certik/gist:9282676

## gistfile1.txt
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run `opium` for our `Sn` test case:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!opium sn.param sn.log all rpt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It saves the pseudopotentials into the file:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head sn.vs_plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     2.714417617e-05          4.476987772 0.0\r\n",
        "     2.749935411e-05          4.476987772 0.0\r\n",
        "     2.785917951e-05          4.476987772 0.0\r\n",
        "     2.822371318e-05          4.476987772 0.0\r\n",
        "     2.859301672e-05          4.476987772 0.0\r\n",
        "     2.896715255e-05          4.476987772 0.0\r\n",
        "      2.93461839e-05          4.476987772 0.0\r\n",
        "     2.973017483e-05          4.476987772 0.0\r\n",
        "     3.011919022e-05          4.476987772 0.0\r\n",
        "     3.051329583e-05          4.476987772 0.0\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the 4 potentials from the file, they are separated using `@`, so we load it to one 3D array `V`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = open(\"sn.vs_plt\").read().split(\"@\")\n",
      "V = []\n",
      "for n in range(4):\n",
      "    V.append(fromstring(t[n], sep=\" \").reshape([-1, 3]))\n",
      "V = array(V)\n",
      "# For the potential number n=0, 1, 2, 3:\n",
      "# R    = V[n, :, 0]\n",
      "# V(R) = V[n, :, 1]\n",
      "# V[n, :, 2] is radius"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the first 3 of them:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for n in range(3):\n",
      "    semilogx(V[n, :, 0], V[n, :, 1], label=\"$V_%d$\" % n)\n",
      "legend(loc=\"best\")\n",
      "title(\"Pseudopotentials\")\n",
      "xlabel(\"r [a.u.]\")\n",
      "ylabel(\"V(r) [a.u.]\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEeCAYAAACZlyICAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2c1f1d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The 4th one is numerically equivalent to the 1st one:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max(abs(V[0, :, 1] - V[3, :, 1]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "1.0800000005417587e-07"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So we'll just ignore it for now. The first 3 potentials are probably for the `s`, `p` and `d` states."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%pylab inline"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"Populating the interactive namespace from numpy and matplotlib\n"
	]
	}
	],
	"prompt_number": 1
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Run `opium` for our `Sn` test case:"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"!opium sn.param sn.log all rpt"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 2
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"It saves the pseudopotentials into the file:"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"!head sn.vs_plt"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	" 2.714417617e-05 4.476987772 0.0\r\n",
	" 2.749935411e-05 4.476987772 0.0\r\n",
	" 2.785917951e-05 4.476987772 0.0\r\n",
	" 2.822371318e-05 4.476987772 0.0\r\n",
	" 2.859301672e-05 4.476987772 0.0\r\n",
	" 2.896715255e-05 4.476987772 0.0\r\n",
	" 2.93461839e-05 4.476987772 0.0\r\n",
	" 2.973017483e-05 4.476987772 0.0\r\n",
	" 3.011919022e-05 4.476987772 0.0\r\n",
	" 3.051329583e-05 4.476987772 0.0\r\n"
	]
	}
	],
	"prompt_number": 3
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Load the 4 potentials from the file, they are separated using `@`, so we load it to one 3D array `V`:"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"t = open(\"sn.vs_plt\").read().split(\"@\")\n",
	"V = []\n",
	"for n in range(4):\n",
	" V.append(fromstring(t[n], sep=\" \").reshape([-1, 3]))\n",
	"V = array(V)\n",
	"# For the potential number n=0, 1, 2, 3:\n",
	"# R = V[n, :, 0]\n",
	"# V(R) = V[n, :, 1]\n",
	"# V[n, :, 2] is radius"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 4
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Plot the first 3 of them:"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"for n in range(3):\n",
	" semilogx(V[n, :, 0], V[n, :, 1], label=\"$V_%d$\" % n)\n",
	"legend(loc=\"best\")\n",
	"title(\"Pseudopotentials\")\n",
	"xlabel(\"r [a.u.]\")\n",
	"ylabel(\"V(r) [a.u.]\");"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEeCAYAAACZlyICAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
	"text": [
	"<matplotlib.figure.Figure at 0x2c1f1d0>"
	]
	}
	],
	"prompt_number": 5
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"The 4th one is numerically equivalent to the 1st one:"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"max(abs(V[0, :, 1] - V[3, :, 1]))"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 6,
	"text": [
	"1.0800000005417587e-07"
	]
	}
	],
	"prompt_number": 6
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"So we'll just ignore it for now. The first 3 potentials are probably for the `s`, `p` and `d` states."
	]
	}
	],
	"metadata": {}
	}
	]
	}