From 761e79850ba5f220fbd63e3b3cdc93257df7da0f Mon Sep 17 00:00:00 2001
From: Mauro Donega <mauro.donega@cern.ch>
Date: Fri, 5 Mar 2021 15:13:37 +0100
Subject: [PATCH] restructuring the repo

---
 notebooks/basicMatplotlib.ipynb               |  547 +++++++
 notebooks/binnedLikelihood.ipynb              |  540 +++++++
 notebooks/centralLimitTheorem.ipynb           |  138 ++
 notebooks/dataRepresentationGraphsHists.ipynb |  791 ++++++++++
 notebooks/iMinuit.ipynb                       | 1339 +++++++++++++++++
 notebooks/leastSquaresFits.ipynb              |  442 ++++++
 notebooks/parentSamplingDistributions.ipynb   |  105 ++
 notebooks/quantiles.ipynb                     |  109 ++
 notebooks/unbinnedLikelihood.ipynb            |  854 +++++++++++
 notebooks/uncertaintyAsSmearing.ipynb         |  187 +++
 10 files changed, 5052 insertions(+)
 create mode 100644 notebooks/basicMatplotlib.ipynb
 create mode 100644 notebooks/binnedLikelihood.ipynb
 create mode 100644 notebooks/centralLimitTheorem.ipynb
 create mode 100644 notebooks/dataRepresentationGraphsHists.ipynb
 create mode 100644 notebooks/iMinuit.ipynb
 create mode 100644 notebooks/leastSquaresFits.ipynb
 create mode 100644 notebooks/parentSamplingDistributions.ipynb
 create mode 100644 notebooks/quantiles.ipynb
 create mode 100644 notebooks/unbinnedLikelihood.ipynb
 create mode 100644 notebooks/uncertaintyAsSmearing.ipynb

diff --git a/notebooks/basicMatplotlib.ipynb b/notebooks/basicMatplotlib.ipynb
new file mode 100644
index 0000000..8400e82
--- /dev/null
+++ b/notebooks/basicMatplotlib.ipynb
@@ -0,0 +1,547 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic plotting with matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n",
+      "[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# equally spaced numbers:\n",
+    "\n",
+    "# give range and number of points \n",
+    "print(np.linspace(1, 2.0, 11))\n",
+    "\n",
+    "# give range and step size\n",
+    "print(np.arange(1, 2.1, 0.1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataset is a parabola\n",
+    "x = np.arange(1, 21., 1.0)\n",
+    "y = x**2 +1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(-3.0, 300, 'LaTex: $\\\\alpha \\\\beta \\\\gamma$')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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nfs6wB4exe7/d6bxM8Tl6GS3DXpvuxXEPHcfMhpnzy2c1zOLKf13JlBlTch9Dz249efa9Z+fHBvD8e88z7p1xAPTo2oONVtuI8544j4bGhvl1ps+ezkVjL1roWH//99/TbdluDBs4jM3X2JwzdjyDIXcN4aNZHy20zz4r9mH85PELlT056Uleev+l3PGbWfUo9tfsCmDnZmWPAsNJneZ1rUvnLsxpnMNeN+/FZw2fMadxDr269+KP3/7jQvXO3eVczhtzHgOvHDh/VNUR/Y9gyFfSzQxHPj2SByY+wP637c9d+98FwFn/PIste2/JFr224Ol3n+aMf5zBSd84iQO3OJCVuqzE4FGDmdUwi8ZoZJWuq3DgFgcW3eRVqPty3TngSwew/cjtiQjmxTy+uuZXOfHrC+6RNfwbw7n6qavZ+bqdaYxGGhob6L1ib47ofwRdO6cZZ4beN5Trn72ebfpswwFbHECPrj247t/X8cK0F9jq8q144rAnWKP7GgD0W60fm/fcnG9e803mxTyE2G7t7Tjh6ye06Xsws+qgKOKOrJIei4jtWyj/R0R8oyyR5TRgwIAYP378kiuamdl8kiZExIA86+SZcqQlc1spNzOzOrXYpipJ3wC6AatK2qXZ21sDT5YrMDMzq05L6uPoDSwPLAesCRResjw2Iv5arsDMzKw6LTZxRMQtAJK+GxHXdkxIZmZWzYq9cvz75Q7EzMxqg28da2ZmubQrcUjaqlSBmJlZbWjvGceZJYnCzKwdfI+NjtXeSQ7z3RjCzKzEfI+NjtfqGYekgyUNLnjd/DGENETXbLGmTYOf/QzWWw+6dYONN4Yrrqh0VFYvRo9OSaOxMT2PHl3piOrf4pqqtidd5AcwDHgXmFrweBeo+5lxrX0+/xx22w0eeAB+9zu4914YMACOOALefrvS0Vk98D02Ol6rTVURcWjB4kcR8VDzOpI8W50t1m23wYQJ8OyzsPnmqWy55eDGG+Gzjr0FutUp32Oj4xXbOX59K+VLniGxTuy7b/pHucYa6fnSS9u+rS22SNsYNAjWWQe23Ta9HjAAzqyR4QYNDXDaafCFL8Dyy6c/1pdfhvHjU3PU5Mmp3p13puNqShoAt98Oq68O/fql5b33hl694L77Ft3PfvvBJpukJgiz1gwcCCed5KTRUYrqHI+IK1t564gSxlLVbrklPe+/P4wa1b5tbbbZgm2ceCIceWRq/3/66dSkU+0aGuDb34ZnnkkjWXr1Sn0Yv/41zJ4NQ4fCWmulumPHpsQwZw689RZcfTVcdFFKvJ06pTonnADPPw/nnQff+c6C/TzySPrcH344naWYWXVo13DciJhYqkDqQQQcfTTsvjvssAPssgucc86i9dTKWDRp4ff+8pe0je23X/A/qoaGhdc57DBYdVUYPjwtf/ABfPObsOKKLSehZ5+FVVZJo1Da6k9/gkcfhbvuSvvffXc49ND0A//3v6dkCDB1KrzzTjrjOOigdIZx+umwzz5pvSZbbw0HHADPPbegrKEBjjoq1d1pp7bHamal167huJLWjYg3SxVMrZPgrLNSU02TESPSj+l//deCspNPbnn9jTaClVdOr6+/Hh58MPURrJTdkXXUqNSpfNVVC9a58srUfLbxxmm5e3fo2zfFse22i+7j009hxgyYkv8mgvP9+c8poX3tawvKevSA6dPht7+F1bJbkzfdHmWrreCrX03J4b774PLLoXdvuPDCBetvtlkaffXBB2n988+HSZNSMjKz6lJ04pDUF9iUBdduCDgd6F+GuGrWHXfAddel0UTLLpv+53zIIQsnjs02a3nd5ZdPTVaQflwfeQQ6F3xD++8PTz0Fr7ySkkyTM86A3/wGLr44NQ0df3z6oW7JwIHpTGCFFdp2fO++Cy+9BD/96cLlc+akM59hwxaUjR+fkt7GG6ek+sUvwh57pOR1880LJ45NNknPL76YPoNTT03H1Ldv2+I0s/IpKnFk12zsCdwDzCt4a/ki1+8K/B3oku3ztoj4jaT1gVHAqsC/gIMiYo6kLsC1pKT0AbBfRLxR1BFVwO23p3b8889PP6y33rrgLOGmm1ISyWvixJabaGbOTEmoMHFAShYDB6ahr60ljSZNZwRtMTFrnFx//QVljY1w7bWwwQapiazJ+PGw5ZaLNs1JsGazK4D69UtJ8sUXUx/IOuvAMce0PU4zK59izziOAL4REY2FhZKeLnL9z4EdIuJTScsCj0v6C+n6kAsiYpSkPwOHASOy548iYkNJ+5Pua75fkfvqcLfemhLH2LELOtGbrLwyvPde/m1uu23aVlMH8uJMnw4/+lH6X/wDD8C558Jxx7Ve/4MP0hlHly7541om6xX78MMFZSNGwAsvpCRRaMKE1N9SaOrUdC3H8ccvXL7ccmmE1mWXpYTz6KPpjM3Mqk+xneNzmicNgIj4dzErR/Jptrhs9ghgB+C2rHwk6awGYHC2TPb+jlJrXcrVo0uXNHKoyejRqbO8LY4+OnUgF/5ANzSkJFXYifzRR6kp7IwzUhPYsGGpyev3v295u2PGpFFQzX/ki/XlL6dkeMopqXP8j3+EX/4yJc7nnksd+rNnp07xKVNS8hs+PH0WN9yQOuX79oWf/3zRbW+6aUoa+++fBgSYWXUq9ozjY0l9I2JSYaGk7SPisWI2IKkTMAHYELgE+A/wcUQ03bd8EtAne90HeBsgIuZKmg6sBrzfbJuHA4cDrLPOOkUeStsMHZr+pz5mTPpha8lpp6WRQJ9+mn48v/Sl9KN55pnprKDwx/KSS1ISeO219KPavXsaqtp0GIMGpf9xH3QQfPxxGrHVvTt873uw116pztSpqYO6c+fUmX7KKSmR3HNPOvt59134wx8WjnGFFVJzUq9ebfscundPcR97bLq2pWfP1B/zrW+lpLn77vDJJws6xg8+OE0vct55sO666f3hw1vuY9lwQ+jaNdU1s+qliCVfwyfpaOBHwIksuOhPwFkR8ZVcO5R6AHcC/wNcHREbZuVrA/dHxJckPQ98qylRSfoPsHVEfNDadgcMGBDjm36trOJOPjklrY8+an34cXP77ZemIXniifLGZmYLSJoQEQPyrFNsU9VPgQtJkxr2zh5rkjq7c4mIj4HRwLZAD0lNZz19gex6YyYBawNk768MfIjVjPHjoX//4pMGpD6R/h6jZ1b1im2q+kFL/RmSZhSzsqSeQENEfCxpeWAnUof3Y8D3SSOrhgB3Z6vcky2Pyd5/NIo5NbKqMWFCuiiwWNOnp2a7tva9WP0ZM8bzT1WrYqccabETPCLuKHI/vYGRWT/HMsAtEXGvpBeAUZJOA54CmqY2uRK4TtJE0plGK70KVq3yjiRbeWWYN2/J9Wzp4HtsVLf2Xjn+k4i4fEn1IuIZYJGrCyLiNRZM3V5YPhvYpz2xmVntaukeG04c1aPYCwBvbOWtHYAlJg4zszya7rHRdMbhe2xUl2LPOHpHxEIj6yX9Efhh6UMys6Wd77FR3YpNHDe0UPZr4Ebg0dKFY2aWDBzohFGtihqOGxGL3CE6G1Zb1FxVZmZWP9p8Pw5JywFdSxiLmZnVgLZ2jgvoAZxS8ojMzKyqFdvH0Ssi2nHPODMzqxd5phwxMzMrunP8lXIHYmZmtaHNneNmZrZ0ajVxSPqjpK8taQOSvibpktKGZWZm1WpxneNnAqdKOh+4C3gSeCd7rw+wDfA94EXg5HIGaWa1z7Pd1o9WE0d2E6VDJa0FHAj8HFgve/sNYCywd/O7ApqZNefZbuvLEofjRsRk4OwOiMXM6pRnu60v7hw3s7Jrmu22UyfPdlsP2nU/DjOzYni22/rixGFmHcKz3dYPN1WZmVkuThxmZpaLE4eZmeXixGFmZrk4cZiZWS5OHGZmlosTh5mZ5eLEYWZmuThxmFmbjRkDZ5yRnm3p4SvHzaxNPOPt0stnHGbWJi3NeGtLBycOM2sTz3i79CqqqUrSRUD3iPixpK8Cw4FVgaER8VI5AzSz6uQZb5dexfZxzAL+lL3+AbAv6W6AFwB7lj4sM6sFnvF26VRsU9XmEfFy9vrTSF4HVipTXGZmVqWKTRzdACStDrxbUD6r5BGZmVlVKzZxNEpaCRgAPAsgScBn5QrMzMyqU7GJ43TgLmCniBgjaTngQuC5skVmZmZVqajEERGjI2KHiDguK5oHPAyMLWZ9SWtLekzSi5Kel/TzrHxVSQ9LejV7XiUrl6SLJU2U9IykLdtwbGZmVgZtuo4jIuZGxL0R8VCRq8wFfhERmwLbAkMlbQacCDwSEf2AR7JlgG8D/bLH4cCItsRpZmal1yEXAEbElIj4V/Z6BvAi0AcYDIzMqo1kwdDewcC12eitsUAPSb07IlazpZ3nn7IladdcVZJ+HBFX5FxnPeCrwJNAr4iYAim5SFojq9YHeLtgtUlZ2ZRm2zqcdEbCOuus04YjMLNCnn/KitHeM44f5qksaQXgduCYiPhkcVVbKItFCiIui4gBETGgZ8+eeUIxsxZ4/ikrRofNVSVpWVLSuCEi7siKpzY1QWXP72Xlk4C1C1bvC0zuqFjNllaef8qK0WpTlaSrgckR8StJV7VUBdikmJ1k13xcCbwYEecXvHUPMAQ4M3u+u6D8KEmjgG2A6U1NWmZWPp5/yoqxuD6O0cDH2esvALuwcBOSgAeL3M92wEHAs5KezsqGkxLGLZIOA94C9sneux/YDZgIzAQOLXI/ZtZOnn/KlqTVxBERIwsW50XEnOZ1JDUWs5OIeJyW+y0AdmyhfgBDi9m2mZl1rGL7OI5vpXxGqQIxM7PaUOyV4xNaKR9c2nDMzKza+Q6AZmaWS1GJQ9IWrZT/orThmJlZtSv2jOMhSV8oLJB0MHBK6UMyM7NqVmzimAScJWltAEn7ka7hKGp2XDOrLM8/ZaVU7FxVPyRd1X2FpIeBfhFxnKQNyxeamZWC55+yUit2VNUrEfEx6cruk4HzsvKJZYzNzErA809ZqRXbOX6UpJ2AgcBWwJ8krS7pyLJGZ2bt5vmnrNSKbao6iTQNyOEREZKGApcD6wF/LlNsZlYCnn/KSq3YxPEicEQ2FQgRMVnSMYC72sxqgOefslIqKnFExE4tlL0paZfSh2RmZtWsXVeOR8RzpQrEzMxqQ7sSh6QrSxWImZnVhqKaqiR1Jc2Qu36zt3YteURmZlbVij3juBC4FehBGmF1FOl2rvuVKS4zM6tSxSaOdSPiJWCliJgaETOBnwLHli80M1scTyNilVLscNxOzetHxH8krV76kMxsSTyNiFVSsWccM7Pn9yWtW1D+eYnjMbMieBoRq6RiE8cDWQf5ucBFktaUtCfwevlCM7PWeBoRq6RiLwBsmlZkrKTfA78E3gCGlikuM1sMTyNilVRsH8d8ETEOGFeGWMwsB08jYpXie46bmVkuuRKHpD+WKxAzM6sNec84NitLFGZmVjPcVGVmZrk4cZhVEV8NbrUg96gqMysPXw1utcJnHGZVwleDW63Imzg+KUsUZuarwa1m5Gqqiog9yxWI2dLOV4NbrSj2Rk6rRsSH5Q7GbGnnq8GtFhTbVDVG0hfLGomZmdWEYhPHp8B+kq6RtGU5AzIzs+pWbOI4KSJOBo4BvivpWknbFLsTSVdJek/ScwVlq0p6WNKr2fMqWbkkXSxpoqRnnKjMzKpLUYkjIh7Knj+OiN+RplPfRdL9kr4OIOncxWziGmDXZmUnAo9ERD/gkWwZ4NtAv+xxODCiuEMxqz6+oM/qUe7rOCQtA+wF9AeuAwZKuhnYo7V1IuLvQPPO9cHAyOz1SGDPgvJrIxkL9JDUO2+cZpXWdEHfySenZycPqxdFJQ5JZ0laRtKhwO2kW8l+LyJuiohzgEOAz3Luu1dETAHIntfIyvsAbxfUm5SVtRTX4ZLGSxo/bdq0nLs3Ky9f0Gf1qtgzjh+SEsYnEfG9iLg1IqLpzYiYBXxcopjUQlm0UEZEXBYRAyJiQM+ePUu0e7PS8AV9Vq+KvQDw7og4agl1Hs+576mSekfElKwp6r2sfBKwdkG9vsDknNs2qzhf0Gf1qth7ji8paZCNusrjHmAIcGb2fHdB+VGSRgHbANObmrTMao0v6LN61CGz40q6CRgErC5pEvAbUi9+uWcAAAnUSURBVMK4RdJhwFvAPln1+4HdgImkvpRDOyJGMzMrTockjoj4QStv7dhC3SAN9zUzsyrkadXNcvK1Gba0842czHLwzZbMfMZhlouvzTBz4jDLxddmmLmpyiwXX5th5sRhlpuvzbClnZuqzMwsFycOMzPLxYnDDF+bYZaH+zhsqedrM8zy8RmHLfV8bYZZPk4cttTztRlm+bipypZ6vjbDLB8nDjN8bYZZHm6qsrrlkVJm5eEzDqtLHillVj4+47C65JFSZuXjxGF1ySOlzMrHTVVWlzxSyqx8nDisbnmklFl5uKnKaopHSplVns84rGZ4pJRZdfAZh9UMj5Qyqw5OHFYzPFLKrDq4qcoqbsyY4kY/eaSUWXVw4rCKyttv4ZFSZpXnpiqrKPdbmNUeJw6rKPdbmNUeN1VZWbjfwqx+OXFYybnfwqy+uanKSs79Fmb1zYnDipJnqg/3W5jVNzdV2RK1penJ/RZm9cuJw5aopaanJSUD91uY1S83VS3Fim1+ctOTmRWq2jMOSbsCFwGdgCsi4swKh1QTih0Gm6f5yU1PZlaoKhOHpE7AJcDOwCRgnKR7IuKFykZW3fIkg7zNT256MrMm1dpUtTUwMSJei4g5wChgcIVjqnp5hsG6+cnM2koRUekYFiHp+8CuEfHjbPkgYJuIOKpZvcOBw7PFLwLPdWigHWt14P3FV1mxO/TbCBAQ8OorMOOzxddfaUX4ZMbi63WIIo6vZtXzsYGPr9ZtHBEr5lmhKpuqSD98zS2S4SLiMuAyAEnjI2JAuQOrFB9f7arnYwMfX62TND7vOtXaVDUJWLtguS8wuUKxmJlZgWpNHOOAfpLWl7QcsD9wT4VjMjMzqrSpKiLmSjoKeJA0HPeqiHh+CatdVv7IKsrHV7vq+djAx1frch9fVXaOm5lZ9arWpiozM6tSThxmZpZLXSUOSb+V9I6kp7PHbpWOqb0k7SrpZUkTJZ1Y6XhKTdIbkp7Nvq/cwwKrjaSrJL0n6bmCslUlPSzp1ex5lUrG2B6tHF/d/N1JWlvSY5JelPS8pJ9n5TX/HS7m2HJ/f3XVxyHpt8CnEXFupWMphWzqlVcomHoF+EE9Tb0i6Q1gQETUxQVWkv4L+BS4NiK+mJWdDXwYEWdmyX+ViDihknG2VSvH91vq5O9OUm+gd0T8S9KKwARgT+AQavw7XMyx7UvO76+uzjjqkKdeqTER8Xfgw2bFg4GR2euRpD/WmtTK8dWNiJgSEf/KXs8AXgT6UAff4WKOLbd6TBxHSXomO6WuudPJZvoAbxcsT6KNX3QVC+AhSROyKWTqUa+ImALpjxdYo8LxlEM9/d0BIGk94KvAk9TZd9js2CDn91dziUPSXyU918JjMDAC2AD4CjAFOK+iwbZfUVOv1LjtImJL4NvA0KwpxGpLvf3dIWkF4HbgmIj4pNLxlFILx5b7+6vKCwAXJyJ2KqaepMuBe8scTrnV/dQrETE5e35P0p2k5rm/VzaqkpsqqXdETMnamd+rdEClFBFTm17Xw9+dpGVJP6w3RMQdWXFdfIctHVtbvr+aO+NYnOwLbfI9an+23LqeekVS96yTDkndgV2o/e+sJfcAQ7LXQ4C7KxhLydXT350kAVcCL0bE+QVv1fx32NqxteX7q7dRVdeRTrcCeAM4oqldslZlQ+MuZMHUK7+vcEglI+kLwJ3ZYmfgxlo/Pkk3AYNIU3FPBX4D3AXcAqwDvAXsExE12cHcyvENok7+7iR9HfgH8CwwLyseTuoLqOnvcDHH9gNyfn91lTjMzKz86qqpyszMys+Jw8zMcnHiMDOzXJw4zMwsFycOMzPLxYnDlmqSDpF0TYX2vbakSzpoX9dIOqSD9nWYpD06Yl9WGU4ctrSbTppmoRJOBW7qoH1NIR1riyTtJ+mbJdrXrcDJ2ezOVod8HYdZBUjqA9wZEVtXOhZIZ17AuxHxQIm2dxHwRETcXIrtWXXxGYeVjaQrJX0o6fRseTVJf5M0Q9KubdjeTyXdk010+aCkyyR1a1bnEUmTJf13tryxpPHZzYe+UlBvQHZTm78vrqlK0vbZjXsek3SfpLMkXZw39hYcSLqivHBfG0t6VNKogrKfSBrdrOwr2c14/lvS3dlnOlbSoS3E/+Ns/Wdaa6qSdDtwInB2Vnd09jmu36zeKpJGZvt7SNLNkm5p/h1kbifdw8LqUUT44UfZHsAZwJDsdVfgBmDbNm6rW7PlTYH/aVbWmTQfzy7Z8iqkH7F+rWyzK3DNYvb5GNCj2T6vKsHncjewYyvvjVpSGelH+d/AutnysqREtE4r29wVOGQx8RwC7LqEmM8GdipYXg64v/n3kr23PPB+pf/9+VGeR83Njmu1JSJOkvS77AxgW+CciHiqsI6ku4GVW1j9hYj4WcHylyUdR5onaS7wCc3a7SNirqSfAH+StCbpBjzDIuLNNh7CU8DXJI2LiGkR8SLwozZuq9B6wDvt3MaJTccVEQ2S/peU2N5q53Zb8ziwlaTXgTci3VysxduMRsQsSY2SekTEx2WKxyrEicM6wjnAGOD+5kkDICKWeFdDSVsDJwE/j4jXs7LepDOa5tubJ+kU0uzCp7UjaRARwyR9DdgrS0QrkiZj/Fdbt1lCzTsoG2n5Hi6l2VnEPZKeAr4O7JvNbPxmRFzayipli8Uqy30cVlaSVibdanM/0j0Njmvjpn4AHNmUNDItjhLKOp5HkO7tsaGkH7Rlh5KWldQlIp6IiEsj4nfAL0kzirbXG9TY3RwlrRgRb0fETRFxRkQMT8X6Vgt1uwKdfLZRn5w4rGyUbkF5DXBSRLwQ6R4AsyT9qg2bexPYpmDbG9DCPREkrQtcAvwkIt6JiF8AW0ga0rxuEXYFzpFU+HeyLrBaG7bV3FgKjqeZVSR1gfkd0h1xrcc7wFZNC5KWkbRTs3s1PCZpjYI6y2XrzGxhe1uz4LakVmfcVGVlIakX8ASpL+JA4H+yRLIHsK2kNSPi6Byb/BNwiaSjSJ2yHwCnATdLujoiDpX0JeAh0t3Zvk/q5+hH+oE+RtIaEXGOJJGSzkqk/zxtJGl0tp8bI+Ky7PUc4MNsHyuQOtJnAEPb8pk0cz2p0/70Ft67CPibpM+BWcC5wI2SjoiISyXtTRoFNUdSQ0Q8ko0YOwGYLenliHg9O/ZLs2NcFVguG1kVpP6Rwh/2vwI7ZZ/DstmxP0Tq12jyDPDrLDmvSPr9uCYi/tHCMexN+k+D1SFfx2FWIZKuBS6LiMeXWLmGSFoJeBTYJiIaKx2PlZ6bqswq59fAAZUOogz2IQ1KcNKoUz7jMDOzXHzGYWZmuThxmJlZLk4cZmaWixOHmZnl4sRhZma5OHGYmVku/w8MECakuJwevgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the simplest way to plot\n",
+    "plt.plot(x, y, '.b') # format is'[marker][line][color]'\n",
+    "\n",
+    "# axis ranges\n",
+    "plt.axis([-5, 25, 0, 500])\n",
+    "\n",
+    "# group relevant font stuff in one place\n",
+    "font = {'family': 'Courier New', # Arial\n",
+    "        'color':  'black',\n",
+    "        'size': 16,\n",
+    "        }\n",
+    "\n",
+    "plt.xlabel('x-axis (units)', fontdict=font)\n",
+    "plt.ylabel('y-axis (units)', fontdict=font)\n",
+    "\n",
+    "plt.title('Title of the plot', fontdict=font)\n",
+    "\n",
+    "plt.text(-3.0, 350, r'some text', fontdict=font, color='g')\n",
+    "plt.text(-3.0, 300, r'LaTex: $\\alpha \\beta \\gamma$', fontdict=font, color='b')\n",
+    "\n",
+    "# make the plot appear\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Markers:  \n",
+    "character\tdescription  \n",
+    "'.'\tpoint marker  \n",
+    "','\tpixel marker  \n",
+    "'o'\tcircle marker  \n",
+    "'v'\ttriangle_down marker  \n",
+    "'^'\ttriangle_up marker  \n",
+    "'<'\ttriangle_left marker  \n",
+    "'>'\ttriangle_right marker  \n",
+    "'1'\ttri_down marker  \n",
+    "'2'\ttri_up marker  \n",
+    "'3'\ttri_left marker  \n",
+    "'4'\ttri_right marker  \n",
+    "'s'\tsquare marker  \n",
+    "'p'\tpentagon marker  \n",
+    "'*'\tstar marker  \n",
+    "'h'\thexagon1 marker  \n",
+    "'H'\thexagon2 marker  \n",
+    "'+'\tplus marker  \n",
+    "'x'\tx marker  \n",
+    "'D'\tdiamond marker  \n",
+    "'d'\tthin_diamond marker  \n",
+    "'|'\tvline marker  \n",
+    "'_'\thline marker  \n",
+    "\n",
+    "\n",
+    "### Line Styles\n",
+    "\n",
+    "character\tdescription  \n",
+    "'-'\tsolid line style  \n",
+    "'--'\tdashed line style  \n",
+    "'-.'\tdash-dot line style  \n",
+    "':'\tdotted line style  \n",
+    "\n",
+    "\n",
+    "### Colors\n",
+    "\n",
+    "The supported color abbreviations are the single letter codes\n",
+    "\n",
+    "character\tcolor  \n",
+    "'b'\tblue  \n",
+    "'g'\tgreen  \n",
+    "'r'\tred  \n",
+    "'c'\tcyan  \n",
+    "'m'\tmagenta  \n",
+    "'y'\tyellow  \n",
+    "'k'\tblack  \n",
+    "'w'\twhite  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x11bbad410>,\n",
+       " <matplotlib.lines.Line2D at 0x11bbad610>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "# you can overlap several plot in one call\n",
+    "plt.plot(x, x, '-b', x, x**2, ':r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": [
+    "# scatter plots\n",
+    "a = np.random.randn(50)\n",
+    "b = a + np.random.randn(50)\n",
+    "plt.scatter(a, b)\n",
+    "plt.xlabel('entry a')\n",
+    "plt.ylabel('entry b')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": [
+    "# plotting categorical variables\n",
+    "names = ['group_a', 'group_b', 'group_c']\n",
+    "values = [1, 10, 100]\n",
+    "plt.bar(names, values)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A figure is composed of subplots\n",
+    "def f(t):\n",
+    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
+    "\n",
+    "t1 = np.arange(0.0, 5.0, 0.1)\n",
+    "t2 = np.arange(0.0, 5.0, 0.02)\n",
+    "\n",
+    "\n",
+    "# create a figure (by default the figure 1)\n",
+    "plt.figure() \n",
+    "\n",
+    "# The subplot() command specifies numrows, numcols, plot_number \n",
+    "# where plot_number ranges from 1 to numrows*numcols. \n",
+    "plt.subplot(211) \n",
+    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
+    "\n",
+    "plt.subplot(212)\n",
+    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mauro/anaconda3/envs/fits/lib/python3.7/site-packages/ipykernel_launcher.py:18: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Move around figures and subplots\n",
+    "\n",
+    "plt.figure(1)                # the first figure\n",
+    "plt.subplot(221)             # the first subplot in the first figure\n",
+    "plt.plot([1, 2, 3])\n",
+    "plt.subplot(222)             # the second subplot in the first figure\n",
+    "plt.plot([4, 5, 6])\n",
+    "plt.subplot(223)             # the first subplot in the first figure\n",
+    "plt.plot([1, 2, 8])\n",
+    "plt.subplot(224)             # the second subplot in the first figure\n",
+    "plt.plot([4, 3, 6])\n",
+    "\n",
+    "\n",
+    "plt.figure(2)                # a second figure\n",
+    "plt.plot([4, 5, 6])          # creates a subplot(111) by default\n",
+    "\n",
+    "plt.figure(1)                # figure 1 current; subplot(212) still current\n",
+    "plt.subplot(221)             # make subplot(211) in figure1 current\n",
+    "plt.title('Easy as 1, 2, 3') # subplot 211 title\n",
+    "\n",
+    "#Save a figure\n",
+    "# plt.savefig(\"test.pdf\") \n",
+    "\n",
+    "# Close and release the memory for figure(1)\n",
+    "plt.close(1)\n",
+    "plt.close(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A figure is composed of subplots\n",
+    "def f(t):\n",
+    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
+    "\n",
+    "t1 = np.arange(0.0, 5.0, 0.1)\n",
+    "t2 = np.arange(0.0, 5.0, 0.02)\n",
+    "\n",
+    "\n",
+    "# Create two subplots sharing y axis\n",
+    "fig, (ax1, ax2) = plt.subplots(2, sharey=True)\n",
+    "ax1.plot(t1, f(t1), 'bo')\n",
+    "ax1.set(title='A tale of 2 subplots', ylabel='Damped oscillation')\n",
+    "\n",
+    "ax2.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
+    "ax2.set(xlabel='time (s)', ylabel='Undamped')\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Figure(432x288) AxesSubplot(0.125,0.125;0.775x0.755)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# by default all subplots in a figure are ordered on a rectangular grid\n",
+    "# to position plots in any arbitrary place use axes\n",
+    "\n",
+    "# create some data to use for the plot\n",
+    "a = np.random.randn(50)\n",
+    "b = a + np.random.randn(50)\n",
+    "\n",
+    "fig, main_ax = plt.subplots()\n",
+    "print (fig, main_ax)\n",
+    "main_ax.scatter(a, b)\n",
+    "main_ax.set_xlim(-5, 5)\n",
+    "main_ax.set_ylim(-5, 20)\n",
+    "main_ax.set_xlabel('x')\n",
+    "main_ax.set_ylabel('y')\n",
+    "main_ax.set_title('title')\n",
+    "\n",
+    "# this is an inset axes over the main axes\n",
+    "right_inset_ax = fig.add_axes([.65, .6, .2, .2])\n",
+    "right_inset_ax.scatter(a,b)\n",
+    "right_inset_ax.set_title('title')\n",
+    "#remove ticks\n",
+    "right_inset_ax.set_xticks([])\n",
+    "right_inset_ax.set_yticks([])\n",
+    "\n",
+    "# this is another inset axes over the main axes \n",
+    "left_inset_ax = fig.add_axes([.2, .6, .2, .2])\n",
+    "left_inset_ax.scatter(a,b)\n",
+    "left_inset_ax.set_title('title')\n",
+    "#remove ticks\n",
+    "left_inset_ax.set_xticks([])\n",
+    "left_inset_ax.set_yticks([])\n",
+    "\n",
+    "# this is another inset axes below the main axes\n",
+    "left_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n",
+    "left_inset_ax.scatter(a,b)\n",
+    "left_inset_ax.set_xlim(0, 0.2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# plots styles\n",
+    "print (plt.style.available)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": [
+    "# Histograms and text\n",
+    "\n",
+    "mu, sigma = 100, 15\n",
+    "x = mu + sigma * np.random.randn(10000)\n",
+    "\n",
+    "# the histogram of the data\n",
+    "n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n",
+    "\n",
+    "plt.xlabel('Smarts')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title('Histogram of IQ')\n",
+    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
+    "plt.axis([40, 160, 0, 0.03])\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Useful links\n",
+    "\n",
+    "Gallery of examples:\n",
+    "https://matplotlib.org/gallery/index.html\n",
+    "\n",
+    "pyplot.plot ('Markers', 'Line Styles', 'Colors'\n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "\n",
+    "pyplot.figure:\n",
+    "https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure\n",
+    "  \n",
+    "line properties:\n",
+    "https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D\n",
+    "\n",
+    "matplotlib fonts:\n",
+    "http://jonathansoma.com/lede/data-studio/matplotlib/list-all-fonts-available-in-matplotlib-plus-samples/\n",
+    "\n",
+    "stlye sheet reference:\n",
+    "https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html\n",
+    "\n",
+    " "
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/binnedLikelihood.ipynb b/notebooks/binnedLikelihood.ipynb
new file mode 100644
index 0000000..1e29157
--- /dev/null
+++ b/notebooks/binnedLikelihood.ipynb
@@ -0,0 +1,540 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Binned Likelihood\n",
+    "\n",
+    "\n",
+    "In this notebook we will be using probfit together with iminuit to perform a Binned Likelihood fit.\n",
+    "\n",
+    "probfit:\n",
+    "https://probfit.readthedocs.io/en/latest/\n",
+    "\n",
+    "iMinuit:\n",
+    "https://iminuit.readthedocs.io/en/latest/index.html#\n",
+    "\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fit an exponential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import exp, pi, sqrt\n",
+    "import probfit\n",
+    "from probfit import BinnedLH\n",
+    "from iminuit import Minuit, describe\n",
+    "from scipy.stats import norm, chi2, lognorm\n",
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate data\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=123456)\n",
+    "\n",
+    "# Generate a toy dataset on an exponential distribution (background)\n",
+    "# pdf = lambda * exp(-lambda * x) ; scale = 1/lambda\n",
+    "data = scipy.stats.expon.rvs(loc= 100, scale = 25, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[5,5])\n",
+    "plt.subplot(111)\n",
+    "plt.hist(data, bins=150, range=[100,400], color='blue', alpha=0.5)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 2')\n",
+    "plt.yscale('log', nonposy='clip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['loc', 'scale']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def exp_func(x, loc, scale):\n",
+    "    return scipy.stats.expon.pdf(x, loc, scale)\n",
+    "\n",
+    "blh = BinnedLH(exp_func, data, bins=150, bound=(100,400))\n",
+    "describe(blh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "loc\n",
+       "</td>\n",
+       "<td>\n",
+       "100.0\n",
+       "</td>\n",
+       "<td>\n",
+       "1.0\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "scale\n",
+       "</td>\n",
+       "<td>\n",
+       "10.0\n",
+       "</td>\n",
+       "<td>\n",
+       "1.0\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------------------------------------------------\n",
+       "|   | Name  |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "-------------------------------------------------------------------------------------------\n",
+       "| 0 | loc   |   100.0   |    1.0    |            |            |         |         |       |\n",
+       "| 1 | scale |   10.0    |    1.0    |            |            |         |         |       |\n",
+       "-------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = Minuit(blh, \n",
+    "           loc=100, scale= 10,\n",
+    "           errordef=0.5,  #remember up is 0.5 for likelihood and 1 for chi^2\n",
+    "           pedantic=False)\n",
+    "\n",
+    "# Show() is the same thing as draw(). But show the figure immediately.\n",
+    "# For all parameters and return vars:\n",
+    "#    https://probfit.readthedocs.io/en/latest/api.html#probfit.costfunc.UnbinnedLH.draw\n",
+    "plt.figure(figsize=[5,5])\n",
+    "plt.yscale('log', nonposy='clip')\n",
+    "plt.ylim([0.1,1000])\n",
+    "blh.show(m, print_par=True)\n",
+    "m.get_param_states()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mauro/anaconda3/envs/fits/lib/python3.7/site-packages/ipykernel_launcher.py:1: LogWarning: x is really small return 0\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "loc\n",
+       "</td>\n",
+       "<td>\n",
+       "100.25\n",
+       "</td>\n",
+       "<td>\n",
+       "0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "scale\n",
+       "</td>\n",
+       "<td>\n",
+       "25.07\n",
+       "</td>\n",
+       "<td>\n",
+       "0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------------------------------------------------\n",
+       "|   | Name  |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "-------------------------------------------------------------------------------------------\n",
+       "| 0 | loc   |  100.25   |   0.25    |            |            |         |         |       |\n",
+       "| 1 | scale |   25.07   |   0.25    |            |            |         |         |       |\n",
+       "-------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.migrad()\n",
+    "plt.figure(figsize=[5,5])\n",
+    "plt.yscale('log', nonposy='clip')\n",
+    "plt.ylim([0.1,1000])\n",
+    "blh.show(m)\n",
+    "m.get_param_states()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mauro/anaconda3/envs/fits/lib/python3.7/site-packages/ipykernel_launcher.py:1: LogWarning: x is really small return 0\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "loc\n",
+       "</td>\n",
+       "<td>\n",
+       " 100.25\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.25\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "scale\n",
+       "</td>\n",
+       "<td>\n",
+       " 25.07\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.25\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------------------------------------------------\n",
+       "|   | Name  |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "-------------------------------------------------------------------------------------------\n",
+       "| 0 | loc   |   100.25  |    0.25   |   -0.25    |    0.25    |         |         |       |\n",
+       "| 1 | scale |   25.07   |    0.25   |   -0.25    |    0.25    |         |         |       |\n",
+       "-------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.minos()\n",
+    "m.get_param_states()"
+   ]
+  },
+  {
+   "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/centralLimitTheorem.ipynb b/notebooks/centralLimitTheorem.ipynb
new file mode 100644
index 0000000..84d85e8
--- /dev/null
+++ b/notebooks/centralLimitTheorem.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Central Limit Theorem (CLT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take N independent uniformly distributed U(–1,1) random variables. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# generate uniformly distributed data between -1 and +1\n",
+    "#\n",
+    "# set the random seed to always reproduce the same distributions\n",
+    "np.random.seed(seed=12345)\n",
+    "\n",
+    "# the uniform method in scipy generates random numbers between loc and loc+scale\n",
+    "x = scipy.stats.uniform.rvs(loc = -1, scale = 2, size=1000)\n",
+    "\n",
+    "plt.figure(figsize=[5,5])\n",
+    "plt.hist(x, bins=100, range=[-10,10], density=False, alpha=1.0)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1296 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sum the uncertainties coming from N sources.\n",
+    "# Assume they are all uniformly distributed:\n",
+    "# (create a sample of N measurements)\n",
+    "\n",
+    "plt.figure(figsize=[15, 18])\n",
+    "\n",
+    "nbins = 100\n",
+    "myrange = [-10,10]\n",
+    "\n",
+    "# one source of uncertainty\n",
+    "err = scipy.stats.uniform.rvs(loc = -1, scale = 2, size=1000)\n",
+    "plt.subplot(331)\n",
+    "plt.hist(err, bins=nbins, range=myrange)\n",
+    "plt.title('1 source of unc.')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 1')\n",
+    "\n",
+    "for i in range(2,10):\n",
+    "    # two sources of uncertainties\n",
+    "    err += scipy.stats.uniform.rvs(loc = -1, scale = 2, size=1000)\n",
+    "    plt.subplot(330+i)\n",
+    "    plt.hist(err, bins=nbins, range=myrange)\n",
+    "    plt.title(str(i)+' sources of unc.')\n",
+    "    plt.xlabel(r'x [units]')\n",
+    "    plt.ylabel(r'Entries / bins size = 1')\n"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/dataRepresentationGraphsHists.ipynb b/notebooks/dataRepresentationGraphsHists.ipynb
new file mode 100644
index 0000000..f8a2cff
--- /dev/null
+++ b/notebooks/dataRepresentationGraphsHists.ipynb
@@ -0,0 +1,791 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Representation Graphs and Histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data =  fall time (t) vs. the height (x) of which an apple is dropped\n",
+    "# Simple dataset (x+/-dx, t+/-dt)\n",
+    "\n",
+    "data_x= [0.49805377, 0.67623611, 0.80522924, 0.97044345, 1.12945511, \n",
+    "    1.28508361, 1.43542144, 1.59138769, 1.72742522, 1.89783378]                                                                                                                      \n",
+    "\n",
+    "data_dx = [0.01, 0.01, 0.01, 0.01, 0.01,\n",
+    "      0.01, 0.01, 0.01, 0.01, 0.01]                                                                                       \n",
+    "\n",
+    "data_t = [0.3304071 , 0.28373072, 0.44070176, 0.49827658, 0.45374148, \n",
+    "     0.52819172, 0.64219285, 0.60636401, 0.59992293, 0.55806461]                                                                                                                     \n",
+    "                                                                                                                                                                                                                                                 \n",
+    "data_dt = [0.05, 0.05, 0.05, 0.05, 0.05,  \n",
+    "      0.05, 0.05, 0.05, 0.05, 0.05]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": [
+    "# create a figure (by default the figure numbering start from 1)\n",
+    "plt.figure(1) \n",
+    "\n",
+    "# plot measurement with errors\n",
+    "plt.errorbar(data_x, data_t, xerr=data_dx, yerr=data_dt,\n",
+    "             marker='o', color='black', label='data', linestyle='none')\n",
+    "\n",
+    "# legend\n",
+    "plt.legend(loc='upper left',fontsize='15', numpoints=1)\n",
+    "\n",
+    "# set axis range\n",
+    "plt.xlim([0,2.0])\n",
+    "plt.ylim([0,0.8])\n",
+    "\n",
+    "# grid lines (sometimes useful)\n",
+    "plt.grid(False)\n",
+    "\n",
+    "# always label the axes (the r'$...$' make the axes have a latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# make the plot appear\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": [
+    "# Any function you draw is computed in on a finite number of points\n",
+    "# With a large enough number of points you achieve what graphically \n",
+    "# seems a smooth curve\n",
+    "def Falltime(x, g):\n",
+    "    return np.sqrt(2*x/g)\n",
+    "\n",
+    "# create a dataset\n",
+    "true_g = 9.8\n",
+    "function_x = np.arange(0, 2.0, 0.01)\n",
+    "function_y = Falltime(function_x, true_g)\n",
+    "\n",
+    "# the simplest way to plot\n",
+    "plt.plot(function_x, function_y,color='blue',label='theory')\n",
+    "\n",
+    "# set axis range\n",
+    "plt.xlim([0,2.0])\n",
+    "plt.ylim([0,0.8])\n",
+    "\n",
+    "# always label the axes (the r'$...$' make the axes have a latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# legend\n",
+    "plt.legend(loc='upper left',fontsize='15', numpoints=1)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": [
+    "# you can also overlap the two plots by putting them together on the same figure\n",
+    "\n",
+    "# plot mean value on top\n",
+    "plt.plot(function_x, function_y, color='blue', label='theory')\n",
+    "\n",
+    "# always label the axes (the r'$...$' make the axes have a latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# plot measurement with errors\n",
+    "plt.errorbar(data_x, data_t, xerr=data_dx, yerr=data_dt,\n",
+    "             marker='o', color='black', label='data', linestyle='none')\n",
+    "\n",
+    "# legend\n",
+    "plt.legend(loc='upper left',fontsize='15', numpoints=1)\n",
+    "\n",
+    "# set axis range\n",
+    "plt.xlim([0,2.0])\n",
+    "plt.ylim([0,0.8])\n",
+    "\n",
+    "# grid lines (sometimes useful)\n",
+    "plt.grid(False)\n",
+    "\n",
+    "# always label the axes (the r'$...$' make the axes have a latex style)\n",
+    "plt.xlabel(r'height $h$ [m]')\n",
+    "plt.ylabel(r'time $t$ [s]')\n",
+    "\n",
+    "# make the plot appear\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Histograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data are 100 points gaussian distributed around mu = 5 with sigma = 1\n",
+    "x = [-0.69, -0.77, -0.037, -0.047, -0.88, 0.5, -1.7, -0.89, 1.4, 0.47, \n",
+    "     -0.081, 1.7, 0.27, -0.77, -0.19, -0.47, 1.1, 0.86, -1.4, 1, -0.78, \n",
+    "     0.36, -0.08, -0.62, -0.31, -0.63, 0.33, -1.1, -1.3, 1.3, 1.2, 1.2, \n",
+    "     -0.45, 0.058, -1.2, 0.73, -0.3, 1.2, -0.48, -0.27, -0.25, 0.077, \n",
+    "     1.8, 2.4, 0.51, 1.3, 2.1, -0.72, 1.1, 0.83, 0.055, -1.2, -3.8, -0.95, \n",
+    "     -0.25, 0.11, -0.38, 0.9, 0.16, 0.38, 2, -0.34, 0.16, -0.41, -1.8, 0.27, \n",
+    "     -1.3, -0.33, -0.33, -0.36, 1.7, -0.52, 0.84, 0.97, 1.8, 1.3, 1.1, -0.21, \n",
+    "     -1.1, -0.039, -0.33, -0.2, 0.81, -1.5, 0.73, 0.37, -0.39, 0.45, -0.44, \n",
+    "     -1.6, 2, -0.44, -0.19, -0.57, -0.094, 0.68, -0.19, 0.56, -0.37, -1.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[10, 10])\n",
+    "\n",
+    "# histogram of the data (default color is blue)\n",
+    "plt.subplot(221)\n",
+    "plt.hist(x, bins=10, range=[-5,5])\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 1')\n",
+    "\n",
+    "\n",
+    "# histogram of the data with area normalized to unity\n",
+    "plt.subplot(222)\n",
+    "plt.hist(x, bins=10, range=[-5,5], \n",
+    "         density=True)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 1')\n",
+    "\n",
+    "\n",
+    "# histogram of the data\n",
+    "plt.subplot(223)\n",
+    "plt.hist(x, bins=[-5,-3,-1,1,2,5])\n",
+    "\n",
+    "# always label the axes (the r'$...$' make the axes have a latex style)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / variable bins size')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Choice of the binning\n",
+    "\n",
+    "plt.figure(figsize=[15, 15])\n",
+    "\n",
+    "j = 0\n",
+    "for i in [1,2,3,5,10,50,100,200,300]:\n",
+    "    j+=1\n",
+    "    plt.subplot(330+j)\n",
+    "    plt.hist(x, bins=i, range=[-5,5])\n",
+    "    plt.xlabel(r'x [units]')\n",
+    "    binsize = 10./i\n",
+    "    label = \"Entries / bins size = \" + str( float('%.1g' % binsize ) )\n",
+    "    plt.ylabel(label)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Normalized Entries / bins size = 0.2')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Shape comparison by overlapping normalized histograms\n",
+    "\n",
+    "# generate two gaussian distributed samples\n",
+    "import scipy.stats\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=12345)\n",
+    "#mu = 1,   sigma = 1\n",
+    "x1 = scipy.stats.norm.rvs(loc=1.0, scale=1, size=10000)\n",
+    "#mu = 1.2, sigma = 1\n",
+    "x2 = scipy.stats.norm.rvs(loc=1.2, scale=1, size=2000)\n",
+    "\n",
+    "plt.figure(figsize=[10,5])\n",
+    "plt.subplot(121)\n",
+    "plt.hist(x1, bins=50, range=[-5,5], color='blue', alpha=0.5)\n",
+    "plt.hist(x2, bins=50, range=[-5,5], color='green',alpha=0.5)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.2')\n",
+    "\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.hist(x1, bins=50, range=[-5,5], color='blue', alpha=0.5, density=True)\n",
+    "plt.hist(x2, bins=50, range=[-5,5], color='green',alpha=0.5, density=True)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Normalized Entries / bins size = 0.2')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mauro/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "/Users/mauro/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/Users/mauro/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "/Users/mauro/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'ratio')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ratio plot\n",
+    "\n",
+    "# generate three gaussian distributed samples centered at 0.8, 1.0, 1.2\n",
+    "import scipy.stats\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=12345)\n",
+    "#mu = 1.0,   sigma = 1\n",
+    "x1 = scipy.stats.norm.rvs(loc=1.0, scale=1, size=10000)\n",
+    "#mu = 1.2,   sigma = 1\n",
+    "x2 = scipy.stats.norm.rvs(loc=1.2, scale=1, size=10000)\n",
+    "\n",
+    "# Plot the two normalized histograms and their ratio\n",
+    "fig, main_ax = plt.subplots()\n",
+    "entries1, bins1, patches1 = main_ax.hist(x1, bins=50, range=[-5,5], color='red', alpha=0.5, density=True)\n",
+    "entries2, bins2, patches2 = main_ax.hist(x2, bins=50, range=[-5,5], color='blue',alpha=0.5, density=True)\n",
+    "\n",
+    "plt.title('Ratio plot', axes=main_ax)\n",
+    "plt.xlabel(r'x [units]', axes=main_ax)\n",
+    "plt.ylabel(r'Entries / bins size = 0.2', axes=main_ax)\n",
+    "\n",
+    "\n",
+    "# if numerator or denominator are zero set the ratio to zero\n",
+    "ratio12 = np.nan_to_num(entries1/entries2, nan=0, posinf=0, neginf=0)\n",
+    "# zoom between 0 and 2 to avoid the outliers\n",
+    "ratio12 = np.clip(ratio12, 0,2.)\n",
+    "\n",
+    "# get the bin center\n",
+    "binscenter = bins1[0:50]+(bins1[1]-bins1[0])*0.5\n",
+    "bottom_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n",
+    "bottom_inset_ax.plot(binscenter, ratio12, marker=\".\", linestyle=\"\", alpha=0.8, color=\"r\")\n",
+    "plt.xlabel(r'x [units]', axes=bottom_inset_ax)\n",
+    "plt.ylabel(r'ratio'    , axes=bottom_inset_ax)\n",
+    "\n",
+    "\n",
+    "# Or the other way around\n",
+    "# Plot the two normalized histograms and their ratio\n",
+    "fig, main_ax = plt.subplots()\n",
+    "entries1, bins1, patches1 = main_ax.hist(x1, bins=50, range=[-5,5], color='red', alpha=0.5, density=True)\n",
+    "entries2, bins2, patches2 = main_ax.hist(x2, bins=50, range=[-5,5], color='blue',alpha=0.5, density=True)\n",
+    "\n",
+    "plt.title('Ratio plot', axes=main_ax)\n",
+    "plt.xlabel(r'x [units]', axes=main_ax)\n",
+    "plt.ylabel(r'Entries / bins size = 0.2', axes=main_ax)\n",
+    "\n",
+    "# if numerator or denominator are zero set the ratio to zero\n",
+    "ratio21 = np.nan_to_num(entries2/entries1, nan=0, posinf=0, neginf=0)\n",
+    "# zoom between 0 and 2 to avoid the outliers\n",
+    "ratio21 = np.clip(ratio21, 0,2.)\n",
+    "\n",
+    "# get the bin center\n",
+    "binscenter = bins1[0:50]+(bins1[1]-bins1[0])*0.5\n",
+    "bottom_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n",
+    "bottom_inset_ax.plot(binscenter, ratio21, marker=\".\", linestyle=\"\", alpha=0.8, color=\"r\")\n",
+    "plt.xlabel(r'x [units]', axes=bottom_inset_ax)\n",
+    "plt.ylabel(r'ratio'    , axes=bottom_inset_ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mauro/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "/Users/mauro/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/Users/mauro/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'ratio')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ratio plot\n",
+    "\n",
+    "# generate three gaussian distributed samples centered at 0.8, 1.0, 1.2\n",
+    "import scipy.stats\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=12345)\n",
+    "#mu = 1.0,   sigma = 1\n",
+    "x1 = scipy.stats.norm.rvs(loc=1.0, scale=1, size=10000)\n",
+    "#mu = 1.0,   sigma = 0.9\n",
+    "x2 = scipy.stats.norm.rvs(loc=1.0, scale=0.9, size=10000)\n",
+    "\n",
+    "# Plot the two normalized histograms and their ratio\n",
+    "fig, main_ax = plt.subplots()\n",
+    "entries1, bins1, patches1 = main_ax.hist(x1, bins=50, range=[-5,5], color='red', alpha=0.5, density=True)\n",
+    "entries2, bins2, patches2 = main_ax.hist(x2, bins=50, range=[-5,5], color='blue',alpha=0.5, density=True)\n",
+    "\n",
+    "plt.title('Ratio plot', axes=main_ax)\n",
+    "plt.xlabel(r'x [units]', axes=main_ax)\n",
+    "plt.ylabel(r'Entries / bins size = 0.2', axes=main_ax)\n",
+    "\n",
+    "\n",
+    "# if numerator or denominator are zero set the ratio to zero\n",
+    "ratio12 = np.nan_to_num(entries1/entries2, nan=0, posinf=0, neginf=0)\n",
+    "# zoom between 0 and 2 to avoid the outliers\n",
+    "ratio12 = np.clip(ratio12, 0,2.)\n",
+    "\n",
+    "# get the bin center\n",
+    "binscenter = bins1[0:50]+(bins1[1]-bins1[0])*0.5\n",
+    "bottom_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n",
+    "bottom_inset_ax.plot(binscenter, ratio12, marker=\".\", linestyle=\"\", alpha=0.8, color=\"r\")\n",
+    "plt.xlabel(r'x [units]', axes=bottom_inset_ax)\n",
+    "plt.ylabel(r'ratio'    , axes=bottom_inset_ax)\n",
+    "\n",
+    "\n",
+    "# Or the other way around\n",
+    "# Plot the two normalized histograms and their ratio\n",
+    "fig, main_ax = plt.subplots()\n",
+    "entries1, bins1, patches1 = main_ax.hist(x1, bins=50, range=[-5,5], color='red', alpha=0.5, density=True)\n",
+    "entries2, bins2, patches2 = main_ax.hist(x2, bins=50, range=[-5,5], color='blue',alpha=0.5, density=True)\n",
+    "\n",
+    "plt.title('Ratio plot', axes=main_ax)\n",
+    "plt.xlabel(r'x [units]', axes=main_ax)\n",
+    "plt.ylabel(r'Entries / bins size = 0.2', axes=main_ax)\n",
+    "\n",
+    "# if numerator or denominator are zero set the ratio to zero\n",
+    "ratio21 = np.nan_to_num(entries2/entries1, nan=0, posinf=0, neginf=0)\n",
+    "# zoom between 0 and 2 to avoid the outliers\n",
+    "ratio21 = np.clip(ratio21, 0,2.)\n",
+    "\n",
+    "# get the bin center\n",
+    "binscenter = bins1[0:50]+(bins1[1]-bins1[0])*0.5\n",
+    "bottom_inset_ax = fig.add_axes([0.125, 0., .775, 0.125])\n",
+    "bottom_inset_ax.plot(binscenter, ratio21, marker=\".\", linestyle=\"\", alpha=0.8, color=\"r\")\n",
+    "plt.xlabel(r'x [units]', axes=bottom_inset_ax)\n",
+    "plt.ylabel(r'ratio'    , axes=bottom_inset_ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2D distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-4.0, 4.0)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8llU+ff7hCwq3b3kkva7tHVuK38tUKmdZmmUqzTP1M25HWcpoStX1dstU/f2qg8gu0BokDvpmZiUa9DeoIzynb2aWUnE+vwcrZx0m6VJJ/ybp/ZKmjXnsX1tpw0HfzKxEF8swtONCsnMAbyNbOOU6SaPzl4e30oCnd8zMEkR3yjBMwuKI+Gz+/dskvRG4XtJplF/M9UsO+mZmJZoV85kmaWZEPAoQEZ+TtBH4JjCnlQYc9EtUzSIoW6c0teZtqrBaWVsbtxavX5sqrFbW1v7Tiwt7fe/ercl9Hnu8uL3pU4rfr62PPJZsa8GCWcX9Wv7Uwu133nBTsq2FT3t64fZbdhZnwhx9VHrt3q/fXHwx5MEHzk7us2hO8a/TT7YWZzs9/ynptXNvXF9cWO4lM4uznaaXFOhLSWWblWWOpT6v7awb3aQsnTINW0TlfLK6O9eNboiIb0s6g6zo2oSSQV/SrS3svzkiTmrlQGZm/aZH8/Qti4iPJ7bfAry8lTbKRvpTgFeUPC7gylYOYmbWrxo2p58k6dSIuGqi55UF/d+PiJ9NcJA/qNwzM7M+0h8hH8iKr7Uf9CPihvHbJB0AHBYRt6aeY2Y2SBo2p58UEee18rwJT+RKuhY4LX/uGmCzpOsi4l2T6qGZmVUm6RjgdLJFVoKsBPOVEbG2dMdcK6f894+IbcCrgX+IiBcAJ7fZXzOzvpHl6Xe2tPKk+iO9F7g079oPyOrqC/iCpPe10kYrKZtTJS0BXguc22Zfa9eLlLKyAlqpx1LpcWXr7e4/u7iA19r7i9eiPWBGer3Vhx4rTqe8L5EWCrB4XnFq6Hdu3Vi4/cEHdybbmjWr+LXs3F68z/RFT0m29cD3ryncfuhJryzcPntG+uO/LZGauGNXuuDagbOL25u2X3FbqbRMgGcu2r9we6p4Xlla7hEHFadvp9ZaLlufOaVf0i8r60FphYrOBp4ZEfsECEkfA24HPjRRA638dP+MLPH/7oi4SdKRwF1tdNbMrO80rAzDXqBo5LOEJ9bbLdXKSH9DRDx79E5ErMv/qpiZDbyGjfTfCVwj6S7g3nzbMuBpwFtbaaCVoP8p4PktbDMzGyijc/pNERHfkPR0ssVWDiXr4nrgpohIX0o9RtkVuS8CXgwsljQ2U2c+2YVbZmYDr0kjfUlzI2I78P0WnlOobE5/OjCX7A/DvDG3bTyxeK+Z2UBTxVuXXSHpo5JeKumXZ+glHSnpbEnfBE4pa6Ds4qzryGo1XzTRlbn9oBfZBWXHSC3NN3t68X+aHk9k6ADsTGT8HLZ/cTGw1NJ7AItmFWfiPPvQucl97t5SnNmzcWPxcoFl5iUygR57NLHE46PVj3HfT+4r3n7Dtcl9Xvj60wu337Iq/auw/5ziLKk3jSwt3H7k/PR7nFoWcVbi85L6THRa6jNelh3Xz5k9UrPKMETESZJeAfw+cLykhcBu4MfAV4GzIqI4jS5XNr3ziYh4J/BpSU/6iUbEaZPqvZlZH2hQzAcgIr4GfK3d/ctO5P5T/vVv223czKzfNWlOvxPKpndW51+vSz1nsiSdAnyS7MTw+REx4YUFZma9NGAxv6XaO8cDf0q2/uJUsnMVERFHTubAkqYAf0dWA3o9cJOkKyPijsm0a2bWKUKNmtPvhFby9C8A/hBYDXTybNFxZFf5rgOQdClZESEHfTNrhoYtojJWPnA+mDFxPCLumWi/VoL+QxHx9Un0LeVQnriiDLLR/q924ThmZm1r4py+pLcB5wH380T5hQCendwp10rQ/66kjwBfBn6ZRxcRN1fv6j6K3sknZQlJWgGsAFi2bNkkD1lN1SJtqQJWZbYmCquVFdBKPVaWmpmyO7He7YZt6YJvd28oLuz2wMYHCrc/tj2dZrlo0bHFD2xPFCObmi4et/CFxSt3poq3HXTCicm2tmx5pHD7u88cSe7zo/uKX+eWRPrpkrkzk209sL24EF4qlXPh3PT7kir2105htVTqcVmxwX5X/V3qiXcAR0dEumpfQitBf3T0PfbTHkD6N6Y164HDxtxfSlYXeh8RsRJYCTAyMlK9VKaZWZtEM0f6ZLMkD7Wz44RBPyJe1k7DLbgJOErSEcB9wOuA3+7SsczMBsk64FpJX2XfGZgJi2G2kr3zgaLtEfHBKj0s2H+PpLeSlW2eAlwYEbdPpk0zs05r6MzVPflten5rWSvTO2MnN2cCpwItLcs1kcleWWZm1m1NDPoR8Wft7tvK9M5Hx96X9LfAle0e0MysX2QLozQn6o+Wx5H0FQoSX1opj9PKSH+82cCkLswyM+sXDRvpT7o8Titz+j/iib8oU4DFwKTm8/tdKmVyasmnY1oiPa6d9MvUGrmptu7YVJxiCbB5Z3E6YSqVFOCYQxcUbn/Kac8pPsa29Hq72xKpiWy5t3j7goOTbaXWyN3vaS8o3L7pvnS224t+7WmF27+/7sHkPqn1c6clPhdbEu89wEO7it//ly5aXLj9sZJ04emp9X4TlTnL0i9TaZ79XElzIp0e6EuaCVwPzCCLwV+KiPPypJZLgYXAzcCZEbHPL8jY8jiSpgPHkMXnH49/bkorI/1Tx3y/B7g/IqonhJuZ9Zls5ayO/0HbBZwYEdslTQNukPR14F3AxyPiUkmfJVsE/TOF/ZJeCXwW+EnezSMk/X4rF9K2Mqff97X0zcza1emLsyIigNGVrablt9Frn0bT1i8mq3lWGPSBjwIvi4i7ASQ9laye/oRBP/l6JE14xW0rzzEz62dStRuwSNKqMbcVT25TUyStATYBV5ON2LeOmUVZT1aqJmXTaMDPrcvbmlDZSP8Zkm4teVzA/q0cxMysH0ltVdncEhHpeh1Avoj5cyUtAC4HnlH0tIL+vDr/9nZJXwMuy593BtkFrxMqC/rHtLB/b9ZoMzOrSTczNiNiq6RrgRcCCyRNzUf7hWVpgP8x5vv7gV/Pv98MHNDKMcsWURmKufxOru1ZViQtlY2TKqBVlo1xz5YdhdtT2SC796bbWjCjOBPouMPnJfc5fH7xWrz//KP7C7cnM3SAPYnX+cZz3ly4/XOfuTzZVsre7VsLt0+Z+5TkPqk+v+pX0vv89IHifWZPLf4Zp9YnBjh4TnExtlQhtrI1clPZOAsSWWBln/vU78ugrpELnU/ZlLQY2J0H/FnAycCHge8CryHL4DkLuGL8vhHxu5M9fjt5+mZmQ6FL2TtLgIvzevj7AZdFxFWS7gAulfQXwC1ka5l0nIO+mVmJTsf8iLgVeF7B9nVki0t11YTZSJLeKqmluSIzs4GibHqnyq2r3ZFepEnWhWglBfUQsvVrL5N0ymQPaGbWT1TxX5edBayWdKmkN0k6pGoDEwb9iPgT4Ciy+aU3AXdJ+qv8YgAzs4GVzek3Z6QfEW+OiOeTXbh1AHCRpP/IY/JL8/MEpVq62Cy/gmxjftuTH+xLkv6m7d6bmVlbIuLOiPh4RJxCdiXvDWS5+jdOtG8rBdfeTvZfii3A+cAfR8RuSfsBdwHvmUzn69ZOOlmqUFUq/RJgTyKlLZVqV5ayuXh+capfap3UjVvTBc9S6Xw/2JAuLPb/biwuhpZaP/WQA4tTPAG+d2NxZvC81JqvC5Yk23rJa15euP2uu4sLqy1fXv1U1d1b0u/lMw8ufp079lRL14V0+m+qeFrZGrmpfVJSP0dIFw6sup50P2l6xmlE7CRbl6SltUlayd5ZBLx6fN5+ROyVdGpiHzOzgTBopzFbKbhWuFxi/lhHVtAyM2ui0Tn9QdLpAnJmZoOjYrG1Xv2nQNKcfIodSU+XdFpepnlCDvpmZiX2y4uutXrrkeuBmZIOBa4Bfhe4qJUdHfTNzBKalrI5tmsRsQN4NfCpiHgVcGwrOzrom5mVaOL0DiBJLwLeQLZ4CrRYVmdoau+UpaGlpNLTUuuElkntsyvRr7J1SlMpeKk0zz2RTqebvl9xWwfMSn803nH8EYXbL7u9uMrm0YuLK0YCzJlRfJytjxRXk5w1N53+uXh+8XFmP7N4Xd3UmrYAzzm8OJ3z5U9dmNxn/rTiKdXV9xdX+XyoZB3iVApman3kdtZUTqVZln32Uvv0eyXNNLFf96+ybcc7gXOAyyPidklHklXpnNDQBH0zs6pET0fvLYuI64DrJM3J768D3t7Kvp7eMTNLaVjBtV92Kyu8dgewNr//HEn/t5V9awn6ks6QdLukvZJKlxUzM6tTQ7N3PgH8JvALgIj4IfDSVnasa6R/G9lZ5+trOr6Z2YRGp3caeCKXiBhfD6Wl5WtrmdMfvZJ30C5vNrPB08PRexX3SnoxEJKmk83nt1QhYWhO5HayUFSqrXakCmuVZVDMnl5cPTXV1pL909kz9zxYvN5u2fqtD+0qzjp5wdI5hdt/9mDx2r0AxxxcvM8vdhQf//m/k15YaF1i7eCXHl6ccbP6Z9uSbb3iqEWF2zc8sjO5Typ758QjFhduL1tTOfXzTxXoS2XolO2Tyigr61eqcOCsxGdyEDQz5vNm4JPAocB64FvAW1rZsWtBX9K3yRZgGe/ciHjSgr8l7awAVgAsW7asQ70zM5uYaF62S14z/8yIeEM7+3ct6EfEyR1qZyWwEmBkZGRw67eaWfOoedPQEfG4pNOBj7ez/9BM75iZtaNZIf+X/l3Sp4EvAo+MboyImyfasZagL+lVwKeAxcBXJa2JiN+soy9mZn3oxfnXD47ZFmSraJWqK3vncuDyOo5tZtaqrOBa88b6EfGydvf19I6ZWYkmhXxJb4yIz0l6V9HjEfGxidoY+qDfTqGodtI8U2lwqe3zStZP3Zoo1JVKmytL/1w4q7iwV1kKXqqw244txUW/Dj8gnf65aGbxY6l1ZTclCrEBHLhsfuH2q9duKdz+jpcUF44DmJYoRHfk/LnJfVLvWWot3LIiaamieqljlKVZVk3NLEtJnjLAa+GmNGygP5rjPK/gsZZ+OEMf9M3M0tSo7J2I+Pv8229HxL+PfUzS8a200bQUVDOzxhjN069y65FPtbjtSTzSNzMr0aSRfr5wyouBxePm9ecDLV0W7aBvZlaiOSEfgOnAXLLYPXZefxvwmlYacNA3M0tp2BW5YxZPuSgiftZOGw76ZmYJTay9k5shaSWwnDFxPCKaeXFWv2snzTO1z7w22kqlAKaUpfOVPZaydWdxymiqMmdZZcql82cVbl+zqXhd2elT0+/Xw7uKq0m+6bilhdt37E6XH9+9t/g1Hjq3uL9QvdJkO5UpUxUzy9JyU5Ux21nreRg1aaQ/xj8DnwXOp8U6+qMc9M3MSnQ65Es6DPhHsirEe4GVEfFJSQvJauksB34KvDYiHkw0syciPtPO8f2n3sysRBdWztoDvDsingG8EHiLpGOB9wHXRMRRwDX5/ZSvSPoDSUskLRy9tXJwj/TNzBKyOf3OjvUjYgOwIf/+YUlryRZDOR04IX/axcC1wHsTzZyVf/3jsU0DR050fAd9M7MS3ZzSl7QceB5wI3Bw/geBiNgg6aDUfhGRriEyAU/vmJklqfI/YJGkVWNuKwpbluYC/wK8MyLSa3fuu897xnx/xrjH/qqVNjzSb0Oq4Fo7WT27EsXLyjIrUo+l2irL7DhkQfH6uWVZPQfOKS7Stu6BRwq3P++QA5Jt3ftQ8bq2Ry8oqidVnrmUep2p15Iqagbww43F2UML5xa/9rK+Ve1XWd+q/uyhepZQWUHBdj7j/a6Nkf6WiBgpb1PTyAL+JRHx5Xzz/ZKW5KP8JcCmgl1fB/xN/v05ZFk8o04B3j9R5zzSNzPrIWU5oBcAa8eVQr6SJ+bqzwKK1hJX4vui+4U80jczS+jGiVzgeOBM4EeS1uTb3g98CLhM0tnAPcAZBftG4vui+4Uc9M3MUlpPw2xZRNxAelR+0gS7P0fStnz/Wfn35PeL52rHcdA3MyvRpAtyI6L6ZdzjOOibmZVQ0+psTpKDvplZQrYwet296CwH/R5JpcGl0ul2l6TgpdYwbad4Wio1MLUOLqRTE0eWFadmlq0F+9QDi9ecfWB78Vq4ZemHqdefSpnc9PCuZFsvPXJx4fay9yX12ILZ0wq370gUTwOYWjHNsyzFN1WkLfVelqVldjJduV94pG9mNkSaNKffCQ76ZmYlPNI3Mxt5IU1MAAAMCElEQVQSgzinX8sVuZI+IulOSbdKulzSgjr6YWZWrq3aO41WVxmGq4FnRcSzgf8kqyFhZtYsFWvp98P8fy1BPyK+FRGjKR3fB4rXszMzq5kq3pquCXP6v0e2RFjf6GR6WioFrqwyZsrsRApeWQXGVGpgmVTaYOr4qZRBSKeMLp5fvN5u2fuSOk6qv0sPSK93Oy+Rllr2XqZSU1MptrOTLcHWHcVr9KbSZVPHgOpVNsvShdv5XPazbE5/sF5z14K+pG+TrQE53rkRcUX+nHPJlg67pKSdFcAKgGXLlnWhp2ZmaYMV8rsY9CPi5LLHJZ0FnAqcFBHJq4oiYiWwEmBkZKT61UdmZpMxYFG/lukdSaeQrf346xFRvIqGmVkD9ENGThV1Ze98GpgHXC1pjaTP1tQPM7OhUstIPyKeVsdxzcyqGrDzuI3I3hlqqUygsnVKU9kVexL7lGXopLIxygqLpbJBHk5kr7Szrm0qeyZ1jLJ+la2Fm5LKnkkVTwOYWvJYkbKMm9R7VlZYLaVqkbSyfg2jAYv5DvpmZqUGLOo76JuZJWQXXA1W1HfQNzNL6ZPSClU46JuZlRiwmO+gb2ZWasCivoO+mVlSf5RLrsJBvw1l6ZQpVYu0la13m0qpSyUMttPfsiJdqRTQVJplWZGuVAGzqqmckF5zNtVWWfpjKv21bF3bdtIpq7aV+ly083mx1nhO38xsSPRLueQqHPTNzMoMWNR30DczK+E5fTOzIeI5fTOzITJgMd9Bvx2pTJyyZeaqFlarO+OiLBOlLFOkqlQmUOr9KsuC2j/R59TPpZ2sotSSkBP1rap2Xr91wQCeyXXQNzMrMWhz+k7gNTMbIh7pm5kliME7keuRvplZCVW8TdiedKGkTZJuG7NtoaSrJd2Vfz2g069jlIO+mVmZTkd9uAg4Zdy29wHXRMRRwDX5/a5w0DczK6GK/yYSEdcDD4zbfDpwcf79xcBvdfZVPMFz+h1UlgKY0skUvKprobbTFqRfZzvr/VY9fq8Ki6XWqO1kWq7TL/tDj+b0D46IDQARsUHSQd06kIO+mVmJNmL+IkmrxtxfGRErO9ahSXLQNzMrUz3qb4mIkYr73C9pST7KXwJsqnzUFnlO38wsYXRh9E7O6SdcCZyVf38WcEUn+l/EI30zs5QuLIwu6QvACWTTQOuB84APAZdJOhu4Bzijs0d9Qi1BX9Kfk52t3kv235g3RcTP6+iLmVmZTp/HjYjXJx46qcOHKlTX9M5HIuLZEfFc4CrgAzX1w8ysXOfz9GtVy0g/IraNuTsH6FzZxhrVnYLXTspkap9Opka28750sspk1RTTMu2khXby52K95oXRO0bSXwK/AzwEvKyufpiZlXHtnRZJ+rak2wpupwNExLkRcRhwCfDWknZWSFoladXmzZu71V0zsyepOrPTD38fujbSj4iTW3zq54Gvkp3BLmpnJbASYGRkZCCmgcysj/RDJK+glhO5ko4ac/c04M46+mFmNpEe5en3TF1z+h+SdDRZyubPgDfX1A8zs6FSV/bO/6zjuMOq01kqnSwg1m8F53qxDm6nueDb5AzaiVxfkWtmVmLAYr6DvplZUhfKMNTNQd/MrNRgRX0HfTOzhEFcGN1B38ysxIDFfAd9M7MyHulb7epOwat6nF4VFku1lVrXtiwtdZDSGQfptdShHy64qsJB38yszGDFfAd9M7MyAxbzHfTNzFLkPH0zs+HiOX0zs2EyWDHfQd/MrMyAxXwH/X7Ubyl4dVeTTKVmeo1aa4Xn9M3MhkZ/LIxShYO+mVnCINbeqWW5RDMzq4eDvpnZEPH0jplZiUGb3nHQt4FRNeOm7Pl1F7Wz5vCJXDOzYeEyDGZmw0P44iwzs+EyYFHfQd/MrITn9M3MhsigzenXmqcv6Y8khaRFdfbDzCxFFW8ttSmdIunHku6W9L7O9zqttpG+pMOAlwP39OJ4M/1/GqtkwIZ31r4OfxQkTQH+jiz+rQduknRlRNzR2SMVq3Ok/3HgPUC61KGZWc1U8V8LjgPujoh1EfEYcClweldfxBi1BH1JpwH3RcQP6zi+mVkrRguuVbm14FDg3jH31+fbeqJrkx6Svg0cUvDQucD7gd9osZ0VwIr87i5Jt3Wmh21bBGypuQ/QjH40oQ/QjH40oQ/QjH40oQ8AR0+2gZtvXv3NWdMqn3OcKWnVmPsrI2LlmPtFfxp6NuPRtaAfEScXbZf034AjgB8q+7O4FLhZ0nERsbGgnZXAynzfVREx0q0+t6IJfWhKP5rQh6b0owl9aEo/mtCH0X5Mto2IOKUTfRlnPXDYmPtLgZ934TiFen56MyJ+BBw0el/ST4GRiGjCyMDMrNtuAo6SdARwH/A64Ld7dXDntJiZ9VBE7JH0VuCbwBTgwoi4vVfHrz3oR8TyCk9fOfFTuq4JfYBm9KMJfYBm9KMJfYBm9KMJfYDm9ONJIuJrwNfqOLYinDFpZjYsvHKWmdkQ6dugX2cJB0l/LulWSWskfUvSU3rdh7wfH5F0Z96XyyUtqKEPZ0i6XdJeST3N2KjzUvYxfbhQ0qY6U4klHSbpu5LW5j+Ld9TUj5mSfiDph3k//qyOfuR9mSLpFklX1dWHpurLoN/rEg4FPhIRz46I5wJXAR+oqR9XA8+KiGcD/wmcU0MfbgNeDVzfy4OOuZT9vwPHAq+XdGwv+5C7COhGWl8Ve4B3R8QzgBcCb6npvdgFnBgRzwGeC5wi6YU19APgHcDamo7daH0Z9Km5hENEbBtzd06N/fhWROzJ736fLN+3131YGxE/7vVxqflS9lERcT3wQK+PO64PGyLi5vz7h8mCXc+u8BzTj4iI7fndafmt578bkpYCrwTO7/Wx+0HfBf2mlHCQ9JeS7gXeQH0j/bF+D/h63Z3ooVovZW8qScuB5wE31nT8KZLWAJuAqyOijn58gmxQuLeGYzde7SmbRTpVwqFbfYiIKyLiXOBcSecAbwXOq6Mf+XPOJfsv/iV19aEGtV7K3kSS5gL/Arxz3P9GeyYiHgeem59fulzSsyKiZ+c7JJ0KbIqI1ZJO6NVx+0kjg36nSjh0ow8FPg98lS4F/Yn6Ieks4FTgpOhS/m2F96KXar2UvWkkTSML+JdExJfr7k9EbJV0Ldn5jl6e5D4eOE3SK4CZwHxJn4uIN/awD43WV9M7EfGjiDgoIpbnF3WtB57f6YA/EUlHjbl7GnBnL48/ph+nAO8FTouIHXX0oUa/vJRd0nSyS9mvrLlPtVA2AroAWBsRH6uxH4tHM8gkzQJOpse/GxFxTkQszePD64DvOODvq6+CfoN8SNJtkm4lm2qqJUUO+DQwD7g6Tx/9bK87IOlVktYDLwK+KumbvThufgJ79FL2tcBlvbyUfZSkLwD/ARwtab2ks3vdB7LR7ZnAifnnYE0+0u21JcB389+Lm8jm9J0y2TC+ItfMbIh4pG9mNkQc9M3MhoiDvpnZEHHQNzMbIg76ZmZDxEHfzGyIOOhb40haLmlnXsOl3TZGJP2f/PsTJL14gue/RNIddZZINusFB31rqp/kpavbEhGrIuLt+d0TgNKgHxH/BtRxQZNZTznoW09J+pV80ZeZkubki208a4J9lo8dgecL6Pxp/v21kj6cL97xn5Jekm8/QdJVedXJNwN/mF+p+pJ84Zfb8sU+eroOgFndGllwzQZXRNwk6UrgL4BZwOc6UIVxakQcl5ceOI+s5svo8X6al6fYHhF/CyDpR8BvRsR9daw2ZlYnB32rwwfJarM8Crx9gue2YrSq5GpgeQvP/3fgIkmXjdnXbCh4esfqsBCYS1YsbmYLz9/Dvp/V8fvsyr8+TgsDmYh4M/AnZKWZ10g6sIU+mA0EB32rw0rgf5Mt+vLhFp5/P3CQpAMlzSBbP6CKh8n+wAAg6akRcWNEfADYwr51+c0Gmqd3rKck/Q6wJyI+ny9u/j1JJ0bEd1L7RMRuSR8kWwLwv6heo/0rwJcknQ68jeyk7lFkq29dA9S69KZZL7m0sjVOnnFzVUSUZvUMynHNesnTO9ZEjwP7T+birKryVM+vkE33mA0sj/TNzIaIR/pmZkPEQd/MbIg46JuZDREHfTOzIeKgb2Y2RP4/iKDz5ujp0d4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# generate two gaussian distributed samples\n",
+    "import scipy.stats\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=12346)\n",
+    "#mu = 0, sigma = 1\n",
+    "x = scipy.stats.norm.rvs(loc=0.0, scale=1, size=10000)\n",
+    "#mu = 0, sigma = 1\n",
+    "y = scipy.stats.norm.rvs(loc=0.0, scale=1, size=10000)\n",
+    "\n",
+    "plt.figure( figsize=[6,5])\n",
+    "plt.hist2d(x, y, bins=(40, 40), cmap='Blues')\n",
+    "cb = plt.colorbar()\n",
+    "cb.set_label('Entires / bin (0.2 x 0.2)')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'y [units]')\n",
+    "plt.xlim([-4.0,4.0])\n",
+    "plt.ylim([-4.0,4.0])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Log scales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Shape comparison by overlapping normalized histograms\n",
+    "\n",
+    "# generate two gaussian distributed samples\n",
+    "import scipy.stats\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=12346)\n",
+    "#mu = 0,   sigma = 1\n",
+    "x1 = scipy.stats.norm.rvs(loc=0.0, scale=1, size=10000)\n",
+    "\n",
+    "plt.figure(figsize=[10, 5])\n",
+    "plt.subplot(121)\n",
+    "plt.hist(x1, bins=50, range=[-5,5], color='blue', alpha=0.5)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 1')\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.hist(x1, bins=50, range=[-5,5], color='blue', alpha=0.5)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 1')\n",
+    "plt.yscale('log', nonposy='clip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# example log-scale; exponential\n",
+    "x = np.arange(0.1, 4, 0.1)\n",
+    "y = np.exp(-x)\n",
+    "\n",
+    "# linear scale\n",
+    "plt.figure(figsize=[10, 5])\n",
+    "plt.subplot(121)\n",
+    "plt.scatter(x, y)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'exp(-x)')\n",
+    "\n",
+    "# log-scale\n",
+    "plt.subplot(122)\n",
+    "plt.scatter(x, y)\n",
+    "plt.yscale('log', nonposy='clip')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'exp(-x)')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# example log-log scale: power law\n",
+    "x = np.arange(0.1, 4, 0.05)\n",
+    "y = 1.5*x**0.3\n",
+    "\n",
+    "# linear scale\n",
+    "plt.figure(figsize=[15, 5])\n",
+    "plt.subplot(121)\n",
+    "plt.scatter(x, y)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'$1.5*x^{0.3}$')\n",
+    "\n",
+    "# log-log\n",
+    "plt.subplot(122)\n",
+    "plt.scatter(x, y)\n",
+    "plt.yscale('log', nonposy='clip')\n",
+    "plt.xscale('log', nonposx='clip')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'$1.5*x^{0.3}$')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/iMinuit.ipynb b/notebooks/iMinuit.ipynb
new file mode 100644
index 0000000..a144c71
--- /dev/null
+++ b/notebooks/iMinuit.ipynb
@@ -0,0 +1,1339 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# iMinuit for LSQ\n",
+    "\n",
+    "\n",
+    "In this notebook we will learn how to use using iminuit (use as example a LSQ fit).\n",
+    "\n",
+    "iMinuit:  \n",
+    "https://iminuit.readthedocs.io/en/latest/index.html#\n",
+    "\n",
+    "Here below a quick summary of:  \n",
+    "https://iminuit.readthedocs.io/en/latest/tutorials.html  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from iminuit import Minuit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": [
+    "# Generate random numbers on a straight line\n",
+    "def line(x, a, b):\n",
+    "    return a + x * b\n",
+    "\n",
+    "data_x = np.linspace(0, 1, 10)\n",
+    "# precomputed random numbers from a normal distribution\n",
+    "offsets = np.array([-0.49783783, -0.33041722, -1.71800806,  1.60229399,  1.36682387,\n",
+    "                    -1.15424221, -0.91425267, -0.03395604, -1.27611719, -0.7004073 ])\n",
+    "data_y = line(data_x, 1, 2) + 0.1 * offsets # generate some data points with random offsets\n",
+    "plt.plot(data_x, data_y, \"o\")\n",
+    "plt.xlim(-0.1, 1.1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Definition of the loss function you want to minimize (here the LSQ)\n",
+    "\n",
+    "def least_squares(a, b):\n",
+    "    yvar = 0.01\n",
+    "    return sum((data_y - line(data_x, a, b)) ** 2 / yvar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Minuit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "a\n",
+       "</td>\n",
+       "<td>\n",
+       "5.00\n",
+       "</td>\n",
+       "<td>\n",
+       "0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "yes\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "b\n",
+       "</td>\n",
+       "<td>\n",
+       "5.00\n",
+       "</td>\n",
+       "<td>\n",
+       "0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "------------------------------------------------------------------------------------------\n",
+       "|   | Name |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "------------------------------------------------------------------------------------------\n",
+       "| 0 | a    |   5.00    |   0.10    |            |            |    0    |         |  yes  |\n",
+       "| 1 | b    |   5.00    |   0.10    |            |            |    0    |   10    |       |\n",
+       "------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Minuit uses introspection to detect the parameter names of your function - it means \n",
+    "#\n",
+    "# A complete instance of Minuit contains the starting point of each parameter (<name>) and \n",
+    "# the step size (<err_name>) for the dimension corresponding to that parameter.\n",
+    "#\n",
+    "# To correctly compute the uncertainties, it needs to know if you are doing a max likelihood \n",
+    "# or a least squares fit:\n",
+    "# errordef = 0.5 for negative log-likelihood functions\n",
+    "# errordef = 1 for least-squares functions\n",
+    "#\n",
+    "# You can specify the limits on the parameters to be fit (\"limit_varname\")\n",
+    "# lower limit: use limit_<name> = (<value>, None) or (<value>, float(\"infinity\"))\n",
+    "# upper limit: use limit_<name> = (None, <value>) or (-float(\"infinity\"), <value>)\n",
+    "# two-sided limit: use limit_<name> = (<min_value>, <max_value>)\n",
+    "#\n",
+    "# You can fix some of the parameters (ignore that dimension in the fit) setting <fix_name> = True/False\n",
+    "# fix_a=True,\n",
+    "\n",
+    "m = Minuit(least_squares, \n",
+    "           a=5, b=5,\n",
+    "           fix_a = True,\n",
+    "           error_a=0.1, error_b=0.1,\n",
+    "           limit_a=(0, None), limit_b=(0, 10),\n",
+    "           errordef=1)\n",
+    "\n",
+    "m.get_param_states()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Once Minuit is constructed you can still fix/release parameters as:\n",
+    "m.fixed[\"a\"] = False\n",
+    "m.fixed[\"b\"] = True\n",
+    "\n",
+    "# Trick to run over all parameters:\n",
+    "for key in m.fixed:\n",
+    "    m.fixed[key] = False\n",
+    "m.migrad()\n",
+    "\n",
+    "# To change the value of a fixed parameter (or reset it to a different value) you can access it as:\n",
+    "m.values[\"a\"] = 0.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MIGRAD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<td colspan=\"2\" title=\"Minimum value of function\">\n",
+       "FCN = 10.39\n",
+       "</td>\n",
+       "<td align=\"center\" colspan=\"3\" title=\"No. of calls in last algorithm and total number of calls\">\n",
+       "Ncalls = 42 (106 total)\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td colspan=\"2\" title=\"Estimated distance to minimum and target threshold\">\n",
+       "EDM = 4.32E-06 (Goal: 1E-05)\n",
+       "</td>\n",
+       "<td align=\"center\" colspan=\"3\" title=\"Increase in FCN which corresponds to 1 standard deviation\">\n",
+       "up = 1.0\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td align=\"center\" title=\"Validity of the migrad call\">\n",
+       "Valid Min.\n",
+       "</td>\n",
+       "<td align=\"center\" title=\"Validity of parameters\">\n",
+       "Valid Param.\n",
+       "</td>\n",
+       "<td align=\"center\" title=\"Is EDM above goal EDM?\">\n",
+       "Above EDM\n",
+       "</td>\n",
+       "<td align=\"center\" colspan=\"2\" title=\"Did last migrad call reach max call limit?\">\n",
+       "Reached call limit\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td align=\"center\" title=\"Did Hesse fail?\">\n",
+       "Hesse failed\n",
+       "</td>\n",
+       "<td align=\"center\" title=\"Has covariance matrix\">\n",
+       "Has cov.\n",
+       "</td>\n",
+       "<td align=\"center\" title=\"Is covariance matrix accurate?\">\n",
+       "Accurate\n",
+       "</td>\n",
+       "<td align=\"center\" title=\"Is covariance matrix positive definite?\">\n",
+       "Pos. def.\n",
+       "</td>\n",
+       "<td align=\"center\" title=\"Was positive definiteness enforced by Minuit?\">\n",
+       "Forced\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "a\n",
+       "</td>\n",
+       "<td>\n",
+       "0.99\n",
+       "</td>\n",
+       "<td>\n",
+       "0.06\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "b\n",
+       "</td>\n",
+       "<td>\n",
+       "1.95\n",
+       "</td>\n",
+       "<td>\n",
+       "0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "------------------------------------------------------------------\n",
+       "| FCN = 10.39                   |     Ncalls=42 (106 total)      |\n",
+       "| EDM = 4.32E-06 (Goal: 1E-05)  |            up = 1.0            |\n",
+       "------------------------------------------------------------------\n",
+       "|  Valid Min.   | Valid Param.  | Above EDM | Reached call limit |\n",
+       "------------------------------------------------------------------\n",
+       "|     True      |     True      |   False   |       False        |\n",
+       "------------------------------------------------------------------\n",
+       "| Hesse failed  |   Has cov.    | Accurate  | Pos. def. | Forced |\n",
+       "------------------------------------------------------------------\n",
+       "|     False     |     True      |   True    |   True    | False  |\n",
+       "------------------------------------------------------------------\n",
+       "------------------------------------------------------------------------------------------\n",
+       "|   | Name |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "------------------------------------------------------------------------------------------\n",
+       "| 0 | a    |   0.99    |   0.06    |            |            |    0    |         |       |\n",
+       "| 1 | b    |   1.95    |   0.10    |            |            |    0    |   10    |       |\n",
+       "------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Migrad performs Variable-Metric Minimization. It combines a steepest-descends algorithm along with \n",
+    "# line search strategy. Migrad is very popular in high energy physics field because of its robustness.\n",
+    "m.migrad()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FMin(fval=10.387015571126001, edm=4.319932643302712e-06, tolerance=0.1, nfcn=42, ncalls=106, up=1.0, is_valid=True, has_valid_parameters=True, has_accurate_covar=True, has_posdef_covar=True, has_made_posdef_covar=False, hesse_failed=False, has_covariance=True, is_above_max_edm=False, has_reached_call_limit=False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# To understand the results of the fit there are two dict-like objects:\n",
+    "# The first one is\n",
+    "pprint (m.get_fmin())\n",
+    "\n",
+    "# The most important one here is is_valid. If this is false, the fit did not converge and the result is useless !\n",
+    "#\n",
+    "# When is_valid = False it can be that the fit function is not analytical everywhere in the parameter space \n",
+    "# or does not have a local minimum (the minimum may be at infinity, the extremum may be a saddle point \n",
+    "# or maximum). Indicators for this are:\n",
+    "#   is_above_max_edm=True\n",
+    "#   hesse_failed=True\n",
+    "#   has_posdef_covar=False\n",
+    "#   has_made_posdef_covar=True\n",
+    "#\n",
+    "# Migrad reached the call limit before the convergence so that has_reached_call_limit=True. \n",
+    "# The used number of function calls is nfcn, and the call limit can be changed with the keyword argument ncall \n",
+    "# in the method Minuit.migrad\n",
+    "#\n",
+    "# Migrad detects converge by a small edm value, the estimated distance to minimum. This is the difference between\n",
+    "# the current minimum value of the minimized function and the next prediction based on a local quadratic \n",
+    "# approximation of the function (something that Migrad computes as part of its algorithm). If the fit did not \n",
+    "# converge, is_above_max_edm is true.\n",
+    "#\n",
+    "# To have a reliable uncertainty determination, you should make sure that:\n",
+    "# has_covariance        = True\n",
+    "# has_accurate_covar    = True\n",
+    "# has_posdef_covar      = True\n",
+    "# has_made_posdef_covar = False \n",
+    "# hesse_failed          = False\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Param(number=0, name='a', value=0.9908538664000521, error=0.05876818861364508, is_const=False, is_fixed=False, has_limits=True, has_lower_limit=True, has_upper_limit=False, lower_limit=0.0, upper_limit=None),\n",
+      " Param(number=1, name='b', value=1.945147699139382, error=0.09907833701567537, is_const=False, is_fixed=False, has_limits=True, has_lower_limit=True, has_upper_limit=True, lower_limit=0.0, upper_limit=10.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The second one is a list of dict-like objects which contain information about the fitted parameters:\n",
+    "pprint(m.get_param_states())\n",
+    "\n",
+    "# Important fields are:\n",
+    "#   index: parameter index.\n",
+    "#   name: parameter name.\n",
+    "#   value: value of the parameter at the minimum.\n",
+    "#   error: uncertainty estimate for the parameter value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# if you need just the basic\n",
+    "print (m.migrad_ok())\n",
+    "print (m.matrix_accurate())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hesse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "a\n",
+       "</td>\n",
+       "<td>\n",
+       "0.99\n",
+       "</td>\n",
+       "<td>\n",
+       "0.06\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "b\n",
+       "</td>\n",
+       "<td>\n",
+       "1.95\n",
+       "</td>\n",
+       "<td>\n",
+       "0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "------------------------------------------------------------------------------------------\n",
+       "|   | Name |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "------------------------------------------------------------------------------------------\n",
+       "| 0 | a    |   0.99    |   0.06    |            |            |    0    |         |       |\n",
+       "| 1 | b    |   1.95    |   0.10    |            |            |    0    |   10    |       |\n",
+       "------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The Hesse algorithm numerically computes the matrix of second derivatives at the function minimum \n",
+    "# (called the Hessian matrix) and inverts it.\n",
+    "# Pros:\n",
+    "#   (Comparably) fast computation.\n",
+    "#   Provides covariance matrix for error propagation.\n",
+    "# Cons:\n",
+    "#   Wrong if function does not look like a hyperparabola around the minimum\n",
+    "\n",
+    "m.hesse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<td/>\n",
+       "\n",
+       "<th>\n",
+       "a\n",
+       "</th>\n",
+       "<th>\n",
+       "b\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<th>\n",
+       "a\n",
+       "</th>\n",
+       "<td>\n",
+       " 0.003\n",
+       "</td>\n",
+       "<td style=\"background-color:rgb(140,140,250)\">\n",
+       "-0.005\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<th>\n",
+       "b\n",
+       "</th>\n",
+       "<td style=\"background-color:rgb(140,140,250)\">\n",
+       "-0.005\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.010\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "---------------------\n",
+       "|   |      a      b |\n",
+       "---------------------\n",
+       "| a |  0.003 -0.005 |\n",
+       "| b | -0.005  0.010 |\n",
+       "---------------------"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Covariance - colored table\n",
+    "m.matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------------\n",
+      "|   |      a      b |\n",
+      "---------------------\n",
+      "| a |  0.003 -0.005 |\n",
+      "| b | -0.005  0.010 |\n",
+      "---------------------\n",
+      "[[ 0.00345475 -0.00490941]\n",
+      " [-0.00490941  0.00981865]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# to access the matrix\n",
+    "cov = m.matrix()\n",
+    "print (cov)\n",
+    "\n",
+    "# or as a numpy array\n",
+    "npcov = m.np_matrix()\n",
+    "print (npcov)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<td/>\n",
+       "\n",
+       "<th>\n",
+       "a\n",
+       "</th>\n",
+       "<th>\n",
+       "b\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<th>\n",
+       "a\n",
+       "</th>\n",
+       "<td>\n",
+       " 1.00\n",
+       "</td>\n",
+       "<td style=\"background-color:rgb(140,140,250)\">\n",
+       "-0.84\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<th>\n",
+       "b\n",
+       "</th>\n",
+       "<td style=\"background-color:rgb(140,140,250)\">\n",
+       "-0.84\n",
+       "</td>\n",
+       "<td>\n",
+       " 1.00\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-----------------\n",
+       "|   |    a    b |\n",
+       "-----------------\n",
+       "| a |  1.0 -0.8 |\n",
+       "| b | -0.8  1.0 |\n",
+       "-----------------"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Correlation - colored table\n",
+    "m.matrix(correlation=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------\n",
+      "|   |    a    b |\n",
+      "-----------------\n",
+      "| a |  1.0 -0.8 |\n",
+      "| b | -0.8  1.0 |\n",
+      "-----------------\n",
+      "[[ 1.         -0.84293683]\n",
+      " [-0.84293683  1.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# to access the matrix\n",
+    "corr = m.matrix(correlation=True)\n",
+    "print (corr)\n",
+    "\n",
+    "# or as a numpy array\n",
+    "npcov = m.np_matrix(correlation=True)\n",
+    "print (npcov)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Minos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Minos implements the so-called profile likelihood method, where the neighborhood around the function minimum \n",
+    "# is scanned until the contour is found where the function increase by the value of errordef. \n",
+    "# Pros:\n",
+    "#  Good for functions which are not very close to a hyper-parabola around the minimum\n",
+    "#  Produces pretty confidence regions for scientific plots\n",
+    "# Cons:\n",
+    "#  Takes really long time\n",
+    "#  Result is difficult to error-propagate, since it cannot be described by a covariance matrix\n",
+    "#\n",
+    "# The results contain information as:\n",
+    "#  At Limit: Whether Minos hit a parameter limit before the finishing the contour.\n",
+    "#  Max FCN: Whether Minos reached the maximum number of allowed calls before finishing the contour.\n",
+    "#  New Min: Whether Minos discovered a deeper local minimum in the neighborhood of the current one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<th title=\"Parameter name\">\n",
+       "a\n",
+       "</th>\n",
+       "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
+       "Valid\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Lower and upper minos error of the parameter\">\n",
+       "Error\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.06\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.06\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Validity of lower/upper minos error\">\n",
+       "Valid\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit limit of any parameter?\">\n",
+       "At Limit\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit function call limit?\">\n",
+       "Max FCN\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"New minimum found when doing scan?\">\n",
+       "New Min\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "\n",
+       "<table>\n",
+       "<tr>\n",
+       "<th title=\"Parameter name\">\n",
+       "b\n",
+       "</th>\n",
+       "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
+       "Valid\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Lower and upper minos error of the parameter\">\n",
+       "Error\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.10\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.10\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Validity of lower/upper minos error\">\n",
+       "Valid\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit limit of any parameter?\">\n",
+       "At Limit\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit function call limit?\">\n",
+       "Max FCN\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"New minimum found when doing scan?\">\n",
+       "New Min\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------\n",
+       "|        a        |            Valid            |\n",
+       "-------------------------------------------------\n",
+       "|      Error      |    -0.06     |     0.06     |\n",
+       "|      Valid      |     True     |     True     |\n",
+       "|    At Limit     |    False     |    False     |\n",
+       "|     Max FCN     |    False     |    False     |\n",
+       "|     New Min     |    False     |    False     |\n",
+       "-------------------------------------------------\n",
+       "-------------------------------------------------\n",
+       "|        b        |            Valid            |\n",
+       "-------------------------------------------------\n",
+       "|      Error      |    -0.10     |     0.10     |\n",
+       "|      Valid      |     True     |     True     |\n",
+       "|    At Limit     |    False     |    False     |\n",
+       "|     Max FCN     |    False     |    False     |\n",
+       "|     New Min     |    False     |    False     |\n",
+       "-------------------------------------------------"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.minos()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "a\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.99\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.06\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.06\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.06\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "b\n",
+       "</td>\n",
+       "<td>\n",
+       " 1.95\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.10\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "------------------------------------------------------------------------------------------\n",
+       "|   | Name |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "------------------------------------------------------------------------------------------\n",
+       "| 0 | a    |    0.99   |    0.06   |   -0.06    |    0.06    |    0    |         |       |\n",
+       "| 1 | b    |    1.95   |    0.10   |   -0.10    |    0.10    |    0    |   10    |       |\n",
+       "------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.get_param_states()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "array([0.99085387, 1.9451477 ])\n",
+      "array([0.05876843, 0.09907874])\n",
+      "array([[0.05866313, 0.09929038],\n",
+      "       [0.05888831, 0.09888435]])\n",
+      "array([[ 0.00345475, -0.00490941],\n",
+      "       [-0.00490941,  0.00981865]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Other ways to access the fits results\n",
+    "pprint(m.np_values())\n",
+    "pprint(m.np_errors())\n",
+    "pprint(m.np_merrors()) # The layout returned by Minuit.np_merrors() follows the convention \n",
+    "                       # [abs(delta_down), delta_up] that is used by matplotlib.pyplot.errorbar.\n",
+    "pprint(m.np_covariance())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot contours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.contour.ContourSet at 0x120abe7d0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[5,5])\n",
+    "m.draw_mncontour('a','b', nsigma=4, numpoints=100)  # nsigma=4 says: draw four contours from sigma=1 to 4v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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": [
+    "m.draw_profile('a');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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": [
+    "m.draw_mnprofile('a');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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": [
+    "px, py = m.profile('a', subtract_min=True)\n",
+    "plt.plot(px, py);"
+   ]
+  },
+  {
+   "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/leastSquaresFits.ipynb b/notebooks/leastSquaresFits.ipynb
new file mode 100644
index 0000000..ab27e6f
--- /dev/null
+++ b/notebooks/leastSquaresFits.ipynb
@@ -0,0 +1,442 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Least squares Fit\n",
+    "\n",
+    "A couple of examples to show how to use:  \n",
+    "- numpy.polyfit  \n",
+    "- scipy.optimize.curve_fit  \n",
+    "\n"
+   ]
+  },
+  {
+   "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\n",
+    "from scipy.stats import norm, chi2, lognorm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a polynomial\n",
+    "We start by fitting a polynomial to a given data set, in particular, a parabola. Compare a linear fit and a parabolic fit and check the goodness of fits with chi squared distributions. Explore how the different uncertainties affect the outcome and uncertainties of the fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create some data distribuited as parabola with normally distributed errors.\n",
+    "def parabola(x, a, b, c):\n",
+    "    return a*x**2 + b*x + c\n",
+    "def error(x, sigma):\n",
+    "    return norm.rvs(0.0, sigma, x.size) \n",
+    "a = -0.1\n",
+    "b = 0\n",
+    "c = 1\n",
+    "sigma_y = 0.0015\n",
+    "\n",
+    "x = np.linspace(0, 1, 21)\n",
+    "y_true = parabola(x, a, b, c)\n",
+    "delta_y = error(x, sigma_y)\n",
+    "y = y_true + delta_y\n",
+    "y_error = sigma_y * np.ones(x.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_polynomial(x, y, degree, weight):\n",
+    "    \"\"\"Fit polynomial of degree to data x, y with y weight = 1/sigma_y\n",
+    "    Return fit, covariance matrix, residuals and chi-squared and degrees of freedom.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    dof = x.shape[0] - degree\n",
+    "    fit, cov = np.polyfit(x, y, degree, w=weight, cov=True)\n",
+    "    residuals = np.sum((y - np.polyval(fit, x))**2 / y_error**2)\n",
+    "    chisq = residuals / (dof)\n",
+    "    return fit, cov, residuals, chisq, dof\n",
+    "    \n",
+    "fit, cov, res, chisq, dof = fit_polynomial(x, y, 2, 1/y_error) # Fit parabola\n",
+    "fit_1, cov_1, res_1, chisq_1, dof_1 = fit_polynomial(x, y, 1, 1/y_error) # Fit line\n",
+    "\n",
+    "def evaluate_chisq(chisq, dof):\n",
+    "    return chi2.sf(chisq, dof)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit with a straight line\n",
+      "a = -0.0955  +/- 0.0060\n",
+      "b = -0.0069  +/- 0.0035\n",
+      "Fit with a parabola\n",
+      "a = -0.0955  +/- 0.0039\n",
+      "b = -0.0069  +/- 0.0040\n",
+      "c = 1.0016  +/- 0.0009\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the fitted parameters and their uncertainties\n",
+    "fitPars = fit_1\n",
+    "errPars = np.sqrt(np.diag(cov_1))\n",
+    "print (\"Fit with a straight line\")\n",
+    "print ('a = {:.4f}'.format(fit[0]), ' +/- {:.4f}'.format(errPars[0]))\n",
+    "print ('b = {:.4f}'.format(fit[1]), ' +/- {:.4f}'.format(errPars[1]))\n",
+    "errPars = np.sqrt(np.diag(cov))\n",
+    "print (\"Fit with a parabola\")\n",
+    "print ('a = {:.4f}'.format(fit[0]), ' +/- {:.4f}'.format(errPars[0]))\n",
+    "print ('b = {:.4f}'.format(fit[1]), ' +/- {:.4f}'.format(errPars[1]))\n",
+    "print ('c = {:.4f}'.format(fit[2]), ' +/- {:.4f}'.format(errPars[2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reduced chi^2:\n",
+      "parabola 0.8805087850199654\n",
+      "line 29.237037028605272\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Reduced chi^2:')\n",
+    "print('parabola', chisq)\n",
+    "print('line', chisq_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi^2 distributions:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.999999999755556, 0.08319587293588597)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Chi^2 distributions:')\n",
+    "evaluate_chisq(chisq, dof), evaluate_chisq(chisq_1, dof_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].set_title('fits')\n",
+    "ax[0].errorbar(x, y, yerr=y_error, fmt='k.')\n",
+    "ax[0].plot(x, y_true, 'k-')\n",
+    "ax[0].plot(x, np.polyval(fit, x), label='parabola', color='blue')\n",
+    "ax[0].plot(x, np.polyval(fit_1, x), label='line', color='green')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[1].plot(y_true - np.polyval(fit, x), '.', color='blue')\n",
+    "ax[1].plot(y_true - np.polyval(fit_1, x), '.', color='green')\n",
+    "ax[1].set_title('residuals')\n",
+    "ax[1].fill_between(ax[1].get_xlim(), -sigma_y, sigma_y, color='grey', alpha=0.4, label=r'$1\\sigma$')\n",
+    "ax[1].legend()\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Draw the plot form lecture notes \n",
+    "plt.figure(figsize=(7, 5))\n",
+    "chisq_arr = np.linspace(0, 100, 1001)\n",
+    "plt.ylabel(r'p-value for test $\\alpha$ for confidence interval') \n",
+    "plt.xlabel(r'$\\chi^2$')\n",
+    "for n in [1, 2, 3, 4, 6, 8, 10, 15, 20, 25, 30, 40, 50]:\n",
+    "    plt.loglog(chisq_arr, chi2.sf(chisq_arr, n))\n",
+    "plt.loglog(chisq_arr, chi2.sf(chisq_arr, dof), 'k-', lw=2, label='dof={0}'.format(dof))\n",
+    "plt.ylim(1e-3, 1.1)\n",
+    "plt.xlim(1, 100)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit of a gaussian\n",
+    "Next, we consider a Gaussian. We are measuring some feature which has a Gaussian distribution in $x$. This could be an inhomogeneous spectral line for $x=E$ the energy of emitted photons. We are interested in the resonance frequency and the linewidth, i. e. we want to estimate them form our observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_parent(x, mu, sigma):\n",
+    "    return norm.pdf(x, mu, sigma)    \n",
+    "\n",
+    "def gaussian_sample(mu, sigma, sample_size):\n",
+    "    return norm.rvs(mu, sigma, sample_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sample\n",
+    "## SAMPLE SIZE\n",
+    "sample_size = 200\n",
+    "##################\n",
+    "\n",
+    "# Prepare toy data\n",
+    "mu = 1540  # True values that we will try to estimate\n",
+    "sigma = 11 # using a least-squares fit\n",
+    "\n",
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "bins = 12\n",
+    "sample = gaussian_sample(mu, sigma, sample_size)\n",
+    "hist = np.histogram(sample, bins=bins, range=(1500, 1580))\n",
+    "bin_width = np.diff(hist[1])[0]\n",
+    "normalization = bin_width * sample_size\n",
+    "x = hist[1][:-1]+bin_width/2\n",
+    "y_error_const = 0\n",
+    "y = hist[0]/normalization + gaussian_sample(0, y_error_const, bins)\n",
+    "y_errors = np.sqrt((np.sqrt(hist[0]) / normalization)**2 + y_error_const**2)\n",
+    "\n",
+    "# Plot our toy measurement results\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.plot(x_arr, gaussian_parent(x_arr, mu, sigma), '-', color='black', label='parent')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save data\n",
+    "data = np.vstack((x, y, y_errors))\n",
+    "np.savetxt('data', data)\n",
+    "np.savetxt('sample', sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data from disk. Format (3,12): (x, y, y_error) x N \n",
+    "data = np.loadtxt('data')\n",
+    "x = data[0, :]\n",
+    "y = data[1, :]\n",
+    "y_error = data[2, :]\n",
+    "# The sample used to generate\n",
+    "sample = np.loadtxt('sample')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function we want to fit to our data set\n",
+    "def model_function(x, *args):\n",
+    "    mu, sigma = args[0:2]\n",
+    "    return norm.pdf(x, mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit Results:\n",
+      "mu = 1541.0 +- 0.3\n",
+      "sigma = 9.9 +- 0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform the fit minimizing least squares\n",
+    "starting_point = [1545, 9]\n",
+    "p_opt, p_cov = curve_fit(model_function, x, y, p0=starting_point, sigma=None, absolute_sigma=False, check_finite=True)\n",
+    "p_err = np.sqrt(np.diag(p_cov))\n",
+    "# pcov(absolute_sigma=False) = pcov(absolute_sigma=True) * chisq(popt)/(M-N)\n",
+    "print('Fit Results:')\n",
+    "print('mu = {:1.1f} +- {:1.1f}'.format(p_opt[0], p_err[0]))\n",
+    "print('sigma = {:1.1f} +- {:1.1f}'.format(p_opt[1], p_err[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_arr = np.linspace(1500, 1600, 101)\n",
+    "\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "plt.xlabel(r'$E$ (meV)')\n",
+    "plt.ylabel('count rate')\n",
+    "plt.errorbar(x, y, yerr=y_errors, fmt='.', ms=7, capsize=3, label='sample')\n",
+    "plt.plot(x_arr, model_function(x_arr, *starting_point), '-', color='grey', label='starting point')\n",
+    "plt.plot(x_arr, model_function(x_arr, *p_opt), '-', color='black', label='fit')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "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": "3.7.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "225.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/parentSamplingDistributions.ipynb b/notebooks/parentSamplingDistributions.ipynb
new file mode 100644
index 0000000..b95ca7e
--- /dev/null
+++ b/notebooks/parentSamplingDistributions.ipynb
@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parent vs. Sampling distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entries / bins size = 0.2')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from scipy.stats import norm\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=[15,5])\n",
+    "plt.subplot(131)\n",
+    "\n",
+    "# the gaussian pdf is the parent distribution\n",
+    "x = np.linspace(-5,5,100)\n",
+    "plt.plot(x, norm.pdf(x),'b-', alpha=0.6)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'pdf(x)')\n",
+    "\n",
+    "\n",
+    "# # from the gaussian parent we can create different sampling distributions (a.k.a. realizations)\n",
+    "import scipy.stats\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=12345)\n",
+    "\n",
+    "plt.subplot(132)\n",
+    "x1 = scipy.stats.norm.rvs(loc=0.0, scale=1.0, size=1000)\n",
+    "plt.hist(x1, bins=50, range=[-5,5], color='blue', alpha=0.5)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.2')\n",
+    "\n",
+    "plt.subplot(133)\n",
+    "x2 = scipy.stats.norm.rvs(loc=0.0, scale=1.0, size=1000)\n",
+    "plt.hist(x2, bins=50, range=[-5,5], color='green',alpha=0.5)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.2')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/quantiles.ipynb b/notebooks/quantiles.ipynb
new file mode 100644
index 0000000..08418ce
--- /dev/null
+++ b/notebooks/quantiles.ipynb
@@ -0,0 +1,109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quantiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0']\n",
+      "['-inf', '-1.282', '-0.842', '-0.524', '-0.253', '0.000', '0.253', '0.524', '0.842', '1.282', 'inf']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x1080 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from scipy.stats import norm\n",
+    "\n",
+    "plt.figure(figsize=[5,15])\n",
+    "\n",
+    "plt.subplot(211)\n",
+    "# cumulative of the gaussian pdf\n",
+    "x = np.linspace(-5,5,100)\n",
+    "plt.plot(x, norm.cdf(x),'b-', alpha=0.6)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'cdf(x)')\n",
+    "\n",
+    "# draw vertical line from (70,100) to (70, 250)\n",
+    "for i in range(0,11):\n",
+    "    step = i*0.1\n",
+    "    plt.plot([-5,5], [step, step], 'k--', lw='0.2')\n",
+    "    plt.plot([norm.ppf(step),norm.ppf(step)], [step, 0], 'k--', lw='0.2')\n",
+    "\n",
+    "plt.subplot(212)\n",
+    "plt.subplots_adjust(top=0.5)\n",
+    "# gaussian pdf \n",
+    "x = np.linspace(-5,5,100)\n",
+    "plt.plot(x, norm.pdf(x),'b-', alpha=0.6)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'pdf(x)')\n",
+    "s =[]\n",
+    "p =[]\n",
+    "for i in range(0,11):\n",
+    "    step = i*0.1\n",
+    "    s.append(\"{:.1f}\".format(step))\n",
+    "    p.append(\"{:.3f}\".format(norm.ppf(step)))\n",
+    "    plt.plot([norm.ppf(step),norm.ppf(step)], [norm.pdf(norm.ppf(step)), 0], 'k--', lw='0.2')\n",
+    "\n",
+    "print (s)\n",
+    "print (p)\n",
+    "\n"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/unbinnedLikelihood.ipynb b/notebooks/unbinnedLikelihood.ipynb
new file mode 100644
index 0000000..1b3f6c6
--- /dev/null
+++ b/notebooks/unbinnedLikelihood.ipynb
@@ -0,0 +1,854 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unbinned Likelihood fits\n",
+    "\n",
+    "In this notebook we will be using probfit together with iminuit to perform an Unbinned Likelihood fit.\n",
+    "  \n",
+    "probfit:  \n",
+    "https://probfit.readthedocs.io/en/latest/  \n",
+    " \n",
+    "iMinuit:    \n",
+    "https://iminuit.readthedocs.io/en/latest/index.html#  \n",
+    "\n",
+    "Here below a quick summary of:    \n",
+    "http://piti118.github.io/babar_python_tutorial/notebooks/04_Fitting.html  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats\n",
+    "from math import exp, pi, sqrt\n",
+    "from probfit import UnbinnedLH\n",
+    "from iminuit import Minuit, describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate data\n",
+    "# set the seed to always get the same samples\n",
+    "np.random.seed(seed=12345)\n",
+    "\n",
+    "# Generate a toy dataset on an gaussian distribution (signal)\n",
+    "#mu = 125, sigma = 1\n",
+    "gdata = scipy.stats.norm.rvs(loc=0, scale=1, size=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entries / bins size = 0.4')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[5,5])\n",
+    "plt.subplot(111)\n",
+    "n, bins, patches = plt.hist(gdata, bins=25, range=[-5,5], color='blue', alpha=0.5)\n",
+    "max = np.amax(n)\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.4')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mean', 'sigma']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from probfit import gaussian\n",
+    "ulh = UnbinnedLH(gaussian, gdata)\n",
+    "describe(ulh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "mean\n",
+       "</td>\n",
+       "<td>\n",
+       "1.00\n",
+       "</td>\n",
+       "<td>\n",
+       "0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "sigma\n",
+       "</td>\n",
+       "<td>\n",
+       "2.00\n",
+       "</td>\n",
+       "<td>\n",
+       "0.10\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------------------------------------------------\n",
+       "|   | Name  |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "-------------------------------------------------------------------------------------------\n",
+       "| 0 | mean  |   1.00    |   0.10    |            |            |         |         |       |\n",
+       "| 1 | sigma |   2.00    |   0.10    |            |            |         |         |       |\n",
+       "-------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = Minuit(ulh, \n",
+    "           mean=1, sigma=2,\n",
+    "           error_mean=0.1, error_sigma=0.1,\n",
+    "           errordef=0.5)#remember up is 0.5 for likelihood and 1 for chi^2\n",
+    "\n",
+    "# Show() is the same thing as draw(). But show the figure immediately.\n",
+    "# For all parameters and return vars:\n",
+    "#    https://probfit.readthedocs.io/en/latest/api.html#probfit.costfunc.UnbinnedLH.draw\n",
+    "plt.figure(figsize=[5,5])\n",
+    "plt.ylim([0.1,max*1.5])\n",
+    "ulh.show(m, bins=25, bound=[-5,5],print_par=True)\n",
+    "m.get_param_states()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "mean\n",
+       "</td>\n",
+       "<td>\n",
+       "0.49\n",
+       "</td>\n",
+       "<td>\n",
+       "0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "sigma\n",
+       "</td>\n",
+       "<td>\n",
+       "0.80\n",
+       "</td>\n",
+       "<td>\n",
+       "0.18\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------------------------------------------------\n",
+       "|   | Name  |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "-------------------------------------------------------------------------------------------\n",
+       "| 0 | mean  |   0.49    |   0.25    |            |            |         |         |       |\n",
+       "| 1 | sigma |   0.80    |   0.18    |            |            |         |         |       |\n",
+       "-------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.migrad()\n",
+    "\n",
+    "plt.figure(figsize=[5,5])\n",
+    "plt.ylim([0.1,max*1.5])\n",
+    "ulh.show(m, bins=25, bound=[-5,5],print_par=True)\n",
+    "m.get_param_states()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<td/>\n",
+       "\n",
+       "<th>\n",
+       "mean\n",
+       "</th>\n",
+       "<th>\n",
+       "sigma\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<th>\n",
+       "mean\n",
+       "</th>\n",
+       "<td>\n",
+       "0.644E-1\n",
+       "</td>\n",
+       "<td style=\"background-color:rgb(250,250,250)\">\n",
+       "0.000E-1\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<th>\n",
+       "sigma\n",
+       "</th>\n",
+       "<td style=\"background-color:rgb(250,250,250)\">\n",
+       "0.000E-1\n",
+       "</td>\n",
+       "<td>\n",
+       "0.322E-1\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-----------------------------\n",
+       "|       |     mean    sigma |\n",
+       "-----------------------------\n",
+       "|  mean | 0.644E-1 0.000E-1 |\n",
+       "| sigma | 0.000E-1 0.322E-1 |\n",
+       "-----------------------------"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<th title=\"Parameter name\">\n",
+       "mean\n",
+       "</th>\n",
+       "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
+       "Valid\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Lower and upper minos error of the parameter\">\n",
+       "Error\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.26\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.26\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Validity of lower/upper minos error\">\n",
+       "Valid\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit limit of any parameter?\">\n",
+       "At Limit\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit function call limit?\">\n",
+       "Max FCN\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"New minimum found when doing scan?\">\n",
+       "New Min\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "\n",
+       "<table>\n",
+       "<tr>\n",
+       "<th title=\"Parameter name\">\n",
+       "sigma\n",
+       "</th>\n",
+       "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
+       "Valid\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Lower and upper minos error of the parameter\">\n",
+       "Error\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.15\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.22\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Validity of lower/upper minos error\">\n",
+       "Valid\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "True\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit limit of any parameter?\">\n",
+       "At Limit\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"Did scan hit function call limit?\">\n",
+       "Max FCN\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td title=\"New minimum found when doing scan?\">\n",
+       "New Min\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "<td style=\"background-color:#92CCA6;\">\n",
+       "False\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------\n",
+       "|      mean       |            Valid            |\n",
+       "-------------------------------------------------\n",
+       "|      Error      |    -0.26     |     0.26     |\n",
+       "|      Valid      |     True     |     True     |\n",
+       "|    At Limit     |    False     |    False     |\n",
+       "|     Max FCN     |    False     |    False     |\n",
+       "|     New Min     |    False     |    False     |\n",
+       "-------------------------------------------------\n",
+       "-------------------------------------------------\n",
+       "|      sigma      |            Valid            |\n",
+       "-------------------------------------------------\n",
+       "|      Error      |    -0.15     |     0.22     |\n",
+       "|      Valid      |     True     |     True     |\n",
+       "|    At Limit     |    False     |    False     |\n",
+       "|     Max FCN     |    False     |    False     |\n",
+       "|     New Min     |    False     |    False     |\n",
+       "-------------------------------------------------"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.minos()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td/>\n",
+       "<th title=\"Variable name\">\n",
+       "Name\n",
+       "</th>\n",
+       "<th title=\"Value of parameter\">\n",
+       "Value\n",
+       "</th>\n",
+       "<th title=\"Hesse error\">\n",
+       "Hesse Error\n",
+       "</th>\n",
+       "<th title=\"Minos lower error\">\n",
+       "Minos Error-\n",
+       "</th>\n",
+       "<th title=\"Minos upper error\">\n",
+       "Minos Error+\n",
+       "</th>\n",
+       "<th title=\"Lower limit of the parameter\">\n",
+       "Limit-\n",
+       "</th>\n",
+       "<th title=\"Upper limit of the parameter\">\n",
+       "Limit+\n",
+       "</th>\n",
+       "<th title=\"Is the parameter fixed in the fit\">\n",
+       "Fixed\n",
+       "</th>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#FFFFFF;\">\n",
+       "<td>\n",
+       "0\n",
+       "</td>\n",
+       "<td>\n",
+       "mean\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.49\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.25\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.26\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.26\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "<tr style=\"background-color:#F4F4F4;\">\n",
+       "<td>\n",
+       "1\n",
+       "</td>\n",
+       "<td>\n",
+       "sigma\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.80\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.18\n",
+       "</td>\n",
+       "<td>\n",
+       "-0.15\n",
+       "</td>\n",
+       "<td>\n",
+       " 0.22\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "-------------------------------------------------------------------------------------------\n",
+       "|   | Name  |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
+       "-------------------------------------------------------------------------------------------\n",
+       "| 0 | mean  |    0.49   |    0.25   |   -0.26    |    0.26    |         |         |       |\n",
+       "| 1 | sigma |    0.80   |    0.18   |   -0.15    |    0.22    |         |         |       |\n",
+       "-------------------------------------------------------------------------------------------"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.get_param_states()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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": [
+    "m.draw_profile('mean');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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": [
+    "m.draw_profile('sigma');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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": [
+    "px, py = m.profile('mean', subtract_min=True)\n",
+    "plt.plot(px, py);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.contour.ContourSet at 0x1a1a52c850>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[5,5])\n",
+    "m.draw_mncontour('mean', 'sigma', nsigma=4, numpoints=100)  # nsigma=4 says: draw four contours from sigma=1 to 4"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/uncertaintyAsSmearing.ipynb b/notebooks/uncertaintyAsSmearing.ipynb
new file mode 100644
index 0000000..cc877e1
--- /dev/null
+++ b/notebooks/uncertaintyAsSmearing.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Error propagation as convolution of measurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the random seed to always reproduce the same distributions\n",
+    "np.random.seed(seed=123456)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance 1 =  4.3561039337307506\n",
+      "Variance 2 =  4.000944746158025\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entries / bins size = 0.1')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Take n measurements of two variables\n",
+    "\n",
+    "n = 1000\n",
+    "\n",
+    "# x1 = gaussian mu = 5 sigma = 2\n",
+    "x1 = scipy.stats.norm.rvs(loc=5.0, scale=2, size=n)\n",
+    "# x2 = gaussian mu = 6 sigma = 2\n",
+    "x2 = scipy.stats.norm.rvs(loc=6.0, scale=2, size=n)\n",
+    "\n",
+    "print (\"Variance 1 = \", x1.var())\n",
+    "print (\"Variance 2 = \", x2.var())\n",
+    "\n",
+    "plt.figure(figsize=[10, 5])\n",
+    "\n",
+    "plt.subplot(121)\n",
+    "plt.hist(x1, bins=50, range=[-5,15])\n",
+    "plt.title(r'$x_1$')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.4')\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.hist(x2, bins=50, range=[-5,15])\n",
+    "plt.title(r'$x_2$')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.4')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance x1+x2 =  8.512618048369532\n",
+      "Variance x1*x2 =  270.95225799791183\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entries / bins size = 1')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sum of variables: the sum of two gaussians is a gaussian (variance = sum of variances)\n",
+    "x1_plus_x2  = x1+x2\n",
+    "# product of variables: the product of gaussians is not a gaussian (see the tails of the distribution)\n",
+    "x1_times_x2 = x1*x2\n",
+    "\n",
+    "print (\"Variance x1+x2 = \", x1_plus_x2.var())\n",
+    "print (\"Variance x1*x2 = \", x1_times_x2.var())\n",
+    "\n",
+    "plt.figure(figsize=[10, 5])\n",
+    "\n",
+    "plt.subplot(121)\n",
+    "plt.hist(x1_plus_x2, bins=50, range=[-10,20])\n",
+    "plt.title(r'$x_1 + x_2$')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 0.6')\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.hist(x1_times_x2, bins=50, range=[-10,100])\n",
+    "plt.title(r'$x_1 x x_2$')\n",
+    "plt.xlabel(r'x [units]')\n",
+    "plt.ylabel(r'Entries / bins size = 1')\n"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
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