From d905b3a780d7a004a721e9860896355d4276f3b8 Mon Sep 17 00:00:00 2001
From: Mauro Donega <mauro.donega@cern.ch>
Date: Sun, 10 May 2020 21:50:59 +0200
Subject: [PATCH] clean up

---
 dataRepresentationGraphsHists.ipynb | 791 ++++++++++++++++++++++++++++
 leastSquaresFits.ipynb              | 442 ++++++++++++++++
 parentSamplingDistributions.ipynb   | 105 ++++
 3 files changed, 1338 insertions(+)
 create mode 100644 dataRepresentationGraphsHists.ipynb
 create mode 100644 leastSquaresFits.ipynb
 create mode 100644 parentSamplingDistributions.ipynb

diff --git a/dataRepresentationGraphsHists.ipynb b/dataRepresentationGraphsHists.ipynb
new file mode 100644
index 0000000..f8a2cff
--- /dev/null
+++ b/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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\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/leastSquaresFits.ipynb b/leastSquaresFits.ipynb
new file mode 100644
index 0000000..ab27e6f
--- /dev/null
+++ b/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/parentSamplingDistributions.ipynb b/parentSamplingDistributions.ipynb
new file mode 100644
index 0000000..b95ca7e
--- /dev/null
+++ b/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
+}
-- 
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