DRosenman/magnetic-torque-balance.ipynb

## magnetic-torque-balance.ipynb
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    "import numpy as np\n",
    "import pandas as pd\n",
    "import linear_regression as lr\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rc(\"savefig\", dpi=120)\n",
    "\n",
    "from IPython.display import Markdown, display, HTML\n",
    "\n",
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   "source": [
    "\n",
    "class torque_balance_data:\n",
    "    m = 0.00128 # MASS OF BALL\n",
    "    g = 9.82\n",
    "    mg = m*g\n",
    "    \n",
    "    \n",
    "\n",
    "    \n",
    "    def __init__(self,data_set, set_number):\n",
    "        self.data_set = np.array(data_set)\n",
    "        self.set_number = set_number\n",
    "        self.B = self.data_set[:,1]\n",
    "        self.R = self.data_set[:,0]\n",
    "        \n",
    "        self.slope = lr.slope(self.B,self.R)\n",
    "        self.intercept = lr.intercept(self.B,self.R)\n",
    "        self.slope_error = lr.slope_error(self.B,self.R)\n",
    "        self.intercept_error = lr.slope_error(self.B,self.R)\n",
    "        self.r = lr.r(self.B,self.R)\n",
    "        \n",
    "        \n",
    "        self.mu = self.slope*torque_balance_data.mg\n",
    "        self.mu_error = self.slope_error*torque_balance_data.mg\n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        #error analysis:\n",
    "        \n",
    "        \n",
    "        \n",
    "    def set_df(self):    #produces a dataframe with the trial data for a set\n",
    "        trial_list = []\n",
    "        i = 1\n",
    "        for trial in self.data_set:\n",
    "            trial_list.append(\"Trial \" + str(i))\n",
    "            i +=1\n",
    "        col = ['R (m)', 'B (T)' , 'I (A)']\n",
    "        df = pd.DataFrame(self.data_set,index = trial_list, columns = col)\n",
    "        df.columns.name = 'SET  ' + str(self.set_number) \n",
    "        return df\n",
    "\n",
    "    def summary_stats(self):\n",
    "        set_name = 'Set ' + str(self.set_number)\n",
    "        ind = [set_name]\n",
    "        col = ['magnetic moment, $\\\\mu$', 'slope', 'R intercept','$r^2$']\n",
    "        results = [self.mu_equation(), self.slope_equation() ,self.intercept_equation(),(self.r)**2]\n",
    "        df = pd.DataFrame([results],index = ind, columns = col)\n",
    "        df.columns.name = 'Summary Stats'\n",
    "        return df\n",
    "    \n",
    "    def slope_equation(self):\n",
    "        return str(round(self.slope,1)) + \" $\\\\pm $\" + str(round(self.slope_error,1)) + \" $\\\\frac{1}{T \\\\cdot s}$\"\n",
    "        \n",
    "       \n",
    "    \n",
    "    def intercept_equation(self):\n",
    "        return str(round(self.intercept,3)) + \" $\\\\pm $\" + str(round(self.intercept_error,3)) + \" $\\\\frac{1}{s}$\"\n",
    "        \n",
    "    def mu_equation(self):\n",
    "        return str(round(self.mu,3)) + \" $\\\\pm $\" + str(round(self.mu_error,3)) + \" J/T\"\n",
    "       \n",
    "    \n",
    "    def plot(self):\n",
    "     \n",
    "        slope_eq = \"$slope = $\" + self.slope_equation()  \n",
    "        mu_eq = '$\\\\mu = $' + self.mu_equation()\n",
    "        axis_font = {'family': 'serif','color':  'darkred','weight': 'bold','size': 12,\n",
    "        } \n",
    "        font_big = {'family': 'serif','color':  'darkred','weight': 'bold','size': 16,\n",
    "        }   \n",
    "        B = self.B\n",
    "        R = self.R\n",
    "        \n",
    "        plt.xlim(B.min()*.95,B.max()*1.05)\n",
    "        plt.ylim(R.min()*.95, R.max()*1.05)\n",
    "        plt.title(\"Set \" + str(self.set_number) + \": R vs B\", fontdict = font_big )\n",
    "        plt.grid()\n",
    "    #measurements scatter plot\n",
    "        plt.scatter(B,R, label = \"Measurements\", color = 'darkred')\n",
    "    \n",
    "    #Best fit line:\n",
    "        slope = lr.slope(B,R)\n",
    "        intercept = lr.intercept(B,R)\n",
    "        B_fit = np.linspace(0,B.max()*1.05)\n",
    "        R_fit = B_fit*slope + intercept\n",
    "        plt.plot(B_fit,R_fit, label = 'Best Fit Line', color = 'darkred')\n",
    "        ax = plt.gca()\n",
    "        plt.text(0.5, 0.75,slope_eq,color = 'darkred', ha='center', va='center', transform=ax.transAxes)\n",
    "        plt.text(0.5,0.65,mu_eq,color = 'darkred',ha='center',va='center',transform=ax.transAxes)\n",
    "        plt.ylabel('R (m)', fontdict = axis_font)\n",
    "        plt.xlabel('B (T)', fontdict = axis_font)\n",
    "        \n",
    "        plt.legend()\n",
    "        \n",
    "    \n",
    "        \n",
    "    def residuals_vs_B(self):\n",
    "        residuals = self.R - self.B*self.slope - self.intercept\n",
    "        axis_font = {'family': 'serif','color':  'darkred','weight': 'bold','size': 12,\n",
    "        } \n",
    "        font_big = {'family': 'serif','color':  'darkred','weight': 'bold','size': 16,\n",
    "        }   \n",
    "        \n",
    "        plt.hlines(0,.95*self.B.min(),1.05*self.B.max(), color = 'k')\n",
    "        plt.scatter(self.B,residuals,color = 'darkred')\n",
    "        plt.xlim(.95*self.B.min(),1.05*self.B.max())\n",
    "        plt.grid()\n",
    "        plt.xlabel('B (T)', fontdict = axis_font)\n",
    "        plt.ylabel('Residual (m)\\n$R-m \\\\cdot B-c$',fontdict = axis_font)\n",
    "        plt.title('Set ' + str(self.set_number) + \": Residuals vs B\", fontdict = font_big )\n",
    "    \n",
    "    @staticmethod\n",
    "    def experiment_stats(set_list):\n",
    "        ind = []\n",
    "        sets = []\n",
    "        col = ['magnetic moment, $\\\\mu$', 'slope', 'R intercept','$r^2$']\n",
    "        #df = pd.DataFrame([set_stats],ind,columns = ,)\n",
    "        for data_set in set_list:\n",
    "            ind.append('Trial ' + str(data_set.set_number))\n",
    "            sets.append(data_set.summary_stats().values[0,:])\n",
    "        df = pd.DataFrame(sets,index = ind, columns = col) \n",
    "        df.columns.name = 'Summary Stats'\n",
    "        return df\n",
    "        \n",
    "        \n",
    "            \n",
    "        \n",
    "            \n",
    "    \n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "\n",
    "\n",
    "def multi_column_df_display(list_dfs, cols=3):\n",
    "    html_table = \"<table style='width:100%; border:0px'>{content}</table>\"\n",
    "    html_row = \"<tr style='border:0px'>{content}</tr>\"\n",
    "    html_cell = \"<td style='width:{width}%;vertical-align:top;border:0px'>{{content}}</td>\"\n",
    "    html_cell = html_cell.format(width=100/cols)\n",
    "\n",
    "    cells = [ html_cell.format(content=df.to_html()) for df in list_dfs ]\n",
    "    cells += (cols - (len(list_dfs)%cols)) * [html_cell.format(content=\"\")] # pad\n",
    "    rows = [ html_row.format(content=\"\".join(cells[i:i+cols])) for i in range(0,len(cells),cols)]\n",
    "    display(HTML(html_table.format(content=\"\".join(rows))))\n",
    "    "
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     "data": {
      "text/markdown": [
       "### RESULTS "
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       "<table style='width:100%; border:0px'><tr style='border:0px'><td style='width:33.333333333333336%;vertical-align:top;border:0px'><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>SET  1</th>\n",
       "      <th>R (m)</th>\n",
       "      <th>B (T)</th>\n",
       "      <th>I (A)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trial 1</th>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.003809</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 2</th>\n",
       "      <td>0.0540</td>\n",
       "      <td>0.003562</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 3</th>\n",
       "      <td>0.0790</td>\n",
       "      <td>0.004658</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 4</th>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.004247</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 5</th>\n",
       "      <td>0.0530</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 6</th>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.005069</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 7</th>\n",
       "      <td>0.0870</td>\n",
       "      <td>0.004521</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 8</th>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.005206</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 9</th>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.003562</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></td><td style='width:33.333333333333336%;vertical-align:top;border:0px'><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>SET  2</th>\n",
       "      <th>R (m)</th>\n",
       "      <th>B (T)</th>\n",
       "      <th>I (A)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trial 1</th>\n",
       "      <td>0.0510</td>\n",
       "      <td>0.003288</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 2</th>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.003493</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 3</th>\n",
       "      <td>0.0650</td>\n",
       "      <td>0.003658</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 4</th>\n",
       "      <td>0.0695</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 5</th>\n",
       "      <td>0.0765</td>\n",
       "      <td>0.004014</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 6</th>\n",
       "      <td>0.0820</td>\n",
       "      <td>0.004261</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 7</th>\n",
       "      <td>0.0885</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 8</th>\n",
       "      <td>0.0950</td>\n",
       "      <td>0.004589</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 9</th>\n",
       "      <td>0.0980</td>\n",
       "      <td>0.004726</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 10</th>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.004932</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Summary Stats</th>\n",
       "      <th>magnetic moment, $\\mu$</th>\n",
       "      <th>slope</th>\n",
       "      <th>R intercept</th>\n",
       "      <th>$r^2$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trial 1</th>\n",
       "      <td>0.353 $\\pm $0.03 J/T</td>\n",
       "      <td>28.1 $\\pm $2.4 $\\frac{1}{T \\cdot s}$</td>\n",
       "      <td>-0.045 $\\pm $2.424 $\\frac{1}{s}$</td>\n",
       "      <td>0.950463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial 2</th>\n",
       "      <td>0.414 $\\pm $0.008 J/T</td>\n",
       "      <td>32.9 $\\pm $0.6 $\\frac{1}{T \\cdot s}$</td>\n",
       "      <td>-0.057 $\\pm $0.627 $\\frac{1}{s}$</td>\n",
       "      <td>0.997114</td>\n",
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       "Summary Stats magnetic moment, $\\mu$                                 slope  \\\n",
       "Trial 1         0.353 $\\pm $0.03 J/T  28.1 $\\pm $2.4 $\\frac{1}{T \\cdot s}$   \n",
       "Trial 2        0.414 $\\pm $0.008 J/T  32.9 $\\pm $0.6 $\\frac{1}{T \\cdot s}$   \n",
       "\n",
       "Summary Stats                       R intercept     $r^2$  \n",
       "Trial 1        -0.045 $\\pm $2.424 $\\frac{1}{s}$  0.950463  \n",
       "Trial 2        -0.057 $\\pm $0.627 $\\frac{1}{s}$  0.997114  "
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BuhhjooBkwGGtXVWEcYuIiJRZvrJxS3Hyq4STiIiIeEfSgQPMuekm4hYvpvU9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19cc722f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TRIAL DATA\n",
    "rball = 0.027 \n",
    "\n",
    "#SET 1 \n",
    "set1_data = []\n",
    "set1_data.append([0.034+rball,0.00137*2.78,2.78]) #TRIAL 1\n",
    "set1_data.append([0.027+rball,0.00137*2.60,2.60])#TRIAL 2\n",
    "set1_data.append([0.052+rball,0.00137*3.4,3.4])  #TRIAL 3\n",
    "set1_data.append([0.042+rball,0.00137*3.1,3.1])  #TRIAL 4\n",
    "set1_data.append([0.026+rball,0.00137*2.5,2.5])  #TRIAL 5\n",
    "set1_data.append([0.0675+rball,0.00137*3.7,3.7]) #TRIAL 6\n",
    "set1_data.append([0.06+rball,0.00137*3.3,3.3])   #TRIAL 7\n",
    "set1_data.append([0.08+rball,0.00137*3.8,3.8])   #TRIAL 8\n",
    "set1_data.append([0.03+rball,0.00137*2.6,2.6])   #TRIAL 9\n",
    "\n",
    "#SET 2\n",
    "set2_data = []\n",
    "set2_data.append([0.024+rball,0.00137*2.4,2.4]) #TRIAL 1\n",
    "set2_data.append([0.032+rball,0.00137*2.55,2.55])#TRIAL 2\n",
    "set2_data.append([0.038+rball,0.00137*2.67,2.67])  #TRIAL 3\n",
    "set2_data.append([0.0425+rball,0.00137*2.8,2.8])  #TRIAL 4\n",
    "set2_data.append([0.0495+rball,0.00137*2.93,2.93])  #TRIAL 5\n",
    "set2_data.append([0.055+rball,0.00137*3.11,3.11]) #TRIAL 6\n",
    "set2_data.append([0.0615+rball,0.00137*3.21,3.21])   #TRIAL 7\n",
    "set2_data.append([0.068+rball,0.00137*3.35,3.35])   #TRIAL 8\n",
    "set2_data.append([0.071+rball,0.00137*3.45,3.45])   #TRIAL 9\n",
    "set2_data.append([0.080+rball,0.00137*3.60,3.60])  #TRIAL 10\n",
    "\n",
    "#Set Objects\n",
    "set1 = torque_balance_data(data_set = set1_data,set_number = 1)\n",
    "set2 = torque_balance_data(data_set = set2_data,set_number = 2)\n",
    "#Set Measurements DataFrame\n",
    "printmd('### RESULTS ')\n",
    "\n",
    "#Measurement dataframes\n",
    "set1_measurements = set1.set_df() \n",
    "set2_measurements = set2.set_df()\n",
    "\n",
    "#Set Analaysis Data Frames\n",
    "multi_column_df_display([set1_measurements,set2_measurements])\n",
    "\n",
    "\n",
    "#Summary Stats From Both Sets\n",
    "display(torque_balance_data.experiment_stats([set1,set2]))\n",
    "\n",
    "#Set plots\n",
    "plt.figure(figsize = (20,16))\n",
    "plt.subplot(2,2,1)\n",
    "set1.plot()\n",
    "plt.subplot(2,2,2)\n",
    "set1.residuals_vs_B()\n",
    "plt.subplot(2,2,3)\n",
    "set2.plot()\n",
    "plt.subplot(2,2,4)\n",
    "set2.residuals_vs_B()\n",
    "plt.savefig('graphs/torquebalance/resultsgraph.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}