{
 "metadata": {
  "name": "",
  "signature": "sha256:d851dd40923e50ed5fff9bed4a1e6f9442e5a1988b5340af4df0205fce225978"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append( '../..' )\n",
      "\n",
      "import SchemDraw as schem\n",
      "from SchemDraw import elements as e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(unit=2)  # unit=2 makes elements with a bit shorter leads\n",
      "d.push()\n",
      "R1 = d.add( e.RES, d='down', label='20$\\Omega$')\n",
      "V1 = d.add( e.SOURCE_V, d='down', reverse=True, label='120V')\n",
      "d.add( e.LINE, d='right', l=3 )\n",
      "d.add( e.DOT )\n",
      "d.pop()\n",
      "\n",
      "d.add( e.LINE, d='right', l=3)\n",
      "d.add( e.DOT )\n",
      "d.add( e.SOURCE_V, d='down', label='60V', reverse=True )\n",
      "d.add( e.RES, label='5$\\Omega$')\n",
      "d.add( e.DOT )\n",
      "d.add( e.LINE, d='right', l=3)\n",
      "d.add( e.SOURCE_I, d='up', label='36A' )\n",
      "d.add( e.RES, label='10$\\Omega$')\n",
      "d.add( e.DOT )\n",
      "d.add( e.LINE, d='left', l=3, move_cur=False )\n",
      "\n",
      "d.add( e.LINE, d='right', l=3)\n",
      "d.add( e.DOT )\n",
      "R6 = d.add( e.RES, d='down', toy=V1.start, label='6$\\Omega$' )\n",
      "d.add( e.DOT )\n",
      "d.add( e.LINE, d='left', l=3, move_cur=False)\n",
      "d.add( e.RES, d='right', xy=R6.start, label='1.6$\\Omega$')\n",
      "d.add( e.DOT, label='a')\n",
      "d.add( e.LINE, d='right', xy=R6.end)\n",
      "d.add( e.DOT, label='b')\n",
      "\n",
      "d.draw(showframe=False)\n",
      "d.save('ex01.eps')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VFbm4uNCZM2fK/LPPysqif/75h+rWrUsTJ06kFy9eyCnSyi0/P5+6dOlCAKhH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\nmErjRMgYY0yl/R8OBzc0qnEKfwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4a4860>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing()\n",
      "I1 = d.add( e.SOURCE_I, reverse=True, label=r'$5\\angle 0^{\\circ}$A' )\n",
      "d.add( e.DOT )\n",
      "d.push()\n",
      "d.add( e.CAP, d='right', label=r'$-j3\\Omega$' )\n",
      "d.add( e.DOT )\n",
      "d.push()\n",
      "d.add( e.INDUCTOR, d='down', label=r'$j2\\Omega$' )\n",
      "d.add( e.DOT )\n",
      "d.pop()\n",
      "d.add( e.RES, d='right', label=r'$5\\Omega$' )\n",
      "d.add( e.DOT )\n",
      "V1 = d.add( e.SOURCE_V, d='down', reverse=True, botlabel=r'$5\\angle -90^{\\circ}$V' )\n",
      "d.add( e.LINE, d='left', tox=I1.start )\n",
      "d.pop()\n",
      "d.add( e.LINE, d='up', l=d.unit*.8 )\n",
      "L1 = d.add( e.INDUCTOR, d='right', label=r'$j3\\Omega$', tox=V1.start )\n",
      "d.add( e.LINE, d='down', l=d.unit*.8 )\n",
      "d.labelI( L1, '$i_g$', top=False )\n",
      "\n",
      "d.draw()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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58vPzkZeXh/Pnz8PJyQkyWdVfNRcXFwDAkSNH6lXHs2fPUFJSgrfeeqte47Q0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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a784358>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing()\n",
      "V1 = d.add( e.SOURCE_V, label='5V' )\n",
      "d.add( e.LINE, d='right', l=d.unit*.75 )\n",
      "S1 = d.add( e.SWITCH_SPDT2_CLOSE, d='up', anchor='b', rgtlabel='$t=0$' )\n",
      "d.add( e.LINE, d='right', xy=S1.c,  l=d.unit*.75 )\n",
      "d.add( e.RES, d='down', label='$100\\Omega$', botlabel=['+','$v_o$','-'] )\n",
      "d.add( e.LINE, to=V1.start )\n",
      "d.add( e.CAP, xy=S1.a, d='down', toy=V1.start, label='1$\\mu$F' )\n",
      "d.add( e.DOT )\n",
      "d.draw()\n",
      "d.save('cap-charge.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a781be0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Inverting opamp\n",
      "d = schem.Drawing(inches_per_unit=.5)\n",
      "op = d.add( e.OPAMP )\n",
      "d.add( e.LINE, d='left', xy=op.in2, l=d.unit/4 )\n",
      "d.add( e.LINE, d='down', l=d.unit/5 )\n",
      "d.add( e.GND )\n",
      "d.add( e.LINE, d='left', xy=op.in1, l=d.unit/6 )\n",
      "d.add( e.DOT )\n",
      "d.push()\n",
      "Rin = d.add( e.RES, d='left', xy=op.in1-[d.unit/5,0], botlabel='$R_{in}$', lftlabel='$v_{in}$' )\n",
      "d.pop()\n",
      "d.add( e.LINE, d='up', l=d.unit/2 )\n",
      "Rf = d.add( e.RES,  d='right', l=d.unit*1, label='$R_f$' )\n",
      "d.add( e.LINE, d='down', toy=op.out )\n",
      "d.add( e.DOT )\n",
      "d.add( e.LINE, d='left', tox=op.out )\n",
      "d.add( e.LINE, d='right', l=d.unit/4, rgtlabel='$v_{o}$' )\n",
      "\n",
      "d.draw()\n",
      "d.save('inv_opamp.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a625c18>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing()\n",
      "o = d.add( e.OPAMP )\n",
      "d.add( e.LINE, xy=o.out, l=.75 )\n",
      "d.add( e.LINE, xy=o.in1, d='left', l=.75 )\n",
      "d.add( e.LINE, d='up', l=1.5)\n",
      "d.add( e.DOT )\n",
      "R1 = d.add( e.RES, d='left', label='$R_1$')\n",
      "d.add( e.GND )\n",
      "Rf = d.add( e.RES, d='right', xy=R1.start, tox=o.out+.5, label='$R_f$')\n",
      "d.add( e.LINE, d='down', toy=o.out )\n",
      "dot = d.add( e.DOT )\n",
      "d.add( e.LINE, d='left', xy=o.in2, l=.75 )\n",
      "d.add( e.DOT )\n",
      "R3 = d.add( e.RES, d='down', label='$R_3$' )\n",
      "d.add( e.DOT )\n",
      "d.add( e.GND )\n",
      "R2 = d.add( e.RES, d='left', xy=R3.start, label='$R_2$' )\n",
      "d.add( e.SOURCE_V, d='down', reverse=True, label='$v_{in}$' )\n",
      "d.add( e.LINE, d='right', tox=Rf.end )#tox=R3.end )\n",
      "d.add( e.LABEL, d='down', xy=dot.start, toy=R3.end, label=['+','$v_o$','-'] )\n",
      "\n",
      "d.draw()\n",
      "d.save('ex03.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UY0xYWr9SVqlUIiYmBidPnsSFCxdw4cIF3Lt3D0VFRQCAFi1aoHPnzujatSt6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\nKIwxwXCgMMYEw4HCGBPM/wH/dtId5NMRhQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a5c4f98>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing()\n",
      "d.add( e.GND )\n",
      "d.add( e.SOURCE_V, label='500mV')\n",
      "\n",
      "d.add( e.RES, d='right', label='20k$\\Omega$')\n",
      "Vin = d.add( e.DOT )\n",
      "d.add( e.LINE, l=.5)\n",
      "O1 = d.add( e.OPAMP, anchor='in1' )\n",
      "d.add( e.LINE, l=.75, d='left', xy=O1.in2 )\n",
      "d.add( e.GND )\n",
      "d.add( e.LINE,xy=Vin.start,d='up',l=2)\n",
      "d.add( e.RES,d='right',label='100k$\\Omega$')\n",
      "d.add( e.LINE,d='down',toy=O1.out)\n",
      "d.add( e.DOT )\n",
      "d.add( e.LINE,xy=O1.out,d='right',l=5)\n",
      "O2   = d.add( e.OPAMP, anchor='in2')\n",
      "Vin2 = d.add( e.LINE, l=.5, d='left', xy=O2.in1)\n",
      "d.add( e.DOT )\n",
      "d.add( e.RES,label='30k$\\Omega$')\n",
      "d.add( e.GND )\n",
      "d.add( e.LINE,xy=Vin2.end,d='up',l=1.5)\n",
      "d.add( e.RES,d='right',label='90k$\\Omega$')\n",
      "d.add( e.LINE,d='down',toy=O2.out)\n",
      "d.add( e.DOT )\n",
      "d.add( e.LINE, xy=O2.out,d='right',l=1, rgtlabel='$v_{out}$')\n",
      "\n",
      "d.draw()\n",
      "d.save('ex04.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LrbqeA2XhRGEiFfP29sauXbtQUFCAe/fuITo6GkOGDCky9s1JwOHh4WjdunWRX7C1a9fG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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a52ad68>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(inches_per_unit=.6)\n",
      "A  = d.add( e.DOT, label='a' )\n",
      "C1 = d.add( e.CAP, label='8nF' )\n",
      "C2 = d.add( e.CAP, label='18nF' )\n",
      "C3 = d.add( e.CAP, botlabel='8nF', d='down' )\n",
      "C4 = d.add( e.CAP, botlabel='32nF', d='left' )\n",
      "C5 = d.add( e.CAP, botlabel='40nF' )\n",
      "B  = d.add( e.DOT, label='b' )\n",
      "C6 = d.add( e.CAP, label='2.8nF', endpts=[C1.end,C5.start])\n",
      "C7 = d.add( e.CAP, endpts=[C2.end,C5.start])\n",
      "C7.add_label( '5.6nF', loc='center', ofst=[-.3,-.1], align=('right','bottom') )\n",
      "d.draw()\n",
      "d.save('cap-net.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RuHz5Mpqamnjt8fHxsLCwQFZWFhISEnDw4EHEx8crLC+TySCVStHe3i5/Ec2Q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\nBoMSEiHEYPwPvBWc/eYwJGUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4a5d68>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(unit=5)\n",
      "V1 = d.add( e.SOURCE_V, label='20V' )\n",
      "R1 = d.add( e.RES, d='right', label='400$\\Omega$' )\n",
      "d.add( e.DOT )\n",
      "d.push()\n",
      "R2 = d.add( e.RES, d='down' )\n",
      "R2.add_label( '100$\\Omega$', loc='center', ofst=[-.9,.05] )\n",
      "d.add( e.DOT )\n",
      "d.pop()\n",
      "L1 = d.add( e.LINE )\n",
      "I1 = d.add( e.SOURCE_I, d='down', botlabel='1A' )\n",
      "L2 = d.add( e.LINE, d='left', tox=V1.start )\n",
      "\n",
      "d.loopI( [R1,R2,L2,V1], 'I1', pad=1.25 )\n",
      "d.loopI( [R1,I1,L2,R2], 'I2', pad=1.25 )  # Use R1 as top element for both so they get the same height\n",
      "\n",
      "d.draw()\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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J/VpaWsjPz5d7HTUZDw8cOIDs7GyMHj0aAODt7Q01NbWqH1VV9Rzh69evyc7O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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4aae10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing(unit=2)\n",
      "R1 = d.add( e.RES, d='down', label='R1' )\n",
      "d.add( e.RES, label='R2' )\n",
      "d.add( e.RES, label='R3' )\n",
      "d.add( e.LINE, d='left' )\n",
      "d.add( e.SOURCE_V, d='up', label='40V', toy=R1.start )\n",
      "d.add( e.LINE, d='right' )\n",
      "d.draw()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GDKA1a9aQXC6ndevWmRzDtm3bbPo/qavExMTQnj17bJbP1N1NVlYWCYLQ7jOjP/3pTyST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\nyABTGYFhAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a48a5f8>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = schem.Drawing()\n",
      "Q1 = d.add( e.BJT_NPN_C, reverse=True, lftlabel='Q1' )\n",
      "Q2 = d.add( e.BJT_NPN_C, xy=[d.unit*2,0], label='Q2' )\n",
      "d.add( e.LINE, xy=Q1.collector, d='up', l=d.unit/2 )\n",
      "\n",
      "R1 = d.add( e.RES, d='up', label='R1', move_cur=False )\n",
      "d.add( e.DOT, lftlabel='V1' )\n",
      "d.add( e.RES, d='right', botlabel='R3', l=d.unit*.75 )\n",
      "d.add( e.DOT )\n",
      "d.push()\n",
      "d.add( e.LINE, d='up', l=d.unit/8 )\n",
      "d.add( e.DOT_OPEN, label='Set' )\n",
      "d.pop()\n",
      "d.add( e.LINE, to=Q2.base )\n",
      "\n",
      "d.add( e.LINE, xy=Q2.collector, d='up', l=d.unit/2 )\n",
      "d.add( e.DOT, rgtlabel='V2' )\n",
      "R2 = d.add( e.RES, d='up', botlabel='R2', move_cur=False )\n",
      "d.add( e.RES, d='left', botlabel='R4', l=d.unit*.75 )\n",
      "d.add( e.DOT )\n",
      "d.push()\n",
      "d.add( e.LINE, d='up', l=d.unit/8 )\n",
      "d.add( e.DOT_OPEN, label='Reset' )\n",
      "d.pop()\n",
      "d.add( e.LINE, to=Q1.base )\n",
      "\n",
      "d.add( e.LINE, xy=Q1.emitter, d='down', l=d.unit/4 )\n",
      "BOT = d.add( e.LINE, d='right', tox=Q2.emitter )\n",
      "d.add( e.LINE, to=Q2.emitter )\n",
      "d.add( e.DOT, xy=BOT.center )\n",
      "d.add( e.GND, xy=BOT.center )\n",
      "\n",
      "TOP = d.add( e.LINE, endpts=[R1.end,R2.end])\n",
      "d.add( e.DOT, xy=TOP.center )\n",
      "d.add( e.LINE, xy=TOP.center, d='up', l=d.unit/8, rgtlabel='Vcc' )\n",
      "d.draw()\n",
      "d.save('SR-Latch.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mGtesjFwup2bNmpU6zgwMDOijjz5S2r6qxznRfyP0WlhYqGW/E6k5uJYsWUIi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\n81xaWhrCw8Ph4+NT5emz3N3dce7cuSo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       "text": [
        "<matplotlib.figure.Figure at 0x10a47b358>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}