxy2p1dc49hisGAQAIi5CVQcViUV1dXXPaurq6VCwW57QtNBI1vz2/aZWGblmjgW6TSRroNg3dsob1WAAABMR0YQZVriKsd3VhIzWtuGIQAIDlRcjKqHw+X7dkAzWtAADILkJWk2OEClg6FrQDCIGQBSAzKM0AoJUQsjJkMRs6AwCAbCJkZQif4oFkMQ0IIE2UcAAAAAiAkAUAABAAIQsAACAA1mQBaDqstQLQDBjJAgAACICQBQAAEAAhCwAAIADWZC2HL3en3QMAALDMGMkCAAAIgJEsAMHFvRpweM2nAvcEAJYPI1nLrLTvrHKPvqkVXzmp3KNvqrTvbNpdAgAAATCStYxK+86q8MJpTU2X749MugovnJYk5TetSrFnAAAgaYxkLaPBXWdmA1bF1HS5HQAAtBZGspaiwasGRye9oXag3VDJHUArYSRrGfV3W0PtAACgeTGStYyKW1bPWZMlSV2d5Xag2TDqBAAXxkjWMspvWqWhW9ZooNtkkga6TUO3rGHROwAALYiQVUepVFIul9OKFSuUy+VUKpWW9Hz5Tas0/LlL9PaXLtXw5y4hYAEA0KLqThea2dcl3SzpqLu/N2pbK+kZSTlJw5Jud/dfmplJekzSxyRNSdrm7j8K0/XwSqWSCoWCpqamJEkjIyMqFAqSpHw+P/fYfWc1uOuMRidd/d2m4pbVBCg0JaYBASAZcUaynpR047y2ByXtcvcrJO2K7kvSTZKuiL4Kkh5PppvpGBwcnA1YFVNTUxocHJzTVql/NTLpcp2rf0WhUQAA2lfdkOXuL0t6Y17zrZJ2RLd3SLqtqv0pL/uBpB4zuyypzi6bL3dLX+7W6MhIzYdHR0bmlG+g/hUAAJhvsWuy+tz9cHT7iKS+6PYGSa9XHXcoajuPmRXMbI+Z7Tl27NgiuxFW3JIL1L8CAADzLXnhu7u7pIbThLsPuftmd9/c29u71G4EUdyyWl2dc9tqlVyg/hUAAJhvsSFrvDINGH0/GrWPSbq86riNUVtTiltyIW4YAwAA7WOxxUifl3SnpO3R9+eq2u8xs6cl/YakyappxaaU37Sq7lWClce5uhAAAFTEKeHwV5I+Imm9mR2S9CWVw9WzZna3pBFJt0eHf1fl8g0HVS7hcFeAPmdSnDAGhEDJBQDIprohy91/f4GHttQ41iV9dqmdAgAAaHZUfAcAAAiAkAUAABDAYhe+N5+q4qEAAAChMZIFAAAQACELAAAggPaZLgSaDKUZAKC5EbKAZUZ4AoD2wHQhAABAAIQsAACAAAhZAAAAAbAmC0gIa60AANUYyQIAAAigbUNWad9Z5R59Uyu+clK5R99Uad/ZtLsEAABaSFtOF5b2nVXhhdOami7fH5l0FV44LUnKb1qVYs+QNUwBAgAWqy1HsgZ3nZkNWBVT0+V2AACAJLRlyBqd9IbaAQAAGtWWIau/2xpqBwAAaFRbhqziltXq6pzb1tVZbgcAAEhCW4as/KZVGrpljQa6TSZpoNs0dMsaFr0DAIDEtOXVhVI5aBGq2hdXDQIAQmvbkIXmEjcUDa/5VOCeAAAQDyELLYURKgBAVrTlmiwAAIDQGMlCqhh5AgC0KkayAAAAAmAkC0EwQgUAaHeMZAE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\n",
          "text/plain": "<Figure size 720x360 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "8113693b117e4b5b81086d9092c4e524": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e459309cae9b444490bcf364944168a5",
       "outputs": [
        {
         "data": {
          "image/png": 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          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
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      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_53378d1af3dc4003a34c40493fe9385d",
        "IPY_MODEL_dba01079f38b45dda63c0e3cc680c8ed",
        "IPY_MODEL_f5282f209b9b4737b3cc9dec1825163c",
        "IPY_MODEL_6266dfabeaee4a1a9a7871f01b0bde98",
        "IPY_MODEL_ef663ff0d6014ae6a22ab6997853b872",
        "IPY_MODEL_1d4d01e6953443f3906dfcac001a036b"
       ],
       "layout": "IPY_MODEL_3e5a67cb2c85445aa37551c3d6509997"
      }
     },
     "c34d009e54444979bf9c433d415a12e0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c3b715a114ba40d093be03b38964f0f6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "c3b779a3d2004b38b628d0fb96e3944c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c3ff3589ba8e4954964fb4187f02a67b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_6a31e7c45d1c419b954cb0cb086bc783",
       "outputs": [
        {
         "data": {
          "image/png": 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       "outputs": [
        {
         "data": {
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          "text/plain": "<Figure size 720x360 with 1 Axes>"
         },
         "metadata": {},
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     "ddcb369f0bdf4b149301235e0633b7df": {
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_206d520a53a74b808d04650746b31e19",
       "outputs": [
        {
         "data": {
          "image/png": 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       "outputs": [
        {
         "data": {
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\n",
          "text/plain": "<Figure size 432x288 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "e4239766cad549c9bdbd0a91c0bf7833": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc2_weight_var1",
       "layout": "IPY_MODEL_491e5240d56b452f940517d3fbd001fc",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_5bd6ff351fa84b8fbabf1554e2d4f56d"
      }
     },
     "e42c063910d64995bb9a11fde034af61": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
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     },
     "e459309cae9b444490bcf364944168a5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e48300fa2dc24f9194ec73be79dba400": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "mc2_shape_conv",
       "layout": "IPY_MODEL_594a86a9367a4739b220bd5ac1d9512f",
       "max": 5,
       "min": -5,
       "style": "IPY_MODEL_d09e6355b86d4e59884090643dcb5722"
      }
     },
     "e4b5bd15768c40279e2fcbcdf1708324": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3d60c0fdeb8646a9bf82085b8c2bb0be",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 720x360 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f2ec3cdedc9e4f7a94ef6bfb2cc53feb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "SigXsecOverSM",
       "layout": "IPY_MODEL_3245048fbe694b06b00b6f1df83ad001",
       "max": 10,
       "style": "IPY_MODEL_8bbaf120ec494ed9ad1214d63d89e167",
       "value": 1
      }
     },
     "f320eb78e56e4869b314e9fd2cf58f61": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_90a60614fc774868b7cbc3f21efbffe8",
        "IPY_MODEL_74998ba983c940869122c1496fe8f201",
        "IPY_MODEL_d2c4955803764a1f853f894092ede456",
        "IPY_MODEL_e3b83918114e4f7b9f95bd82c69177d5"
       ],
       "layout": "IPY_MODEL_5b7c437cf5f94c0bbf307c71fa2835c1"
      }
     },
     "f4510231652a46008f8c8302a7ee809f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_591eab1f74aa4709b7cf9727e2246a30",
       "outputs": [
        {
         "ename": "KeyError",
         "evalue": "'width'",
         "output_type": "error",
         "traceback": [
          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
          "\u001b[0;32m~/.local/share/virtualenvs/pyhf-EFAVEj2h/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;32m<ipython-input-58-eb293ff643af>\u001b[0m in \u001b[0;36mplot\u001b[0;34m(ax, **parsettings)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mpars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpyhf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensorlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggested_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparsettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mpars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparnamedict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mmccounts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpected_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpars\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'samples'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mbottom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mKeyError\u001b[0m: 'width'"
         ]
        }
       ]
      }
     },
     "f4d93303e5314f08be47ca8c312b660a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f5282f209b9b4737b3cc9dec1825163c": {
      "model_module": "@jupyter-widgets/controls",
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