Muon_Jupyter_Script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "filename=\"sifted22-10-18-16-52.txt\"  # change the name of the file here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "xColumn = 1                   # Specify the column containing the x data points \n",
    "yColumn = 2                   # Specify the column containing the y data points \n",
    "include_x_errors = True      # Enter True or False only (True if x errors needed on plot)\n",
    "include_y_errors = True       # Enter True or False only (True if y errors needed on plot)\n",
    "xErrColumn = 0                # Specify the column containing the x errors \n",
    "yErrColumn = 3                # Specify the column containing the y errors \n",
    "header = False                 # Does your data file contain one of more header lines (e.g. column titles)\n",
    "head_lines = 0                # Enter the number of header lines in your data file\n",
    "save_graph = True             # Enter True or False only (True if you wish to save the figure)\n",
    "CSV = False                     # Is the data separated by commas (True if yes - e.g. a .csv file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_file=\"figure.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_function(t,A0,tau, B, C):  \n",
    "# In the brackets should be the x parameter (the dependent variable) followed by\n",
    "# the parameters of the equation to be extracted from the fit\n",
    "        \n",
    "    global args                                 # Don't touch this - this enables us to remember the variable names\n",
    "    args = fit_function.__code__.co_varnames    # Don't touch this - this enables us to remember the variable names\n",
    "    \n",
    "    return A0*np.exp(-t/(tau)) + B + C*t      \n",
    "# Be sure to use np.xxx() for any functions\n",
    "# such as trig functions (e.g. np.cos() ) or np.exp( ), etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "guess = (1000,2,0,0) # Enter the initial guess for the parameters of the fit function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code will fit the specified function and extract\n",
      "4 parameters with the following inital guesses:\n",
      "\n",
      "A0 = 1000\n",
      "tau = 2\n",
      "B = 0\n",
      "C = 0\n"
     ]
    }
   ],
   "source": [
    "fit_function(1,*guess)            \n",
    "# Calls the function to be fitted, with x=1 and the initial guesses. This\n",
    "# is just to extract the names of the arguments\n",
    "    \n",
    "\n",
    "if len(args)-1 != len(guess):     # Checks the number of paramaters and the number of guesses\n",
    "    print(\"ERROR - mismatch between number of parameters and number of guesses\")\n",
    "else:\n",
    "    print(\"This code will fit the specified function and extract\")\n",
    "    print(len(args)-1,\"parameters with the following inital guesses:\\n\")\n",
    "    for i in range(len(guess)):         # For each parameter....\n",
    "        print(args[i+1],\"=\",guess[i])   # ..prints the name of each parameter and its initial guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4813 rows of data successfully stored in array\n"
     ]
    }
   ],
   "source": [
    "raw_data = open(filename,'r')       # Open the data file again\n",
    "lines=0\n",
    "\n",
    "# Read the data: count the number of lines\n",
    "i=0\n",
    "for data_line in raw_data: \n",
    "    lines+=1\n",
    "raw_data.seek(0)\n",
    "# print(lines)\n",
    "\n",
    "# Create an array to store all the time values\n",
    "times=np.zeros(lines)\n",
    "\n",
    "# Read the data: store all the time values in an array\n",
    "for data_line in raw_data:                       \n",
    "        row = data_line.strip()                   \n",
    "        data = row.split()               \n",
    "        times[i]=float(data[0])/1000\n",
    "        i=i+1 \n",
    "        # print(i,times[i-1],float(data[0])/1000)        #!Error will appear about index out of range if blank lines are at the end of the data file       \n",
    "print(i,\"rows of data successfully stored in array\")\n",
    "raw_data.close()\n",
    "\n",
    "# converts to a histogram with X (first number) bins in the range (Y,Z) microseconds \n",
    "y,edges=np.histogram(times,20,range=(0,20))\n",
    "\n",
    "if min(y)==0: print(\"TOO MANY BINS, one of the bins has zero counts!!\")\n",
    "\n",
    "# converts the bin \"edges\" in x values that correspond to the centre of the bin\n",
    "x=edges[:-1].copy()\n",
    "binWidth=(x[1]-x[0])\n",
    "x+=binWidth/2\n",
    "\n",
    "# y and x are now arrays of the histogram values (y) and times in us (x). This can now be plotted. \n",
    "\n",
    "# The errors in y and x are the square root of the number of counts and half the bin width. \n",
    "\n",
    "xError=np.zeros(len(x))\n",
    "xError.fill(binWidth/2)\n",
    "yError=np.sqrt(y)\n",
    "\n",
    "# The arrays x and y and their errors can now be plotted using matplotlib and fitted with a curve. \n",
    "# See the Jupyter_Plot_Curve_Fit.ipynb script, for examples on how to fit curves and plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.37612440e+03  1.94170560e+00  1.62079958e+01 -5.32183519e-01]\n"
     ]
    }
   ],
   "source": [
    "# Performs a fit calling the numpy polyfit function (polynomial order 1):\n",
    "\n",
    "# If y errors are included, they will be used as weights in the fit calculation:\n",
    "\n",
    "if include_y_errors:                                                         # Are y errors are included?\n",
    "    para,covar = curve_fit(fit_function, x, y,p0=guess,sigma=yError)\n",
    "else:\n",
    "    para,covar = curve_fit(fit_function, x, y,p0=guess)            # Performs an \"unweighted\" fit\n",
    "\n",
    "# Extract the parameters from the fit:\n",
    "\n",
    "errors=np.sqrt(np.diag(covar))                     # Creates a list called \"errors\" from the covariance matrix\n",
    "\n",
    "x_fit=np.linspace(min(x),max(x),1000)    # Creates an x array to be used for drawing the fitted curve\n",
    "y_fit=fit_function(x_fit,*para)                    # Creates a y array containing the fitted curve\n",
    "\n",
    "print(para)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fit Results:\n",
      "\n",
      "A0 = 2376.124403991017 +/- 79.33074655276401\n",
      "tau = 1.9417056000657424 +/- 0.06691608708483997\n",
      "B = 16.20799583735706 +/- 9.392668691424527\n",
      "C = -0.5321835190058481 +/- 0.580090302048334\n"
     ]
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 150   # Controls the size and resolution of the figures as displayed in Jupyter\n",
    "\n",
    "plt.errorbar(x,y,yerr=yError,xerr=xError,fmt='s',color='black',markersize=4,elinewidth=1,capsize=2,label='data')\n",
    "plt.plot(x_fit,y_fit,'-',color='blue',linewidth=1, label=\"linear fit\")\n",
    "    \n",
    "#plt.xlim(0,2.5)\n",
    "#plt.ylim(0,25)\n",
    "plt.xlabel('X-Axis Label (units)',fontsize=14)\n",
    "plt.ylabel('Y-Axis Label (units)',fontsize=14)\n",
    "plt.title(\"This is a title\",fontsize=16,loc=\"center\")\n",
    "#plt.minorticks_on()\n",
    "#plt.text(7.5,95,\"Here is some text on the graph\",color='red',fontsize=10,weight=\"normal\",fontstyle=\"italic\")\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend()\n",
    "#plt.grid(True)\n",
    "\n",
    "if save_graph:\n",
    "    plt.savefig(fig_file,dpi=500)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nFit Results:\\n\")\n",
    "for i in range(len(para)):\n",
    "        print(args[i+1],\"=\",para[i],\"+/-\",errors[i])\n",
    "        "
   ]
  }
 ],
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