experiment-notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def heating_curve(time_series, temperature_series, TIME_UNIT):\n",
    "    '''\n",
    "        Plot the heating curve.\n",
    "        \n",
    "        Author: Wenjie Chen \n",
    "        E-mail: wenjiechen@pku.edu.cn\n",
    "        \n",
    "        args:\n",
    "            time_series : [numpy.array] A serie of time in minutes.\n",
    "            temperature_series : [numpy.array] A serie of time in Celsius.\n",
    "            TIME_UNIT : [string] Either 'h' or 'min' or 'd', change the unit of time axis.\n",
    "            \n",
    "        returns:\n",
    "            A figure.\n",
    "            \n",
    "        example:\n",
    "            heating_curve(t_series, T_series, 'h')\n",
    "    '''\n",
    "    # data preparation\n",
    "    t_series = time_series.copy()\n",
    "    T_series = temperature_series.copy()\n",
    "    for i in range(len(t_series) - 1):\n",
    "        t_series[i + 1] = t_series[i + 1] + t_series[i]\n",
    "    scale = {'min' : 1, 'h' : 60, 'd' : 1440}\n",
    "    t_series = t_series / scale[TIME_UNIT]\n",
    "    t_series_str = tuple([str(t) for t in np.round(t_series, 2)])\n",
    "    T_series_str = tuple([str(T) for T in T_series])\n",
    "    \n",
    "    plt.figure(figsize = (10, 5))\n",
    "    \n",
    "    # plot the reference line\n",
    "    T_pool = [] # check if the horizental line has already been plot\n",
    "    for i in range(len(t_series) - 2):\n",
    "        plt.plot([t_series[i + 1], t_series[i + 1]], [T_series[i + 1], min(T_series)], 'k--')\n",
    "        if T_series[i + 1] in T_pool:\n",
    "            continue\n",
    "        else:\n",
    "            plt.plot([t_series[i + 1], min(t_series)], [T_series[i + 1], T_series[i + 1]], 'k--')\n",
    "            T_pool.append(T_series[i + 1])\n",
    "        \n",
    "    # plot the curve\n",
    "    plt.plot(t_series, T_series, 'k', linewidth = 3)\n",
    "\n",
    "    # set up the axis\n",
    "    plt.xticks(t_series, t_series_str)\n",
    "    plt.yticks(T_series, T_series_str)\n",
    "    plt.xlim(min(t_series), max(t_series))\n",
    "    plt.ylim(bottom = min(T_series))\n",
    "    plt.xlabel('time / ' + TIME_UNIT)\n",
    "    plt.ylabel('temperature / $^\\circ$C')\n",
    "    plt.title('Heating Curve')\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reaction_substances_mass(molar_mass, molar_ratio, mass_needed):\n",
    "    '''\n",
    "        Calculate the needed substantces' masses for a certain reaction.\n",
    "        \n",
    "        Author: Wenjie Chen \n",
    "        E-mail: wenjiechen@pku.edu.cn\n",
    "        \n",
    "        args:\n",
    "            molar_mass : [numpy.array] An array of molar_mass (the reaction product should be in the first place).\n",
    "            molar_ratio : [numpy.array] An array of molar_mass (should be the same order as molar_mass).\n",
    "            mass_needed : [double] The needed mass for the reaction product.\n",
    "            \n",
    "        returns:\n",
    "            masses_needed : [numpy.array] The calculated masses for the substances.\n",
    "            \n",
    "        example:\n",
    "            molar_mass = np.array([387.44, 105.99, 240.79, 159.60])\n",
    "            molar_ratio = np.array([3, 3, 2, 3])\n",
    "            print(reaction_substances_mass(molar_mass, molar_ratio, 27.0))\n",
    "    '''\n",
    "    masses = molar_mass * molar_ratio\n",
    "    masses_needed = mass_needed / masses[0] * masses\n",
    "    masses_needed = np.round(masses_needed, 4)\n",
    "    return masses_needed[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def XRD_scan_reader(FILEPATH):\n",
    "    '''\n",
    "        Read the XRD scanning data from the txt file.\n",
    "        \n",
    "        Author: Wenjie Chen \n",
    "        E-mail: wenjiechen@pku.edu.cn\n",
    "        \n",
    "        args:\n",
    "            FILEPATH : [string] The path of the data file.\n",
    "            \n",
    "        returns:\n",
    "            (tt, counts) : [tupple] Two numpy arrays of data.\n",
    "            \n",
    "        example:\n",
    "            (tt, counts) = XRD_scan_reader(\"./data/templateDataFile.txt\")\n",
    "    '''\n",
    "    import numpy as np\n",
    "    import csv\n",
    "    datablock = []\n",
    "    \n",
    "    # read data\n",
    "    with open(FILEPATH, newline='') as data_file:\n",
    "        data_text = data_file.read()\n",
    "        data_csv = data_text.split(\"[Data]\")[0]\n",
    "        spamreader = csv.reader(data_csv.splitlines(), delimiter=',', quotechar='|')\n",
    "        line_num = 0\n",
    "        for row in spamreader:\n",
    "            if line_num == 0:\n",
    "                print(f'Column names are {\", \".join(row)}')\n",
    "            else:\n",
    "                datablock.append([float(row[0]), float(row[1])])\n",
    "            line_num = line_num + 1\n",
    "        print(f'Processed {line_num} lines.')\n",
    "    \n",
    "    # process data\n",
    "    datablock = np.transpose(np.array(datablock))\n",
    "    \n",
    "    return (datablock[0], datablock[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def XRD_scan_curve(tt, counts):\n",
    "    '''\n",
    "        Plot the XRD curve.\n",
    "        \n",
    "        Author: Wenjie Chen \n",
    "        E-mail: wenjiechen@pku.edu.cn\n",
    "        \n",
    "        args:\n",
    "            tt : [numpy.array] 2theta data.\n",
    "            counts : [numpy.array] Counts data.\n",
    "            \n",
    "        returns:\n",
    "            A figure.\n",
    "            \n",
    "        example:\n",
    "            XRD_scan_curve(tt, counts)\n",
    "    '''\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure(figsize = (14, 7))\n",
    "    plt.plot(tt, counts)\n",
    "    plt.xlabel('$2 \\\\theta$ / degree')\n",
    "    plt.ylabel('counts')\n",
    "    plt.title('XRD result')\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## play ground"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  6.8391  10.3582  10.2984]\n"
     ]
    }
   ],
   "source": [
    "molar_mass = np.array([387.44, 105.99, 240.79, 159.60])\n",
    "molar_ratio = np.array([3, 3, 2, 3])\n",
    "print(reaction_substances_mass(molar_mass, molar_ratio, 25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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zWw/MAebombWxN3fuXI4fPw7ANddcQ926dU/aH94gROJJ156IRNu9997Lf/zH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7f6ac6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_series = np.array([0, 180, 540, 90, 720, 360])\n",
    "T_series = np.array([20, 850, 850, 1200, 1200, 20])\n",
    "heating_curve(t_series, T_series, 'd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}