{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96f35096",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "Write a python macro ExerciseGraph.py.\n",
    "\n",
    "1. Create a graph with symmetric errors and 5 points.\n",
    "\n",
    "2. Set the following points: (1.0, 2.1), (2.0, 2.9), (3.0, 4.05), (4.0, 5.2), (5.0, 5.95)\n",
    "\n",
    "3. Set the errors on x to 0.0 and the errors on y to 0.1 (all at once).\n",
    "4. Draw the graph including the axes and error bars.\n",
    "5. Create a one dimensional function f(x) = ax + b and fit it to the graph.\n",
    "\n",
    "## Bonus questions:\n",
    "\n",
    "Programatically obtain the two parameters a and b and their estimated uncertainties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94542f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /Users/yvonne/miniforge3/lib/python3.10/site-packages (3.7.0)\n",
      "Requirement already satisfied: numpy>=1.20 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (1.24.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (4.38.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: six>=1.5 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: mplhep in /Users/yvonne/miniforge3/lib/python3.10/site-packages (0.3.26)\n",
      "Requirement already satisfied: numpy>=1.16.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (1.24.2)\n",
      "Requirement already satisfied: matplotlib>=3.4 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (3.7.0)\n",
      "Requirement already satisfied: uhi>=0.2.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (0.3.3)\n",
      "Requirement already satisfied: packaging in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (23.0)\n",
      "Requirement already satisfied: mplhep-data in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (0.0.3)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (3.0.9)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (4.38.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (1.4.4)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (9.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (2.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (1.0.7)\n",
      "Requirement already satisfied: six>=1.5 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3.4->mplhep) (1.16.0)\n",
      "Requirement already satisfied: uproot in /Users/yvonne/miniforge3/lib/python3.10/site-packages (5.0.3)\n",
      "Requirement already satisfied: numpy in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from uproot) (1.24.2)\n",
      "Requirement already satisfied: packaging in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from uproot) (23.0)\n",
      "Requirement already satisfied: awkward>=2.0.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from uproot) (2.0.8)\n",
      "Requirement already satisfied: awkward-cpp==9 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from awkward>=2.0.0->uproot) (9)\n",
      "Requirement already satisfied: typing-extensions>=4.1.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from awkward>=2.0.0->uproot) (4.5.0)\n",
      "Requirement already satisfied: scipy in /Users/yvonne/miniforge3/lib/python3.10/site-packages (1.10.1)\n",
      "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from scipy) (1.24.2)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{sys.executable}\u001b[39;00m\u001b[38;5;124m -m pip install uproot\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{sys.executable}\u001b[39;00m\u001b[38;5;124m -m pip install scipy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m fig, ax\u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39msubplots()\n\u001b[1;32m      8\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGraph\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot([\u001b[38;5;241m1.\u001b[39m,\u001b[38;5;241m2.\u001b[39m,\u001b[38;5;241m3.\u001b[39m,\u001b[38;5;241m4.\u001b[39m,\u001b[38;5;241m5.\u001b[39m], [\u001b[38;5;241m2.1\u001b[39m,\u001b[38;5;241m2.9\u001b[39m,\u001b[38;5;241m4.05\u001b[39m, \u001b[38;5;241m5.95\u001b[39m])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install matplotlib\n",
    "!{sys.executable} -m pip install mplhep\n",
    "!{sys.executable} -m pip install uproot\n",
    "!{sys.executable} -m pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19c6619e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import uproot\n",
    "import numpy as np\n",
    "import awkward as ak\n",
    "import mplhep as hep\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c91b1256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12e6e3e20>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots()\n",
    "ax.set_title(\"Graph\")\n",
    "x_data=[1.,2.,3.,4.,5.]\n",
    "y_data=[2.1,2.9,4.05,5.2, 5.95]\n",
    "plt.plot(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f7284f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_error=[0.1, 0.1, 0.1, 0.1, 0.1]\n",
    "plt.errorbar(x_data,y_data, yerr=y_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "695ce2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12e9fba60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit_function(x, *coeff):\n",
    "    a,b = coeff\n",
    "    return np.multiply(a,x)+b\n",
    "\n",
    "\n",
    "# Get the fitted curve\n",
    "optimizedParameters, pcov = curve_fit(fit_function, x_data, y_data, p0=[1.,1]);\n",
    "y_fit=fit_function(x_data, *optimizedParameters)\n",
    "\n",
    "plt.errorbar(x_data,y_data, yerr=y_error, label=\"data point\")\n",
    "\n",
    "plt.plot(x_data, y_fit, color=\"r\", label=\"fit\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b029e9e",
   "metadata": {},
   "source": [
    "## Output the fit parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0389de7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "param1:  1.0\n",
      "param2:  1.040000000000087\n"
     ]
    }
   ],
   "source": [
    "print(\"param1: \", optimizedParameters[0])\n",
    "print(\"param2: \", optimizedParameters[1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}