Thresholds_Testing.ipynb

{
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  "name": "",
  "signature": "sha256:8c0fb6289c8a3aaee03e433dfb13e00df5764fb0f1966682ba17c34a77ef0e99"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Testing for Threshold Models"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here I would like to run testing to get some numerics ready for threshold models.  The idea is as follows:  We set up a network with a particular voltage.  AS will switch until the none carry a current above the threshold.  At this point, we increase the voltage slightly, enough to cause the next memristor to switch. This will increase the current and likely cause other memristors to switch down the line.  When all memristors have switched, we collect the number of memristors that switch for each increase and bin these to hopefully observe a power law."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setting the threshold"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I need to know what threshold to apply for the memristors.  To that end, I need to look at random resistor networks voltage distributions and decide where would be sensible to put it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy as sp\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "import generate_adjacency_matrix as gen_adj\n",
      "import resistor_networks as rnets\n",
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "L = 128\n",
      "p = 0.6\n",
      "random_lattice = gen_adj.cubic_2d_random((L,L), p)\n",
      "v_external = np.empty(L*L) * np.nan\n",
      "voltage = 1\n",
      "v_external[0:L] = voltage\n",
      "v_external[-L:] = 0\n",
      "\n",
      "rnet = rnets.ResistorNetworkCC(random_lattice, v_external)\n",
      "rnet.solve_voltages('spsolve')\n",
      "\n",
      "#fig, ax = plt.subplots(figsize=(10,10))\n",
      "#rnet.display_grid(ax, (L,L), 'voltage')\n",
      "rnet.conductivity()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "0.16117248605541634"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_voltage_drops(G, voltages):\n",
      "    rows, cols = G.nonzero()\n",
      "    drops_list = []\n",
      "    for i, j in zip(rows, cols):\n",
      "        if i < j:\n",
      "            drop = np.abs(voltages[i] - voltages[j])\n",
      "            if drop > 1e-12:\n",
      "                drops_list.append(drop)\n",
      "    return drops_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "voltage_drops = calculate_voltage_drops(rnet.G, rnet.voltages)\n",
      "len(voltage_drops)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "15030"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist, bins = np.histogram(np.log10(voltage_drops), 150)\n",
      "\n",
      "fig, ax = plt.subplots(figsize=(12, 12))\n",
      "ax.scatter(10**bins[1:], hist)\n",
      "#ax.set_yscale('log')\n",
      "ax.set_xscale('log')\n",
      "#ax.set_ylim(1, hist.max()*2)\n",
      "ax.set_xlim((10**bins[1:].min())/2, 2*(10**bins[1:].max()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(2.8415690589665816e-08, 0.048124833778032006)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SSf3gB3epa9cuuummm6yOBg8xkgwAAHCBXnzxNdXW/krSdZImqbb2cf3pT1lWx4IXUJIB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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x297fe90>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max(voltage_drops)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "0.024062416889016003"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So a threshold of 0.1 or 0.05 would leave most of the memristors unswitched.  To confirm, I'll switch all memristors above a threshold repeatedly until none others want to switch.  Then I'll look at the voltages of bonds below that threshold.  I think a R_ON value of something like 0.01 might be appropriate althrough I should check at 0.001 and so on to confirm."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def switch_memristors(G, voltages, I_thresh, G_ON=100., G_OFF=1.):\n",
      "    rows, cols = G.nonzero()\n",
      "    switch_counter = 0\n",
      "    for i, j in zip(rows, cols):\n",
      "        if i < j:\n",
      "            current = G[i, j] * np.abs(voltages[i] - voltages[j])\n",
      "            if G[i, j] == G_OFF and current >= I_thresh:\n",
      "                G[i, j] = G_ON\n",
      "                G[j, i] = G_ON\n",
      "                switch_counter += 1\n",
      "    return G, switch_counter\n",
      "\n",
      "def find_maximum_unswitched_voltage(G, voltages, G_ON=100., G_OFF=1.):\n",
      "    rows, cols = G.nonzero()\n",
      "    max_unswitched_voltage = 0\n",
      "    for i, j in zip(rows, cols):\n",
      "        if G[i, j] == G_OFF and i < j:\n",
      "            drop = np.abs(voltages[i] - voltages[j])\n",
      "            if drop > max_unswitched_voltage:\n",
      "                max_unswitched_voltage = drop\n",
      "                indices = (i, j)\n",
      "    return max_unswitched_voltage, indices\n",
      "\n",
      "def find_maximum_switched_voltage(G, voltages, G_ON=100., G_OFF=1.):\n",
      "    rows, cols = G.nonzero()\n",
      "    max_switched_voltage = 0\n",
      "    for i, j in zip(rows, cols):\n",
      "        if G[i, j] == G_ON and i < j:\n",
      "            drop = np.abs(voltages[i] - voltages[j])\n",
      "            if drop > max_switched_voltage:\n",
      "                max_switched_voltage = drop\n",
      "                indices = (i, j)\n",
      "    return max_switched_voltage, indices"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_threshold = 0.02\n",
      "G_OFF = 1\n",
      "G_ON = 100\n",
      "max_unswitched_voltage, indices = find_maximum_unswitched_voltage(rnet.G, rnet.voltages)\n",
      "avalanche_size = []\n",
      "conductivities = []\n",
      "conductivities.append(rnet.conductivity())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "while max_unswitched_voltage != 0:\n",
      "    counter = 0\n",
      "    while max_unswitched_voltage*G_OFF > I_threshold:\n",
      "        rnet.G, num_switched = switch_memristors(rnet.G, rnet.voltages, I_threshold)\n",
      "        counter += num_switched\n",
      "    \n",
      "        rnet.solve_voltages('spsolve')\n",
      "        max_unswitched_voltage, indices = find_maximum_unswitched_voltage(rnet.G, rnet.voltages)\n",
      "    \n",
      "    avalanche_size.append(counter)\n",
      "    conductivities.append(rnet.conductivity())\n",
      "    voltage = voltage * I_threshold / max_unswitched_voltage + 1e-15\n",
      "    rnet.external_voltages[0:L] = voltage\n",
      "    rnet.solve_voltages('spsolve')\n",
      "    max_unswitched_voltage, indices = find_maximum_unswitched_voltage(rnet.G, rnet.voltages)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-14-9b9060aecd50>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexternal_voltages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvoltage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolve_voltages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'spsolve'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mmax_unswitched_voltage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfind_maximum_unswitched_voltage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvoltages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m<ipython-input-12-cf8c86789f6c>\u001b[0m in \u001b[0;36mfind_maximum_unswitched_voltage\u001b[1;34m(G, voltages, G_ON, G_OFF)\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mmax_unswitched_voltage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mG\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mG_OFF\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m             \u001b[0mdrop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvoltages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mvoltages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mdrop\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mmax_unswitched_voltage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/scipy/sparse/csr.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    231\u001b[0m             \u001b[1;31m# [i, j]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misintlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_single_element\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    234\u001b[0m             \u001b[1;31m# [i, 1:2]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/scipy/sparse/csr.pyc\u001b[0m in \u001b[0;36m_get_single_element\u001b[1;34m(self, row, col)\u001b[0m\n\u001b[0;32m    287\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m             \u001b[0mcol\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mM\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    290\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"index out of bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "hist, bins = np.histogram(np.log10(avalanche_size), bins = 20)\n",
      "ax.plot(10**bins[1:], hist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:2: RuntimeWarning: divide by zero encountered in log10\n",
        "/usr/local/lib/python2.7/dist-packages/numpy/core/function_base.py:88: RuntimeWarning: invalid value encountered in multiply\n",
        "  y = _nx.arange(0, num) * step + start\n",
        "/usr/local/lib/python2.7/dist-packages/numpy/core/function_base.py:88: RuntimeWarning: invalid value encountered in add\n",
        "  y = _nx.arange(0, num) * step + start\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[<matplotlib.lines.Line2D at 0x4785e10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sc+fOadKkSTE1LpdLoVAouhwKheR2u3X69Gm1tbUpIyNDktTe3q4HH3xQDQ0N/R6ntLQ0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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x50f88d0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "4731"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avalanche_size"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "[21, 3, 0, 0, 0, 0, 1, 1, 1, 3, 2, 4, 1, 1, 3]"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rnet.conductivity()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "0.38450422832717518"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rnet.G, counter = switch_memristors(rnet.G, rnet.voltages, 0.015)\n",
      "rnet.solve_voltages('spsolve')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 181,
       "text": [
        "5"
       ]
      }
     ],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max_voltage_unswitched, unswitched_indices = find_maximum_unswitched_voltage(rnet.G, rnet.voltages)\n",
      "max_voltage_switched, switched_indices = find_maximum_switched_voltage(rnet.G, rnet.voltages)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'rnet' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-5-e3c3badaede7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmax_voltage_unswitched\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munswitched_indices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfind_maximum_unswitched_voltage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvoltages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmax_voltage_switched\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mswitched_indices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfind_maximum_switched_voltage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvoltages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mNameError\u001b[0m: name 'rnet' is not defined"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Maximum unswitched voltage was %f at %d, %d\" % (max_voltage_unswitched, unswitched_indices[0], unswitched_indices[1])\n",
      "print \"Maximum switched voltage was %f at %d, %d\" % (max_voltage_switched, switched_indices[0], switched_indices[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Maximum unswitched voltage was 0.021513 at 4891, 5019\n",
        "Maximum switched voltage was 0.000644 at 4251, 4379\n"
       ]
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "voltage = voltage * 0.015 / max_voltage_unswitched\n",
      "rnet.external_voltages[0:L] = voltage\n",
      "rnet.solve_voltages('spsolve')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 175,
       "text": [
        "100.0"
       ]
      }
     ],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}