tschoppi/Zeff_vs_ionisationE.ipynb

## Zeff_vs_ionisationE.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:a1afa05241c45de3acd18bf6f9f4297061ab392dc66c8c85672d441833c8ec1a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the data files."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ionization = np.loadtxt(\"ionization.dat\", usecols=(0,2))\n",
      "Zeff = np.loadtxt(\"Zeff.dat\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Join the two data arrays on common points (element numbers), thus eliminating all the missing values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "joined = [[num, n, ion, z] for num, ion in ionization for num2, n, z in Zeff if num==num2]\n",
      "joined"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[[1.0, 1.0, 13.59844, 1.0],\n",
        " [2.0, 1.0, 24.587409999999998, 1.6879999999999999],\n",
        " [3.0, 2.0, 5.3917200000000003, 1.2789999999999999],\n",
        " [4.0, 2.0, 9.3226999999999993, 1.9119999999999999],\n",
        " [5.0, 2.0, 8.2980300000000007, 2.4209999999999998],\n",
        " [6.0, 2.0, 11.260300000000001, 3.1360000000000001],\n",
        " [7.0, 2.0, 14.534140000000001, 3.8340000000000001],\n",
        " [8.0, 2.0, 13.61806, 4.4530000000000003],\n",
        " [9.0, 2.0, 17.422820000000002, 5.0999999999999996],\n",
        " [10.0, 2.0, 21.564599999999999, 5.758],\n",
        " [11.0, 3.0, 5.1390799999999999, 2.5070000000000001],\n",
        " [12.0, 3.0, 7.6462399999999997, 3.3079999999999998],\n",
        " [13.0, 3.0, 5.9857699999999996, 4.0659999999999998],\n",
        " [14.0, 3.0, 8.1516900000000003, 4.2850000000000001],\n",
        " [15.0, 3.0, 10.486689999999999, 4.8860000000000001],\n",
        " [16.0, 3.0, 10.360010000000001, 5.4820000000000002],\n",
        " [17.0, 3.0, 12.967639999999999, 6.1159999999999997],\n",
        " [18.0, 3.0, 15.75962, 6.7640000000000002],\n",
        " [19.0, 4.0, 4.3406599999999997, 3.4950000000000001],\n",
        " [20.0, 4.0, 6.1131599999999997, 4.3979999999999997],\n",
        " [21.0, 4.0, 6.5614999999999997, 4.6319999999999997],\n",
        " [22.0, 4.0, 6.8281000000000001, 4.8170000000000002],\n",
        " [23.0, 4.0, 6.7462, 4.9809999999999999],\n",
        " [24.0, 4.0, 6.7664999999999997, 5.133],\n",
        " [25.0, 4.0, 7.4340200000000003, 5.2830000000000004],\n",
        " [26.0, 4.0, 7.9024000000000001, 5.4340000000000002],\n",
        " [27.0, 4.0, 7.8810000000000002, 5.5759999999999996],\n",
        " [28.0, 4.0, 7.6398000000000001, 5.7110000000000003],\n",
        " [29.0, 4.0, 7.7263799999999998, 5.8419999999999996],\n",
        " [30.0, 4.0, 9.3941999999999997, 5.9649999999999999],\n",
        " [31.0, 4.0, 5.9992999999999999, 6.2220000000000004],\n",
        " [32.0, 4.0, 7.8994, 6.7800000000000002],\n",
        " [33.0, 4.0, 9.7886000000000006, 7.4489999999999998],\n",
        " [34.0, 4.0, 9.7523800000000005, 8.2870000000000008],\n",
        " [35.0, 4.0, 11.81381, 9.0280000000000005],\n",
        " [36.0, 4.0, 13.999610000000001, 9.3379999999999992],\n",
        " [37.0, 5.0, 4.17713, 4.9850000000000003],\n",
        " [38.0, 5.0, 5.6948999999999996, 6.0709999999999997],\n",
        " [39.0, 5.0, 6.2171000000000003, 6.2560000000000002],\n",
        " [40.0, 5.0, 6.6338999999999997, 6.4459999999999997],\n",
        " [41.0, 5.0, 6.7588499999999998, 5.9210000000000003],\n",
        " [42.0, 5.0, 7.0924300000000002, 6.1059999999999999],\n",
        " [43.0, 5.0, 7.2800000000000002, 7.2270000000000003],\n",
        " [44.0, 5.0, 7.3605, 6.4850000000000003],\n",
        " [45.0, 5.0, 7.4588999999999999, 6.6399999999999997],\n",
        " [47.0, 5.0, 7.5762, 6.7560000000000002],\n",
        " [48.0, 5.0, 8.9938000000000002, 8.1920000000000002],\n",
        " [49.0, 5.0, 5.7863600000000002, 8.4700000000000006],\n",
        " [50.0, 5.0, 7.3438999999999997, 9.1020000000000003],\n",
        " [51.0, 5.0, 8.6083999999999996, 9.9949999999999992],\n",
        " [52.0, 5.0, 9.0096000000000007, 10.808999999999999],\n",
        " [53.0, 5.0, 10.45126, 11.612],\n",
        " [54.0, 5.0, 12.129799999999999, 12.425000000000001]]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now transpose the array so that the columns become rows (which makes reading the plotting code so much easier if you would continue without reassigning the variables)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "joined = np.array(joined).T\n",
      "atn = np.array(joined[0])\n",
      "n = np.array(joined[1])\n",
      "ion = np.array(joined[2])\n",
      "zeff = np.array(joined[3])\n",
      "joined"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "array([[  1.     ,   2.     ,   3.     ,   4.     ,   5.     ,   6.     ,\n",
        "          7.     ,   8.     ,   9.     ,  10.     ,  11.     ,  12.     ,\n",
        "         13.     ,  14.     ,  15.     ,  16.     ,  17.     ,  18.     ,\n",
        "         19.     ,  20.     ,  21.     ,  22.     ,  23.     ,  24.     ,\n",
        "         25.     ,  26.     ,  27.     ,  28.     ,  29.     ,  30.     ,\n",
        "         31.     ,  32.     ,  33.     ,  34.     ,  35.     ,  36.     ,\n",
        "         37.     ,  38.     ,  39.     ,  40.     ,  41.     ,  42.     ,\n",
        "         43.     ,  44.     ,  45.     ,  47.     ,  48.     ,  49.     ,\n",
        "         50.     ,  51.     ,  52.     ,  53.     ,  54.     ],\n",
        "       [  1.     ,   1.     ,   2.     ,   2.     ,   2.     ,   2.     ,\n",
        "          2.     ,   2.     ,   2.     ,   2.     ,   3.     ,   3.     ,\n",
        "          3.     ,   3.     ,   3.     ,   3.     ,   3.     ,   3.     ,\n",
        "          4.     ,   4.     ,   4.     ,   4.     ,   4.     ,   4.     ,\n",
        "          4.     ,   4.     ,   4.     ,   4.     ,   4.     ,   4.     ,\n",
        "          4.     ,   4.     ,   4.     ,   4.     ,   4.     ,   4.     ,\n",
        "          5.     ,   5.     ,   5.     ,   5.     ,   5.     ,   5.     ,\n",
        "          5.     ,   5.     ,   5.     ,   5.     ,   5.     ,   5.     ,\n",
        "          5.     ,   5.     ,   5.     ,   5.     ,   5.     ],\n",
        "       [ 13.59844,  24.58741,   5.39172,   9.3227 ,   8.29803,  11.2603 ,\n",
        "         14.53414,  13.61806,  17.42282,  21.5646 ,   5.13908,   7.64624,\n",
        "          5.98577,   8.15169,  10.48669,  10.36001,  12.96764,  15.75962,\n",
        "          4.34066,   6.11316,   6.5615 ,   6.8281 ,   6.7462 ,   6.7665 ,\n",
        "          7.43402,   7.9024 ,   7.881  ,   7.6398 ,   7.72638,   9.3942 ,\n",
        "          5.9993 ,   7.8994 ,   9.7886 ,   9.75238,  11.81381,  13.99961,\n",
        "          4.17713,   5.6949 ,   6.2171 ,   6.6339 ,   6.75885,   7.09243,\n",
        "          7.28   ,   7.3605 ,   7.4589 ,   7.5762 ,   8.9938 ,   5.78636,\n",
        "          7.3439 ,   8.6084 ,   9.0096 ,  10.45126,  12.1298 ],\n",
        "       [  1.     ,   1.688  ,   1.279  ,   1.912  ,   2.421  ,   3.136  ,\n",
        "          3.834  ,   4.453  ,   5.1    ,   5.758  ,   2.507  ,   3.308  ,\n",
        "          4.066  ,   4.285  ,   4.886  ,   5.482  ,   6.116  ,   6.764  ,\n",
        "          3.495  ,   4.398  ,   4.632  ,   4.817  ,   4.981  ,   5.133  ,\n",
        "          5.283  ,   5.434  ,   5.576  ,   5.711  ,   5.842  ,   5.965  ,\n",
        "          6.222  ,   6.78   ,   7.449  ,   8.287  ,   9.028  ,   9.338  ,\n",
        "          4.985  ,   6.071  ,   6.256  ,   6.446  ,   5.921  ,   6.106  ,\n",
        "          7.227  ,   6.485  ,   6.64   ,   6.756  ,   8.192  ,   8.47   ,\n",
        "          9.102  ,   9.995  ,  10.809  ,  11.612  ,  12.425  ]])"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(zeff, ion, s=20, c=atn, cmap=\"Greys\")\n",
      "xlabel(r\"$Z_\\mathrm{eff}$\")\n",
      "ylabel(r\"$E_\\mathrm{ion} / \\mathrm{eV}$\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.text.Text at 0x7fb8e9d06dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XBiApKYmrrrqKIUOG0KBBAzIyMli1ahXz589nzZo1tGnThuuuu47PP/+c6dOn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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb8ea533d90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(zeff**2, ion, s=20, c=atn, cmap=\"Greys\")\n",
      "xlabel(r\"$Z_\\mathrm{eff}^2$\")\n",
      "ylabel(r\"$E_\\mathrm{ion} / \\mathrm{eV}$\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x7fb8e9bb0bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UPIIrpZv6UUi+/PnPf6Znz5506dKF1q1bU6NGjbxqqDMVHh6e19fCzMjKyqJm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\n/Pp//G3iAlnkmCsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb8e9c6fd90>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(zeff**2/n**2, ion, s=20, c=atn, cmap=\"Greys\")\n",
      "colorbar(label=\"Atomic Number\")\n",
      "\n",
      "mslope = 13.6\n",
      "mx = np.linspace(0, 2)\n",
      "my = mx*mslope\n",
      "plot(mx, my, 'r-')\n",
      "\n",
      "xlabel(r\"$Z_\\mathrm{eff}^2 n^{-2}$\")\n",
      "ylabel(r\"$E_\\mathrm{ion} / \\mathrm{eV}$\")\n",
      "tight_layout()\n",
      "savefig(\"firstresult.png\", figsize=(7,5), dpi=80)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mm0lLsx3czW8KOWggDXhJVVsAnYHnRKS5u9w6QA8g173os53XHtdE/wO4Voqu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\nCUs4xhhjioQlHGOMMUXCEo4xxpgi8f8BZi6kD9AsNPYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb8e9c12750>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(zeff**2/n**2, np.log(ion), s=20, c=atn, cmap=\"Greys\")\n",
      "xlabel(r\"$Z_\\mathrm{eff}^2 n^{-2}$\")\n",
      "ylabel(r\"$\\ln E_\\mathrm{ion} / \\mathrm{eV}$\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x7fb8e9991750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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x2223lTxOTU0lNTW1vHEiIiUlpcxbQmZmZrJz506OPPLIklVCN2/eTL169TAz\nGjduTHp6Ohs2bCAxMZH4+HjatGnDvffeyymnnPKTmb5xcXEMGzaMe++9l549exIdHU1eXh733nsv\n11133U9GNlU3S5YsAX46c3nx4sVBxRGpsdLS0khLSyvXeyK+wI4V/Ut/Gljq7g/+zGHTgLHAy2bW\nD9j2c/0NpYtDTefuXH/99Tz66KPExcXRokULZs+eTcuWLRkwYAD/93//R6NGjUhMTKRx48Z8//33\nREVFMWTIEKZMmUK9evX2e96tW7eSmJhYMow0Li6O+Ph4tm3bVq2LQ+/evVm6dCmlJzr26dMnwEQi\nNdO+H5z/+te/HvQ9lZ4hXV5mdiLwAUVDYvde/CagNRSt41R83CPAqUAW8Ft3/2o/56pVM6SnTJnC\n5ZdfTnJyMrGxsaSnp9OyZUvmzp0LwLRp0/jd735HZmYmxx9/PI899hidO3c+6CJ6W7ZsoVOnTrRs\n2ZLDDz+cH374gdzcXFauXFmtF+DbtWsXQ4cOZcmSJbg7vXr1YtasWdSvXz/oaCI1WllmSEe8OIRT\nbSsON910E6+99hodO3YEYPfu3Xz99dds3bq10uf+/PPPufDCC8nIyKBr1668+uqrlV7YLxIKCwtZ\ntWoVZkaHDh20OJ5IGFTJ8hlSNfLy8vj666/ZsmUL7du3Jyoqii1bttC2bduwnL9Pnz6sWLEiLOeK\npKioKDp16hR0DJE6R3cOASssLOTuu+/mvvvuIz8/n0aNGrFt2zZiYmLIy8ujf//+DB48mJ07d/L0\n009jZlx99dXceOON+hQtIhWiO4ca4O677+aRRx4hOTm55O5hzJgxTJo0ib59+5KcnMxjjz1GdHQ0\nN9xwA5s3b+bJJ5+kadOmVbZonoiI7hwCtHPnTpKTk2ndunXJqKEVK1aQmJjIxo0befDBBzEz/vKX\nv3DMMccwY8YM4uLiyMrKonv37nzxxRcB/wQiUhOFbSc4Cb+0tDTatWvHpk2byMvLK/l+Xl4e69at\nIyYmpqTZqF69ekydOpU+ffowZMgQBg4cyJIlS/j++++Dii8itZyalQKwZ88ezj33XP785z+Tm5vL\n+PHj2blzJ3l5eaxdu5aRI0fy6aefMnnyZLp27cquXbswM5o0aQJA48aNadKkCcuXL6dFixYB/zQi\nUhvpziEAGzcWzedLSUmhf//+3HXXXWzdupWuXbvyzTff8PLLL/Pxxx8TGxvLrFmzGDBgAFFRUWzb\ntg0o2n6ee+IaAAAQEUlEQVRz27ZttGvXLsgfQ0RqMd05BOCII44gPz+fJUuW0K1bN5o3b86ePXu4\n6667Sn7ht2jRgkmTJpW8Z8CAAVx++eUceuihbN26lfHjx9OmTZugfgQRqeVUHAIQFxfH5MmTGT16\nNC1btiQjI4NbbrklZDG+LVu28O677xIdHc3pp5/ON998w/Lly2nXrl3Y5j+IiOyPRisFaPPmzSxb\ntoxWrVrRunXrn7y2evVqjj/+eA455BAKCgoAmD9/fkm/g4hIRWm0UjWxY8cOLrroItq1a8fxxx/P\n/PlF21ckJSVxwgknhBQGgOuvv56WLVty3HHH0b9/fxISErj99tsjHV1E6igVhwgYPXo0mzZt4o47\n7mDIkCEMHz6ctWvXHvA9GRkZP1kxtXHjxgd8j7sT7ruovWs7acisSN2j4lDF9uzZw9tvv82f/vQn\nWrduzdChQ+nduzdz5sw54PtSU1NZvXo1+fn55OXlsWbNGgYNGhRynLszbtw4GjduTIMGDbjyyivJ\nz8+vdO7FixfToUMHhg8fztFHH13p7T5FpGZRh3QVi4mJITY2lszMTJo2bYq7s2XLFho0aHDA9915\n552sWbOGf//73wBceumlXH311SHH/fOf/2Tq1KnMnDmT+Ph4rr76au666y5uvfXWSuUeNWoU/fr1\nIyUlhaysLJ599lmGDBnC4MGDK3VeEakZ1CEdAffccw8TJ07k1FNPZcWKFWRmZvLRRx+RkJBw0Pfm\n5uZiZsTFxe339VGjRnH88cczcuRIAD766COefPJJPvjggwrndXdiY2MZN25cyX4Pb731FqNGjeKa\na66p8HlFpHpQh3Q1ccMNN/Dwww9Tv359Tj31VObNm1emwgAQHx//s4UBiuZMLFu2rOT5smXLSEpK\nqlReM6N9+/YsXboUgJycHL777juOPvrocp1nwYIFdOrUibi4OLp168a3335bqVwiEjm6c6hG3J2X\nXnqJr776is6dO3PxxRcfdKe29evXc/zxx5OcnEy9evWYN28eaWlpHHPMMZXK8uWXXzJs2DAaNmzI\n1q1bGTNmDA899FCZlwnfvn077du3B4rWhtq9ezfx8fF89913xMfHVyqbiFSOdoKrYa644gqmTZtG\ns2bN2LJlC127dmX69OlERR34Bm/Lli288cYb5Ofnc8YZZ9CyZcuw5Nm+fTtLly4lKSmp3BvufPjh\nh4wcOfInd0g7d+7k448/DpnsJyKRpeJQg2zcuJH27dszcuRI4uLiKCgoYObMmcycOZPevXsHHa/c\nli5dSt++fWnUqBFRUVEUFBSQmZlJeno6zZo1CzqeSJ2mPocaJCsri3r16hEbGwtAdHQ0iYmJ7Nq1\nK+BkFdOlSxfOPfdcsrKySr7Gjh2rwiBSQ+jOoZooKCigW7du1KtXjw4dOrB+/Xq+++47li1bxiGH\nHBJ0vApxd6ZNm8by5cvp3r07p556atCRRAQ1K+3Xu+++y6RJk4iJiWHs2LH06tWritKV3/fff89v\nf/tbFi1aRPv27Xn22Wc56qijgo4lIrWMisM+pk2bxu9//3tuueUWsrKy+Mc//sE777xTrQqEiEhV\nU3HYx5AhQ/j9739fMmHs3nvvJT09nSeeeKKqIoqIVDvqkN5Hfn7+T4ZWJiQkhGUdIhGR2qZOra30\n61//mmuvvZb77ruPXbt2MX78eF555ZWfPX758uXMnz+fpk2bMmTIkIPONxARqS3qVHG4+OKLiYqK\n4qGHHiIuLo7nnnuOgQMH7vfY//znP1xyySUMGDCAZcuW0bVrV15++WUVCBGpE+pUn0NZuTtNmzbl\nqaeeIiUlhby8PEaMGMH48eM5/fTTw349EZFIUp9DBeXn57N161Z69uwJULJwnDa9EZG6QsVhP2Jj\nY0lJSeHRRx/F3Vm2bBlz587luOOOCzqaiEhEqFnpZ6xZs4azzz6b//73v8TFxTFx4kTGjBlTJdcS\nEYkkzXMIg6ysLBISEtQRLSK1hoqDiIiEUIe0iIhUiIqDiIiEUHEQEZEQgRQHM3vGzDaa2eKfeT3V\nzLab2YLir1sinVFEpC4LavmMZ4EJwAsHOOZ9dx8RoTwiIlJKIHcO7j4PyDzIYQfsSRcRkapTXfsc\nHOhvZovM7C0z6xJ0IBGRuqS6rsr6FdDK3bPNbBgwFegcVJj8/Hw+/vhjcnJy6NevX43d01lEpKyq\nZXFw952lHs80s0fN7DB337rvsbfddlvJ49TUVFJTU8OaZffu3Zx66qls2LCBQw45hI0bN5KWlkb7\n9u3Deh0RkaqSlpZGWlpaud4T2AxpM2sLTHf37vt5rSmwyd3dzPoCr7p72/0cV+UzpP/2t78xa9Ys\n7rjjDqKiopg8eTLp6em8+eabVXpdEZGqUpYZ0oHcOZjZS8BAIMnMMoBbgVgAd38COAe4wszygWzg\n/CByAqxatYpjjz22ZG2l3r17M3fu3KDiiIhERFCjlS5w9xbuHufurdz9GXd/orgw4O4T3b2bu/d0\n9/7u/mkQOQH69OnD7NmzycrKorCwkOnTp5OSkvKTY3bu3Mno0aNp1aoVKSkpfPppYHFFRMJCC+8d\nRGFhIVdccQWTJ08mNjaWY445hunTp3PYYYeVHDN8+HDS09Pp1q0bmzdvZsGCBSxcuJA2bdpUaTYR\nkYrQqqxhtHXrVnJycmjevDlm//szzc/PJyEhgTFjxhATU9RK9/HHH/PHP/6Riy++OCLZRETKo9r2\nOdREpe8USouOjiYmJobdu3fTsGFD3J3s7GwSExMjnFBEJHx05xAG//jHP7j33ntp27Yt27dvJy4u\njs8++4yEhISgo4mIhFCzUgRNnz6duXPn0rx5c/7whz/QoEGDoCOJiOyXioOIiITQTnAiIlIhKg4i\nIhJCxUFEREKoOIiISAgVBxERCaHiICIiIVQcREQkhIqDiIiEUHEQEZEQKg4iIhJCxUFEREKoOIiI\nSAgVBxERCaHiICIiIVQcREQkhIqDiIiEUHEQEZEQKg4iIhJCxUFEREKoOIiISAgVBxERCaHiICIi\nIVQcREQkhIqDiIiEUHEQEZEQKg4iIhJCxUFEREKoOIiISAgVBxERCRFIcTCzZ8xso5ktPsAxD5vZ\nCjNbZGa9IplPRKSuC+rO4Vng1J970cxOAzq6eyfgMuCxSAWrrLS0tKAjhFCmsquOuZSpbJQpvAIp\nDu4+D8g8wCEjgOeLj50PNDazppHIVlnV8S+DMpVddcylTGWjTOFVXfscWgIZpZ6vA44MKIuISJ1T\nXYsDgO3z3ANJISJSB5l7ML9zzawtMN3du+/ntceBNHd/ufj5t8BAd9+4z3EqGCIiFeDu+34A/4mY\nSAUpp2nAWOBlM+sHbNu3MMDBfzgREamYQIqDmb0EDASSzCwDuBWIBXD3J9z9LTM7zcxWAlnAb4PI\nKSJSVwXWrCQiItVXde6Q/llmdqqZfVs8Se7PQeeBsk3sizQza2Vm75nZf81siZldXQ0y1TOz+Wa2\n0MyWmtn4oDPtZWbRZrbAzKYHnQXAzL4zs6+LM30WdB4AM2tsZq+b2TfF///6VYNMRxX/Ge392l5N\n/q7fWPxvb7GZTTaz+GqQ6ZriPEvM7JoDHlvT7hzMLBpYBgwB1gOfAxe4+zcB5zoJ2AW8sL9O9iCY\nWTOgmbsvNLMGwJfAyGrwZ1Xf3bPNLAb4ELje3T8MMlNxrj8CKUBDdx9RDfKkAynuvjXoLHuZ2fPA\n++7+TPH/v0R33x50rr3MLIqi3wt93T3jYMdXYY62wFzgGHfPNbNXgLfc/fkAM3UDXgL6AHuAt4Hf\nu/uq/R1fE+8c+gIr3f07d98DvAycGXCmskzsizh33+DuC4sf7wK+AVoEmwrcPbv4YRwQDQT+y8/M\njgROA54idBh1kKpNFjNrBJzk7s8AuHt+dSoMxYYAq4IsDMV2UPQLuH5xEa1PUdEK0tHAfHfPcfcC\n4H3g7J87uCYWh/1NkGsZUJYao/iTTC9gfrBJij7dmdlCYCPwnrsvDToT8ABwA1AYdJBSHJhtZl+Y\n2e+CDgO0A340s2fN7Csz+6eZ1Q861D7OByYHHaL4bu8+YC3wPUUjLmcHm4olwElmdljx/7fhHGBy\ncU0sDjWrHawaKG5Seh24pvgOIlDuXujuPSn6iznAzFKDzGNmpwOb3H0B1eiTOnCCu/cChgFXFjdd\nBikGOBZ41N2PpWgk4f8LNtL/mFkccAbwWjXI0gG4FmhL0d16AzP7VZCZ3P1b4O/AO8BMYAEH+DBU\nE4vDeqBVqeetKLp7kP0ws1jg38Akd58adJ7Sipsk3gR6BxylPzCiuI3/JeBkM3sh4Ey4+w/F//0R\neIOiJtUgrQPWufvnxc9fp6hYVBfDgC+L/7yC1hv42N23uHs+MIWiv2eBcvdn3L23uw8EtlHUf7tf\nNbE4fAF0MrO2xZ8UzqNo0pzsw8wMeBpY6u4PBp0HwMySzKxx8eMEYChFn2AC4+43uXsrd29HUbPE\nXHf/dZCZzKy+mTUsfpwI/AIIdCScu28AMsysc/G3hgD/DTDSvi6gqLhXB98C/cwsofjf4RAg8OZT\nMzui+L+tgbM4QBNcdZ0h/bPcPd/MxgKzKOrMfDro0Tfwk4l9hxdP7PuLuz8bcKwTgNHA12a29xfw\nje7+doCZmgPPF48qiQJedPc5AebZn+rQdNkUeKPo9woxwL/c/Z1gIwFwFfCv4g9mq6gmE1SLC+gQ\noDr0zeDui4rvPr+gqOnmK+DJYFMB8LqZHU5RZ/kf3H3Hzx1Y44ayiohI1auJzUoiIlLFVBxERCSE\nioOIiIRQcRARkRAqDiIiEkLFQUREQqg4iIhICBUHEREJoeIgUkFm1tvMBprZ/wWdRSTcVBxEKq43\nRUugJxWvfFshKjJSHak4iFSQuz9O0Ro1MZVcCj0sRUYknGrcwnsikWZmT1C0a92nQAPgz0A3d99E\n0arAd5tZbPHOhOXm7o8Xb39b2SIjEjYqDiIHt8zd7wcws38Dl7j7JjO7CBgADAJ+f7CTmFkLoPT+\n4jvc/ZPix5UuMiLhpFVZRQ6geM+JVu6+3Mz+CDRz97D2DZQqMoUUbfheEM7zi1SEioNIGZjZ8cBd\nwBB3P+A+08Xbef6Sog3cAbq6+51VHFEkrNQhLXIQxZujTABGu3uhmcUf5C17P3Gtc/c3gI5VGlCk\nCqjPQeQAird4fB74f+7+vZn1oWgf8++LXz8UOBfIoKjDeu/jDu7+uZk1AnYHEl6kEnTnIHJgNwFf\nuPvs4ueD3f37Uq93p6jDeubex8B7QE7x68OAt8zshEgFFgkH3TmI/Awz6wtcB9xpZjcA/YHVZnYG\nkAkcA+QDh5jZh0AH4BAgi//1N+wC2gBLIhxfpFLUIS1STmb2T+BeoBfwA4C7v29mA/c+DjCeSFio\nWUmk/F4HmgLfAEcBKcXfL/1YpEbTnYOIiITQnYOIiIRQcRARkRAqDiIiEkLFQUREQqg4iIhICBUH\nEREJoeIgIiIhVBxERCSEioOIiIT4/5o6cqjv+Y/1AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb8e9c125d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(zeff**2/n**3, ion, s=20, c=atn, cmap=\"Greys\")\n",
      "colorbar(label=\"Atomic Number\")\n",
      "xlabel(r\"$Z_\\mathrm{eff}^2 n^{-3}$\")\n",
      "ylabel(r\"$E_\\mathrm{ion} / \\mathrm{eV}$\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0x7fb8e99041d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mtTKBExUVxfTp00lNTeW6667j008/ZcGCBdSqVatwn0suuYSRI0eiquTn51Ot\nWjWuueYaPvjgAwYOHOhi9MZUvPImeBF5WUT2iMjaItu6isgKEVklIt+ISBcv1/2NiIwXkY3As3iG\nDY5S1WRVneJL7KGU4As5hXYrtgdBamoqvXv3Jicnh4SEBGJjY2nduvUp++Tl5bFt2zaioqIKf3E/\n//xzZs6cSbt27dwI25hwVFwp+mngYacHzN+c9ZJsBDoBf1DVnk5Szy9LAKHUi2aPiNRX1d0i0gAo\ndobx8ePHFy4nJyeTnJxcMdFFiOHDh3PRRRfRpk0bCgoKmDVrFq+88gp33HFH4T5PPvkky5cvZ8SI\nEQDMmzePvn372r+1CWkpKSmkpKQE/LzlLb+o6pci0uy0zRlADWe5JuBtCNargOuBL0RkPvAev9xo\n9UkoJfgPgKHAP5yfxU6EWDTBn+kyMjKYP38+sbGxDBgwoPDGqTc7d+6kR48egKdck5SUxI4dO07Z\n54svvuDcc88lNjYWgDZt2rBkyZLAfwBjAuj0Bt+jjz4akPMGuJvkX4ElIjIRTwXldyXtqKqzgdki\nUhUYCIwE6orIC8AsVV1Q2sVcKdGIyFvA18BvRGSHiAwDngL6ikg60MdZN44ffviB/v3706JFCy69\n9FIWLlxI+/bt+b//+z+efvppzjvvPPbt21fqebp27crKlStRVY4ePcqWLVsKu0ee1LRpU3bv3l24\nvnv3bpo0aRLwz2RMOAjwTdZpwH2q2gRPwn65tANU9aiqvqmqlwONgVX42MvQnmQNAzk5OXTo0IHr\nr7+ea665hvfff59JkybRs2dPunf3jBg6d+5cunXrxsSJE72e6+eff+byyy9n3bp15OfnM3bsWP72\nt7+dss/u3bvp1q0blSpVAiA7O5tly5bRoEGD4HxAY4IgUP3gV6xYUex7K1euZOXKlYXrU6dO/dX1\nnBLN3CLP+xxW1erOsgCZqlqDILEEHwZWr17N4MGDWbVqVeG2unXrcv7551OrVi3OOecc0tPTiY2N\n5a233ir1fKrKwYMHiY+P59tvv2XOnDlUr16d2267jaSkJACOHDnCp59+iqpyySWX+FT+MSaUBCrB\nf/PNNz7t26VLF18S/LfASFX9XEQuBp5S1RJ70vgrlGrwpgQJCQkcPHiQ48ePU6VKFX744QeOHTvG\nihUriI2NZd68edSsWZPHH/fp2QdEhMTERGbNmsUNN9xAXl4eMTExTJkyhbVr11K3bl2qVavGlVde\nGeRPZkxlEJXgAAASKElEQVToK+9NVqcU3QuoIyI78PSauR14XkQqAced9aCxFnwYUFVuvvlmtmzZ\nwmWXXcbzzz9PVlYWiYmJABw6dIgaNWqwdevWMv0ytmjRgl27dhEdHV14nbFjxzJ27NigfA5jKlKg\nWvCpqak+7du5c+egDDbmj5DsB29OJSK88sor3HrrrRw4cICzzjqrsIcLQKVKlUhISPCa3BctWsT9\n99/PhAkT2L9/PwDHjh075Zj8/HwOHz4cvA9iTBhy+0lWf1iJJkxERUVxyy2eMf6nTZvGqFGjCseL\nyc7Opl+/05+n+MWrr77KPffcQ35+PtHR0bz44ousXbuWa665htdee40TJ06gqsTExHDFFTZChDFF\nhcCcrOVmCT4M3XLLLWzbto2JEydSUFDAddddx9///vdf7XfkyBHWrFnDqFGjEBEqV64MwOHDh3n9\n9deZPHkyIsKMGTNISEjgmWee4Xe/K7FbrjFnpFBtnfvCavBhTFVR1V+1MA4fPsySJUu44447aNCg\nAStXrqRq1aqF++Xm5jJu3DibiclEtEDV4NesWePTvh06dLAavAkcEflVcn/vvfdo0qQJt9xyC4cO\nHeLhhx/muuuuIzs7m/z8fPLy8hAR+vfv71LUxoQXq8GbCrFr1y6++eYbCgoK6NGjx68m3sjIyODO\nO+9k+vTpnHPOObz99tsMGTKEpUuXsnnzZtLT02ncuDFTpkyhffv2Ln0KY8JLqCZvX1iCDxOfffYZ\nV111Ffn5nsHk8vLyuOeee3j66acLW/Hp6em0bNmS1q1bc/vtt7N582aqVq1Kr169qFu3Li+99BJ/\n/OMf3fwYxoSdcE7wVqIJcTk5OcyYMYNrr72WxMRE+vTpw5gxY2jSpAmTJ08mISGBCRMmoKo0a9aM\nrVu38tJLL7Fr1y6GDx/ObbfdRpcuXTh69CjXXXed2x/HGFOBrAUfotavX89NN93EunXriI+PJzMz\nk9zcXFq2bMnjjz9Ow4YNmTBhAtnZ2fz3v/+lefPm3HDDDTz22GM89NBDdOvWrfABplatWrFmzZqw\nbokY45Zw7iYZvpFHsO3bt9OxY0d++uknGjZsSFRUFLGxsVSpUoV33nmHhIQELr30UqpUqUKtWrXo\n1q0bCxcuBOC2225j5MiRbNy4kezsbFSVNWvW0LFjR5c/lTHhyW6ymoDJycmhU6dOREdHF06jV6dO\nHTIzMzl8+DCqSpMmTdi1axfNmjUDPDdfL7zwQtatW8fFF19MdnY2x44dY/LkyVSrVo2zzjqLd999\n18VPZUz4CtXk7QtL8CHmb3/7G4cPHy6c9FpEKCgoQFVp0KABO3fuJDo6mvnz57N9+3YOHz7MoUOH\nmD17Nl27diU7O5vKlSsTGxtLdnY2U6dOZeDAgWH9NdMYN4Vzgrf/60PI448/zrPPPktMTAzR0dHs\n3LmT/fv38+OPP1K9enVatmxJUlIS27dvJzc3l9WrV9O6dWs2bdpEzZo1+eGHHwrHcI+OjqZSpUrs\n3bvXkrsxfrASjfHbxo0befrpp2natCk7d+4kJyeHypUrc+jQIQoKCujQoQPgeXq1YcOGpKamkpCQ\nQFxcXOE56tSpQ25uLpUqVaKgoIC8vDxatmzp1kcyxrjMmnYhYsOGDVSuXJkWLVoU1t6PHz9Ofn4+\nlSpV4vDhw/z4448cOHCAjz/+mFq1ap2S3AFmzJhBQUEB+fn5HD9+nCFDhtCnTx83Po4xEcNa8MZv\nkydP5uDBgxw9epR27dpRu3Zt1q5dS2xsLK1atSrsJtmgQQOaN29e7Dl69uzJli1bSEtLo379+rRt\n27aCP4UxkSdUk7cvLMGHgMzMTFJTU2nfvj3Lly8nNjaW3NxcmjVrRkZGBn379uWCCy5AVXnggQdI\nT08vMXknJSVxySWXVPAnMCZy+TGj08tAf+DnIlP2/RO4HMgFtgLDVPVQgEL9FSvRhIDY2NjCXjL9\n+vWje/fuVK9enYYNG9K2bVuWLFkCeIYnOHbsGPHx8S5HbMyZw48SzSvA6RM1LADaqGoHIB0I6pCu\n1oIPAQkJCdx8883MnTuXevXqsW/fPlSVpKQkNm/eTEZGBnPnzmXp0qX8/ve/L7FEY4wJvPK24FX1\nS2fS7aLbFhZZXQ5cXe7AfGAt+BDxwgsv0K9fP9atW0dGRgZJSUmsXbuWTZs2UVBQwMaNG/nTn/7E\nG2+8UewvXEZGBhs2bKCgoMCF6I0x5XAL8HEwL2At+BAya9YsOnToQL169di+fTtZWVkkJCTw008/\nkZCQUOwx+fn5XHrppaSkpBAdHU10dDQfffQRvXr1quDojYlMwbjJKiJjgVxV/V/AT16EJfgQkZGR\nQW5uLvn5+dSqVYtatWqxY8cOKleuXGJyBxg+fDhff/01PXr04He/+x2LFi1iwIAB/Pzzz4UPPRlj\nyq+kBL906VKWLVtWnvPdDFwGXOxXYD6wBB8iatasSV5eHuvWrQMgLi6O1atXM2HChBKP+de//sWb\nb75JfHw8qamprFmzhquvvpp169bx448/0rp164oK35iIVVKC7969O927dy9cf/bZZ305Vz/gQaCX\nqmYHKMSSrxdOc5xG8pysmzdvpmPHjrRp04Z9+/axf/9+4uPjycjIKPEXrFq1aiQkJBAbGwt4uls2\nadKEbdu2sWvXLmrWrFmRH8GYkBKoOVl37Njh076NGzc+5Xoi8hbQC6gD7AEewdNrJg444Oy2VFXv\n8idGb6wF76Jdu3bxzDPPcPDgQVSVFi1aFPZvz83NZebMmSUmd1UlJyeH6tWrF24rKCggPT2dcePG\nWXI3JkD86EVzfTGbX/YvmrIJuV40IrJdRNJEZJWIrHA7nmDZu3cv3bp1o6CggM6dO/Pxxx9z5MiR\nwvePHTtGlSpVSjxeRLj88svJysoiLy+PrKwsTpw4weuvv84jjzxSER/BGBPiQi7BAwokq2pHVe3q\ndjDB8vbbb9OzZ0/Gjh1LWloajRs3Zvfu3Xz11VekpaXxxRdf8MQTT3g9xxtvvMHAgQOJjo6mUaNG\nfPbZZzbnqjEBZmPRBF5o/msFUE5ODlWrVuWyyy7j7LPPZtSoUbzzzjusWrWKAQMG8NRTT9G3b1+v\n54iPj+fVV1+tmICNOUOFavL2RcjdZBWR74FDQD7woqq+VOS9iLnJ+t1339G1a1eqV6/OV199VTix\nR69evZgzZw7t2rVzO0RjwlqgbrJmZGT4tG+DBg38vl6ghWKJ5kJV7QhcCtwtIhe5HVAwtG7dmhde\neIGcnJzCp09VlRMnTtgEHcaEkHAu0YRcC74oEXkEOKqqzzjrWvQGYnJyMsnJyS5F57+CggL69u1L\n1apV6devH/PmzSMrK4sFCxZYkjemjFJSUkhJSSlcf/TRRwPSgt+zZ49P+9arVy/kWvAhleBFJB6I\nVtUjIpKAZ+S1R1V1gfN+xJRoTsrKyuKJJ55gw4YNtGnThtGjR9tokcYEQKBKNOGc4EPtJms9YJbz\ndScGePNkco80R44c4YEHHmD58uU0btyYSZMm2ZOnxoSgUC2/+CKkWvCliaQWfL9+/Thy5AhVqlRh\n37597Nq1i02bNpGYmOh2aMZEhEC14Pfu3evTvnXr1g25FrwVel2QmZlJSkoKaWlpZGZmEhMTw6FD\nh5g+fbrboRljTmM3WStIpLTgjx07RmJiIhdccAHnnHMOAGvXriUuLo7PP//c5eiMiQyBasHv37/f\np31r164dci34UKvBnxESEhKoX7/+KcP5VqlSxevQBMYYd4Rq69wXVqJxySOPPMLKlSvJyMhg165d\npKWlMXToULfDMsZEEGvBu2TYsGHk5OQwZcoUoqKiePrppxk0aJDbYRljThPOLXirwRtjIlKgavCZ\nmZk+7VuzZs2Qq8FbicYYYyKUJXhjjPHCn26SIlJTRGaIyEYR2SAi3SoydqvBG2OMF37W4P8FfKyq\n14hIDJAQmKh8YwneGGOCQERqABep6lAAVT2BZyj0CmMlGmOM8cKPEk1zYK+IvCIi34rIS86AihXG\nErwxxgRHDNAJ+D9V7QQcA/5a0QEYY4wpQUk1+C+++IIvv/zS26E/AT+p6jfO+gwqOMFbP3hjTEQK\nVD/4o0eP+rRv1apVf3U9EfkCGK6q6SIyHqiiqn/xJ6aysBa8McZ44WcvmnuBN0UkDtgKDAtIUD6y\nFrwxJiIFqgWflZXl077x8fH2JKsxxpiKYSUaY4zxIpwHG7MWvDHGRChL8MYYE6GsRGOMMV6Ec4nG\nErwxxngRzgneSjTGGBOhrAVvjDFeWAveGGNMyLEWvDHGeGEteGOMMSHHWvDGGOOFteADRET6icgm\nEflORCpsSE1jjCmJP5Nuuy1kEryIRAP/BvoB5wLXi8hv3Y3KfykpKW6HUGbhFnO4xQsWs6kYIZPg\nga7AFlXdrqp5wNvAQJdj8ls4/k8RbjGHW7xgMYcTa8EHRkNgR5H1n5xtxhhjyiGUbrLaTB7GmJAT\nqq1zX4TMjE4i0g0Yr6r9nPXRQIGq/qPIPqERrDEmLARiRqeKvF6ghVKCjwE2AxcDu4AVwPWqutHV\nwIwxJkyFTIlGVU+IyD3AJ0A0MM2SuzHGlF/ItOCNMcYEVij1ovGJiFwrIutFJF9EOrkdjzfh9uCW\niLwsIntEZK3bsfhCRBqLyGLn92GdiNzndkylEZHKIrJcRFaLyAYRedLtmHwhItEiskpE5rodiy9E\nZLuIpDkxr3A7HreEXYIH1gJXAl+4HYg3Yfrg1it44g0XecBIVW0DdAPuDvV/Y1XNBnqr6nlAe6C3\niPRwOSxf3A9sIHx6uymQrKodVbWr28G4JewSvKpuUtV0t+PwQdg9uKWqXwIH3Y7DV6q6W1VXO8tH\ngY3AWe5GVTpVzXIW4/DcbzrgYjilEpFGwGXAVCCkeomUIpxiDYqwS/BhxB7cqkAi0gzoCCx3N5LS\niUiUiKwG9gCLVXWD2zGVYjLwIFDgdiBloMCnIpIqIre5HYxbQqYXTVEishCoX8xbY1Q1LGqAhM9X\n2bAnIlWBGcD9Tks+pKlqAXCeiNQAPhGRZFVNcTmsYonI5cDPqrpKRJLdjqcMLlTVDBGpCywUkU3O\nN9QzSkgmeFXt63YMAbATaFxkvTGeVrwJIBGJBWYCb6jqbLfjKQtVPSQiHwGdgRSXwylJd2CAiFwG\nVAaqi8hrqnqTy3F5paoZzs+9IjILT8n0jEvw4V6iCeUaWyrQWkSaiUgccB3wgcsxRRTxPEM+Ddig\nqs+6HY8vRKSOiNR0lqsAfYFV7kZVMlUdo6qNVbU58Efgs1BP7iISLyLVnOUE4Pd4OmecccIuwYvI\nlSKyA0+viY9EZJ7bMRVHVU8AJx/c2gC8E+oPbonIW8DXwNkiskNEhrkdUykuBG7A0xNllfMK9V5A\nDYDPnBr8cmCuqi5yOaayCIfSYz3gyyL/xh+q6gKXY3KFPehkjDERKuxa8MYYY3xjCd4YYyKUJXhj\njIlQluCNMSZCWYI3xpgIZQneGGMilCV4Y4yJUJbgjTEmQlmCN2FDRDqLSC8RecjtWIwJB5bgTTjp\njOfR8zrOCJLlIiJtRSRZRJ4IXGjGhB5L8CZsqOp/8MziFOPnsMCtgO+ApIAEZkyIsrFoTMgRkRfx\nzHa0DKgK/AVoq6o/i8hgYAFwyJkpq7zXOAfopaovBiJmY0JRSI4Hb854m1V1EoCIzARudZL7UKAn\n0Bu4s7STiMhZQLsimw6r6lKnhv880EpEzg6TKSCNKTNrwZuQ4oyR3lhV00Xkz0B9VQ3oTVURuRBP\nefIPwARVzQnk+Y0JFZbgTUgSkd8Bfwcucaa487bvRcDVwOfOpjaq+niQQzQm5NlNVhNyRKQ2MAW4\nQVULRKRSKYecbKX8pKqz8NxENeaMZzV4E1KcafimA39V1V0i0gXP/La7nPdrAdcCO/DchD253FJV\nv3Emsj7uSvDGhBhrwZtQMwZIVdVPnfWLVXVXkffb4bkJO+/kMrAYyHbevxT42KmzG3NGsxa8CRki\n0hUYCTwuIg8C3YHvReT/AQeB3wIngOoisgRoCVQHjvFL/f0o0BRYV8HhGxNy7CarCXki8hIwEegI\nZACo6uci0uvksovhGROyrERjwsEMoB6wEfgNcL6zveiyMeY01oI3xpgIZS14Y4yJUJbgjTEmQlmC\nN8aYCGUJ3hhjIpQleGOMiVCW4I0xJkJZgjfGmAhlCd4YYyKUJXhjjIlQ/x9l4hOnMc1+KgAAAABJ\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb8e98f0850>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This seems to give the nicest linear-looking results.\n",
      "\n",
      "Now, we simply need to find some nice-looking linear line to go through that cluster of points."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zarray = np.array(zeff**2/n**3)\n",
      "scatter(zarray, ion, s=20, c=atn, cmap=\"Greys\")\n",
      "colorbar(label=\"Atomic Number\")\n",
      "\n",
      "mslope = 6\n",
      "minter = 4\n",
      "mx = np.linspace(0, 5)\n",
      "my = mx*mslope + minter\n",
      "plot(mx, my, 'r-')\n",
      "\n",
      "xlabel(r\"$Z_\\mathrm{eff}^2 n^{-3}$\")\n",
      "ylabel(r\"$E_\\mathrm{ion} / \\mathrm{eV}$\")\n",
      "xlim(0,5)\n",
      "ylim(0,40)\n",
      "tight_layout()\n",
      "savefig(\"endresult.png\", figsize=(7,5), dpi=80)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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o6rfZjtkEXJpHsnkFmK2qPpWoya7Qo6hFmWhMCXT8OIwfD2+9BX/6kzPLLDra\n66iM8URBW7B5bOty6hhf+qKXANNFJAxnOrV7qlbI68TiOWXHhL7MTJgyxZll1rWrM625fn2vozLG\nU0HQXfoaztj92myTxHxiycYUSmZmJocOHaJy5cr+22tj8WJnXCY8HP7zH+jQwT/XNSbEBcF+NtuA\n+PwmGrBkYwph6dKl9O3bl+TkZCIiIvjXv/5Fj8IspNy8GYYNcyYBvPQS3HqrjcsYk00QtGw2A/NF\n5FucSjLg56nPxvzB8ePHueGGGxg+fDidO3fm559/pn///qxbt46qVavm72JHj8KLL8IHHzibmU2a\nBGXLBiZwY0JYkCSbzTi1MCNx19n4cqInyUZEPsJZGLRPVZu7r40F7gf2u4eN9LXAmyl6mzZtIjo6\nms6dOwPQunVr6tevz2+//UaXLl18u0hGBnz0EYwZAz17QlwcZFuwaIz5I6+TjaqOdeMo7z4/5uu5\nXrVsPgYmApOzvabAa740x4z3atWqxYEDB9i1axe1a9fm4MGDbNu2jTp16vh2gXnznHGZSpXg66+h\ndevABmxMMeD1fjbuQtDJwDnu8/3APaq6Nq9zPUk2qrrIrc9zOs/biMY3VatW5YUXXuDBBx/kkksu\nIT4+nsGDB3NuXiv5N2xwtmBeu9aZ0ty3r21iZoyPvG7ZAO8BQ1R1PoBbxuw9oGNeJwbbmM3jInI3\nsBIYmn37URN8HnvsMWJiYoiPj6dp06a0atXq7AcfPgzPPQeTJztlZj7/HKKiii5YY4qBIEg2ZU8l\nGgBVjRWRcr6cGExTfd4GGgEtgd04paxNkLv44ovp37//2RNNejrJEyaQ0bQpmpQE8fHOXjOWaIzJ\nt4JunuZHm0VktIg0FJFGIvIssMmXE4OmZZO98JuIfIBT8C1HY8eOzXocExNDTExMIEMzBZT59dfs\nvesuEo4e5c/R0ci6dcyIjKSy14EZEyCxsbHExsYG7PpBsM7mPmAccKpuziL3tTwVujZaQbljNl9l\nm41WS1V3u48HA21V9fYczrPaaMEuPh6GDuXI6tX8pUIFhs6bR7noaIYOHUpGRgb//Oc/vY7QmCLh\n79po8+bN8+nYyy+/3G/3PS2Gm1X1P3m9lhNPutFE5DOcGjvni8h2EbkPeFlE4kTkF6AbkGNlUlM4\nqsrBgwfJyMjw/8UPHIBHH4WYGOjZk1HXXUftBx+kQsWKhIeHc99997Fy5Ur/39eYEiIIutGe8fG1\nM3iSbFTn32q7AAAdN0lEQVT1NlWtraqRqlpPVT9S1btV9RJVbaGqN6jqXi9iK85Wr15N3bp1qV+/\nPpUrV2bmzNx2gc2HtDR49VW48EJnxX9CAjz5JPXOO49FixaRmelUtliwYAENGzb0zz2NKYG8SjYi\n0ktEJgJ1RORNEZnofv2T3wty5ipoxmxMYKWnp9OrVy+aNWtG06ZN2bt3L3fffTdr1qyhfkELXKrC\nzJnOgH/TprBwoZNwXIMGDeKbb76hS5cuVK5cmS1btjB37lw/fSJjSh4PZ6PtAn4C+rh/ngrkKD72\nQlmyCUIZGRmMHz+emTNnUr58eUaPHk3Xrl0Ldc1du3aRkpJC06ZNAahRowY1a9YkLi6uYMlm9WoY\nMgT27nXK/1999RmHlClThnnz5rF48WJOnDhBhw4dqFSpUqE+h7/s27eP+++/n+XLl1O3bl3efvtt\n2rZt63VYxuTKq2Sjqr/g7Oj5f6p6qiYaItIFeB54NK9rBNPUZ+MaN24cX375JRMmTGDAgAH069eP\nuLi4Ql2zatWqpKWlcfjwYQBSU1PZv38/devWzd+F9uyB++93ksvNN8Mvv+SYaE4pVaoUMTEx9OrV\nK2gSjapyww030KBBA+bNm8cjjzzCtddey+7du70OzZhceT1mo6ppItJaRCaIyBbgOSDBl3OtZROE\nPvnkE2bNmsVFF10EwNq1a5k6dSqXXFKg3VgBKFu2LG+99RaDBw+mTp067N27l3vvvZeWLVv6doFT\n2zC/+ircey+sW+eUmglBR44cYe3atcyaNYuwsDD69evHF198wZIlS+jXr5/X4RlzVl61bETkfOA2\noD9O/cr/AGGqGuPrNSzZBKGoqCgSExOznicmJlKrVq1CX3fAgAG0b9+eVatWkZmZScuWLcnMzMy9\n3pKqs9p/+HBo1Qp+/BEaNy50LF4qW7Ys6enp7Nu3j5o1a5KRkcH27dupWLGi16EZk6uCJpuzFD9u\nB7wFRADpwJ9y2Xn5N2AWcLWqbnPPH5KfGCzZBKFhw4Zx2223MXz4cLZt28b06dNZvny5X65dq1Yt\n7rnnHvbt20dmZiaNGzdm1qxZlM2ppP+KFU6xzOPH4eOPoXt3v8TgtaioKEaNGsW1115Lv379WL58\nObVq1aJ7Mfl8pvgqRMsmp+LH44HRqvq9iPRyn5/tP0FfnJbNQhH5Dqdlk69gbMwmCA0YMICJEyfy\n888/k5KSwpIlS/I/tnIWI0eOpH79+kybNo1p06YRFRXF888//8eDduyAu++GPn1gwABYubLYJJpT\nRo0axRtvvEFkZCS33347s2bNCobV2cbkqqBjNqq6CDh82su7gVPN+UrAzrPdV1VnqGp/4GKcqgGD\ngWoi8raIXOVT7KG2Gt8qCBROt27d6N+/P+3btwfghx9+4Mcff2TGjBmQnAwTJsCbb8LDD8OIEVC+\nvMcRGxO6/F1B4Mcff/Tp2Pbt259x3xyqtjQAFuNs7xIGdFDV7fmIpwpwE3Crql6e1/HWjVbCNGvW\njLlz59KuXTtUldjYWNq0agWffAIjR0KnTvDTT2CLL40JOmfrRvvpp5/4+eef83u5D4EnVHW6iNwM\nfARc6evJqnoIZ3uB93w53lo2xcy2bduYOXMmERER3HTTTWds0XzkyBGuvvpq9u7dS0ZGBjdUr87f\nwsIIDwtzZpt1zHNbCmOMj/zdsvF17Nb9ZTKvls1RVa3gPhbgiKoGbJaMtWyKkTVr1tCpUydOnjyJ\niDBmzBhWr15N7WxbLVeqVIn//ve/bJgzh9pvvEGFtWuRv/4Vbr/dKTVjjAlafp76/D8R6aaqC4DL\ngfX+vPjp7KdLMTJ48GCSkpJISUnhxIkTHDhwgM6dO/Pxxx+T1Ro8doxSY8Zw4R13ULFDB2TdOrjz\nTks0xoSAgk4QyKH48QDgQWC8iKzGqQLwYCBjt5ZNMbJv3z6ydzGqKklJSYwaNYpd27czqk4dGD0a\nevSAuDioU8fDaI0x+VXQlo2q3naWty4reDT5Y8mmGLnuuuvYuHEjycnJgFMqpkWLFlweFsY1f/kL\ntG/vFM60GmDGhKQg2Ba6wGyCQDGSnp7ONddcw5w5c1BVbmzenDFJSdTet4+n0tOZlJwMIfyP1ZhQ\n4+8JAqtWrfLp2FatWgVk87TCsI76YmTcuHFs3bqVQffcw6sifPTrr8SVLk3nKlWocP/9lmiMCXFe\nF+IsDOtGKyaOHj3Km6+9xqYRI6gycSJ7e/em7YIFRAB3DBzIM8/4tJmeMSaIBWsi8YUlm2Li5KxZ\nLEtNpeKcOaR8+SXlmzenfp8+DBkyhN69e3sdnjHGDyzZmAJRVfbu3UuZMmUKXnH4t99g6FCqbNjA\nCw0aUKpdOx445xwWfPYZ8fHxtGvXzr9BG2M8E8rJxsZsPHLo0CG6dOnCRRddRO3atRk0aBC5TXzY\nunUrHTt2pGLFipx//vmMHz6cbTfcgHbtCldeicTH89SiRayNj6dz58689957fPPNN2dUEDDGhK5Q\nHrPxZDbaWfZWqAL8G2gAbAFuUdUjOZxbLGaj3XHHHQA888wzJCUl8cADDzB06FDuueeeM449efIk\nTZo0YdeuXZTKzOThzExGqPJ9+fL8dP31vD5lStD+AzOmJPP3bLT4+Hifjm3WrJnNRnN9DPQ87bUR\nwGxVbQrMdZ8XWytWrKB///6EhYVRoUIFevfuzcqVK3M8dvPmzRw8cIBrMjP5JSODq4Brypbl+Pjx\nTF+0CF8rwRpjQlsot2w8STZn2VvhemCS+3gScEORBlXEGjRowHfffcehQ4fIyMhg5cqVNGjQIMdj\nq+zYwczkZF7IyGBIeDjXhYcTr8o555zDeeedx549e4o4emOMF0I52QTTBIEaqrrXfbwXqOFlMIG0\ne/dutm3bxn//+18++OADKlSowEUXXcRjjz32xwP37oUxY6g6Ywbx559Pz4QEMlRBlbZt25KUlERc\nXBxt2rTx5oMYY4pUsCYSXwRTssmiqioiZx2YGTt2bNbjmJgYYmJiiiAq/xk4cCDVqlWjb9++pKam\n8vnnn/PAAw9QunRp54DUVHjjDRg/Hu6+m+WTJzO6f3/aderEiRMn2LlzJ0uWLCEhIYFPPvmE+vXr\ne/uBjDEAxMbGEhsbG7Drh3Ky8axcTQ57KyQAMaq6R0RqAfNV9YIczgv4BIGjR48yefLkrL1f2rq1\nxD777DOeffZZkpOT6devH6+99hqRkZH5vn7t2rXp06cPlStXBmDJkiW0adOGV195BaZNg2HD4JJL\nnF0zmzRh3LhxfPfdd3Tq1AmAY8eOMXXqVA4cOBDS//iMKe78PUFg/XrfdgFo2rSpTRDIxZfAqalY\n9wAzvAji6NGjdOzYkfnz53PkyBGuu+46pk+fTmxsLI899hhXXXUVd911F/Pnz2fYsGEFukejRo3Y\nvHkzABkZGezatYsOkZHQrRs8/zx88AHMmAFNmgBQpUoVjh07lnX+4cOHqVixoiUaY0zI8Grq82dA\nN6AqzvjMGGAm8DlQHw+nPr/55pssXLiQTz/9FIAFCxYwaNAgWrduzYIFC6hZsybt27cnIiKCGTNm\nZCWN/EhISKB79+5ER0dTLjGRCRERdEpO5u/Vq7Pg3HMZNWYMHTp0yDr+2LFjtG3blvDwcKKjo1m3\nbh2TJ0/m+uuv99vnNsb4n79bNhs2bPDp2CZNmgRdy8aqPp9m3LhxJCUl8fzzzwOwfft2LrvsMpKS\nkihXrhwiQlJSEldccQU7d+7kl19+KdB9Enfv5uAzz1Bv+nR+7diRngsWcCg9HVUlIiKChQsX/mHg\nPykpiSlTppCYmMhVV11F69at/fJ5jTGB4+9k87///c+nYxs3bhx0ySYoJwh4qWfPnvTp04eePXvS\nqFEjhg8fnpVkKlSoADj/gObMmcPMmTN9vu7atWuZP38+lStVon9mJhVHj6Zi+/awahW3XXMNhzMy\nKFXK+XacPHmSd999l/feey/r/OjoaB555BH/flhjTEgpaNf5WRbSTwCuBdKAjcAAVU30U6hnsGRz\nmssuu4y33nqLRx55hMTERHr37k2TJk1Ys2ZN1jFhYWG0aNGCXr16+XTNWbNm0b9/fzoAf01NZX1U\nFI2//JKoK64Acv4HZOMxxpjTFeLnwsfARGByttd+AIaraqaIvASMJICL6S3Z5OCmm27ipptuyno+\nbdo07r33XsLCnPkUKSkpPP3002c9X1X56KOP+O6776hSpQorp03jn6mpdMzM5LnSpflXeDivbd3K\nfe7xTz/9NI899hgnT57M6kZ7+OGHA/kRjTEhqBDbQi9yZwBnf212tqfLgH4FDswHlmx80K9fP1JT\nU5kwYQKqytChQ7nlllv+cMzhw4d5/PHHWbFiBeHh4aSnpzNmyBDqTJnCiwcP8n5kJE9GR5MsQkZ6\nOgcOHMg6995776Vs2bK89957lClThtGjR9OqVaui/pjGmJLrPuCzQN7AJgjkQFWZPXs2a9asISMj\ng0svvZTOnTufdU2NqtKtWzfq1avH7bffzvfffkv6hx8yPiqKUj160HnRIn49dozIyEgyMjLIzMxk\n3rx5XHbZZQH9HMYYb/l7gsCWLVt8OrZhw4Zn3Pf0tY3ZXh8FtFZVa9kUtcGDB/P555+TlJREpUqV\nAGeAfuDAgdSqVYu+ffv+vtof2L9/P2vWrOG9997j0PTpDHznHTIjI7m3QgVWr15N627dOLp6NZs3\nbyY6OpqJEydaojHG5NvZutGWLl1aoIK8InIv0Bu4olCB+XKvkt6y+eKLL5g7dy6bN29m2bJlpKam\nIiJccMEF1KhRg2bNmvHhhx9y7NgxmjdvzvHjxylXrhyLFi2iTJkygLsQtEYNFnXoQMqSJUy+6CKS\nr70WRJg1axbr169nzZo1NGjQwAb+jSlB/N2y2bp1q0/HNmjQIM+WjYj0BF4FuqnqgTMu4mcltmVz\n+PBhunbtSkJCApUrVyYlJYVKlSrRp08fvvvuO44cOUKbNm34xz/+QVRUFPfccw8XXnghqsqkSZOY\nNGmSM4ifmEiF55/nR1XeWb2aN6Kjubp1a+q4SaVmzZrUq1ePhg0bevuBjTEh79QkpfzKvpBeRLYD\nf8aZfRYJzHZ/CV6qqn/yU6hnKJHJZseOHVx00UUkJSXRqFEjIiIiUFV27NjB5MmTEREiIiKYO3du\nVmmZOnXqAM5vKtWrV2ffrl3wzjswdixbmjXjirAw9qenc/z4cb766ivuu+8+UlJS+OWXX3jppZc8\n/sTGmOKgELPRbsvh5Y8KF03+lKhkc/z4cZ588kk+//xz0tLSUFXCw8MB55sYFhZGgwYNqFSpUtbk\ngMqVK9O4cWO+/fZbbrzxRg4dOkTF5csZunEj1K/PiS++oNmVVxJVujRlIiKIjIzk0KFDvPLKK5Qq\nVYphw4Zl7cppjDElVYkas7nuuuuIi4vjyJEjqCoZ7qr9KlWqkJKSwr59++jQoQOlS5dmy5YtlCpV\nimPHjlGuXDmOHTtGpT17eC0sjE5VqlDhvffghhv438aNtG7dOmv8BpzimpMnT6ZXr142RmNMCebv\nMZudO3f6dGydOnWsXI1XtmzZwtdff02PHj3YuHEjBw4cIDU1Nav7LDMzk3r16mXNMktNTWXnzp3M\nnTuXLT//TONPP6VlcjKRzz4LTzwBUVEAWd1raWlpREZGkp6eTkpKCs2aNbNEY4zxq1D+mVIiko2q\ncs011wCQnp5O48aNOX78OEePHuXEiROEhYURERHBnj17EBFSU1PZt28fUz/7jI4rV9Lx+eehXz+Y\nOROqV//DtcuUKcP06dO58cYbSU1NJTU1lXffffesWzwbY0xBhXKyKRHdaHv27KFx48bUqVOH3bt3\nU7duXY4cOUJiYiJdunRhyZIllC1blkGDBrFixQqiIiMpPW8eb5crR+mmTeHVV+Hii3O9R1JSElu3\nbqVOnTpZa3OMMSWbv7vRdu/e7dOxtWrVsm40L5QvX5709HQaNWpEdHQ0Bw8e5NChQ7Rt25YKFSpQ\nvXp1du7cSe3atXmoUycu/vhjjh88yLEJEyh9993gw28T0dHRNGvWrAg+jTGmpArllk0w7dQZMOXK\nlWPEiBGsWLGCY8eOkZiYSNmyZalZsyZpaWns2rWLJhUrIn/6E5eNHMnbO3fywRNPUO2ee86aaBIS\nEujZsyetW7dm+PDhZGRkFPGnMsaUNCLi01cwKhHdaKe0bduW1atXc+GFF7JhwwaioqI4mZTEE8Az\npUoRW7cuy666is7XX0/Pnj1zvMbJkyd55513GDp0KK1ateKcc84hNjaWNm3asHDhwqD9Rhtjip6/\nu9H27dvn07HVq1cPum60EpVsmjZtyubNm+nbty8CXLR+PXf+8gtRrVtT59NP4fzzcz0/LS2Nrl27\nEhcXR1hYGCdPnuT2228nLS2NL7/8ksWLF9OiRYsCxWaMKX78nWz279/v07HVqlULumRTbMdsTk1p\nzsjIoEGDBiQnJ7N7926qVKnCvu+/Z0JGBmVTUhhXqxYfLVsG7uLO3Hz66aesX7+eKlWqICKkpKQw\nffp0+vXrR3h4OElJSUXwyYwxJVUo95wE3ZiNiGwRkTgRWSUiywtyjbS0NG666SbatGlDhw4d6NGj\nB0uXLqVJdDT/qVCB/yQn81W5clwaFsYTX36ZVUUgL3v27EFVs77hERERHD9+nK+//prw8HBr1Rhj\nzFkEXbIBFIhR1Vaq2q4gFxg/fjwpKSls3LiRIUOGsHvTJuL692f23r0cjYpizM03s7FHD0pFRVGt\nWjWfr9u5c2fS09OzdtQ8evQo4Mx2W7JkCdHR0QUJ1xhjfBLKEwSCtRutUH9bcXFx3HbbbTz3l79Q\naupUlh87xqaqVemanMzRxESqJySwd+9ebrzxRurXr+/zdbt06cKECRMYMmQIaWlpXHbZZcycOZOq\nVasWJlxjjPFJsCYSXwTdBAER2QQkAhnAu6r6/mnv5zlBYMSIEUStXk3vOXO4uEkTkp9/nrT27Xny\nySeJjIykRo0atGzZkjvvvLNAJbtVlfT0dCIiIvJ9rjGm5PD3BIFDhw75dGyVKlVsgoAPOqnqbhGp\nhrPPQoKqLvL57O3beW7TJo7Mm8czIjz4z39Su25dwPnGd+zYkUcffbRQAZ7agsAYY4pSQfezCQZB\nl2xUdbf7534RmQ60A/6QbMaOHZv1OCYmhpiYGEhKgvHj4e9/J+LRR0letYp9zzzDgIEDGTRoEBs2\nbGDRokW8+eabRfhpjDElSWxsLLGxsV6HEZSCqhtNRMoC4ap6TETKAT8A41T1h2zH/LEbLTMTpkyB\nUaOgWzf0xRcZ8re/8eGHH1KuXDnS0tJo0aIFDRo0YPTo0Zx77rlF/rmMMSWTv7vREhMTfTq2YsWK\n1o2WhxrAdHcQrBTwafZEc4bFi+HJJ6FUKfjPf6BDB76cOZOpU6ciIpQqVYqjR4+ybt065s6dG9KD\na8YYUxgiUgn4AGiGM+v3PlX9sajuH1TJRlU3Ay3zPHDzZhg2DJYtg5degttuy6phtmbNGhITE2nf\nvj0NGzYkLS2NqVOnMnfuXHr06BHgT2CMMYFTyF+Y3wC+UdWbRKQUUM4/UfkmNEeb2raFFi0gIQFu\nv/0PxTKbNGnCsWPHsqY0R0ZGUqdOHTZs2OBVtMYY4ykRqQh0UdWPAFQ1XVV965Pzk9BMNnFx8Oyz\nULbsGW/dcsstVKpUKSu5JCcns3//fpo3b17UURpjjF8VYlFnI2C/iHwsIj+LyPvuGHnRxR5MEwR8\n4cs6m7i4OHr06EFmZiYnTpxgxIgRjB49uogiNMYYh78nCPhafzE6OvoP9xWRS4GlQEdVXSEifwOO\nquoYf8Tmi6Aas/GXSy65hG3btrFp0yaqVq1K9dO2cjbGmOJk4cKFLFqU63LEHcAOVV3hPp8KjAh4\nYNkUy5aNMcYEA3+3bI4fP+7TseXKlTvjviKyELhfVdeLyFigjKoO90dsviiWLRtjjDFneBz4VEQi\ngY3AgKK8ubVsjDEmQPzdsklOTvbp2LJlywbdos7QnI1mjDEmpFg3mjHGhIhQroJiLRtjjDEBZy0b\nY4wJEdayMcYYY3JhLRtjjAkR1rIxxhhjcmEtG2OMCRHWsjHGGGNyYS0bY4wJEdayMcYYY3JhLRtj\njAkRodyysWRjjDEhIpSTTdB1o4lITxFJEJENIlJkey0Eq9jYWK9DKFL2eYu3kvZ5ze+CKtmISDjw\nFtATuAi4TUQu9DYqb5W0/5z2eYu3kvZ5/U1EfPoKRkGVbIB2wP9UdYuqngT+BfTxOCZjjDGFFGzJ\npg6wPdvzHe5rxhhT4oVyyyaoduoUkX5AT1V9wH1+J3CZqj6e7ZjgCdgYY/Lgz506vbivvwTbbLSd\nQL1sz+vhtG6yBNtfoDHGFIVQ/9kXbN1oK4EmItJQRCKB/sCXHsdkjDGmkIKqZaOq6SLyGPA9EA58\nqKq/eRyWMcaYQgqqMRtjjDHFU7B1o51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       "text": [
        "<matplotlib.figure.Figure at 0x7fb8e9ab6690>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}