{
 "metadata": {
  "name": "",
  "signature": "sha256:34611d8789dd0b60070b79e959d5ae409bbe969f91d62e6b9edd8191e3aa7e07"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook generates the images used in the documentation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append( '../..' )\n",
      "\n",
      "import SchemDraw as schem\n",
      "import SchemDraw.elements as e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def drawElements( elm_list, n=5, dx=1, dy=2, ofst=.8, fname=None, **kwargs ):\n",
      "    x = 0\n",
      "    y = 0\n",
      "\n",
      "    d = schem.Drawing(fontsize=12)\n",
      "    for elm in elm_list:\n",
      "        A = d.add( elm, xy=[(d.unit+1)*x+1,y], label=elm['name'], **kwargs )\n",
      "        x = x + dx\n",
      "        if x >= n:\n",
      "            x=0\n",
      "            y=y-dy\n",
      "\n",
      "    d.draw()\n",
      "    if fname is not None:\n",
      "        d.save( fname )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elements_2term = [\n",
      "e.RES,\n",
      "e.RES_VAR,\n",
      "e.RBOX,\n",
      "e.CAP,\n",
      "e.CAP2,\n",
      "e.CAP_P,\n",
      "e.CAP2_P,\n",
      "e.INDUCTOR,\n",
      "e.INDUCTOR2,\n",
      "e.DIODE,\n",
      "e.DIODE_F,\n",
      "]\n",
      "\n",
      "drawElements(elements_2term, n=4, fname='2term.png', lblofst=.6, lblloc='center' )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAC2CAYAAAC1SNU1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntxNt0bw6AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0585f8>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sources = [\n",
      "e.SOURCE,\n",
      "e.SOURCE_V,\n",
      "e.SOURCE_I,\n",
      "e.SOURCE_SIN,\n",
      "e.SOURCE_CONT,\n",
      "e.SOURCE_CONT_I,\n",
      "e.SOURCE_CONT_V\n",
      "]\n",
      "drawElements( sources, n=4, dy=2.2, fname='sources.png', d='right', lblofst=.4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAACNCAYAAADb9SJbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0589b0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "meters = [\n",
      "e.METER_V,\n",
      "e.METER_I,\n",
      "e.METER_OHM\n",
      "]\n",
      "drawElements( meters, n=3, dy=2.2, fname='meters.png', d='left', lblofst=.4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAABHCAYAAAAHtjB9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmk5EMByez98AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d1cdf60>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grounds = [\n",
      "e.GND,\n",
      "e.GND_SIG,\n",
      "e.GND_CHASSIS,\n",
      "]\n",
      "drawElements( grounds, fname='grounds.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAA4CAYAAABqv+r2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0bbf60>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "switches =[\n",
      "e.SWITCH_SPST,\n",
      "e.SWITCH_SPST_OPEN,\n",
      "e.SWITCH_SPST_CLOSE,\n",
      "e.SWITCH_SPDT,\n",
      "e.SWITCH_SPDT_OPEN,\n",
      "e.SWITCH_SPDT_CLOSE,\n",
      "e.SWITCH_SPDT2,\n",
      "e.SWITCH_SPDT2_OPEN,\n",
      "e.SWITCH_SPDT2_CLOSE,\n",
      "]\n",
      "drawElements( switches, n=3, dx=1.2, dy=3, fname='switches.png', lblloc='center', lblofst=.8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAdkAAAENCAYAAABD6B87AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d213978>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(fontsize=12)\n",
      "op = d.add( e.OPAMP )\n",
      "d.add( e.LINE, xy=op.in1, d='left', l=.5, lftlabel='in1' )\n",
      "d.add( e.LINE, xy=op.in2, d='left', l=.5, lftlabel='in2' )\n",
      "d.add( e.LINE, xy=op.out, d='right', l=.5, rgtlabel='out')\n",
      "d.draw()\n",
      "d.save('opamp.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAJ4AAABeCAYAAADSba3PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA8OMwgjbsV/zAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d23f6a0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(fontsize=12)\n",
      "bjt = d.add( e.BJT_NPN )\n",
      "d.add( e.LINE, xy=bjt.base, d='left', l=.3, lblofst=.2, lftlabel='base' )\n",
      "bjt.add_label( 'emitter', loc='center', ofst=[.3,-1.3] )\n",
      "bjt.add_label( 'collector', loc='center', ofst=[.3,1.0] )\n",
      "d.draw()\n",
      "d.save('bjt_anchors.png')\n",
      "\n",
      "d = schem.Drawing(fontsize=12)\n",
      "fet = d.add( e.NFET )\n",
      "d.add( e.LINE, xy=fet.gate, d='right', l=0, lblofst=.2, rgtlabel='gate' )\n",
      "fet.add_label( 'source', loc='center', ofst=[-.5,-1.3] )\n",
      "fet.add_label( 'drain', loc='center', ofst=[-.5,1.0] )\n",
      "d.draw()\n",
      "d.save('fet_anchors.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAGkAAABhCAYAAADV0Y9XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0e92b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAGkAAABhCAYAAADV0Y9XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d26ba20>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bjt = [\n",
      "e.BJT,\n",
      "e.BJT_NPN,\n",
      "e.BJT_PNP,\n",
      "e.BJT_NPN_C,\n",
      "e.BJT_PNP_C,\n",
      "]\n",
      "drawElements( bjt, n=5, dy=2.5, fname='bjt.png', lblloc='top')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAABKCAYAAAB928rrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGIhJREFUeJzt3XlQFGf6B/BvDzc6BoThkCheCAweYFbBE9czuwUJSmTj\nZlOaTdyYlBuPjZvUehBEjZsynnG3jGxCEk3iIglG1OiKm1WC64EcHhFLUS7BLMLgCAzC9PP7w2J+\nMXIMdM80DM+niqqEed9+v2O/vPNMT0+3QEQExhhjjDHWaSqlAzDGGGOMdXdcUDHGGGOMScQFFWOM\nMcaYRFxQMcYYY4xJxAUVY4wxxphEXFAxxhhjjEnEBRVjjDHGmERmFVQDBw6Eq6sr1Go1+vbti6io\nKJSWlgIA3nnnHbz44osoKSlB7969oVaroVaroVKpHvn/77//3qJPhLXP0vtxwYIFUKlUOHfunOl3\n169fh0r1/9NsypQpcHFxgVqthkajQWxsLCoqKszuz+Rhjbng5OQEtVoNDw8PzJw5EwUFBabtq1Qq\npKSkmNo3NTVBpVKhuLi43f5MGltfB44ePYrJkyejT58+8PLywpQpU3Dw4MEO/Rv1BLa6Bly7dg1z\n586FRqOBm5sbRo0ahS1btkAURSn/XGYxa4YKgoD09HTo9XqUl5fD29sbf/zjHx9p079/f9y/fx96\nvR56vR4AkJ+fb/r/CRMmyJ+edYg19mPfvn2xatWqNjPs3LkTer0e165dg06nw7Jly8zuz+Rh6bkg\nCALeeust6PV6lJaWwsvLCwsWLDA93rdvX8THx7e6yLXXn3WeLa8D+/fvR1xcHBYsWICysjL8+OOP\nWLt2LRdULbDFNeDGjRsIDw+Hv78/Ll26BJ1Oh5SUFGRnZ5vyW1KH3/o7OTkhNjYWV65csUQeZiWW\n2I+CIGD+/PnIz8/HyZMn223v7u6OOXPm4NKlS53qz+Rh6b9pFxcXzJs375H9/PTTT8PR0RF79uzp\ncH8mH1taB4gIy5cvx5o1a/D73/8earUaADB58mR8+OGHnXsyPYStrAHx8fGYOHEiNm3aBG9vbwDA\nsGHDsGfPHjzxxBPSn0g7zC6omu9QU1dXh3379mHcuHEWC8Usx9L70dXVFX/5y1+wcuXKdjNUVlYi\nNTUVo0eP7lB/Jg9Lz4Xm7d+/fx979+417WcigiAISExMREJCAoxGY4f6M+lscR0oKChAaWkpnnvu\nuc4H72FsbQ3IyMhQdP+bVVAREWJiYuDu7g43NzdkZGTgzTfftHQ2JjNr7EdBEPDqq6+iuLgY3377\nbYsZ3njjDbi7uyM0NBR+fn7YvHmz2f2ZPCw9F4gImzZtgru7OwICAlBXV4fk5GTT44IgIDo6GhqN\nBrt37+5wf9Z5troO3L17FwDg6+srz5Owcba4Bty9e1fR/W/2OVQHDhxAdXU1GhoasGPHDkRGRuLO\nnTuWzsdkZK396OjoiNWrV2P16tUQBOGxDDt27EB1dTVKS0vx2WefwcPDw+z+TB6WnguCIGDFihWo\nrq5GeXk50tLSMGjQINPjze88161bh/Xr16OhoaFD/Vnn2eo60Ny/vLxcvidhw2xxDfDw8MDt27dl\nyd8ZHT6HShAEzJ49G3Z2dsjMzOQXvG7KUvux+Y9kwYIF0Ol0SE1NtWp/1nGWngstjdds+vTpGDp0\nKHbu3Gl2fyYfW1oHAgMD0b9/f+zfv7/jgXs4W1kDpk+fruhrRofPoSIiHDhwADqdDlqtlhe9bsaS\n+/Gn27C3t0dCQgL++te/ttmuM/2ZPKw1F9p7bP369XjvvffM7s+ks8V1QBAEbN68GYmJiUhOTsa9\ne/cgiiIyMzPx6quvduKZ2D5bWwMSEhKQlZWFP//5z6YjbdevX8eLL76ImpqaDm+vo8wuqKKjo6FW\nq/HEE09g9erV+OSTTxAcHAwALVazfOSqa7LkfhQE4ZH28+bNQ79+/Vo83C+lP5OHNedCW4+NHz8e\n4eHhj/yurf5MOltdB2JjY7Fv3z589NFH8PPzg4+PD9asWYOYmBiz8/cktrYGDB48GKdPn8atW7cQ\nEhICNzc3PPfccxgzZozpW5+WJJDEUnT58uUA8MgJhaz74f3ImvFc6Ll43zOA50FnSboEtU6nw7Fj\nx/CLX/xCrjxMAbwfWTOeCz0X73sG8DyQotMFVXp6OoYOHYqIiAjExcXJmYlZUUf3Y0hIiOm2Az/9\n+eKLL6yQllkSz4Weqzvt+5/e+uSnP3x7M+m6wzz41a9+1eKYGzdutNiY5pL8kR9jjDHGWE/Hd51l\njDHGGJNIkYLq+vXrfPE1hszMTKUjMIXdvXuX7wvKcO7cuccu7Mh6FqPRiKysLKVjSKJIQbVjxw78\n85//VGJo1oXMmDED9fX1SsdgCjp06FCXOPeBKWvZsmU4d+6c0jGYggoKCvDKK68oHUMS/siPMcYY\nY0wiLqgYY4wxxiTigooxxhhjTCJ7pQMwxhizLZWVlTh16hRycnKQm5uLq1evora2FkajEc7OzvDz\n80NoaCjCwsIwduxYvnejDSIiXLx4EWfPnjXNg7KyMhgMBtjZ2aFXr14ICgoyzQNvb2+lI0tm9YLq\nwYMHuHr1KgIDA609NOtCfvjhB4iiCJWKD5L2VKIoIiEhAV5eXkpHYTIQRREZGRlISkrC0aNHMWHC\nBIwePRrz58+HVqtFnz59oFKpYDAYUFRUhNzcXGRmZiIxMRHl5eX4+uuvodVq0bdvX6WfCpOgqqoK\ne/fuRVJSEvR6PSZPnozQ0FDMnTsX/v7+cHZ2hiiKuHfvHq5cuYLc3FwkJSXh2LFjUKlU+Ne//oVp\n06Z1z9cGsqKKigqaNGkSRUdHU01NjTWHZl3IN998QxqNhj7++GOlozCFVFRUEAACQCkpKUrHYRKd\nPHmStFothYaG0s6dO6m6utqsfnl5eTRw4ECKi4uj559/ntzc3CgxMZEaGhosnJjJraGhgRITE8nN\nzY3mzZtHJ06cIKPR2G4/o9FIvXv3JgAUGRlJoaGhpNVq6eTJk1ZILS+rFVTnz5+nAQMG0KpVq8z6\nR2a2RxRFSkxMJD8/Pzp9+rTScZhCDh8+bCqmbty4oXQcJkFVVRUtXLiQ/Pz8KDU1lURRNLtvSkoK\neXp60t69e02/KyoqoqioKNJqtZSZmWmJyMwCMjMzSavVUlRUFBUVFZndr6SkxLQW7N+/n4gevk6k\npqaSn58fLVy4kKqqqiwVW3ZWKaj27t1Lnp6e/E60B9Pr9RQbG0vh4eFUVlamdBymkKVLlxIAsre3\np8bGRqXjMAkKCgpo8ODBtGjRItLpdGb3MxqNtGrVKhowYACdP3/+scdFUaSUlBTy8fGhDz74QM7I\nzAI++OAD8vHxoZSUlA4V1Pv37zcVUyUlJY89rtPpaNGiRTR48GAqKCiQM7LFWLSgampqojfffJMG\nDRpE+fn5lhyKdWGFhYU0YsQIeumll6i+vl7pOEwhAQEBBIBmzJihdBQmUX5+Pvn4+FBSUlKH+tXU\n1FB0dDRNmjSJ7ty502bbwsJCGjZsGK1du1ZKVGZBa9eupWHDhlFhYWGH+i1cuJAAUO/evdv9xGr3\n7t3k4+PTLWoIWQqqkJCQx35XVVVFM2fOpKlTp1JlZaUcw7AurqV5cPz4cfL29qbt27d36N0L676a\n58GdO3do6tSplJ2dbXonun37doXTMalu3bpFvr6+9OWXX7bapqW1oKCggIKCgui1114z+xyp8vJy\nCgoKoq1bt3Y6L7OMrVu3UnBwMFVUVLTapnkePHjwgJ599lk6cuQI+fn5EQCKiYkxe6wvvviCfH19\nO/RxohJkKah+fm77pUuXaMiQIbR06VI+rN+D/HQeiKJIW7ZsIW9vbzpx4oSCqZi1AaCmpiYaN24c\nqVQqUzGVl5endDQmUVNTE02ePJk2btzYZrufvyYcPnyYNBoNffjhhx0e8+bNm+Tp6Uk5OTkd7sss\n48KFC6TRaOjWrVtttmueB0uWLCFHR0fTWtDRI5tERO+++y5FRkZSU1NTpzJbg+wFVVpaGnl6elJy\ncrIcm2bdSPM8qK+vp/nz59PIkSPp5s2byoZiVgeA1qxZQ87OzqYFdNCgQfTgwQOlozGJzH1Ra14L\nRFGkjRs3kq+vr6STzD/77DMKDg6murq6Tm+DyaO2tpaCg4Npz5497bYFQAcPHiQXFxfTWmBvb0/F\nxcUdHtfcYl5JApH0K6oJggCj0Yh169Zh9+7dSE1NxdixY6VulnUzgiCgtLQUc+bMgb+/Pz7++GP0\n6tVL6VjMygRBgIODAxobG2FnZwcHBwfExcUhKSkJDg4OSsdjnVRUVISnnnoKFy5cwIABA9psKwgC\namtr8fLLL+P69ev4+uuv8eSTT3Z6bCLCb37zG4SEhCA+Pr7T22HSJSQk4PLly9i3bx8EQWizrSAI\n6NWrF2prawEAzs7OGDduHD7//HP4+Ph0eOzi4mKMHj3arDmoBNkKqtmzZ6OiogKpqanw9fWVIxvr\nZgRBQL9+/bB48WK8/fbb7f6xMdtz+/Zt+Pn5AXi4eC5cuBBvv/02+vXrp3AyJtWKFSsgiiLef//9\ndtsKgoCwsDAMHz4cu3btgouLi+Txr1y5gqlTp+LWrVtwdnaWvD3WcQaDAf7+/vjuu+8QHBzcZtvG\nxkY4OjoCABwcHBAVFYWEhASMGDFCUobly5fD3t4e7733nqTtWITUQ1yiKBIAeuWVV8hgMEjdHOum\nTp06RQAoPT1d6ShMQf7+/gSA/vSnP9Hdu3eVjsNkotfrycPDw6yP8MvLywkAbd68WfYvosyaNYsv\nCKygjz76iJ5++mmz2j7zzDOmk887+i3AthQWFpKHhwfp9XrZtikX2Y5QiaLIRyR6MJ1OB3d3d74n\nVw+Xnp6O6Ohongc2Jjk5GWlpaUhLS2u3bVNTExwcHCwyBw4dOoQNGzbg+++/l33brH3jx4/HqlWr\n8Otf/7rdtv/9738xbtw4i8yDZ599FnPmzMH8+fNl37YUst0sh4upns3NzU3pCKwLiIqKUjoCs4Cs\nrCxMmzbNrLb29pa7ReyUKVOQm5uLhoYGi43BWtbQ0IC8vDxERkaa1T4iIsJiWaZNm4asrCyLbb+z\nuuHdBxljjFnT2bNnER4ernQM9OrVC0OHDkV+fr7SUXqcvLw8BAQEdIkvGoWHh+Ps2bNKx3gMF1SM\nMcZaZTAYUFBQgFGjRikdBQAwZswYnD9/XukYPc758+cxZswYpWMAAEaNGoWrV6/CYDAoHeURXFAx\nxhhrlU6nQ58+feDk5KR0FACAj48PKisrlY7R41RWVnbqUgeW4OzsDLVajZqaGqWjPKLNgkoQBLN+\nmG0aPny42XOA54Ht4nnQszU2NqKqqqrLzAFHR0c8ePDAomOwxzU2NmLXrl08D9rQZkFFD6+k3u4P\ns02XLl0yew7wPLBdPA96NicnJ7i5uXWZOdDQ0MDXoVKAk5MT/vCHP3SZeWAwGLrcPOCP/BhjjLXK\n3d0ddXV10Ov1SkcB8PCK7XzxaOvz9fVFUVGR0jEAAHq9HvX19V3u2+VcUDHGGGuVg4MDRo4ciQsX\nLigdBcDDbxzyrc2sb+zYsV3mm3XZ2dkYNWpUl7uVFRdUjDHG2hQeHo4zZ84oHQNVVVWoqKho97Yn\nTH5arRbl5eWorq5WOgrOnDnTJS7j8XOSC6rmz0m72slhzLoyMjKUjsC6gCNHjigdgVnA5MmTcejQ\nIbPalpWVAQBEUZQ9x5EjRzB+/HjY2dnJvm3WNjs7O4wfPx6HDx82q70lj2YdPnwYkyZNstj2O0u2\nI1TTp0/Hjz/+KNfmWDfTvO/VarVFFlLWPTR/LHTx4kWFkzA5RUdH48aNG8jNzW23bfNVzO3s7PC/\n//1PtgxEhK1bt+L111+XbZusY1577TVs27bNrBPOKyoqAAAHDx6UNUNOTg4KCwsRHR0t63blILmg\nav5qZGRkJMaMGdNlPmdn1jVv3jwAwP3792FnZ2d6l8p6lpUrVwIApk6ditTUVIXTMLk4ODhg8eLF\n2Lp1a7ttBw8ebPpvLy8vfPvtt7JkyMrKQnV1Nd/eSEFRUVGoqqrC6dOn2237zDPPAHhYhK1bt062\nb/1t3boVixcv7nLnTwEApN9fmah5MykpKeTp6Umff/65HJtl3QwAKi4uJgAEgFJTU5WOxBQAgM6f\nP0/9+/en1atXk9FoVDoSk0FlZSVpNBrKzs5uty0AamxsJHt7ewJAy5YtkzR2U1MTTZgwgf7+979L\n2g6T7m9/+xtNnDiRmpqa2m0LgMrKyig8PJxiY2NJr9dLGjs7O5s0Gg3dvXtX0nYsRdaCiogoLy+P\nBg0aRCtWrDDrH5zZjuZ5YDQaqXfv3gSAFi5cqHAqZm3N86CiooImTpxI0dHRVFNTo3AqJoe9e/dS\nUFAQ1dbWttnup68JM2bMIAAUGBjY6XHXr19Pv/zlL7k47wKamppoypQptGHDhnbbNs+D+vp6euml\nl2jEiBFUWFjYqXFra2spMDCwSx+wkb2gInr4Tmbq1Kk0a9YsqqqqkmMI1g38fB7ExMQQAOrfvz+J\nokjJyclUXV2tUDpmLT+dBw0NDbRo0SIKDg6mgoICBVMxufz2t7+lRYsWtdnm52vBtm3bTEeua2pq\n6NChQ3Tt2jWzxjtz5gx5eXlRcXFxpzMzeRUXF5NGo6EzZ8602e6n80AURdq+fTt5e3vT8ePHOzzm\nokWL6IUXXuhwP2uySEFFRNTY2EhLly6loUOH0uXLl+UYhnVxLc2DpKQk00KqUqlo5syZJIqiAumY\ntbQ0D3bt2kUajYaOHDmiQCImp+rqatJqtZSQkNBqm5bmQF5enmktaH6j1d5HQHl5eeTj40MHDhyQ\nnJvJKy0tjXx8fCg/P7/VNi3NgxMnTpC3tzdt2bLF7NeCd955h7RaLel0uk7ntQZZCqqQkJBWH0tO\nTiZPT09KS0uTYyjWhbU2D1auXGlaRF1dXWnTpk1WTsasqbV5cOrUKfL19aWNGzdyUd3NVVRUkFar\npaVLl7b4MVxrcyAjI8O0Fjg6OtKcOXNanQsnT54kLy8v2rdvn6zZmXy+/PJL8vLyolOnTrX4eGvz\n4ObNmzRy5EiaP38+1dfXt7p9o9FIS5cupZCQEKqoqJAlsyXJUlC158yZM/Tkk09SQkICfwbeA40f\nP55cXFxMC6mTk1O7h4qZbSouLqannnqKnn/++XbPw2FdW1VVFUVGRlJkZCRdvXrVrD7Lli0jJyen\nR4qqXbt2PdLGYDBQfHw8aTQaOnbsmCWiMxkdPXqUNBoNxcfHk8FgMLvf/fv3ae7cuTR27FgqLS19\n7PGrV6+a5ld3OXXIKgUVEdHt27cpIiKCZs+eTffu3bPWsKwLMBqN9M0339Dw4cNNi6lare42fyRM\nXnV1dfS73/2OwsLC6NatW0rHYRI0NTXRtm3byMPDgxISEswqks+ePUszZswwrQUqlYpyc3NJFEXK\nyMigwMBAmj17NpWUlFjhGTA5lJSUUExMDAUFBdGJEyfMPgItiiJt2LCB+vXrR1lZWUT08OTzhIQE\n8vDwoG3btnWrL7dZraAievjO4+WXX6aQkBD+Y+mBRFGk7777jkJDQwkADRw4UOlITCGiKNKmTZvI\nx8eHTp8+rXQcJlFxcTHFxsZS37596fXXX6cLFy602+fKlSsUHR1NAMje3p60Wi0NGTKEvvrqKysk\nZpbw1Vdf0ZAhQ2jkyJG0fft2sy9vkJ6eTm5ubjRt2jRyd3en2NjYbvklBIFIpqttmYmI8MILLyAi\nIgJvvPGGNYdmXci///1vTJ8+HXq9Hq6urkrHYQp5//33kZ+fj08++UTpKEwGJSUlSE5Oxj/+8Q8A\nQFhYGEJDQxESEgK1Wg2VSgWDwYCioiLk5uYiNzcXBQUFaGxsRHx8PN566y2oVHyL2e5MFEX85z//\nQVJSEtLT0xEQEIDQ0FCEhobC398fzs7OEEURer0ely9fRm5uLnJycmAwGCCKIrKzs9G/f3+ln0an\nWL2gAoAlS5Zg8ODBWLJkibWHZl2Ii4sLqqqq4OLionQUppBPP/0Ux48fx6effqp0FCYjURRx48YN\n5OTkIDc3Fz/88APq6upgNBrh5OQEPz8/hIWFISwsDCNGjMCsWbOwceNGTJw4UenoTEa1tbW4ePEi\ncnJykJOTg7KyMjQ0NMDOzg6urq4IDg5GaGgowsLC0NDQgLi4OFy5ckXp2J1mr3QAxhhjtkWlUiEg\nIAABAQGIi4tTOg5TSK9evRAREYGIiIh223bnQqoZH1tljDHGGJOICyrGGGOMMYkUKagcHBy65p2i\nmVU5OzvzCag9HK8FDOB5wGxjDihyUrpOp4OjoyN/u6uHu337Nvr166d0DKYgg8GA2tpaeHh4KB2F\nKai8vBze3t78BqsHIyKUl5d369cERQoqxhhjjDFbwm8HGGOMMcYk4oKKMcYYY0wiLqgYY4wxxiTi\ngooxxhhjTCIuqBhjjDHGJOKCijHGGGNMov8De8VQun4QrzsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d24c358>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fet = [\n",
      "e.NFET,\n",
      "e.PFET\n",
      "]\n",
      "d = schem.Drawing(fontsize=12)\n",
      "d.add( e.NFET, label='NFET', lblloc='top' )\n",
      "d.add( e.PFET, label='PFET', lblloc='top', xy=[3,0]  )\n",
      "d.draw()\n",
      "d.save('fet.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAKUAAABOCAYAAABBqNabAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+Q4VAOyoAsyCGAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0dcef0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(fontsize=12)\n",
      "P = d.add( e.POT, botlabel='POT' )\n",
      "d.add( e.LINE, xy=P.tap, d='up', l=.5 )\n",
      "P.add_label( 'tap' )\n",
      "d.draw()\n",
      "d.save('pot.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAHQAAABPCAYAAAA6J+FJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0e4860>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(fontsize=12)\n",
      "d.add( e.LINE, d='right', l=1 )\n",
      "d.add( e.DOT_OPEN )\n",
      "d.add( e.LABEL, d='down', label=['+','LABEL','-'] )\n",
      "d.add( e.DOT_OPEN )\n",
      "d.add( e.LINE, d='left', l=1 )\n",
      "d.draw()\n",
      "d.save('label.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAABzCAYAAADkHj42AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d1f82e8>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Connecting elements\n",
      "\n",
      "d = schem.Drawing(fontsize=12)\n",
      "d.add( e.LINE, l=4, label='LINE' )\n",
      "d.add( e.DOT, label='DOT' )\n",
      "d.add( e.LINE, l=2 )\n",
      "d.add( e.DOT_OPEN, label='DOT_OPEN' )\n",
      "d.draw()\n",
      "d.save('connectors.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPAAAAAjCAYAAACq/4cTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d205da0>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(fontsize=12)\n",
      "R1 = d.add( e.RES )\n",
      "d.labelI( R1, '10mA' )\n",
      "d.add( e.LABEL, d='up', l=.5 )  # To bump the margins...\n",
      "d.draw()\n",
      "d.save('labeli.png')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAHQAAAA1CAYAAACQns/uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmhmcoWYGZ6iZwRlqZvwPss9X2f2v2WcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d261320>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Test loop current label\n",
      "d = schem.Drawing()\n",
      "R1 = d.add( e.RES )\n",
      "C1 = d.add( e.CAP, d='down' )\n",
      "D1 = d.add( e.DIODE_F, d='left')\n",
      "L1 = d.add( e.INDUCTOR, d='up' )\n",
      "d.loopI( [R1, C1, D1, L1], d='cw', label='$I_1$' )\n",
      "d.draw()\n",
      "d.save('loopi.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAIEAAAB+CAYAAAADZHKcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d236780>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing()\n",
      "d.add( e.RES, label='100K$\\Omega$' )\n",
      "d.add( e.CAP, d='down', botlabel='0.1$\\mu$F' )\n",
      "d.draw()\n",
      "d.save( 'testschematic.eps' )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAKgAAACJCAYAAAC4u3MnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d0e4e10>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(fontsize=12)\n",
      "d.add( e.RES, label='Rin' )\n",
      "op = d.add( e.OPAMP, anchor='in2' )\n",
      "d.add( e.RES, xy=op.out, label='Ro' )\n",
      "d.draw()\n",
      "d.save('opamp_anchor.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAASQAAABeCAYAAACKN2n/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQniDAokQwhsUSIQQ3vgv+JdSD29BHy0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d285dd8>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(fontsize=12)\n",
      "D1 = d.add(e.DIODE, label='D1' )\n",
      "D1.add_label( '1N4001', loc='bot' )\n",
      "D1.add_label( 'a', loc='lft' )\n",
      "D1.add_label( 'b', loc='rgt' )\n",
      "d.draw()\n",
      "d.save('label_positions.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAIEAAAA9CAYAAACKqApeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d058978>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}