wackywendell/gist:10186578

## gistfile1.txt
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Sample Notebook"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is text! Text can include *formatting*, and $\\LaTeX$ equations: $a^2 + b^2=c^2$."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1 + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1: Python is Expressive"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "\"Programming is the art of telling someone else what you want a computer to do.\" *-I don't know who*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(url=\"http://imgs.xkcd.com/comics/python.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://imgs.xkcd.com/comics/python.png\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.core.display.Image at 0x7fb6925e1048>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mytext = \"This is some sample Python code\"\n",
      "words = mytext.split(\" \")\n",
      "for word in words:\n",
      "    print(word.capitalize())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "This\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Is\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Some\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sample\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Python\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Code\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python has:\n",
      "  - Straightforward function definitions\n",
      "  - Variable-length arguments\n",
      "  - Simple control structures (for loops)\n",
      "  - list comprehensions\n",
      "  - Classes and objects\n",
      "  - Lists, Sets, Dictionaries"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def my_function(x):\n",
      "    \"A pointless function.\"\n",
      "    print(\"my_function!\", x)\n",
      "\n",
      "def my_function2(*args):\n",
      "    \"Another pointless function.\"\n",
      "    for arg in args:\n",
      "        my_function(arg)\n",
      "\n",
      "my_function2(1000, 2000, 3000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "my_function! 1000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "my_function! 2000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "my_function! 3000\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Classes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class fibonacci:\n",
      "    def __init__(self, maxlength=10):\n",
      "        self.n1 = 0\n",
      "        self.n2 = 1\n",
      "        self.max = maxlength\n",
      "    \n",
      "    def next(self):\n",
      "        self.n1, self.n2 = self.n2, self.n1 + self.n2\n",
      "        return self.n1\n",
      "    \n",
      "    def __iter__(self):\n",
      "        while self.max > 0:\n",
      "            yield self.next()\n",
      "            self.max -= 1\n",
      "\n",
      "print(\"Fibonacci:\", *fibonacci(20))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fibonacci: 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 2584 4181 6765\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Dictionaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = {'a':1, 'b':2, 'c':[3,4,5]}\n",
      "d['a'] = 0\n",
      "d['d'] = 'WORD'\n",
      "\n",
      "for k,v in d.items():\n",
      "    print(k, v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "c [3, 4, 5]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "b 2\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "a 0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d WORD\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A Concrete Example"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -1 ~/numsim/data/3dpackingsF | head -n20"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "n1000_r3_m0.12_s130_P1e-6.npz\r\n",
        "n1000_r3_m0.12_s290_P1e-6.npz\r\n",
        "n1000_r3_m0.15_s131_P1e-6.npz\r\n",
        "n1000_r3_m0.15_s291_P1e-6.npz\r\n",
        "n1000_r3_m0.18_s132_P1e-6.part.npz\r\n",
        "n1000_r3_m0.18_s292_P1e-6.part.npz\r\n",
        "n1000_r3_m0.1_s129_P1e-6.npz\r\n",
        "n1000_r3_m0.1_s289_P1e-6.npz\r\n",
        "n1000_r3_m0.22_s133_P1e-6.part.npz\r\n",
        "n1000_r3_m0.22_s293_P1e-6.part.npz\r\n",
        "n1000_r3_m0.27_s134_P1e-6.part.npz\r\n",
        "n1000_r3_m0.27_s294_P1e-6.part.npz\r\n",
        "n1000_r3_m0.33_s135_P1e-6.part.npz\r\n",
        "n1000_r3_m0.33_s295_P1e-6.part.npz\r\n",
        "n1000_r3_m0.4_s136_P1e-6.npz\r\n",
        "n1000_r3_m0.4_s296_P1e-6.part.npz\r\n",
        "n1000_r4_m0.12_s138_P1e-6.npz\r\n",
        "n1000_r4_m0.12_s298_P1e-6.npz\r\n",
        "n1000_r4_m0.15_s139_P1e-6.npz\r\n",
        "n1000_r4_m0.15_s299_P1e-6.npz\r\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Python has a module for everything"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pathlib import Path # a Path object represents a filesystem location\n",
      "from decimal import Decimal # a Decimal object represents a decimal number (not binary)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datadir = Path('numsim/data/3dpackingsF/')\n",
      "\n",
      "files = [f for f in datadir.glob('*.npz') if 'part' not in f.stem] # list iterators!\n",
      "print(\"First file:\", files[0])\n",
      "print(\"Found\", len(files), \"files.\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "First file: numsim/data/3dpackingsF/n600_r5_m0.12_s82_P1e-6.npz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Found 239 files.\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Dictionaries (hashmaps) and lists are part of the language"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# An 'outer' dictionary, keeping track of all files\n",
      "filelists = dict()\n",
      "for f in files:\n",
      "    # turn the name into a dictionary of parameters\n",
      "    params = {s[0]:Decimal(s[1:]) for s in f.stem.split('_')}\n",
      "    # Add the filename to the parameter dictionary\n",
      "    params['filename'] = f\n",
      "    # The key for the outer dictionary will be (n,r)\n",
      "    mykey = params['n'], params['r']\n",
      "    \n",
      "    # Now: if (n,r) is not a key in the outer dictionary, make it a key pointing to a list\n",
      "    # Append this parameter dictionary to that list\n",
      "    filelists.setdefault(mykey, []).append(params)\n",
      "    \n",
      "print(\"Filelists has\", len(filelists), \"Elements\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Filelists has 19 Elements\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for n,r in sorted(filelists):\n",
      "    myfiles = filelists[n,r]\n",
      "    ms = sorted([par['m'] for par in myfiles])\n",
      "    print(\"%4s %4s: %2d files,\" % (n,r, len(myfiles)), *ms)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200    3: 23 files, 0.1 0.1 0.1 0.12 0.12 0.12 0.15 0.15 0.15 0.18 0.18 0.18 0.22 0.22 0.22 0.27 0.27 0.33 0.33 0.33 0.4 0.4 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200    4: 21 files, 0.1 0.1 0.1 0.12 0.12 0.12 0.15 0.15 0.15 0.18 0.18 0.18 0.22 0.22 0.22 0.27 0.27 0.27 0.33 0.33 0.33\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200    5:  9 files, 0.1 0.1 0.1 0.12 0.12 0.12 0.15 0.15 0.15\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400    3: 22 files, 0.1 0.1 0.1 0.12 0.12 0.12 0.15 0.15 0.15 0.18 0.18 0.18 0.22 0.22 0.22 0.27 0.27 0.33 0.33 0.4 0.4 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400    4: 19 files, 0.1 0.1 0.1 0.12 0.12 0.12 0.15 0.15 0.15 0.18 0.18 0.18 0.22 0.22 0.22 0.27 0.27 0.33 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400    5: 17 files, 0.1 0.1 0.12 0.12 0.12 0.15 0.15 0.15 0.18 0.18 0.18 0.22 0.22 0.27 0.27 0.33 0.33\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400    6: 12 files, 0.1 0.1 0.1 0.12 0.12 0.12 0.15 0.15 0.15 0.18 0.18 0.18\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600    3: 16 files, 0.1 0.1 0.1 0.12 0.12 0.15 0.15 0.15 0.18 0.18 0.22 0.22 0.22 0.33 0.4 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600    4: 12 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.18 0.18 0.22 0.22 0.4 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600    5: 12 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.18 0.18 0.27 0.27 0.4 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600    6:  9 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.18 0.27 0.27\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800    3: 14 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.18 0.18 0.22 0.22 0.27 0.33 0.33 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800    4:  8 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.18 0.22\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800    5:  7 files, 0.1 0.1 0.12 0.15 0.15 0.18 0.22\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800    6: 12 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.22 0.22 0.27 0.27 0.33 0.33\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000    3:  7 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000    4:  8 files, 0.1 0.1 0.12 0.12 0.15 0.15 0.18 0.22\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000    5:  6 files, 0.1 0.12 0.12 0.15 0.15 0.18\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000    6:  5 files, 0.1 0.1 0.15 0.15 0.18\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2: Scientific Data"
     ]
    },
    {
     "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"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['f', 'gray', 'pink']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = eye(3)\n",
      "m[1,2] += 1\n",
      "m[2,1] += 1\n",
      "m"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([[ 1.,  0.,  0.],\n",
        "       [ 0.,  1.,  1.],\n",
        "       [ 0.,  1.,  1.]])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eigenvalues, eigenvectors = eig(m)\n",
      "print(eigenvalues)\n",
      "print(eigenvectors)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 2.  0.  1.]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.          0.          1.        ]\n",
        " [ 0.70710678  0.70710678  0.        ]\n",
        " [ 0.70710678 -0.70710678  0.        ]]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Importing Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mydata = loadtxt('hardspheres.msd')\n",
      "time = mydata[:,0]\n",
      "msdLarge = mydata[:,1]\n",
      "msdSmall = mydata[:,2:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Graphing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "loglog(time, msdLarge)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fda4a960e10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ioqKAvW5Yz0TxAcRERIHjPP6t5/WJE42OU11ycSDKoQjCBxATEVFIczQRmhpS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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fda4b2301d0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Fitting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "loglog(time, msdLarge)\n",
      "idx = (time < max(time)/10) & (time > max(time)/100)\n",
      "m,b = polyfit(log(time[idx]), log(msdLarge[idx]), 1) # polyfit, just like Matlab\n",
      "plot(time, time*exp(b), ls='--')\n",
      "axvline(time[idx][0], color='black', ls=':')\n",
      "axvline(time[idx][-1], color='black', ls=':')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.lines.Line2D at 0x7fda4a8261d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OHoRUWISwzz93dXMqxDVRRERkRaVSYf78+a5uhlsqNJixbNtpbDx0HgpIaPK/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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fda4a5456d8>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Bells and Whistles"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(8,4))\n",
      "loglog(time, msdLarge, label='Large Particle', color='blue')\n",
      "msdSmallMean = mean(msdSmall, axis=1)\n",
      "sdev = std(msdSmall, axis=1)\n",
      "loglog(time, msdSmallMean, label='Small Particles', color='red')\n",
      "fill_between(time, msdSmallMean-sdev, msdSmallMean + sdev, color='red', alpha=0.3)\n",
      "\n",
      "idx = (time < max(time)/10) & (time > max(time)/100)\n",
      "for ys, color in [(msdLarge, 'blue'), (msdSmallMean, 'red')]:\n",
      "    slopefit = exp(mean(log(ys[idx]) - log(time[idx])))\n",
      "    label = r'Fit to $6Dt$' if color =='red' else None\n",
      "    plot(time, slopefit*time, color='black', ls=':', label=label)\n",
      "\n",
      "for t in time[idx][0], time[idx][-1]:\n",
      "    axvline(t, color='black', alpha=0.4, zorder=-200)\n",
      "\n",
      "xlim(min(time), max(time))\n",
      "\n",
      "ylabel(r'MSD, $\\left<|\\vec r(t+\\Delta t) -\\vec r(t)|\\right>$')\n",
      "xlabel(r'Time, $\\Delta t$')\n",
      "\n",
      "legend(loc=2)\n",
      "savefig('examplefigure.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ptm///v0AdCiHQ0aPq8jISPz8/MjMzGTevHn861//MuskGgwG9u7dS7BxEZOH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\nAADAhCIBAAAAgMn/AN3lLRT670SbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fda4a1eee80>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Putting Things Together"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_phi(params):\n",
      "    d = np.load(str(params['filename']))\n",
      "    diameters = d['sigmas'][-1]\n",
      "    return sum(diameters**3*np.pi/6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(8,4))\n",
      "for n,r in sorted(filelists):\n",
      "    if n != 600: continue\n",
      "    lst = filelists[(n,r)]\n",
      "    \n",
      "    ms = np.array([float(params['m']) for params in lst])\n",
      "    phis = np.array([get_phi(params) for params in lst])\n",
      "    plot(ms, phis, 'o', label=r)\n",
      "\n",
      "xlim(0, 0.55)\n",
      "xlabel(r'Relative volume fraction $m$')\n",
      "ylabel(r'Volume fraction $\\phi$')\n",
      "legend(title='Size ratio $r$')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.legend.Legend at 0x7fda4a8be3c8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u7h/bgJ9jdzfJZorqiB0bGwstLS3s2LEDQ4YMqfE5cuQIAOB///sfhgwZgpCQ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fda4a51e518>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Further down the rabbit hole..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sim2d as sim\n",
      "from cplot import CirclePlot\n",
      "sim.seed()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "1397145733"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab qt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "A Packing Solver"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "phi0=0.01\n",
      "sigmas = [1.0]*40 + [1.4]*40\n",
      "masses = [s**2*pi/4 for s in sigmas]\n",
      "Vs = sum(masses)\n",
      "\n",
      "box = sim.OriginBox(sqrt(Vs/phi0))\n",
      "atoms = sim.atomvec(masses)\n",
      "neighbors = sim.neighborlist(box, max(sigmas), max(sigmas)*1.7)\n",
      "interaction = sim.Hertzian(neighbors)\n",
      "\n",
      "for s,a in zip(sigmas, atoms):\n",
      "    a.x = box.randLoc()\n",
      "    a.v = sim.Vec()\n",
      "    interaction.add(sim.HertzianAtom(a, 1.0, s))\n",
      "\n",
      "neighbors.update_list(True)\n",
      "integrator = sim.collectionNLCG(box, atoms, 0.1, 1e-4, [interaction], [neighbors],[],10.0, 1000, 10, 1e-20)\n",
      "integrator.setForces()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "locs = [tuple(a.x) for a in atoms]\n",
      "cplot = CirclePlot(locs, sigmas, L=box.L(), draw_contacts=True)\n",
      "\n",
      "import matplotlib.animation as anim\n",
      "\n",
      "def update(i):\n",
      "    for j in range(1000):\n",
      "        integrator.timestep()\n",
      "    locs = array([tuple(a.x) for a in atoms])\n",
      "    cplot.ax.set_title(r'$\\Sigma |\\vec f| = %.3g$' % sum([a.f.mag() for a in atoms]))\n",
      "    return cplot.update(locs, L=box.L())\n",
      "\n",
      "ani = anim.FuncAnimation(cplot.fig, update, frames=100)\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "A Lennard-Jones Simulation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "phi=0.4\n",
      "N = 8\n",
      "sigmas = list(linspace(1,2,8)) * N\n",
      "masses = [s**2*pi/4 for s in sigmas]\n",
      "Vs = sum(masses)\n",
      "L = sqrt(Vs/phi)\n",
      "\n",
      "box = sim.OriginBox(L)\n",
      "atoms = sim.atomvec(masses)\n",
      "neighbors = sim.neighborlist(box, max(sigmas), max(sigmas)*1.7)\n",
      "interaction = sim.LJgroup(neighbors)\n",
      "\n",
      "E = 0\n",
      "for s,a in zip(sigmas, atoms):\n",
      "    a.x = box.randLoc()\n",
      "    a.v = sim.randVec() * 0.1\n",
      "    interaction.add(sim.LJatom(1.0, s, a))\n",
      "\n",
      "neighbors.update_list(True)\n",
      "integrator = sim.collectionVerlet(box, atoms, 0.001, [interaction], [neighbors],[])\n",
      "integrator.setForces()\n",
      "\n",
      "# Settle things down a bit\n",
      "for dt in logspace(-8,-2, 10):\n",
      "    for i in range(10000):\n",
      "        integrator.timestep()\n",
      "        integrator.scaleVelocitiesT(0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "locs = [tuple(a.x) for a in atoms]\n",
      "cplot = CirclePlot(locs, sigmas, L=box.L(), draw_contacts=False)\n",
      "\n",
      "import matplotlib.animation as anim\n",
      "\n",
      "def update(i):\n",
      "    for j in range(1000):\n",
      "        integrator.timestep()\n",
      "        #box.shear(1e-5, atoms)\n",
      "    locs = array([tuple(a.x) for a in atoms])\n",
      "    return cplot.update(locs, L=box.L())\n",
      "    print(i)\n",
      "\n",
      "ani = anim.FuncAnimation(cplot.fig, update, frames=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Shearing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "phi=0.78\n",
      "N = 8\n",
      "sigmas = list(linspace(1,3,8)) * N\n",
      "masses = [s**2*pi/4 for s in sigmas]\n",
      "Vs = sum(masses)\n",
      "L = sqrt(Vs/phi)\n",
      "T = 0.01\n",
      "\n",
      "box = sim.LeesEdwardsBox(sim.Vec(L,L))\n",
      "atoms = sim.atomvec(masses)\n",
      "neighbors = sim.neighborlist(box, max(sigmas), max(sigmas)*1.7)\n",
      "interaction = sim.LJgroup(neighbors)\n",
      "\n",
      "E = 0\n",
      "for s,a in zip(sigmas, atoms):\n",
      "    a.x = box.randLoc()\n",
      "    a.v = sim.Vec()\n",
      "    interaction.add(sim.LJatom(1.0, s, a))\n",
      "\n",
      "neighbors.update_list(True)\n",
      "integrator = sim.collectionOverdamped(box, atoms, 0.01, 1.0, [interaction], [neighbors],[])\n",
      "integrator.setForces()\n",
      "integrator.scaleVelocitiesT(T)\n",
      "\n",
      "# Settle things down a bit\n",
      "for dt in logspace(-16,-2, 20):\n",
      "    #print(dt, integrator.energy())\n",
      "    integrator.setdt(dt)\n",
      "    for i in range(1000):\n",
      "        integrator.timestep()\n",
      "print(integrator.potentialenergy())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.011090048629645128\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "locs = [tuple(a.x) for a in atoms]\n",
      "cplot = CirclePlot(locs, sigmas, L=box.L(), gamma=box.get_gamma(), draw_contacts=False)\n",
      "\n",
      "import matplotlib.animation as anim\n",
      "\n",
      "def update(i):\n",
      "    for j in range(2000):\n",
      "        box.shear(3e-5, atoms)\n",
      "        integrator.timestep()\n",
      "        #integrator.scaleVelocitiesT(0.0001)\n",
      "    locs = array([tuple(a.x + sim.Vec(0,box.L()/2)) for a in atoms])\n",
      "    print(integrator.temp())\n",
      "    return cplot.update(locs, L=box.L(), gamma=box.get_gamma())\n",
      "    print(i)\n",
      "\n",
      "ani = anim.FuncAnimation(cplot.fig, update, frames=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "6.137435795365128e-05\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}