akashin/Lab4

## Lab4
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from sklearn import linear_model\n",
      "import pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = [20.3, 20.97, 22.00, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]\n",
      "dH = [1.5 * 1.2, 1.52 * 1.2, 1.55 * 1.2, 1.9 * 1.2, 1.8 * 1.2, 1.9 * 1.2, 2.1 * 1.2, 2.6, 2.8, 2.9, 3.1, 3.2, 3.4, 3.6, 3.8, 4.1, 4.4, 4.6]\n",
      "conc = [3 / 5, 3 / 5, 2.9 / 5, 2.9 / 5, 3 / 5, 2.9 / 5, 2.8 / 5, 3 / 5, 3.1 / 5, 3 / 5, 2.9 / 5, 2.6 / 5, 2.4 / 5, 2.4 / 5, 2.4 / 5, 2 / 5, 2 / 5, 2/ 5]\n",
      "\n",
      "rho = 1\n",
      "g = 9.8\n",
      "R = 8.31\n",
      "\n",
      "def processExperiment(t, dH, conc):\n",
      "    t = array(t)\n",
      "    T = t + 273.3\n",
      "    dH = array(dH)\n",
      "    conc = array(conc)\n",
      "    dP = rho * g * dH\n",
      "    print(list(zip(T, dP)))\n",
      "#     plot(T, dP, 'bo')\n",
      "    invT = 1 / T;\n",
      "    logP = log(dP)\n",
      "    linear = linear_model.LinearRegression()\n",
      "    linear.fit(matrix(invT).T, matrix(logP).T)\n",
      "    slope = linear.coef_\n",
      "    print(R * slope)\n",
      "    \n",
      "    ddP = array([dP[i + 1] - dP[i] for i in range(0, len(dP) - 1)])\n",
      "    dT = array([T[i + 1] - T[i] for i in range(0, len(T) - 1)])\n",
      "    slope1 = (ddP / dT) * R * T[:-1] * T[:-1] / dP[:-1]\n",
      "    print(slope1.mean())\n",
      "    \n",
      "    fig = plt.figure(figsize=(8, 15))\n",
      "    fig.add_subplot(2, 1, 1)\n",
      "    ax = fig.gca()\n",
      "    pl.grid()\n",
      "    ax.set_xlabel('t, \u00b0C', size=25)\n",
      "    ax.set_ylabel('$\\Delta$P, Pa', size=25)\n",
      "    pl.xticks(fontsize=14)\n",
      "    pl.yticks(fontsize=14)\n",
      "    pylab.ylim(0, 60)\n",
      "    pylab.xlim(0, 50)\n",
      "    \n",
      "    plot(t, dP, 'bo')\n",
      "    \n",
      "    fig.add_subplot(2, 1, 2)\n",
      "    ax = fig.gca()\n",
      "    pl.grid()\n",
      "    ax.set_xlabel('1 / T', size=25)\n",
      "    ax.set_ylabel('$\\log{\\Delta P}$', size=25)\n",
      "    pl.xticks(fontsize=14)\n",
      "    pl.yticks(fontsize=14)\n",
      "    pylab.ylim(2, 4)\n",
      "#     pylab.xlim(0.003, 0.004)\n",
      "    \n",
      "    plot(invT, logP, 'bo')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "processExperiment(t, dH, conc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[(293.60000000000002, 17.640000000000001), (294.26999999999998, 17.8752), (295.30000000000001, 18.228000000000002), (296.30000000000001, 22.344000000000001), (297.30000000000001, 21.168000000000003), (298.30000000000001, 22.344000000000001), (299.30000000000001, 24.696000000000002), (300.30000000000001, 25.480000000000004), (301.30000000000001, 27.440000000000001), (302.30000000000001, 28.420000000000002), (303.30000000000001, 30.380000000000003), (304.30000000000001, 31.360000000000003), (305.30000000000001, 33.32), (306.30000000000001, 35.280000000000001), (307.30000000000001, 37.240000000000002), (308.30000000000001, 40.18), (309.30000000000001, 43.120000000000005), (310.30000000000001, 45.079999999999998)]\n",
        "[[-42530.89561503]]\n",
        "44119.1013412\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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R4g3l5eVKS0tzexhRLZoyzsycobKygmaOz9SaNY+5MKJ/iKacbUXG5tk074X1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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f23fedd9ad0>"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "44119 * 0.05"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "2205.9500000000003"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}