Michaelson speed of light - hypothesis testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ecdf(data):\n",
    "    #Compute ECDF for a one-dimensional array of measurements.\n",
    "    # Number of data points: n\n",
    "    n =len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1,n+1) / n\n",
    "\n",
    "    return x, y\n",
    "\n",
    "def bootstrap_replicate_1d(data, func):\n",
    "    bs_sample=np.random.choice(data, len(data))\n",
    "    return func(bs_sample)\n",
    "\n",
    "def draw_bs_reps(data, func, size=1):\n",
    "    \"\"\"Draw bootstrap replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: bs_replicates\n",
    "    bs_replicates = np.empty(size)\n",
    "\n",
    "    # Generate replicates\n",
    "    for i in range(size):\n",
    "        bs_replicates[i] = bootstrap_replicate_1d(data,func)\n",
    "\n",
    "    return bs_replicates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  Expt  Run  Speed\n",
      "0           1     1    1    850\n",
      "1           2     1    2    740\n",
      "2           3     1    3    900\n",
      "3           4     1    4   1070\n",
      "4           5     1    5    930\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('Michaelson_speed_of_light.csv')\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "299852.0\n"
     ]
    }
   ],
   "source": [
    "constant=351.77381511027687\n",
    "data=df['Speed']*constant\n",
    "michelson_speed_of_light_mean=np.mean(data)\n",
    "print(michelson_speed_of_light_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Michaelson light speed data is normal distributed\n"
     ]
    },
    {
     "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": [
    "mean= np.mean(data)\n",
    "std=np.std(data)\n",
    "samples = np.random.poisson(mean, size=10000)\n",
    "x,y=ecdf(data)\n",
    "x_theor,y_theor = ecdf(samples)\n",
    "_ = plt.plot(x_theor, y_theor)\n",
    "_ = plt.plot(x, y, marker='.', linestyle='none')\n",
    "_ = plt.xlabel('light speed)')\n",
    "_ = plt.ylabel('poisson CDF')\n",
    "plt.show()\n",
    "\n",
    "#normal distribution\n",
    "samples= np.random.normal(mean,std, size=10000)\n",
    "x_norm,y_norm=ecdf(samples)\n",
    "_ = plt.plot(x, y, marker='.', linestyle='none')\n",
    "_ = plt.plot(x_norm, y_norm)\n",
    "_ = plt.xlabel('light speed)')\n",
    "_ = plt.ylabel('normal CDF')\n",
    "plt.show()\n",
    "\n",
    "print(\"Michaelson light speed data is normal distributed\")\n",
    "\n",
    "\n",
    "_ = plt.hist(data, bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing Michaelson speed of light with Newcombs speed of light\n",
      "Michelson speed of light differs -8.0 meters\n",
      "0     299015.742844\n",
      "1     260320.623182\n",
      "2     316604.433599\n",
      "3     376405.982168\n",
      "4     327157.648053\n",
      "          ...      \n",
      "95    330675.386204\n",
      "96    334193.124355\n",
      "97    281427.052088\n",
      "98    284944.790239\n",
      "99    306051.219146\n",
      "Name: Speed, Length: 100, dtype: float64\n",
      "small variations in values.  The darker dots are the variations.\n"
     ]
    },
    {
     "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": [
    "def diff_from_newcomb(data, newcomb_value=299860):\n",
    "    return np.mean(data)-newcomb_value\n",
    "\n",
    "print('Comparing Michaelson speed of light with Newcombs speed of light')\n",
    "\n",
    "newcomb_speed_of_light_mean=299860 #km/s\n",
    "\n",
    "diff_from_newcomb= diff_from_newcomb(michelson_speed_of_light_mean)\n",
    "\n",
    "print(\"Michelson speed of light differs {0} meters\".format(diff_from_newcomb))\n",
    "\n",
    "michelson_shifted = data - diff_from_newcomb\n",
    "\n",
    "print(michelson_shifted)\n",
    "\n",
    "x=np.arange(0,len(michelson_shifted))\n",
    "\n",
    "_=plt.scatter(x=x,y=data, c='red',s=1)\n",
    "_=plt.scatter(x=x,y=michelson_shifted,c='blue',alpha=0.3)\n",
    "plt.show\n",
    "\n",
    "print('small variations in values.  The darker dots are the variations.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEM 2765.452345998784\n",
      "Standard Deviation of the replicates2767.087033879578\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:19: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[296482.97137494 300915.32144533 298523.25950258 ... 302709.36790239\n",
      " 301513.33693102 294301.97372126]\n",
      "0.5021\n"
     ]
    }
   ],
   "source": [
    "bs_replicates = draw_bs_reps(michelson_shifted, np.mean,10000)\n",
    "\n",
    "plt.clf()\n",
    "x,y=ecdf(data)\n",
    "x_replicate,y_replicate = ecdf(bs_replicates)\n",
    "_ = plt.plot(x_replicate, y_replicate)\n",
    "_ = plt.plot(x, y, marker='.', linestyle='none')\n",
    "_ = plt.xlabel('light speed)')\n",
    "_ = plt.ylabel('replicates CDF')\n",
    "plt.show()\n",
    "\n",
    "sem = np.std(data) / np.sqrt(len(data))\n",
    "print(\"SEM {0}\".format(sem))\n",
    "\n",
    "bs_std = np.std(bs_replicates)\n",
    "print(\"Standard Deviation of the replicates{0}\".format(bs_std))\n",
    "\n",
    "# Make a histogram of the results\n",
    "_ = plt.hist(bs_replicates, bins=50, normed=True)\n",
    "_ = plt.xlabel('mean speed of light (km/s)')\n",
    "_ = plt.ylabel('PDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "print(bs_replicates)\n",
    "\n",
    "p_value= np.sum(bs_replicates<= newcomb_speed_of_light_mean)/10000\n",
    "\n",
    "print(p_value)\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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}