diff --git a/notebooks/ubinnedLikelihood.ipynb b/notebooks/ubinnedLikelihood.ipynb
deleted file mode 100644
index 37ae9fc08ee321bac1b67d5f261e040db1896dec..0000000000000000000000000000000000000000
--- a/notebooks/ubinnedLikelihood.ipynb
+++ /dev/null
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Unbinned Maximum Likelihood fit\n",
-    "Implement by hand an unbinned maximum likelihood fit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import print_function\n",
-    "import numpy as np\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import curve_fit, minimize, fsolve\n",
-    "from scipy.stats import norm, chi2, lognorm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "measurements = np.array([97.8621, 114.105, 87.7593, 93.2134, 86.6624, 87.4629, 79.7712, \\\n",
-    "91.5024, 87.7737, 89.6926, 133.506, 91.4124, 94.4401, 97.3968, \\\n",
-    "108.424, 103.197, 88.2166, 142.217, 89.0393, 102.438, 95.7987, \\\n",
-    "94.5177, 96.8171, 90.903, 132.463, 92.3394, 84.1451, 87.3447, \\\n",
-    "92.2861, 84.4213, 124.017, 90.4941, 95.7992, 92.3484, 95.9813, \\\n",
-    "88.0641, 101.002, 97.7268, 137.379, 96.213, 140.795, 99.9332, \\\n",
-    "130.087, 108.839, 90.0145, 100.313, 87.5952, 92.995, 114.457, \\\n",
-    "90.7526, 112.181, 117.857, 95.2804, 115.922, 117.043, 104.317, \\\n",
-    "126.728, 87.8592, 89.9614, 100.377, 107.38, 88.8426, 93.3224, \\\n",
-    "138.947, 102.288, 123.431, 114.334, 88.5134, 124.7, 87.7316, 84.7141, \\\n",
-    "91.1646, 87.891, 121.257, 92.9314])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1-D Maximum likelihood fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have a set of measurements which are distributed according to the sum of two Gaussians (e.g. this can be signal and background).\n",
-    "\n",
-    "$\\rho = \\frac{1}{3}\\frac{1}{\\sqrt{2\\pi \\sigma^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p}{\\sigma}\\right)^2} + \\frac{2}{3}\\frac{1}{\\sqrt{2\\pi \\sigma_b^2}} e^{-\\frac{1}{2}\\left(\\frac{x-p_b}{\\sigma_b}\\right)^2}$  \n",
-    "\n",
-    "where for one of the two peaks the parameters are known already\n",
-    "\n",
-    "$p_b = 91.0$  \n",
-    "$\\sigma_b = 5.0$  \n",
-    "  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this is my model for the data\n",
-    "def likelihood_point(x, position, width):\n",
-    "    gauss1 = 1./np.sqrt(2*np.pi*width**2)*np.exp(-0.5*((x-position)/(width))**2.0)\n",
-    "    gauss2 = 1./np.sqrt(2*np.pi*5**2)*np.exp(-0.5*((x-91)/(5))**2.0)\n",
-    "    f = 1./3.\n",
-    "    return f * gauss1 + (1-f)* gauss2 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we assume the width of the peak we want to fit is already known: $\\sigma = 15.0$.\n",
-    "Perform a 1-D Maximum Likelihood fit for the position of the peak $p$.\n",
-    "\n",
-    "Complete the functions below which return the likelihood and negative log likelihood (NLL)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def likelihood_1d(params):\n",
-    "    return np.prod([likelihood_point(x, params[0], 15.0) for x in measurements])\n",
-    "\n",
-    "def nll_1d(params):\n",
-    "    return -np.log(likelihood_1d(params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Minimize the NLL and give the best-fit result, including asymetric errors and plot the NLL."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Optimization terminated successfully. \n",
-      "\n",
-      "Fit results:\n",
-      "     fun: 291.4301328503986\n",
-      "     jac: array([-3.81469727e-06])\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 44\n",
-      "     nit: 4\n",
-      "    njev: 22\n",
-      "  status: 0\n",
-      " success: True\n",
-      "       x: array([117.72327492])\n",
-      "\n",
-      "Uncertainty scan:\n",
-      "negative error: [3.3120601]\n",
-      "positive error: [3.3909765]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Cee7rQ1ZHUUp1kDGGx1ceoEdksNeM2dIWLXQ7hAT6c/vkZNZmlbD7iA7apZQnWX/wBJtzSrn7ogFeMaLi2Wih2+mWcX2ICA7gH2uyrY6ilLJT8975fnpHhTArzbNnI7KHFrqdokIDuWVcEh/vLCTvRJXVcZRSdvhqfxHbD5/ipxenEBzg3XvnoIXeIXdM7kuAvx/PrdZj6Uq5O2MMT6w8QFK3MG4ck2B1HJfQQu+AHl1CmJOWxDtbCyg4WW11HKXUWXy6+xi7j5Tzs4tTCPT3jarzjb+lA/34wn74ieheulJurL6xib98tp+BPSO4blS81XFcRgu9g3pFhfKDsQm8lZ5P4anTVsdRSrXirfR8skuqeODyQfj7nXXCNa+ihX4O7po6AIB/6HXpSrmd6roGnvoii7HJXbn4/B5Wx3EpLfRzEB8dyo1jEnhzS75OU6eUm1myLofiiloemjEIEd/ZOwct9HP2k6kDaGwyPK976Uq5jdKqOp7/OpvLBvdkTJ9uVsdxOS30c5TYLYzrR8XzxqbDFFXoXrpS7uCZLw9SXdfAA9PPszqKJbTQO+HuiwZQ39jEYr17VCnL5ZdW88+NefwgNZEBPSKtjmMJLfROSI4J59qR8fxz42FKKmutjqOUT3ty5QFE4L5LBlodxTJa6J1097QB1DY08uLaHKujKOWz9haW896OI9w2qS9xUSFWx7GMFnon9Y+N4KoRvVm6IZfSqjqr4yjlk/78WSZdQgK568L+VkexVLuFLiJLRKRIRHa3sXyqiJSJyA7b4xHHx3Rv91w0gNP1jbygx9KVcrlvDpWwen8xd1/Un6gw75xazl727KG/AkxvZ521xpiRtsejnY/lWVJ6RnLNiN688k2OXvGilAs1Nhn+8PE+4qNDmTsh2eo4lmu30I0xa4BSF2TxaPddMpCGRsOzXx60OopSPmP5tgL2FJbz4IxBhAR6//C47XHUMfQJIpIhIp+IyBAHvaZHSY4J5wdjE3lj82HyS3UkRqWcrbK2gb98tp/RSdFcNbyX1XHcgiMKfRvQxxgzAngaeL+tFUVkgYiki0h6cXGxA97avfxsWgoiwt9WZVkdRSmv9/zqQxRX1PLfVw72uVv829LpQjfGlBtjKm3PVwCBIhLTxrovGGNSjTGpsbGxnX1rtxMXFcK8CX14d1sBB4sqrI6jlNcqOFnN4rXZXDOyN6OSulodx210utBFJE5svx5FJM32mic6+7qe6q6pAwgN9OeJlQesjqKU13rs0/0APDB9kMVJ3Is9ly2+CWwAzhORAhG5Q0QWishC2yo3ArtFJANYBMwyxhjnRXZv3cKD+NGUfqzYdYxdBWVWx1HK62zNO8lHGYX8+IJ+xEeHWh3HrYhV3ZuammrS09MteW9nq6ipZ8qfv2JEQjSv3p5mdRylvEZTk+H6576h8NRpvrp/KuHBAVZHcjkR2WqMSW1tmd4p6gSRIYH8ZGp/vj5QzKZsnz36pJTDfZhRyI78U/zy8vN8sszbo4XuJHMnJNOzSzB//Xw/PnwESimHOV3XyGOfZjI0vgs3jE6wOo5b0kJ3kpBAf346LYUtuSdZfcD7LtFUytUWr83maFkN/33FYPx8aJ7QjtBCd6IfpCaS1C2Mxz7JpLFJ99KVOlf5pdX8ffVBZg6LY1y/7lbHcVta6E4UFODHA9PPI/NYBe9szbc6jlIe63//vRdB+M0Vg62O4ta00J3simG9GJ0UzV8/P0BVbYPVcZTyOF/tL+Lzvcf56cUD6K2XKZ6VFrqTiQgPXzGY4opa/qHD6yrVITX1jfz2wz30iwnnR5P7WR3H7Wmhu8CYPl25YngvXlhziGNlOryuUvZavCabvBPV/PbqIQQFaF21R79DLvLQ9EE0NcFfP99vdRSlPEJ+aTXPrj7IjKFxXDDQ+8Z+cgYtdBdJ7BbG/EnJtvGbdUgApdrz3YnQK/VEqL200F3o7osGEB0ayB8+3qc3Gyl1Ft+eCL1n2gAdr6UDtNBdKCo0kHsvTuGbQyf4an+R1XGUcku1DY387tsToVP6Wh3Ho2ihu9gt4/vQNyac/7cik4bGJqvjKOV2Fq/JJtd2IjQ4QKeV6wgtdBcL9PfjoRmDOFhUyZtb9GYjpVrKLani6S/1ROi50kK3wGWDezKubzeeXHmAU9V1VsdRyi0YY3j4/V0E+fvxP1f55NTEnaaFbgER4X+uGsKp6joe/1xnNlIKYPm2I6w/eIIHZgwiLirE6jgeSQvdIoN7d2HuhGRe35TH7iN6GaPybSWVtfz+472M6dOVW9KSrI7jsbTQLfTzSwfSLTyIRz7YTZOOxqh82O//vZeq2gb+dP0wHRq3E7TQLRQVGsiD0wex7fAplm8rsDqOUpb4+kAx7+8o5K6pA0jpGWl1HI+mhW6xG0YnMDopmj99kknZ6Xqr4yjlUtV1DTz83i76x4Zz90X9rY7j8bTQLebnJzx6zVBOVtfx5Eo9Qap8y5MrD1Bw8jR/vH64XnPuAFrobmBofBS3jOvD0g257C0stzqOUi6xq6CMl9blMDstibS+3ayO4xW00N3E/ZedR3RY8wlSHedFebuGxiYeencnMRHBPDRjkNVxvIYWupuICgvkwennkZ53kne3HbE6jlJO9dzqQ+wpLOfRa4YQFRpodRyvoYXuRm4ak8jIxGj++EkmZdV6glR5pz2FZSz6MourRvRm+tBeVsfxKu0WuogsEZEiEdndxnIRkUUiclBEdorIaMfH9A1+fsLvr20+QfrHT/ZZHUcph6traOIXb2UQHRbEo1fr7f2OZs8e+ivA9LMsnwGk2B4LgOc6H8t3DY2P4keT+7JsSz7fHCqxOo5SDrVoVRaZxyr40/XD6BoeZHUcr9NuoRtj1gClZ1nlGmCpabYRiBYR/X9UJ9x3yUD6dA/j1+/uoqa+0eo4SjlERv4pnvv6EDeOSeDi83taHccrOeIYejzQchzYAtvn1DkKDfLnj9cNI/dENU99kWV1HKU6raa+kV+8nUGPyGAeuUqnlHMWRxR6awMvtHrdnYgsEJF0EUkvLi52wFt7r4kDYrg5NZHFa7N18C7l8R7/fD8Hiyp57IbhdAnRq1qcxRGFXgAktvg4AShsbUVjzAvGmFRjTGpsrA5e355fzzyfbuFBPLh8p85upDzWltxSXlyXw5xxSTpphZM5otA/BObarnYZD5QZY4464HV9XlRYII9ePYQ9heW8tC7H6jhKdVh1XQP3v51BQtdQfj3zfKvjeL2A9lYQkTeBqUCMiBQA/wMEAhhjngdWADOBg0A1cJuzwvqi6UPjuGxwT55YeYDLh8SRHBNudSSl7PboR3s5XFrNm3eOJyK43bpRndTud9gYM7ud5Qa422GJ1H8QEf732qFc8vjX/OrdXbxx5zhEdLxo5f5W7DrKsi353DW1P+P7dbc6jk/QO0U9QM8uIfxq5vlsyD7B65sOWx1HqXYdOXWah5bvZERiNP916UCr4/gMLXQPMWtsIlNSYvjDx/vIKamyOo5SbWpobOK+ZdtpMrBo1kgC/bVmXEW/0x7Cz0/4y40jCArw4+f/2qFXvSi39cxXB9mSe5LfXzuUPt31nI8raaF7kLioEP5w3VB25J/i76sPWR1Hqe/ZklvKolVZXD8qnmtH6f2FrqaF7mGuHN6ba0b25m+rsthZcMrqOEp9p6y6nnvf3E5itzAevXao1XF8kha6B3r06qH0iAzm5//awek6HetFWc8Yw6/e20lRRS2LZo3SSxQtooXugaLCAvnrTSM4VFzFY59mWh1HKd7cnM+KXce4//LzGJEYbXUcn6WF7qEmDYjhtknJvPJNLmsO6Lg4yjo7C07x2w/3MCUlhgVT+lkdx6dpoXuwB6cPYkCPCH75TganquusjqN80MmqOu765zZiI4NZNGsUfn5605uVtNA9WEigP0/dPJITlXU8uHynTi6tXKqxyXDvv3ZQXFHL328ZrRNWuAEtdA83ND6Kh2YM4rM9x1myPtfqOMqH/G1VFmsOFPPbq4focXM3oYXuBe6Y3JdLB/fkjyv2sf3wSavjKB/wVWYRi1ZlceOYBGanJbb/BcoltNC9gIjw1xtHEBcVwj1vbNfj6cqp8kurue9fOxjcqwu/v3aoDhbnRrTQvURUWCDPzhlNUUUNv3grg6YmPZ6uHK+mvpGF/9yKMYbnfziGkEB/qyOpFrTQvciIxGgennk+qzKLWLw22+o4yssYY3j4vd3sKSznyZtHktQ9zOpI6gxa6F5m3sRkZg6L48+f7Sc9t9TqOMqL/GNNNsu3FfCzi1O4+PyeVsdRrdBC9zIiwp9uGE5C11DueWM7pVV6PF113md7jvHYp5lcMbwX912cYnUc1QYtdC/UJaT5eHppVR33LtuuQ+2qTtlTWMZ9y3YwPCGax28aoTcPuTEtdC81ND6K/712CGuzSvjDin1Wx1Eeqqi8hh+9mk50WCCLb9WToO5Oh0TzYjePTSLzWAUvr89lUFwkN49NsjqS8iA19Y3cuTSdstP1vL1wAj26hFgdSbVD99C93MMzz2dKSgy/eX83W/QkqbJTU5PhF29nsPNIGU/dPJIhvaOsjqTsoIXu5QL8/Xhm9mgSu4ax8LWt5JdWWx1JeYCnVmXx8c6jPDR9EJcNibM6jrKTFroPiAoLZPG8VOoam7hzaTpVtQ1WR1Ju7PVNeSxalcUPUhNYcIEOh+tJtNB9RP/YCJ6dM5oDxyv4+b926J2kqlUrdh3lN+/vZtqgHvzhumF6W7+H0UL3IRcMjOXhKwbz+d7jPLHygNVxlJv55mAJ9y3bweikrjw7ZzSB/loPnsaun5iITBeR/SJyUEQeamX5VBEpE5Edtscjjo+qHOH2ScncnJrIM18d5PVNeVbHUW5iV0EZdy5Np29MOEvmjSU0SC9P9ETtXrYoIv7As8ClQAGwRUQ+NMbsPWPVtcaYK52QUTmQiPD764ZSXFnLf7+/m+7hQUwf2svqWMpC2cWVzH95M9FhQbx6expRYYFWR1LnyJ499DTgoDEm2xhTBywDrnFuLOVMgf5+PDtnNCMTo/nZmzvYcOiE1ZGURY6V1XDrS5sBeO2ONOKi9FpzT2ZPoccD+S0+LrB97kwTRCRDRD4RkSEOSaecJjTInyXzx9KnexgLlqazp7DM6kjKxUqr6pi7ZBOnqut45bY0+sVGWB1JdZI9hd7aae4zL5HYBvQxxowAngbeb/WFRBaISLqIpBcX60z1VosOC2LpHWlEhgQwb8kW8k5UWR1JuUhpVR1zFm8k70Q1i+emMixBbxzyBvYUegHQco6pBKCw5QrGmHJjTKXt+QogUERiznwhY8wLxphUY0xqbGxsJ2IrR+kVFcrSO9JoaGpi7pLNFFXUWB1JOdmJylrmLN5ITkkVL85LZeKA7/1TVR7KnkLfAqSISF8RCQJmAR+2XEFE4sR2waqIpNleVw/MeogBPSJ5ef5Yisprmb9kC2XV9VZHUk5SUlnLnMWbyCmp4qV5Y5mSojtW3qTdQjfGNAD3AJ8B+4C3jDF7RGShiCy0rXYjsFtEMoBFwCxjjN654kFGJXXluR+O5mBRJbe8tFHnJfVCJbY987zSKpbMH8vkFN0z9zZiVe+mpqaa9PR0S95bte2rzCJ+/M+tDIiN4PUfjaNreJDVkZQDFFc0l3n+yWqWzBurh1k8mIhsNcaktrZMbwVT/+GiQT1YPDeVg8WVzF68kROVtVZHUp1UVFHzf2U+X8vcm2mhq++5cGAsS+aNJaekijmLN1Gipe6xsosrueG5bzhy6jQvz09jYn8tc2+mha5aNTklhpfnjyWvtIrZL2ykuEJL3dNsP3ySG5/fQHVtI2/eOZ4J/btbHUk5mRa6atPEATG8PD+NgpOnmfXCBo6V6SWNnuLLzOPMWbyJiOAAlt81kRGJ0VZHUi6gha7OakL/7rx6exrHymq47u/ryTxWbnUk1Y630vO5c+lW+vcIZ/ldE0mOCbc6knIRLXTVrrS+3Xhr4QSajOGm5zaw/mCJ1ZFUK4wxPPNlFg+8s5OJ/buzbMEEYiODrY6lXEgLXdllSO8o3vvJJHpHhzJvyWaWby2wOpJqobahkV+9u4u/fn6A60bF89K8sUQE6xzwvkYLXdmtd3Qoby2cQFrfbvzi7QyeXpWF3j9mvWNlNdz8j40s25LPPRcN4PGbRhAUoP+0fZH+1FWHRIUG8sptaVw/Op7HVx7goeW7qG9ssjqWz9qSW8qVT68j63gFz/9wNPdffh5+fjptnK/S/5OpDgsK8OPxm0aQ0DWMRauyyD1RxdNzRtEjUsfSdhVjDK9tzOPRj/aS2C2MN+8cR0rPSKtjKYvpHro6JyLCf106kCdvHkFGwSmuWLSOjdk6Hpsr1NQ38sA7O3nkgz1cODCW9++epGWuAC101UnXjUrgg7snExkSwJzFG3lu9SGamvS4urNkHa/ghue+4e2tBdx7cQqL56YSFapTxqlmWuiq086Li+TDeyYzY1gvHvs0kzuXpusQvA7W1GR4eX0OVz69jqNlNbw4N5WfXzpQj5er/6CFrhwiIjiAZ2aP4ndXD2FNVjFXPL2WjPxTVsfyCsfKapj38mZ+99FeJg2I4bP7LuCSwT2tjqXckBa6chgRYd7EZN768QSMgeuf+4Y/f5pJTX2j1dE81kcZhVz+1BrSc0/yh+uG8tK8VL1ZSLVJC1053Kikrqz42RSuHxXP31cf4opFa9mad9LqWB6luKKWe5dt56dvbic5JpyPfzaZW8b1wTYxmFKt0gkulFN9faCYX7+7i8Ky09w2sS/3Xz6QsCC9WrYtDY1NvLYxjyc+P0BNQyN3XzSAey4aQIC/7nupZmeb4EILXTldZW0Dj32SyWsb80jqFsafrh+mkyy0YnNOKY98sJvMYxVMSYnht1cPoX9shNWxlJvRQlduYWP2CR5cvpO8E9VcOrgnD04fxIAeWlhFFTX8aUUm724/Qu+oEB65ajCXD4nTwyuqVVroym2crmtkyfocnlt9iNP1jcwam8i9l6T45F2mp6rrWLIuhyXrc6lraGLBBf34yUX99ZCUOistdOV2SipreXpVFq9vOkxQgB8LLujHnVP6Ee4DIwSerKrjpXU5vPJNLpW1DcwYGscD0wfRV8ctV3bQQlduK6ekir98lsmKXceIiQhi3oRkbhnfh27hQVZHc7jSqjpeXJvNq9/kUlXXyBXDevHTiwcwKK6L1dGUB9FCV25v2+GTPPVFFmsOFBMc4Mf1o+O5bVJfBnrBGCV7CstYtjmfd7cVUF3fyMxhvfjZtBTOi/P8v5tyPS105TGyjlewZH0u724roLahiSkpMdw+qS8XDIzF34Nuc6+sbeCjjEKWbT5MRkEZQQF+XDmsF3dN7a8DaalO0UJXHudkVR1vbD7M0g25HC+vJTYymBlD45g5rBdjk7u5ZbnXNzaxNe8kH+w4woc7Cqmqa2RgzwhmpyVx3ah4osO87zCScr1OF7qITAf+BvgDLxpj/nTGcrEtnwlUA/ONMdvO9ppa6MoedQ1NfL73GP/OOMpX+4uobWgiNjKY6UO+Lfeult50U1pVx+r9RXyZWcTXB4qpqGkgJNCPK4f3ZnZaEqOTovXyQ+VQnSp0EfEHDgCXAgXAFmC2MWZvi3VmAj+ludDHAX8zxow72+tqoauOqqpt4MvMIlbsai73mvomwoP8GZkUzZikrozq05XRiV2JCnPOcLLGGI6cOs2ewnL2HClj3cEStuefwhiIiQhm2qBYpg3qweSUWJ3PUznN2Qrdnq0uDThojMm2vdgy4Bpgb4t1rgGWmubfDhtFJFpEehljjnYyu1LfCQ8O4KoRvblqRG+q6xpYvb+YTdkn2Hr4JM+uPkSjbRz2lB4RDOndhfiuocRHh9n+bH6EBvmf9T3qGpooraqjpLKWkspaiipqOXCsgj2F5ew9Wk7Z6eZhgf0EhsZHce/FKUwb1IOhvaN0KFtlOXsKPR7Ib/FxAc174e2tEw9ooSunCAsKYOawXswc1gto3nvPKDjFtryTpOedZEvuST7aefS7kv9WZHAAgQF++PsJAX6Cv58QaDtkU1pV911htxQc4MeguEhmDuvFkN5dGNK7C4PiurT7y0EpV7On0Fvb7TjzOI096yAiC4AFAElJSXa8tVL2CQ8OYGL/GCb2/78xYhoamzheUcuRk6c5cqqaIydPU1JZR0NTE41NhoZG0/xnk6HJGLqGBRETEUz3iOY/YyOD6B4eTELXUB0cS3kEewq9AEhs8XECUHgO62CMeQF4AZqPoXcoqVIdFODv992hFuhmdRylnM6e3Y4tQIqI9BWRIGAW8OEZ63wIzJVm44EyPX6ulFKu1e4eujGmQUTuAT6j+bLFJcaYPSKy0Lb8eWAFzVe4HKT5ssXbnBdZKaVUa+y6tsoYs4Lm0m75uedbPDfA3Y6NppRSqiP0TI9SSnkJLXSllPISWuhKKeUltNCVUspLaKErpZSXsGz4XBEpBvLO8ctjgBIHxnEUd80F7ptNc3WM5uoYb8zVxxgT29oCywq9M0Qkva3RxqzkrrnAfbNpro7RXB3ja7n0kItSSnkJLXSllPISnlroL1gdoA3umgvcN5vm6hjN1TE+lcsjj6ErpZT6Pk/dQ1dKKXUGtyx0EVkiIkUisrvF524SkT0i0iQiqWes/ysROSgi+0XkcnfIJSKXishWEdll+3OaO+RqsTxJRCpF5H53ySUiw0Vkg235LhEJsTqXiASKyKu2PPtE5FfOyHSWXH8RkUwR2Ski74lIdItlVm73reZyg+2+ze+XbblV2/3Zfo6O2+6NMW73AC4ARgO7W3zufOA8YDWQ2uLzg4EMIBjoCxwC/N0g1yigt+35UOCIO3y/WixfDrwN3O8OuWge+XMnMML2cXc3+TnOAZbZnocBuUCyC3NdBgTYnj8GPGZ7bvV231Yuq7f7VnO1WG7Vdt/W98uh271b7qEbY9YApWd8bp8xZn8rq19D8z+4WmNMDs1jsqdZncsYs90Y8+2sTXuAEBEJtjoXgIhcC2TbcjlNB3NdBuw0xmTY1jthjGl0g1wGCBeRACAUqAPKXZjrc2NMg+3DjTTPBgbWb/et5nKD7b6t75fV231buRy63btloXdQWxNUu5MbgO3GmFqrg4hIOPAg8Durs5xhIGBE5DMR2SYiD1gdyOYdoIrmCc8PA381xpSe/Uuc5nbgE9tzd9ruW+Zqyert/rtcbrbdt/x+OXS7t2uCCzdn1wTVVhGRITT/F+syq7PY/A540hhTKdLat84yAcBkYCzNs16tEpGtxphV1sYiDWgEegNdgbUi8oUxJtuVIUTkYaABeP3bT7Wymsu3+1Zyfft5S7f7VnK5xXbfSi6HbvfeUOh2TVBtBRFJAN4D5hpjDlmdx2YccKOI/BmIBppEpMYY84y1sSgAvjbGlACIyAqaj0NaXehzgE+NMfVAkYisB1Jp/q+7S4jIPOBK4GJjO9CKG2z3beSyfLtvI5fl2/1Zfo4O2+694ZDLh8AsEQkWkb5ACrDZ4kzYzmJ/DPzKGLPe4jjfMcZMMcYkG2OSgaeA/+cGZQ7Nc9YOF5Ew2/HqC4G9FmeC5sMs06RZODAeyHTVm4vIdJoPFVxtjKluscjS7b6tXFZv923lsnq7P8vP0bHbvbPO9HbmAbxJ8zHLepp/g90BXGd7XgscBz5rsf7DNJ/l3w/McIdcwG9oPva6o8Wjh9W5zvi63+Lcs/0d/Tn+kOYTVruBP7tDLiCC5qsi9tD8D+2XLs51kOZj5d9uQ8+7yXbfai432O7b/H5ZvN2f7efosO1e7xRVSikv4Q2HXJRSSqGFrpRSXkMLXSmlvIQWulJKeQktdKWU8hJa6Eop5SW00JVSyktooSullJf4/7b9skRICT+rAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "solution = minimize(nll_1d, [100.0], method='CG')\n",
-    "print (solution.message,\"\\n\")\n",
-    "print (\"Fit results:\")\n",
-    "print (solution)\n",
-    "\n",
-    "min_pos = solution.x[0]\n",
-    "min0 = solution.fun\n",
-    "\n",
-    "# scan numberically the likelihood to get the uncertainties\n",
-    "scan_points = np.linspace(110.0,126.0,50)\n",
-    "plt.plot(scan_points, [nll_1d([x]) - min0 for x in scan_points])\n",
-    "\n",
-    "nll_1sigma = lambda x: nll_1d([x]) - min0 - 0.5\n",
-    "\n",
-    "print(\"\\nUncertainty scan:\")\n",
-    "print(\"negative error:\", min_pos - fsolve(nll_1sigma, min_pos-0.5))\n",
-    "print(\"positive error:\", fsolve(nll_1sigma, min_pos+0.5) - min_pos)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2-D Likelihood fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we perform the 2-D Maximum Likelihood fit, fitting for both $\\sigma$ and $p$ at the same time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def likelihood(params):\n",
-    "    return np.prod([likelihood_point(x, params[0], params[1]) for x in measurements])\n",
-    "\n",
-    "def nll(params):\n",
-    "    return -np.log(likelihood(params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Minimize the NLL and find the best-fit result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "position = 118.3155 \n",
-      "width = 13.6297\n"
-     ]
-    }
-   ],
-   "source": [
-    "solution = minimize(nll, [120.0, 10], method='CG')\n",
-    "print(\"position = {:.4f}\".format(solution.x[0]), \"\\nwidth = {:.4f}\".format(solution.x[1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a 2D contour plot of the 1, 2 and 3 $\\sigma$ contours of the NLL and plot the best-fit solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scanA = np.linspace(110.0,130.0,50)\n",
-    "scanB = np.linspace(5,20,50)\n",
-    "minValue = nll(solution.x)\n",
-    "Z = [[nll([a,b]) - minValue for b in scanB] for a in scanA]\n",
-    "p1 = plt.contour(scanB, scanA, Z, [0.01,0.5, 2.0, 4.5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute numerically the error matrix of the NLL for the 2-D fit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[10.89066093 -2.44351183]\n",
-      " [-2.44351183  4.98113048]] \n",
-      "sigma(position): 3.3001001396079648 sigma(width): 2.2318446354879837\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.misc import derivative\n",
-    "\n",
-    "# compute the error matrix\n",
-    "A = np.linalg.inv([\n",
-    "    [\n",
-    "        derivative(lambda x: nll([x, solution.x[1]]), solution.x[0], n=2),\n",
-    "        derivative(lambda y: derivative(lambda x: nll([x, y]), solution.x[0]), solution.x[1])\n",
-    "    ],\n",
-    "    [\n",
-    "        derivative(lambda x: derivative(lambda y: nll([x, y]), solution.x[1]), solution.x[0]),\n",
-    "        derivative(lambda y: nll([solution.x[0], y]), solution.x[1], n=2)\n",
-    "    ]\n",
-    "])\n",
-    "print(A, \"\\nsigma(position):\", np.sqrt(A[0,0]), \"sigma(width):\", np.sqrt(A[1,1]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}