correlationcoefficient.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "event = np.ones(1000)\n",
    "event[100:200]=5\n",
    "event[400:500]=5\n",
    "event[800:900]=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal01 = np.ones(1000)\n",
    "signal01[100:200]=100\n",
    "signal01[400:500]=100\n",
    "signal01[800:900]=100\n",
    "\n",
    "signal02 = np.ones(1000)\n",
    "signal02[100:200]=20\n",
    "signal02[400:500]=20\n",
    "signal02[800:900]=20\n",
    "\n",
    "signal03 = np.ones(1000)\n",
    "signal03[100:200]=200\n",
    "signal03[400:500]=200\n",
    "signal03[800:900]=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(4,1,1)\n",
    "plt.plot(event)\n",
    "plt.title('event')\n",
    "plt.subplot(4,1,2)\n",
    "plt.plot(signal01)\n",
    "plt.title('singal 01')\n",
    "plt.subplot(4,1,3)\n",
    "plt.plot(signal02)\n",
    "plt.title('singal 02')\n",
    "plt.subplot(4,1,4)\n",
    "plt.plot(signal03)\n",
    "plt.title('singal 03')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.833030277982336 45.36749938006281 8.706893820416097 79.65054927619771\n"
     ]
    }
   ],
   "source": [
    "std_e = np.std(event)\n",
    "std01 = np.std(signal01)\n",
    "std02 = np.std(signal02)\n",
    "std03 = np.std(signal03)\n",
    "print(std_e,std01,std02,std03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   3.36   83.16]\n",
      " [  83.16 2058.21]] [[ 3.36 15.96]\n",
      " [15.96 75.81]] [[3.36000e+00 1.11160e+02]\n",
      " [1.11160e+02 6.34421e+03]]\n"
     ]
    }
   ],
   "source": [
    "cov01 = np.cov(np.array([event,signal01]),bias=True)\n",
    "cov02 = np.cov(np.array([event,signal02]),bias=True)\n",
    "cov03 = np.cov(np.array([event,signal03]),bias=True)\n",
    "\n",
    "print(cov01,cov02,cov03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00163249 0.04040404]\n",
      " [0.04040404 1.        ]] [[0.21052632 1.        ]\n",
      " [1.         4.75      ]] [[2.30134041e-02 7.61360119e-01]\n",
      " [7.61360119e-01 4.34529370e+01]]\n"
     ]
    }
   ],
   "source": [
    "r01=cov01/(std_e*std01)\n",
    "r02=cov02/(std_e*std02)\n",
    "r03=cov03/(std_e*std03)\n",
    "\n",
    "print(r01,r02,r03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "r01 = np.corrcoef(event,signal01)\n",
    "r02 = np.corrcoef(event,signal02)\n",
    "r03 = np.corrcoef(event,signal03)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r01 is: [[1. 1.]\n",
      " [1. 1.]]\n",
      "r02 is:[[1. 1.]\n",
      " [1. 1.]]\n",
      "r03 is:[[1.         0.76136012]\n",
      " [0.76136012 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(f'r01 is: {r01}')\n",
    "print(f'r02 is:{r02}')\n",
    "print(f'r03 is:{r03}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cov(x,y): \n",
    "    mean_x = np.mean(x)\n",
    "    mean_y = np.mean(y)\n",
    "\n",
    "    dev_x = [i - mean_x for i in x]\n",
    "    dev_y = [i - mean_y for i in y]\n",
    "\n",
    "    numerator = sum([dev_x[i] * dev_y[i] for i in range(len(x))])/len(dev_x)\n",
    "\n",
    "    denominator_x = np.sqrt(sum([i**2 for i in dev_x])/len(dev_x))\n",
    "    denominator_y = np.sqrt(sum([i**2 for i in dev_y])/len(dev_y))\n",
    "    corr_coef = numerator / (denominator_x * denominator_y)\n",
    "    print(\"Correlation Coefficient:\", corr_coef)\n",
    "    print(f'the covariance is:{numerator}')\n",
    "    print(f'the std of x is:{denominator_x}')\n",
    "    print(f'the std of y is:{denominator_y}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Coefficient: 0.7613601192666659\n",
      "the covariance is:111.15999999999856\n",
      "the std of x is:1.8330302779823469\n",
      "the std of y is:79.65054927619745\n"
     ]
    }
   ],
   "source": [
    "cov(event,signal03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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