tests/examples/spice-sim-intro/spice-sim-intro.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Spicing-It-Up!-(Sorry)\" data-toc-modified-id=\"Spicing-It-Up!-(Sorry)-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Spicing It Up! (Sorry)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Why?\" data-toc-modified-id=\"Why?-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Why?</a></span></li><li><span><a href=\"#Installation\" data-toc-modified-id=\"Installation-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Installation</a></span></li><li><span><a href=\"#Examples\" data-toc-modified-id=\"Examples-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Examples</a></span><ul class=\"toc-item\"><li><span><a href=\"#Current-Through-a-Resistor\" data-toc-modified-id=\"Current-Through-a-Resistor-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Current Through a Resistor</a></span></li><li><span><a href=\"#Transient-Response-of-an-R-C-Filter\" data-toc-modified-id=\"Transient-Response-of-an-R-C-Filter-1.3.2\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>Transient Response of an R-C Filter</a></span></li><li><span><a href=\"#A-Voltage-Controlled-Voltage-Source\" data-toc-modified-id=\"A-Voltage-Controlled-Voltage-Source-1.3.3\"><span class=\"toc-item-num\">1.3.3&nbsp;&nbsp;</span>A Voltage-Controlled Voltage Source</a></span></li><li><span><a href=\"#A-Current-Controlled-Current-Source\" data-toc-modified-id=\"A-Current-Controlled-Current-Source-1.3.4\"><span class=\"toc-item-num\">1.3.4&nbsp;&nbsp;</span>A Current-Controlled Current Source</a></span></li><li><span><a href=\"#A-Transmission-Line\" data-toc-modified-id=\"A-Transmission-Line-1.3.5\"><span class=\"toc-item-num\">1.3.5&nbsp;&nbsp;</span>A Transmission Line</a></span></li><li><span><a href=\"#A-Transformer\" data-toc-modified-id=\"A-Transformer-1.3.6\"><span class=\"toc-item-num\">1.3.6&nbsp;&nbsp;</span>A Transformer</a></span></li><li><span><a href=\"#A-Transistor-Amplifier\" data-toc-modified-id=\"A-Transistor-Amplifier-1.3.7\"><span class=\"toc-item-num\">1.3.7&nbsp;&nbsp;</span>A Transistor Amplifier</a></span></li><li><span><a href=\"#XSPICE-Parts\" data-toc-modified-id=\"XSPICE-Parts-1.3.8\"><span class=\"toc-item-num\">1.3.8&nbsp;&nbsp;</span>XSPICE Parts</a></span></li><li><span><a href=\"#A-Hierarchical-Circuit\" data-toc-modified-id=\"A-Hierarchical-Circuit-1.3.9\"><span class=\"toc-item-num\">1.3.9&nbsp;&nbsp;</span>A Hierarchical Circuit</a></span></li><li><span><a href=\"#Using-SPICE-Subcircuits\" data-toc-modified-id=\"Using-SPICE-Subcircuits-1.3.10\"><span class=\"toc-item-num\">1.3.10&nbsp;&nbsp;</span>Using SPICE Subcircuits</a></span></li></ul></li><li><span><a href=\"#The-Details\" data-toc-modified-id=\"The-Details-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>The Details</a></span><ul class=\"toc-item\"><li><span><a href=\"#Units\" data-toc-modified-id=\"Units-1.4.1\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>Units</a></span></li><li><span><a href=\"#Available-Parts\" data-toc-modified-id=\"Available-Parts-1.4.2\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>Available Parts</a></span></li><li><span><a href=\"#Startup\" data-toc-modified-id=\"Startup-1.4.3\"><span class=\"toc-item-num\">1.4.3&nbsp;&nbsp;</span>Startup</a></span></li><li><span><a href=\"#Miscellaneous\" data-toc-modified-id=\"Miscellaneous-1.4.4\"><span class=\"toc-item-num\">1.4.4&nbsp;&nbsp;</span>Miscellaneous</a></span></li></ul></li><li><span><a href=\"#Future-Work\" data-toc-modified-id=\"Future-Work-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Future Work</a></span></li><li><span><a href=\"#Acknowledgements\" data-toc-modified-id=\"Acknowledgements-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Acknowledgements</a></span></li></ul></li></ul></div>"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spicing It Up! (Sorry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[SKiDL](https://xesscorp.github.io/skidl/docs/_site/index.html) is a Python package for describing the interconnection of electronic devices using text (instead of schematics). [PySpice](https://pyspice.fabrice-salvaire.fr/) is an interface for controlling an external SPICE circuit simulator from Python. This document demonstrates how circuits described using SKiDL can be simulated under a variety of conditions using PySpice with the results displayed in an easily-shared [Jupyter](http://jupyter.org/) notebook.\n",
    "\n",
    "![SKiDL + PyCircuit + ngspice + matplotlib data flow.](dataflow.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are existing SPICE simulators that analyze schematics created by CAD packages like KiCad.\n",
    "There are also versions with their own GUI like LTSpice.\n",
    "What advantages does a combination of SKiDL, PySpice and ngspice offer?\n",
    "\n",
    "* The circuit description is completely textual, so it's easy to share with others who may not have a schematic editor or GUI.\n",
    "* It can be archived in a Git repository for the purpose of tracking versions as modifications are made.\n",
    "* The documentation of the circuitry is embedded with the circuitry, so it's more likely to be kept current.\n",
    "* It makes the entire Python ecosystem of tools available for optimizing, analyzing, and visualizing the behavior of a circuit under a variety of conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook assumes you're using ngspice version 30.\n",
    "To install ngspice for linux, do:\n",
    "\n",
    "    sudo apt-get update\n",
    "    sudo apt-get install ngspice\n",
    "    \n",
    "For Windows:\n",
    "\n",
    "1. [Download ngspice-30_dll_64.zip](https://sourceforge.net/projects/ngspice/files/ng-spice-rework/30/ngspice-30_dll_64.zip/download)\n",
    "1. Unpack the zip file into `C:\\Program Files`. The top-level folder should be named `Spice64_dll` so PySpice can find it.\n",
    "1. Change subdirectory `dll-vs` to `bin_dll`.\n",
    "1. Make sure to run 64-bit Python. Otherwise, it will be unable to run the 64-bit DLLs.\n",
    "    \n",
    "Next, for either OS, install SKiDL:\n",
    "\n",
    "    pip install skidl\n",
    "    \n",
    "After that, you'll have to manually install PySpice (***it must be version 1.3.2 or higher***):\n",
    "\n",
    "    pip install \"PySpice>=1.3.2\"\n",
    "    \n",
    "Finally, place an `spinit` file in the same folder as the notebook you're trying to run.\n",
    "This contains the ngspice initialization commands as discussed in the [ngspice manual](https://sourceforge.net/projects/ngspice/files/ng-spice-rework/30/ngspice-30-manual.pdf/download).\n",
    "Typically, I just enable the use of Pspice models and set the number of processing threads as follows:\n",
    "\n",
    "    set ngbehavior=ps\n",
    "    set num_thread=4\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following examples demonstrate some of the ways of using SKiDL and PySpice to simulate electronics. While shown using the Jupyter notebook, these examples will also work by placing the Python code into a file and executing it with a Python interpreter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code snippet is needed at the beginning of every example.\n",
    "It loads the `matplotlib` package for generating graphs, and SKiDL + PySpice packages for describing and simulating circuitry."
   ]
  },
  {
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     "start_time": "2021-04-29T13:32:59.546445Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: KICAD_SYMBOL_DIR environment variable is missing, so the default KiCad symbol libraries won't be searched.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'kicad': ['.'], 'skidl': ['.', '/media/devb/Main/xesscorp/KiCad/tools/skidl/skidl/libs'], 'spice': ['.']}\n"
     ]
    }
   ],
   "source": [
    "from skidl import *\n",
    "print(lib_search_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:00.206520Z",
     "start_time": "2021-04-29T13:32:59.947166Z"
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     "text": [
      "{'kicad': ['.'], 'skidl': ['.', '/media/devb/Main/xesscorp/KiCad/tools/skidl/skidl/libs'], 'spice': ['.']}\n"
     ]
    }
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   "source": [
    "# Load the package for drawing graphs.\n",
    "import matplotlib.pyplot as plt\n",
    "# Omit the following line if you're not using a Jupyter notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the SKiDL + PySpice packages and initialize them for doing circuit simulations.\n",
    "from skidl.pyspice import *\n",
    "print(lib_search_paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Current Through a Resistor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example connects a 1 K$\\Omega$ resistor to a voltage source whose value is ramped from 0 to 1 volts.\n",
    "The current through the resistor is plotted versus the applied voltage."
   ]
  },
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     "text": [
      "   V    I (mA)\n",
      "===============\n",
      "  0.00  -0.00\n",
      "  0.10   0.10\n",
      "  0.20   0.20\n",
      "  0.30   0.30\n",
      "  0.40   0.40\n",
      "  0.50   0.50\n",
      "  0.60   0.60\n",
      "  0.70   0.70\n",
      "  0.80   0.80\n",
      "  0.90   0.90\n",
      "  1.00   1.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # This will clear any previously defined circuitry.\n",
    "\n",
    "# Create and interconnect the components.\n",
    "vs = V(ref='VS', dc_value = 1 @ u_V)  # Create a voltage source named \"VS\" with an initial value of 1 volt.\n",
    "r1 = R(value = 1 @ u_kOhm)            # Create a 1 Kohm resistor.\n",
    "vs['p'] += r1[1]       # Connect one end of the resistor to the positive terminal of the voltage source.\n",
    "gnd += vs['n'], r1[2]  # Connect the other end of the resistor and the negative terminal of the source to ground.\n",
    "\n",
    "# Simulate the circuit.\n",
    "circ = generate_netlist()              # Translate the SKiDL code into a PyCircuit Circuit object.\n",
    "sim = circ.simulator()                 # Create a simulator for the Circuit object.\n",
    "dc_vals = sim.dc(VS=slice(0, 1, 0.1))  # Run a DC simulation where the voltage ramps from 0 to 1V by 0.1V increments.\n",
    "\n",
    "# Get the voltage applied to the resistor and the current coming out of the voltage source.\n",
    "voltage = dc_vals[node(vs['p'])]       # Get the voltage applied by the positive terminal of the source.\n",
    "current = -dc_vals['VS']               # Get the current coming out of the positive terminal of the voltage source.\n",
    "\n",
    "# Print a table showing the current through the resistor for the various applied voltages.\n",
    "print('{:^7s}{:^7s}'.format('V', ' I (mA)'))\n",
    "print('='*15)\n",
    "for v, i in zip(voltage.as_ndarray(), current.as_ndarray()*1000):\n",
    "    print('{:6.2f} {:6.2f}'.format(v, i))\n",
    "\n",
    "# Create a plot of the current (Y coord) versus the applied voltage (X coord).\n",
    "figure = plt.figure(1)\n",
    "plt.title('Resistor Current vs. Applied Voltage')\n",
    "plt.xlabel('Voltage (V)')\n",
    "plt.ylabel('Current (mA)')\n",
    "plt.plot(voltage, current*1000) # Plot X=voltage and Y=current (in milliamps, so multiply it by 1000).\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transient Response of an R-C Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows the time-varying voltage of a capacitor charged through a resistor by a pulsed voltage source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:00.694516Z",
     "start_time": "2021-04-29T13:33:00.493154Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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V7s/xkg6O4g1cdjd16JlSFCUxksKwUBRFURSlYZAUhoWr+v7HYyy0jkUQOOubURlX4igrxFWvkODFuu1uqnUsFMUTksKwUBRFURSlYZAchoXreIMU6hXiNBPGVR0LR2N22DbDWd8Mp91NRbubKooXJIdhoSiKoihKgyApDAt3e+/By3Qt38VKMozBOPVMucJlXIkrXP0/a4yFoiROUhgWrnEZ5OaKVCwk5KpAlivcBCUritLYSQrDwtY3cFnhwA1OxuxwyK7iSly7ptzUaEk9XFUbVZRkIykMC9e4KSTk9haYijfgwJuQOf6UnTXXUxSlUZMUhoXGWAQoM3iRlbjqIpGKq3fXg3YS4+Go2qiiJBtJYVikIq5vfyl5/025GIvUkaooinckhWFh6xsETyquZF1mC7iu3eGKVMv+sfLdyFSzRlESx3fDQkTSRWS+iLzhtyxXpOZetHMFAiflRpyS32tFURIlCI/FT4DlfgqwMRYuKvVptkCwuOpu6nr17q7aqCuc1e5Qw0ZREsZXw0JEegHnAf/0U45rUjHfPxVXllrHIgiZiqI0dvz2WNwN/AKoqO0AEblOROaIyJyioqK4hNg6FsHjOsYi1Uo6aIxFgDKDF1lNvhtvnBo2ipI4vhkWIjIF2GmMmVvXccaYB40xo4wxozp37uyXOr7iZGWpdSwCR+tYJKdMRVG8xU+PxQRgqohsAJ4FzhCRJ/0QZMD9EssBqZYtkKpzTqp5plyidSwUJXF8MyyMMTcbY3oZY7KBbwIfGmMu90ueCyrbabtY2QUv8kT5jhRw5SKHVIyxcCEzVAYtBbe8FCVZSIo6FjiqY+GaVFvJpupqMtU8Uy5JzW+YonhLRhBCjDEfAR8FIStIwjffFAyxcDbJu1zJBj1i15Ocmx449jEVg7EVJVlICo+FwTivKeGCVKtj4XqidUUq1rFwhWuDXVGSgaQwLFxxPMbCRb5/amaFuCyQFfR1dr3149IT56xAlqIoCZMUhoWrXiGucTJmx3UsUpFUrGPhCtcGu6IkA0lhWLgmNWMs3Mh11U4bHGSFBCuupnwn2U5hoam1zacoyURSGBauKjK6JtWyBVxvDaQUqfgPhXuDXVGSgaQwLFxRee9NwQqFqeQydjXFur7GLnAZY6EuC0XxhqQwLAwmRfPuNVsgCFoe2sTPM56HsmNO5LvY/jlp2/O8nHkrlB4NXLYrvrb9r9x97NbUtOgUxUOSwrBwRfiGn5JdIB1VGw18jjWG8XNv4L8yXqHZpo8Dlh2suEqKd3DGujsZnrYGs2WOExUCN6W2zOOUPS8xqmIx7C8MWrqiJBVJYVhojEWAMo0hTzalzqpu9Xu0O7AKgMztdfbT843AL/NHdxz/vfDLoKUHjzHw3v87/nfhbHe6KEoSkBSGhStc9ZCwMt1M7F1XPM47TX9Fxw2vBy/cBBw8Wl4G/7mV4pZ9WFbRh6YBGxZO4lh2Lod5jzO/+6Wsr+gKm4OdZI/HWAR4nVe+BRs/45VuP+YITWFzChhTiuIjSWFYpMjauQaBr2SP7qfXgr8A0GZnCtx85z8BRStYkv9TZlXkkbljvjU2AiZQz9R/boXM1szufS3zzEA7ySazd6q81I65Uw4zO1zICumvHgtFSZCkMCxc4aqHhDM+v5+M0mL2mla0KZoXuPhAYyyOHYTpd0DWyWzrfhbzKwaSVnoYdi4LSAEH8/n6T2D1e3DqjRxt0o75FQORQ0Wwd0NgKlR2Nw1K4LzHYPcaOPt/MJLB4rQ82L4ISo8EpYGiJB1JYVjYGIvUC7IIdMiH98Dn97Gnz2QeKz+HFvtXwdEDASoQMF/+Ew7thLN/ByJ29Q5QOCtwVQK5zMbAh7dD6x4w5jpEYF5FaMzJujVQehQ+uQuyToaccwFYIjlQUQZbF7jVTVEaMUlhWLjieA+J4GUHLnPG3VBykC0n/ZS5FTmIqYAtAcccGBPMJHvsIMy8B/qfCb3HIgKbTSfKWnQJdJIN9BKvmw6FX8ApP4MmzQBYabIwTVoGujUQaB2LeY9D8VaY9N8ggggsScuxrwUcW6IoyUSSGBYBTTgNjMACGYu3w6wHYcglHGufw4KKAfYTT9a96NkPwuHddsKpRDjWbaSTMfvujTPGbvu06QUjrrQygXLSMT1HJOckW3oEPr0L+kyAvqdWPr1X2kH7vsn73VaUAEgSw8INx2Mskry76Wf/B+UlcPqvACimBYfa5QS+LRBIjMWxYuutGHA29BoFHDfgjnYdCXvXw8Ein5WwBJb5s+Z964k59eeQ0fREHXqOhu1LoORQIKpUdgrx+zrPfRQObq/0VoC9zsYAWWOSP2hVUXwkaQyLFAyxCGbMh3bB3Mdg6KXQsX+lzAOdRtibb0VFAEoEyOwH4cheOP3mGi8d7TbS/hJwzIGv1znsrWjbG066vIZMkzUGTDlsne+jEgFTchg+/QtknwLZE2u+3ms0HNwB+zYFr5uiJAFJYVg467TpUH5gMr94AMqOwsSfnvD0gU4j4NgBKFoRkCIBBOmWHIKZf4OB50CvkZVPh0Ue6zIE0poEtjUQyCVe9xFsnWdjKzIya7xc0WO0/SWgrYGwl8bXbb4FT9nA3JAHLoxIyBOYNcY+kaxBq4riM0lhWIB6LHzh6AGY/RDkT4HOOWGpAOzvPNz+WfiFz0oEyPyn4MgeOOXnEV82Gc2h+9DA9999vcwz/gqtusKwy06UGf5ytegAHQckzyRbXgYz77VeiT4TIh/TZRAEHLSqKMlEUhgWznZCHdaxCETmnIfh2H6Y+LMaLx1p2RtadAo2Y8DPIN3yMvj8XsgaC71PjizfAL3GwJZ5trCSz/juldq6wGaDnPyDykyQGjpg7JgLZwfiJquU4NeFXv4q7NsIE26oYZmLhIaYngHJGrSqKAGQ4VoBr0jF7qa+jrn0KHxxP/Q73d5kwzKlyi9ZY53UdfCF5a/aPfXJf6z7uKzRMOsB2LEEegwPRje/XFMz74HM1jDymrqPyxoNC5+2gasd+vmjSxAYA5/dDR0HQu5X6z6212j7+ZQegSbNA1GvsTN37twuGRkZ/wQGkySLViUiFcCSsrKy744cOXJnpAOSwrBw1TejcmJ3IN/3MS961gawXfRQZPkAvcfCyjdtlkSrzv7qQ+hj9mOONcZuCXQcCDlfqfHy8biO0Ood7AreZ8PC18yfPeth6csw7npo3q52HQzWgATYNMt3w6KyjoUfJ1/3ka2qOfVeSIs078nxTzxrrC2UtXkO9D3FD22SjoyMjH9269Ytv3PnznvT0tI0pSZJqaiokKKiooLt27f/E5ga6ZiksSpTMcbCN4eFMfDF36HbkBNy/GuIDE84jd1lvP5j2LYQxv9XLRNOFdplQdss2DgjGN3w6TJ/fh9Iut0GiSSzqtDO+dCsXaBj9oVwPMnQS+s/tvdYQGDT576rlUQM7ty58wE1KpKbtLQ007lz5/1Yz1TkYwLUxzdcfYtd9grxVeb6T6BoOYz9fu0WmwG6n2SzJDYFE8BpHRY+TLOf3wctu9Q64dTI/ukzHjbO9N9T5dfpj+yzmRFDLoE2Peo/Pi3t+Jh9J5QV4vVKYecKG08y5roatTrCVMZYADRvD10Hw4bPvNUjuUlToyI1CF3nWu2HpDAswEGnz2Rm1t+hRUcYfHHdxzVpBj1HNu6V7J51sPo/MOrbtQYw1qDPBDhUBLtW+6ubXyx8BkoPw9jvRf+ePuNhz1pbhbUx8uVDkN4URl4d/Xv6jLdbXmUlvqmlKMlIUhgWLutY9JHtSEXwrbTD8j1nz3pY+bYN6Isw0dZYSWZPtNkFx4r90OZEjA9bXl/+C9LS65xwasgMF1UKyKDydMwVFTaFuNcY6HFS7TKrf7v6jLePQY3Zy5Md3Q8LnoHBX4eWneqRWeVmkj0Byo7YbTKlUfDLX/6y24ABAwbl5OQU5OXlFXz44YctXes0ZsyY3Ozs7MG5ubkFI0aMyFu4cGFkl1mIn/3sZz1uvfXWrkHp5wdJYVgAToIsMrbN5eOmP2Po2n8ELts3N/nsh+xEO/o79YgPKZA9wVZm3NQIs0NKDsP8JyB/KrTpXu/hlR95h352r97nSdaXS7z2Q+t5GHNddDqEleg2DDJb+b4d4ssiYcEzUHoIxlwb2/t6h40p3Q5pDLz//vst33333XaLFy9etmrVqmXTp09f1a9fv4TdTaWliaeWP/744+tWrly5bNq0abt++tOfZiV8wgZOUhgWrjb1ms37JwC9ij51It/zfehjB+1EW3BBrXvvNSRmjYW0jEBuvp7XsVjyol3N1jPh1Fi9i9jtkA0zAnGXeRpXMvtBG09ScEHdMquLTM+w13pDI/PSVFTYbZBeo09Im65N5gmXs1Vn6JQbUGyJkihbtmxp0qFDh7LmzZsbgO7du5dlZ2eXArz66qut8/PzC3JycgouueSS7CNHjghAz549h2zbti0D4JNPPmkxZsyYXLBegwsvvLDviBEj8i666KK+hYWFGWeffXb/3Nzcgtzc3IL//Oc/LQHuv//+DkOGDMnPy8srmDZtWp+ysrq912eeeebBjRs3NgVo0aJFZVrZI4880v7rX/96dvXjf//733fp37//oJycnIIpU6b0Azhw4EDaJZdckj1kyJD8/Pz8gieffLIdwJw5c5qFdcnJySlYvHhxnZ4RP6k33VREugATgB7AEWAJMMcY06CaRATurzi8h8xVrwPQ7uBqu/rNbBGYeF+ms6Uv2TLdo+tf2VXegDNbQo8RjS/IzRg7yXYdDL3HRf2WSvqMt5/X3g3Qoa9vKnrKnnWw+j049aaI5bsj6lB9a+CD38Gh3dCyo8fKheV5zLoPYfeaWtOm66XPeFjyb6got548JSpuenFh1qrtxZ7eEHO6tT5858XDCmt7/cILLzzwhz/8oUd2dvbgiRMnHrjsssv2nHfeeQcPHz4s3/ve9/q+9957K4cOHXrsa1/7Wvadd97Z+dZbb41YgyHM6tWrm82aNWtFq1atzHnnndfvlFNOKb711lvXlpWVsX///vR58+Y1e/HFFzvMmTNnRdOmTc3ll1/e++9//3vH66+/fndt53zppZfa5uXlHYl2zPfcc0+3jRs3Lm7evLnZtWtXOsB///d/d580adKBF154YcOuXbvSR40alT916tQD9957b+cf/vCHO37wgx/sOXr0qNRn5PhJrR4LEZkkIu8CbwJfAboDBcAtwGIR+a2ItAlGzbpxUsdi4TNIeQl3lF5GuilzUtrac2Nq7mN2hVZL5UmoZSWZPdE2qTp20GuNTsB4GWNROAu2L4bR3633pLWOGQJZzXo25jmPgKTBqHoKYlHLdytcAntTAGP26tv95cPQsnO9HpqwzBp3kj4TrLG9fbE3+ii+0bZt24olS5Ys+9vf/raxc+fOZVdddVX/e+65p+PChQub9erV69jQoUOPAVx99dW7P/vss9b1nW/y5Mn7WrVqZQBmzpzZ+qabbioCyMjIoGPHjuXvvPNO6yVLlrQYNmxYfl5eXsFnn33WZt26dRG9BFdeeWW/vLy8gs8//7zVX//611qNo+rk5uYe+drXvtb3/vvv79CkSRMD8NFHH7X5v//7v+55eXkFEydOzD127JisWbMmc9y4cYfuuuuu7r/+9a+7rV69OjOsuwvq8lh8FbjWGFOjxZ+IZABTgLOBf0d6s4g0Az4BmobkvGiM+U3CGtdCoCEWxsDcRynrMZon153NrzJfIG39J9D/jABV8Pg7s2MpbJkD594R1Yd5gvjsCfDZX+xkPeBMb/Xyi3mP26qTQ78R9VtOWL13yoXmHWycxfBv+aCgxwWyykttNkjO5OhSTMM6VFWhx3DIaGaNqfzzvdOtNnmJUrwDVr0D435Ua4ppvVQGrc6sM9hVOZG6PAt+kpGRwZQpU4qnTJlSPHTo0CNPPPFEx9GjRx+u7fj09HRTEerQfOTIkRMW2i1btqzTK2+MkUsuuWT3fffdt6U+vR5//PF1p5566gl6VN3KDm/NVGf69Omr33777davvvpq2z//+c/dV65cudQYw4svvrhm2LBhx6oeO2LEiKOnnHLKoZdffrntlClTBt57770bp06dGkBUfU3qirG4K5JRAWCMKTPGvGKMiWhUhDgGnGGMGQacBEwWkdqXwo2JjTNh1ypKhl3BYZpR1Hawrf0QMJ4aU3Mfg/RMGPrNumVGWklmnWyLLQUQzOjJSvZYsa06Ofgiu5VTDxElVtZ28D/mwJPLvOpdmyI74oroZEYSmtHUxioEMWYvBr3wGRtYPDz6Mdcw2Nv2hPbZjTulOkVYuHBh06pxBfPnz2/eq1evkmHDhh3dsmVL5pIlS5oCPP744x1POeWUYoBevXqVzJgxowXA888/3762c0+YMKH4zjvv7AxQVlbG7t270ydPnnzgjTfeaL9ly5YMgB07dqSvWrUquj1GoGPHjqXz5s1rVl5ezquvvlpDdnl5OWvXrs08//zzi++7774tBw8eTN+/f3/6pEmTDtx1111dwwbRjBkzmgMsW7YsMz8//9gtt9yy89xzz923YMECZ7Xo6zIsFojI+yLyHRFpF+uJjSXsG28S+vHNNRNojMXcR6FpW0ryrHt1a/sxdivg6P7AVPD0gyw9Ykt4558f9d75CfKbtrKr2cYSZ7HkJVvHIcoJJ0yN1XSfCTbGYv9mz1SrU14izH8CWnWDAWfHpkP1J/pMsNsCR/Z5pdmJ8rwatDF2zFknV+nMGyd9JljDoqJBhZUp1Thw4ED6lVde2Tcc7LhixYrmf/rTn7a2aNHC/P3vf99wySWX9M/JySlIS0vjxhtvLAK49dZbt/7iF7/oPXjw4Pz09PRav3wPPPDApo8//rh1Tk5OweDBgwvmz5/fbOTIkUdvueWWLWeeeWZOTk5OwRlnnJFTWFjYJFp9f/vb32654IILBowYMSKva9euNVJPysrKZNq0aX3DMr/73e/u7NSpU/kf//jHrWVlZZKXl1cwYMCAQbfccktPgCeffLJDTk7OoLy8vILly5c3/973vldrrIff1LUV0hM4C/gmcIeIfAE8A7xqjIkq+ERE0oG5wADgPmNMjZxEEbkOuA6gd+/esWkfItAQi8N7YNmrMOJKpIld7W5rP5rh6x+0nozcmr0m/MIzY2rZa9YoiqJ4UK0ryeyJtoJlyaGovADxYIzxZiU7/0nonAe9RkV1eK0ywz0k1n8CJ03zQLEY5UfLgW02aHPCT2x2RzQya/t29TsNPv4jbPjUt+0QKz9BNn1hgzYn/jQmmRFvJX1Ps5VKty/S7ZAGzCmnnHJ4/vz5KyK9dsEFFxRfcMEFy6o/P3ny5IMbNmxYUv35v/zlL1ur/p2VlVX2wQcfrK1+3LXXXrv32muv3VuXXrNnz14Z6flrrrlm7zXXXFPjvVVlz507t8Z7W7VqZZ5++umN1Z+/4447tt9xxx0NooJdrR4LY0y5MeZdY8w1QBbwMHABsF5Enorm5KFznAT0AsaISI3a4saYB40xo4wxozp3jr+Rleepl7Wx9CUoP3aCS3ln26F27znA7RBPjal5j9vaDNnRN1uqsbLMPgUqSht+t9Oilba3yfDLY56xa3zmXQbZwMC1073Tr6o8r0608BkwFTF7aCDCde45Cpq0tA29fMCzMc9/wtbdKLgw8XP1O90++jRmRUk2oqpjYYwpAZYBy4EDQH4sQowx+4DpwOQY9Yvu/EFWslj4LHQpgG5DK5dV5elNbY7/+mDrWXhiTO3daGtQnDQtsaVx75Nt3xAfb76eNDed/6StuxFNI6pKapGalmZXs+s+8tVtllBciTF2zH0mQMf+0cusTWRGpg3W9XuSTeRCHz1wPIamaavoRYpEvoytu9r/eTUsFCUq6jQsRCRLRG4SkXnAG6Hjpxpj6q40Y9/bORybISLNsRkkEd1UXhCIv2L3Wtj8JQz75gl3XmOM7QK6Y7HN8Q8Az4ypxc/bxyHRZ0dY+dVo2soaVz6t3j2hvNQahjmToVWXmN8e8TPvPwkO7YSdNbysCeNJvMGmL2ylzTi8FVCLB6HfJLvNsM/7wH9P7LNlr4RiaK704GQh+p1uO52WHvXunIqSpNRVx2Im8BnQBZt2mmuMuc0YE61x0B2YLiKLgC+B/xhj3khY4wgEFmOx8FlAbFfI6vQ9zT5uCM5rkbAxZQwset6uZtv3iU5mXUL7T7L70AeLEtUsIglf53UfWSPgJA/TQ4NwkydyoRc/D01aeBsP0dC3Bha/AB36Rx1DU5Vajbl+k6DsqJN6NYrS2KjLY/ErINsYc5MxZm6sJzbGLDLGDDfGDDXGDDbG/C5+NevH9xALY2DRc/amGqoDcILMHsNtXYT1H/usyHF1EmbbAti1KqZaDnXK7z/JPvo6ySZwoRe/AM3awYCz4hIZccxte0HHgb54ahK+xmUldksg96sxbQnUq0OXfFsW3IfrHPYKxb39c2Cr3ZIccom3N4U+4+0WWkM1phSlAVGXYXEq0K62F0XkDBGZ4rlGcRCIx2LTF7Bvo90GiUR6hs2MWPNBcC6URO+bC5+ztSuiqEp4XGQdQrufBM3bw7oGuB1SchiWv2HHGmU566jpd7pNR/SpvXbcl3nth3Bkb2QPW30y65qUReyY133U8FIwl7wEmLjGDHUEjzZtZTvCqmGhKPVSl2GxGHhDRD4QkTtF5BcicquIPCEii4HzgQaTAuBpo6ZILHzGRsPnHbelwhIr7YgBZ1rjY886f3XBA9ulvMw24cqZbI2B2DWo+VRaut0SWvuhb8ZV3Fd51du2w2U8k2x9B/SfZPf0N8+OSzXfWPyCrQ6aSDXU2i5j/0lweJf3sSUheXE7GxY/b72HnQbE/NZ6ZfafBFsX2JRzpUGyadOmjClTpvTLysoaPGjQoPzTTjttwKJFi3xvxrVhw4YmkydP7gcwc+bM5s8991zbRM7Xq1evIdXbq3/729/O+vWvf92ttveEm5qtXLky8+9//3uHROQnSl3ppq8aYyYA3weWAunYjJAngTHGmJ8aY/zZTI8R37NCykpsQFj++XW7lMMu9jXv+6tPiISMqXUf2UqMMWVHRHPzPQOKt9m0Tg9JOJBx8YvQusfxEs1ekj3RVh71aTUbV/bPsYOw8i0YdCGkR12z57jM+g4IxxQ1JO9U0SrYtjDmQOQTqOtr1u90e4CDKrtK/VRUVDB16tQBp556anFhYeGSpUuXLv/jH/+4ZevWrbH/A8RIdnZ26TvvvLMOYM6cOS3efPPNmAyL6q3ZL7zwwj2PP/54pXFQXl7Om2++2f6qq66q16pdvXp10+eee65hGhZhjDGrjTGPGmP+YIy5O1TbIurubIHhp8Ni3Ue2gNTgi04UGbrhVxo2HfraoLGADIuEWPw8NGsLA2OrxBim1nk+HGex9sP49KqHuFayh/fA6v/Y6xdHh8rK61zbmJu1hZ4jPY+zSMiWWvmW9aLEuSVQqUNtM23bntApx/sxhx7j+nde/IJtslbt/zRa6jXUe4yApm0aljGlVPLGG2+0zsjIML/4xS8qF7zjxo07Mnny5IP79+9PGzduXE5BQUF+Tk5OZavxlStXZvbt23fQ1KlT+/br12/Q5MmT+xUXF6cB3Hjjjd0HDx6cP3DgwEGXXXZZn3AJ7SVLljQdP358Tm5ubkFBQUH+0qVLm65cuTJz4MCBg44ePSp/+MMferz++uvt8/LyCh566KH2O3bsSD/rrLP65+TkFAwbNixv1qxZzaFma/aqY7nyyiv3vPLKK5XGwdtvv926Z8+eJTk5OSW33XZb14EDBw4aOHDgoN/97nc10tt+/etf95wzZ06rvLy8gt/+9rddVq5cmTly5MjcgoKC/IKCgvxwy/fy8nIuv/zy3n379h00fvz4gaeddtqARx55pD3Ap59+2mL06NG5gwYNyp84ceLAjRs3xmScRVeGr4Hje0jD0pehaVsbGV4fA86yBadKj0KTZr6qFbe7uOwYrHwb8qfG3JypXpntekPHAfbmO+6HcSpYk4Su8fLXbfGuIRd7pk8N+p8BH//Jl5bicU+ybbNsSet4ZEYjtP+ZMPcRG7+S6WmH7Ngxxo6576nQulZvcf2nqevF9Ax7/tXve9xqNwl55UdZ7Fzm7ZeiS8FhLryv1hznRYsWNR82bFjEhmMtWrSoePPNN9d06NChYtu2bRljx47NmzZt2j6ADRs2NPvHP/6x4Zxzzjl0ySWXZN95552df/e73+246aabdv75z3/eBnDhhRf2ffbZZ9tOmzZt/7Rp0/reeOON26+88sp9hw8flvLyctm6dWsGQLNmzczNN9+8dc6cOS0ff/zxTQBXXXVV1rBhww6///77a1977bXWV111Vd8VK1YsgxNbs1fVd8yYMUfS0tL4/PPPm48bN+7I008/3f7iiy/e/emnn7Z4+umnO86dO3e5MYaRI0fmn3nmmcUTJkyoXOzffvvtW+66666u06dPXwNQXFyc9umnn65q0aKFWbx4cdPLLrus35IlS5Y//vjj7QsLCzPXrFmzdMuWLRmDBw8efPXVV+8+duyY/PjHP+795ptvrunRo0fZQw891P7GG2/s+cILL2yI9lJFVSCrMeDbv3hZCax8E/LOqxH0VyPGAqxhUXbE99bSCW0NrJ1uW0EPujB++XW92P8M2zek7FhdR8VFXNs/i1+wxk73k+KUaalzy23gOYCBtR/EJSMScW/xHd5jPUaDL7JFvBLRoS4Vcs6xKZgepliH5cW8/bNtAexdn5CHJiqROefCgc2+1C1R/KOiokJuuOGGXjk5OQWTJk3K2blzZ+bmzZszALp161ZyzjnnHAK44oords+cObMVWC/B0KFD83JycgpmzpzZesmSJc337t2btmPHjswrr7xyH0CLFi1M69at64xgnj17duvvfOc7uwGmTp1avG/fvow9e/akwYmt2atz0UUX7X7yySc7lJaW8t5777W/4oor9n700UetvvrVr+5r06ZNRdu2bSvOO++8vdOnT6+zBXxJSYlMmzYtOycnp+CSSy7pv3bt2mYAn376aauLLrpob3p6Or179y47+eSTiwEWLVrUdPXq1c3POOOMnLy8vII777yze6zbScnhscBHwyK8DRLtJJw9AdKb2lWNz23U4x7zslet+z68Tx6TzCikDjgbZj9ojQuP2qjHbUYdLLIZG6fc6O8Ks8dwW9571btxpe/WRcxqr3wbKspiyvapITOag/pMsAHNq961E65Llr1m41xyv5rQaeo12MNN3Fa/B10HJSQrqanDs+AXQ4YMOfLKK69EjET/xz/+0WH37t0ZixcvXt60aVPTs2fPIeE26dWNWBHh8OHD8vOf/7zPrFmzlg0YMKD0Zz/7WY+jR496vhCvqzX7lVdeuXfy5MkDJ02aVJybm3s4KyurLB4Zt99+e9cuXbqU/vvf/15fUVFB8+bNR9Z1vDFGBgwYcGTBggVxF7Ss94MSkZxQZsiS0N9DReSWeAX6hW9zRh3bIJX1Dao+mdnSBgj6HGcR90Qb9sDk1vTAxCS/LgX6ngIZze2E4zGxT7Jv2T4ZBVMTllnnmNPS7KSz5n2bceMBcTullr9ut0F61Fsgt34d6noxo6kNaFz9nmf7kZV1LGK5zsbA8tfs965F/DFrUYls092W81/1XtxyFH84//zzi0tKSuTPf/5zp/Bzs2bNav7OO++02r9/f3qnTp1KmzZtal5//fXWW7durbz5bdu2LfP9999vCfDUU091GD9+/MHDhw+nAXTr1q1s//79aa+//np7gPbt21d069at5IknnmgHcOTIEQnHZIRp06ZN+cGDByufGzt2bPEjjzzSEWwcSPv27cs6dOhQb572oEGDjrVv377slltu6fWNb3xjD8CkSZMOvvXWW+2Ki4vTDhw4kPbWW2+1nzRpUnHV97Vt27b84MGDlcFk+/fvT+/evXtpeno6999/f8fy8nIAJk6cePCVV15pX15eTmFhYcasWbNaAwwdOvTonj17MsKfybFjx2TOnDkx7etHY4E9BNwMlIItfIXteNpw8CvGoo5tkDoZcBbsWgn7NvmkmCUuY2r9x9YDE+dqNiqZTZrbCWfVO95NOPGeZ/nr0D4butbof+c9OefA0X227LuHxHSdjxXbbZD88xOytqPeisg5B/YXws7lcctKmKIVtsR4fvzGY5iovmU559pme0fqbGqpBExaWhqvvfba2g8//LBNVlbW4AEDBgz65S9/2bNnz56l3/3ud/csXLiwZU5OTsFjjz3WsW/fvpW12bOzs4/ee++9Xfr16zdo3759GTfeeGNRp06dyr/1rW8V5efnD5o0aVLOsGHDDoWPf/LJJ9ffd999XXJycgpGjRqVV1hYeILn/ytf+UrxqlWrmoeDN//0pz9tnT9/foucnJyCX//61z0fffTR9dGO6eKLL96zfv36Zpdffvk+gIkTJx6eNm3a7hEjRuSPHDky/4orriiqGl8BNj4jPT3d5ObmFvz2t7/tcsMNN+x85plnOubm5hasWLGiWfPmzSsArrrqqr3du3cvGTBgwKBLL72076BBgw63a9euvFmzZubZZ59d+6tf/apXbm5uwaBBgwo+/vjjmCrsRbMV0sIYM7vajcabJZmH+FLHop5tkLDMGnPegLPgvV/b1euob3uvVySZ0bLsFRvZ3r+mByYm+fXdgnPOtbUjilZCl7yEZFUlpqt8dL+9hid/P8FJ1j7W+5H3P8O641e/C33GxS0vTFyXeNW7tvuuRyW86zXoBp5jH1e/B10LPJBnH2O6Wstes+/IS6xeX9RfkYHnwCd32mJ4fgYEKzGTnZ1d+tZbb0UsJBTJtb9y5crMjIwMXn311RqT/T333LP1nnvu2Vr9+SFDhhz74osvVlV/fvXq1UsBunbtWr5kyZITLO3333+/Rsv16q3ZI3HrrbfuvPXWW3dWfe62227bcdttt+2ofuzhw4fnAzRt2tRU12/VqlWVQUEPPPDAFoD09HQeeOCBzW3btq3Yvn17+ujRo/NHjhx5GGD8+PFH5syZE3fNgGg8FrtEpD+h+5yIXAxsi1egH/hWx2LZK9Fng1Slcy6062P3un0kZmOqvBRWvAm5X4k5G+S4zCgJ77mveicuOdWJb5J9z2aDeLCSjYpmbaH3OM/d5DFd5+Wv23LbWWMTkxmtyDY9oNsQa1i4Yvlrtrtu664Jnyoqg73nSGjR0aYwK0oj5uyzzx6Yl5dXMGHChLybbrppW+/evT1xGkTjsfgR8CCQJyJbgPXA5V4I9xLPYywqyu2kmHNurdsgx1eypuYLeefBl/+yhYri7NNQF3EZUxs+te7bBIL6KuXXJ75Nj9Be9Lsw8YaE5YWJ6TovfxVadYOesTejOkFmpWcqis885xz4z62282e7rITkxrz9U3rETnbDLo2rXkdEHaI5aOA58Nnd9rsVVxXXmvKivs6718KOJXDuHQnJtTKjFJqWbr2Sa/5j7xMefdZK8OTm5paEPQ2pyOzZs72tZBgimgJZ64wxZwGdgTxjzERjzAY/lIkXX+pYbP4SDu+2q/t4yP2qdUl7mH5YnZiNqRVv2aDKBLJVYpKZM9l2g/SgBHLM17jksM3MyZ+ScMplTAwMeWrWeLeajfozX/OBLVvuwTZITF+tgeeCKfetKFqdLH/dPnq19ROtwT7wHHt/2DLPE7lJQkVFRYUW90gBQte51gDUaLJCfiYiPwO+B1wb+vs7InKSd2omjucei5VvQVqTqNIlI056vcfZ1duKtzxWrA6Z9b1h1Ts2tqJJ88TlR3NQzmSbkbHGO+Mq6lXl2g9sPREvJpxoYyzA022wmO3l5a/b7q3ZpyQsu1KHaJToNcpuDXgx5kqBUV7n5a/bVN92vROWHdMtJBxPs9Kf/+9GypKioqK2alwkNxUVFVJUVNQWWFLbMdFshYwK/YSWBkwBFgHfF5EXjDH/m7CmCeJLhMWKt2wPiGZx9pJJz7ArudXv2vTDdO9LhsRkTO1YaqP3T70pUanRH1pZ2+FtGOpTaenaWPGmNez6TExIbsxIKIjwy4fg6AFo1iYYuRXlNs4h59y4eoPUIJYvV1q69ewte81mUnndPbY2Du6ELXNg0q89O2XUBnuLDvb+sOINOOs3nslvzJSVlX13+/bt/9y+fftgkqj4olKDCmBJWVnZd2s7IJrZrhcwwhhzEEBEfgO8iW2rPhdwbliAx1khu1bD7tUw5rq6ZdYnMu+rsOhZ2PS5zbH3kJiNqVWh1aRHhYyi2v9PS7Pylr1mq3DGGTAaM+FJdsDZnhh0ESus1kX++fDFfXY7ZPDX45Ybk1dq8xw4ssfzQlVRG3T5U2H+k7ZB18CzEpBnicquCQdPejXmWG8h+efDWzfazKfOud7o0IgZOXLkTiCgSGmlIRONVdkFqFqbuRToGmpE5n3N5jhIuPNldcIu3dzJiZ2n/5m2Cqdv7tIY7oQr37EFkxLoowBxbDnlX2DLhyfY+TOmS7xlnt3/dlUNMmuMzcwI7/8nSFTbP6vesa75/t5UOo3ZTO97GmS2thkaQbHqHWgdKljlETHdScLprUGOWVEaAdEYFk8Bs0TkNyFvxQzgaRFpCTSYgvmexlisfBu6Dql337bebIGmraDfadYt77HxE9PpDu6ELXPjD0RNhH6n2ZTdZd7cfKNbyb5ru1x6VFL9+MQe5Yeelm6zgla9Z5vRxU0MF3n1e6G4nnYJyEtAhSbNbKfclW9Zj1GC8uq9zGUltudNzrme/fPH7PVs0x16jfHMgFSUZCGarJD/wQZu7gv9fN8Y8ztjzCFjzLf8VS86PJ2yD+22mQxeTcK5X4V9G21KnMdEfT9d9S5gbDBlojJjfUNGU+v5WfGGraMRBKvetXUcEijvnDD559sMDQ9abNf7me8rtN8vDz00cc3V+efDoSJbldJvNs2EkuLjWTheEevNJH8KbFvoe5VdRWlMRBVgY4z5EngGeBnYKSKJh2A3VNb8x2YyRLENElUPibwpdvW89BVP1DtODHfAVe9Am162kJFX0mO5AedPtaWuPeiCWe+q8sA22L7oeEVID4g5xgJsZkaztgmtZqOWFy5O5cPWT0zz7MCz7dZfImOu7BVSz3Ve9a6V1S/2Rnq1EZcxVbkd8oZneihKYyeadNOpIrIaWxjr49CjvyUlY8TTXYY179tMhu7DvTlfq852kln6sufbIVHdB0uPeuoyjrmdNdiU3SYtE9oOaQiTbExkZFoP0cq3EvbU1PuRr37P9kPplJOQnBNkxhMM3bS13X5a/rpPxWWqsOpdGxCd2dLT08acfdSxv+1Do9shilJJNB6L/wFOBlYZY/oCZwFf+KpVHMQ14VWnosIW+el/RlRFlaLeeR98EexZa1fSHhH1fXvT59Yl7+EKHmK8ATdpbitSrngjsf13opxk2/SCLon3raguM+apMv98W41yw2dxyY1KXukRWPex3RLwocVvzPZB/hSb1rx1fkLy6hzJrjX2/8mDrb2qxP3p5Z9v/8+Ka7RvUJSUJBrDotQYsxtIE5E0Y8x0bF2LBoNna6NtC2w2gUeR9ZXknW8j9pe+7Olpo5pH1n5oC31le1PPIf6b71S7/77p87jeHpUhU3Ys5J05x5dJNmb6nwmZrRK+7nV6D9Z/aguBeeyhifvjy/2q/b4tfclTfU4g3H/GY2MZ4nS0FFwIGNtbSFGUqAyLfSLSCvgEeEpE/gocquc9gePJNBIuvx1lNkHYS1LvzahlR9tG3MPtkKjPsvZD26DJ434lMQ9j4DmQ0cyDSbYONs4IeWc8nmRr62JbH5ktbHbIslet0RMjUclb874t0+6R4VhDh1jN9hYdbB+NJS9ZD2Cs8sIei7ou9NoPoFMutO8T8/nrIm5jqkue3Q5Z/KKn+ihKYyUaw+IC4DDwU+AdYC22+mbDwav93DUfQvdhNi7CawZ9DfZuiNtFHIl698GLt9tsgSjKkkctM96bb9NWNtNm6ctxxRxEdYnXTrerZY+LkSXE4Itt4GoCZc3rnmQ/hOwJnhcfS8hQH3IxHNgSt3eqTkqPwsaZtjS9D8R9JxlyMWyebf/HFSXFicawuNUYU2GMKTPGPGaMuQf4pd+KxUrCnu+j+22aXAzbIDGJzDsP0jI83w6pk7WhVEeP6jkkzJBv2K2msF5xUOd1Xjfdemc8DuhL6LvVfxI07wBLfFjN7t9sK8T282eSjZvcr0CTFrD4hbhPUavRXPgFlB31ZcwJVe8NV1hd8m9vlFGURkw0hsXZEZ5zUGmpdjzxV6z/xHZojGN1H5W7uEUHO8EvfTkuF3ENmcbUP+Gt/dBmuHT1Ls007m0BsC7y5u1h8fMxv7VecQeLYPtiT9MPa+gQz6DTm8CgC23vmWMHY5MXTr2s7YBKw9GHSTaaVOrayGxpYy2WvRqzd6pecWunWwM9e0IcikUhP17vZ7vetnaKbocoSu2GhYj8QEQWA7kisqjKz3psE7IGRcIxFms+sCWJe42JXmasQodcYiPmN82M8Y1xEM5w6Tcp2LbhdZGRaQPdVrwZ8yQbptZV5fqP7WM/770zCX+3hlxiAyy9Lu2+bjq06uppBoxnDLnE9i6J0ztV6//Wuul2Am/aOn7dYpUZLUMugZ3LbMM/RUlh6ppxngbOB14LPYZ/RhpjLg9At6hJOMTCGBsQ1vfUuDozRi0/b4rNEljwTMwyasiknglvx2I4vMvT+ApIIPUyzNBvQOnhmCfZeleS66bbglQ9TopXs/p1iPeNWSfbFNgYV7N1BjJWVNj+K/1O9yUDptIzFe8J+p9hW7jHuB1S53U+tBu2LfJ16yehW0nBhTb7S70WSopTl2GRDhwAfgQUV/lBRBzWSo5MQnUs9qyzJXkHxLbajVlmZgt781n2CpQcju29sRIOFmxo++9ZJ0PbLFj0XFxvj/iRGwNrP7KGYVp6QupFFprg+9PSbC2TtR/AoV2eqGQNx90N7/qGyciEggsS8k7VYP1HgPEtcDNh86xVZ2voLX7Rk+1ORWms1GVYzAXmhH7mVvuZ479q0RNzSlx1wm70vqfHKT8GTroMSg7aYlEJYEw9hs3aD21sReuuCcmpXX6cn3lamo2gXzvdxkVEK6+uF3evhQOb7U3dRxLyjA37JlSUxbSCPy4vwnUObzH4NebKGIsEBn3SNJv+G0P3zzqlrQ17pTyqihtJfqLez2GXwf5NnpSvV5TGSq2GhTGmrzGmX+inb7WffvWdWESyRGS6iCwTkaUi8hNvVa8mL5E3r//Utl/u2N8rdWqn93ho2xsWJr4dUiulR6Fwtq+BjAkx9Js2UDaOIM6IhBt9+bR6TyhbIEzXQXZCnPeEN+nR66ZD53zbYbOhkjUWOg6A+U/G/NYaNrMx1rDwyysVUWgc5E+x3XzjGLOiJAtRRfWF+oX8OfQTbQ2LMuDnxpgCbEnwH4mIL1FmCd2njbEll7NPif/GEosCaWl29bruIziwNT551LOS3PwllB+zY/KYhGMswBYU6jUa5j0e9WdX52HrPrJR+R3qtXcTImHP2PArYOdSW+E1Bnk1vpalR2Dj575tCUCcjddqnERg+OW2cNnutVG9pVZ5u9eEvFINdOsnTJPm1iO3/DWbwq4oKUg0Tcj+CPwEWBb6+YmI3FHf+4wx24wx80K/FwPLgZ6JqVuXnnG+sWglHNoZd1GluOQO+6btoBpnnEG9bPjUdlTtM86f83vB8CugaIU1gmKgxvZPeZn1OPkUxGhlenSiwV+31UfnPZHYeQpnWcPR560fTxj6TftdXPBUTG+r4SWqzPo53Ru9Isr0iOGX21obWtNCSVGi8Vh8FTjbGPOwMeZhYDIxVt4UkWxgODArwmvXicgcEZlTVBT9nntVElpVrf/EPvY9Ne5TxCy+Y38bxJiAW9xQx4S34TNbQbRZ27jOXRfiicsCG8zYpKX1WiTC9kVwbD/0DWDbJ9ExN29ne6YsftF6HeoTV1tDrg0z7GTd2z/D0ZOmfmC3agacDQuejrIBXS0f8oYZdrvSZ6+UJ/QYblOAdTtESVGiLXDQrsrvMc1WoT4j/wZuMMYcqP66MeZBY8woY8yozp19KKVdHxs+sTEP7bPjenvct99R19gOjWHDxitKj1gvgE+9IzyjaWsY/DXbU+JYcf3H1zbJbpxhH/v4UzAposxEGH65NYSWJxC8u3EGdBsKzdp4p5efDL8cirfZgOIoOcGuMcaOOXuir83ljhcFS9CCDG8BbZkLO5YlrpiiNDKiMSz+AMwXkUdF5DFsVsjt0ZxcRJpgjYqnjDG+tTuspz5h7VRU2NV9gr0l4uuIeIGtQjn3kTiF1hJUWDgbyksgO34PTF0cbxXvQQDiiKts1sCSBL4aG2bYVWwAQYyeVHjNPsXGg8yP3lNzgveg9ChsnuO74ehJjEWYnMnQomNU3qmI8navhYM7fDUePWfopbZC6PwEt70UpRFSV+XN+0RkgjHmGWzw5UtYI2GcMabe4ACxd8N/AcuNMX/xSmFP2bkUjuz1JcixXpo0h2HTYPnrcHCnd+fd8FnITX6yd+f0i16jbZfKaCacSNN6RYWtYtqYJpy0NBh+pfVU7Vod+/u3zLHxFY1pzBmZNvV05VvxBSxv/Mw+BuSF88SYatnJFsRb8JT/NWsUpYFRl8diFfBnEdmA7WxaaIx5zRizPcpzTwCuAM4QkQWhn68mpm5kouqbEYn1oVzzBDwWIhL/6n3UNba2QRx7sbXGWGz4FLqf5JubPKEeEpFONvIqO1luWxiTfMAahkf3+796lwT6o0Ri5FW2C+uX/6zzsIgxFhtm2Gd8Dsw9Hkrj0aBHfcfGWMx9tM7DwtJOuM4bZkDLLjZ11Uc8SSuuyphr7ffTjwZ0itKAqauOxV+NMeOA04DdwMMiskJEfiMiOfWd2BjzmTFGjDFDjTEnhX48bpaQIOs/sW70tr3cyO800HpL5j7qTaW+ksPWTd6Q2obXx0nfsp0wZz1Y52ERJ/UN4fiK8d7r5SetutitsAVPx16VcuNn0HWw3UZrTHToCwPPtt/1spLo31cZXzHB1/iKE0R6daI+E2ytkdkPeWiVKkrDp94YC2PMRmPMn4wxw4HLgAuxqaMNiphvOWE3eoKrXSHBe8aoa2DfxpgC2yDkpan+5ObZUFHq69ZOwj0kqtO8nd2PXvyC7QVRr/wqbJxhA2/b9fZKm8gyvV69A4y5Do4dqLNIWI06FmUlUBhMYK6nnqkwo6+1sRIrXq/1kONempACezfAgS2BjtnTE4651mYuxZhWrSiNmWjqWGSIyPki8hTwNrASuMh3zfymaIV1U/qYshcVeefbDpWzHkj8XBs+s02QGkN8RVXGXGfjBuY9VushNeY3Y2DjTN/aZ/tO1hjoNiS21ezWebZLamMd84CzbPbV7Lq3gE5gQyi+ok9wWU4JZ4VUZeil0LSNvc6KkiLUFbx5tog8DGwGrgXeBPobY75pjHk1KAWjwfbNiPFNmz63jwlOwiIJrt4zMu1Kbs37tlhXlESMsdj0hZ2sfGgpHcaXlWzXAutl+fJftuBVnfJDCuxaZbu3BrAN4mmGROVJxV73ncusgRSBGt1Nw5Ns7yDG7LFnCmzg6qjvWE9hLa3Fa3hpNs6AFp2gc66XmkTEl42Wpq1s/5Blr8TUG0dRGjN1eSxuBmYC+caYqcaYp40xhwLSy38KZ9mAsPZ9XWsCo75tKzJ+cX/85ygvtfEVjc1bEWbs92zJ5lraqddYRVauZBvp6h1gyCW2tXi03qqNM2zhpZYdfVXLV4ZfDhnNo/+ub5hhjceA4ivAY2MKYPR3bQr4nH95fWZFaZDUFbx5hjHmn8aYvUEqFA8GE3tE96bP7SSc4A1LkMRXsi07WpfpwmejijOACN1Nty+ybvKssQkqUzee1rGoSs5XbLzEF3VPsiesZAOqxOhVsdEaZLawRuXyNyL20qjMkECsJ6dwdmCBqp4Vi6pOiw429XTR81BcM8HshEyY/Zttp9CAjEffbJfOObaWx+wHNfVUSQmirbyZXBzYCvs2NazV/ck/tP0F5jwc3/s3haqlN6QxxUJ6Bpz8fesmL6wZ6FZjetv0hY2PCXAl6wtjvwfpTeDzv9V93M5lUHLQloJv7Iz7kfWwzfp73ccVhr/T/hrL1fElgWP8j+Hwblj4tA8nV5SGRVIYFjHHWGz6wj56MQmLR6v3Lnk2uO3Lh6DsWL2HWy9NFQpn2RV/mx6J61IXfsRYhBlxld0amHF33eL3b7aZAj57Z6pJ9X71DtC6m21Kt+DpGnvwpuryPTzJZo3xXoc68CVJsmN/KJgKXz5co5z7CXUsCr+0qchdB/uhRQ08648SiT7joedImPm3KHumKErjJSkMi5gpnGVvWN2GutbkRMZdb9PxFj4T2/uMsWMKeGXnOU1b2QyRFW/UCGQ9YU4vnG0fs0YHp5ufjPsv6636so7MgcLZ0Kqb76m1gTH+J7ZnSl1VVwtn2ck4vUlweuHDNh9YS2n8j2Hvevv9VpQkJikMizo7fUZi0+ee3bAkrIAX9Dvd6vXpX+rNjjCG4wEP+zbZJk8BrOB9yRaoytjv2eC+GffUooDYmgAZzQMzDH2LsQjTOQdyzwvtwR+Pjz4eY4GtUZI1OrCtH8+rjVan10gbO/H5/XZbJETYSyNlR23cUK8kMR4B8s+3weIz7tGCWUpSkxSGRUwcK4bti93Xr4iECJx6ky2YFUsZ4Eo3eSP3WIDtsTDiClj0HOzfUvn0CavIwtm2NXXAK1lfmfAT27dmbs1aHplHimyhqGS4vlWZ8BObCbSoZuuhpjsX2XL3Dsbs25yflg7jr7cl7L3uaqwoDYikMCxsFcooV3Kb54Cp8GzbIOE6FtXJmWz3lD+9q84y39ZhERrzpi8gszV0HeSlJhGpXDD7ueIad729RjPvrfFSevkx21ckwG2Qym+Wn4vM3mNtLY8Zd0PpESsuJK/N7gX2l17BxVcc/2/ycdADz4Huw+CTO0/wWgA03zHH/hKgxyIQZ9BJl0PrHvDRH9VroSQtSWFYxMSmL2z3zwBv0jEhAqf83BaAWv5adO8pnAW9RtkVUTLQvg+cdJnNkAl3wwzdgzseWGbLlifb6h3g9JttjE21zKC2u+ZDeqadhJMJETvmvRtqeC2abZtjm4415podkWjSDE75mc1+Wv+xa20UxReSwrAwEH3ZvM1f2sZAHnX/tHUsPF55FFwAHQfalVxtXouwyKP7bRXDgNNMfV9rnXoTmHLrualC532hLqhBrt7D8QZ+jzp7AvQ9FT67O1TvwMprs2ue7VjbpJm/8iPg+6I6Z/IJXgsrz9Bsx9zAjUfPu5vWxvAr1GuhJDVJYVhEjTGwZa4NHGvIpKXDab+EHUtg6Uu1HiaC3drBBHYTDqxqRPtsewOe+xjsK6yc0jvtW2hfa9U5KE2C5fSb4dDOSq9FE8povXtx4GmmgZUHieC16CM7SD+yO/Axh/F9rq/0WnyuXgslKUkOw8JEOeHtWQdH90HPUZ6JFvHpRjT46zbW4sPf19h/hiqr5y3zAIGeI3xQonYCWWideqP9gD+5MyzVeiwC3sYKIqykkj7joe9pMONupOQQg2QDaRUl7ibZIITkTLYemY//Fyk/xkhZZZ8P2mMRZK21sNdi+h3qtVCSjuQwLKJly1z72LOBeyzANmw681ab9z7/iYiHCNgxdcqBZm0DUcvXIkLVadsLRl4NC54ibe96eskumh/b5WySDYwz/h8cKqLr0n8yMi00yQZuTAV4nUXgzP8H+zbSd8OzjEhbTXlmG+jkf+OxSPi+5QXWa3HaTTY+qpb+OIrSWEkKw8LWsYjiRrh5ji2M1TnPM9mCj6u6gefYEs4f/alGjwFjOL6148BQ8qUKZSRO+TmkN6X1p79jROVK1s22QGALy6zRUHAh3Zb8g8npsznasie06R6Q8BMJbMwDzoL+Z5C38gEmpS+gpOtwa1wHSODF4YdfYQNU3/9tvXVrFKUxkRSGRdRsmRuqf5DhWpPoEIGzboOD2yP2VWhTusPuxwe4DRL4zbd1NzjlpzRb8xY/yHiNsvTm0MX/tFrnnHUbUlHO6LRVFHcaHrh4Jy1Yzv4fMksP0FN2c7S7d9uVsRKYMZXeBM78DexaCQueCkioovhPUhgWto5FPZQds5X8PJ6ERTzobloXfcbZzp+f/gWKd1Q+bYDuB5faP1x4LIIUNu56ylr3JD+tkD1tCwI3DH2vNhqJDn0pyvkmAAc7DglS8gkEsi0QpttgNnc/G4ASB4aFE2Mq/3y7zfXRHyrrlyhKYycpDIuo2LEEyksaR3xFdc693faS+OB3Jzzd/eAyW98goCZN4Ojm26Q5xRNuAWBf6xwHCrihcMRN3F46jR0DLg1ctquesXNPup0flPyEo1mnONIgYANSBCb/Ec67CzKCTydWFD9IGsOi3glvyzz76GFGCIRjLHy+FXXsDyf/ABY8WRmAaoyxhkW3oZCR6a/8CAQdyH4k90K+XXIji/N+EqxgqsZYBDvo8oyWPFQ+hfLM1oHKrUrQ17k0oyVvV4xFJPhib4EGrFal10jIO8+R1a4o3pMUhkVU977Nc6BlF5tp0Bg59Sar/9u/AmNIo4KuB5cH7oFxdfM1InxYMYKyjJZO5LvExSeeynNc0AakoiQbSWFYQBQ33y1zbdlrr++YftWxqE6zNnDWb2yXy4XP0F+2kFlxxNnWjqtbr7NVJcGPuSFMb0HPsZXdTR1c5lQ2phTFS5LCsKj35ndkL+xeHXgRKc8ZNs2mn75zM2emhbd2AjYsHN18U3oV6eQzT91ZNoW/aYriCUlhWEA9dSy2zrePPkzCgd5+09Jg6r1QephfZDzH0fTW0KFfkBpU4myid7iSDX71Hqy8iDoEPM02gCEripIgSWFY1Hvz27rAPvYIvh6A53TOgdN+AcDhzA7BFxFy5rFwI7ch4GL7J5W3BVL5u6YoXpAUhgXUs5DdthDa9YHm7b2X6+IOPOEGXig7lQ/73RS8bMc4CWRM4W0BV7iJsdDrrChekBSGRb0rjG0LbWtm3+QHvMRJb8JNZd+nsF2wTZoglXfeIWhHfdgT52SSDeuQkhGrrhVQlMZNUhgWQO0z3tH9tpFX96H+iE3tmTZwXKwq9RoHj5PrHLhERUlOksKwqHNVtX2xfex+kn/yfTtzLfKcpuSFylunYiCjo9W7mzoWjuqVNAB3QUPQQVEaM74ZFiLysIjsFJElfsk4QV5tt99tC+2jT1shusoJFi0WlRrodVaUxoufHotHgck+nj86ti2E1t2hVRffRLhavTvJFgjr4CjewCWuwg1cbguoZ0pRlFjxzbAwxnwC7PHr/NWp9d67bZHtp+GbXF3mBImbQEa9xkHjMmBVUZTEcB5jISLXicgcEZlTVFQU1zlqzcooOQy7VvqaEQLuigi5LHusK9ng5Dm9zilYIKsh6KAojRnnhoUx5kFjzChjzKjOnTvHfZ6I994dS8FU+GpY6ConWLSHRGrgpiiYXmhF8QLnhoUX1LrC2LbAPvrtsXDVqClYsSGZoayQgOU2hFWkq7gSl4GMqemZagBKKEojJikMC6hlVbl9ka226WOrdF3kBIvLgFUlONQzpSiNFz/TTZ8BPgdyRWSziHzHL1m1LjDCFTd9vmO4yxYIWDAuV7LuV5EpFWPhzDPVAK6zawUUpZGT4deJjTGX+XXuSNRYyZaVwI5lMO6HQaqhKIqiKClNUmyFRFzl7FoFFaW+pppaxOFK1p3vNiUzYQKWe1yeu9zLoD1Fx2u0BI+z/iiKkmQkhWEBESacHUvtY9dBgeuiKIqiKKlKUhgWEVcYO5dCWhPoOMBX2dagSZ0qlKmZLRDujxL06r0BdDcNWG6lPIepMA0hzkNRGjNJYVhAJI/FMuicB+lNnOijKIqiKKlIUhgWEdcXO5ZC1wLfZQupmS0QPOHVe+q0Ta+MK3Eg21UXWyprtDhMK1aHhaIkRFIYFpYqN6LDe6B4q8ZXKIqiKErAJIVhUWNVtXOZfeziv2Eh4m7/303ZY/uo2QIB4DD757hEzf5RFCU2ksKwgGo3oh0hw0I9FoqiKIoSKEliWFRbY+xcakt5t+7mu2RBgq/p4DTG4kQdgsLtStZNtkAq9wpx2gdHXRaKkhBJYlhUuxHtWApdB2vxf0VRFEUJmKQwLE5YYVRUwM7l0MX/jBBwE2PhdiXrqIdE5UrWXbxBKmb/BH+d3Wf/aB0LRUmMpDAsoMrNd99GKDmo8RWKoiiK4oCkMCxOWF/sDDZwU6rLD4DUjLFwWIXSebyBy+yfYOU6rd0R1kEdFoqSEElhWECVm2+4R0jnPHfKKIqiKEqKkhSGxQk1FXYshfZ9oWmrQGSLOOhuGpbtciXrKhMmUKlhmY7iDcLynXqmUijjSetYKIonJIVhAVVuRDuXa3yFoiiKojgiKQyLyhVGWQnsWRv4Nkjwq3eX8QZucv0bxErWUXdTJzhavTv1xjnqYqsoyUZSGBYQug/uWQcVZdA517U6iqIoipKSJIVhUbnAKFphHwM0LMRBWkhDWE8Fv5J1mS8Q1sGNPKd1LAL3TLlvCqMOC0VJjKQwLCDkot+1ChDoONC1OoqiKIqSkiSFYVG5yilaAe16Q2aLwGSLuKxj4Wb1br00qZctELhnqiHUsXDkH3OZCaMoSmIkhWFRSdEqrV+hKIqiKA5JCsPCAGLK7VZI55xAZQsSfBS5w23osFxX29Bu+6O4ibJwunpPpe6m2rRQUTwhKQwLgPYl26D8mHosFEVJCA3eVJTESA7DwkDnoxvs752CTTV1EmPhcCVr5QZfbbSq7MBlhh5Tq3aHq2qjDrubBi5RUZKT5DAsqGJYBLwVogSDriKVoNC26YqSGElhWBig09EN0LoHNGsbqGyXqxyXMRaucLP37kBoVfkOs0JckYrXWVGShaQwLAA6H1nvzFvhykXuksDLmDeAVaSrAlkuScnvdgPQQVEaM0lhWBhTQaejG50EbrqMJHdax8IRLqtQuiIVazo4rVeiKEpCJIVh0Y3dNK04DJ0ceSySXF4kdCWbfPIi6hC4Z8o9DUEHRWnMJIVh0Y/N9hcXHovAJVaR7SorxOGoU3Elm4rxBi67myqKkhi+GhYiMllEVorIGhH5lV9y+rPF/uKoq2lKtdMO65Dk8iKRknElqeilaQhKKEojxjfDQkTSgfuArwAFwGUiUuCHrIFs4mBGB2jZyY/T143LeIOUE+xqJesWN96DFIwrcX2hFSVJyPDx3GOANcaYdQAi8ixwAbDMUylH9jJBFrGzxQBaeXri6PloZRFn/+XjwOSVV7hfUT07exPvL9sRmLwjpeWByaqNu99fzaMzNgQmr/hoWWCyauPmlxbTIjM9MHl7D5cEJqs2rnn0SzLTg90lbt8ik+e/Py5QmYriF34aFj2Bwip/bwbGVj9IRK4DrgPo3bt37FKatmVL2xEcHvRN+sWnZ0J8d2I/PltTFLjcIb3acmpO58DlAvzXpAEs334gcLlj+3bkpKx2gcvt1Kop10zIZseBo4HLbtcikz4dWwYuN797ay4dlUXxsdLAZffp2JJmTYIzZsKM7duRrw3vybGy4I3YNs2aBC5TUfxC/NpPFJGLgcnGmO+G/r4CGGuMub6294waNcrMmTPHF30URVGSERGZa4wZ5VoPRQnjp79vC5BV5e9eoecURVEURUlS/DQsvgQGikhfEckEvgm85qM8RVEURVEc41uMhTGmTESuB94F0oGHjTFL/ZKnKIqiKIp7/AzexBjzFvCWnzIURVEURWk4JEXlTUVRFEVRGgZqWCiKoiiK4hlqWCiKoiiK4hlqWCiKoiiK4hm+FciKBxEpAjbG+fZOwC4P1WkM6JiTn1QbL+iYY6WPMcZNGV5FiUCDMiwSQUTmpFr1OR1z8pNq4wUds6I0dnQrRFEURVEUz1DDQlEURVEUz0gmw+JB1wo4QMec/KTaeEHHrCiNmqSJsVAURVEUxT3J5LFQFEVRFMUxalgoiqIoiuIZjd6wEJHJIrJSRNaIyK9c6+M3IpIlItNFZJmILBWRn7jWKShEJF1E5ovIG651CQIRaSciL4rIChFZLiLjXOvkNyLy09D3eomIPCMizVzr5DUi8rCI7BSRJVWe6yAi/xGR1aHH9i51VJREaNSGhYikA/cBXwEKgMtEpMCtVr5TBvzcGFMAnAz8KAXGHOYnwHLXSgTIX4F3jDF5wDCSfOwi0hP4MTDKGDMYSAe+6VYrX3gUmFztuV8BHxhjBgIfhP5WlEZJozYsgDHAGmPMOmNMCfAscIFjnXzFGLPNGDMv9HsxdrLp6VYr/xGRXsB5wD9d6xIEItIWOBX4F4AxpsQYs8+pUsGQATQXkQygBbDVsT6eY4z5BNhT7ekLgMdCvz8GXBikToriJY3dsOgJFFb5ezMpMMmGEZFsYDgwy7EqQXA38AugwrEeQdEXKAIeCW3//FNEWrpWyk+MMVuAPwObgG3AfmPMe261Coyuxphtod+3A11dKqMoidDYDYuURURaAf8GbjDGHHCtj5+IyBRgpzFmrmtdAiQDGAE8YIwZDhwiyd3jobiCC7BGVQ+gpYhc7lar4DG2BoDWAVAaLY3dsNgCZFX5u1fouaRGRJpgjYqnjDEvudYnACYAU0VkA3a76wwRedKtSr6zGdhsjAl7o17EGhrJzFnAemNMkTGmFHgJGO9Yp6DYISLdAUKPOx3royhx09gNiy+BgSLSV0QysYFerznWyVdERLD77suNMX9xrU8QGGNuNsb0MsZkY6/xh8aYpF7JGmO2A4Uikht66kxgmUOVgmATcLKItAh9z88kyQNWq/AacFXo96uAVx3qoigJkeFagUQwxpSJyPXAu9gI8oeNMUsdq+U3E4ArgMUisiD03H8bY95yp5LiE/8FPBUymtcB1zjWx1eMMbNE5EVgHjb7aT5JWOpaRJ4BTgc6ichm4DfAH4HnReQ7wEbgG+40VJTE0JLeiqIoiqJ4RmPfClEURVEUpQGhhoWiKIqiKJ6hhoWiKIqiKJ6hhoWiKIqiKJ6hhoWiKIqiKJ6hhoXS6BGRjiKyIPSzXUS2hH4/KCL3+yTzBhG50oPzPCsiA73QSVEUpSGg6aZKUiEitwEHjTF/9lFGBrbWwghjTFmC5zoNuNwYc60nyimKojhGPRZK0iIip4vIG6HfbxORx0TkUxHZKCIXicj/ishiEXknVCYdERkpIh+LyFwReTdcZrkaZwDzwkaFiHwkIv8nInNEZLmIjBaRl0RktYj8PnRMSxF5U0QWisgSEbk0dK5PgbNCxoqiKEqjRw0LJZXojzUKpgJPAtONMUOAI8B5IePiXuBiY8xI4GHg9gjnmQBUb4hWYowZBfwdW475R8Bg4GoR6QhMBrYaY4YZYwYD7wAYYyqANcAwT0eqKIriCF0lKanE28aYUhFZjC0B/07o+cVANpCLNQb+Y1tVkI5t312d7tTsYRHuUbMYWBpugS0i67CN8hYDd4nIn4A3jDGfVnnvTmw3z1Tq3qooSpKihoWSShwD6yUQkVJzPMCoAvu/IFijYFw95zkCNIt07tC5jlV5vgLIMMasEpERwFeB34vIB8aY34WOaRY6p6IoSqNHt0IU5Tgrgc4iMg5se3oRGRThuOXAgFhOLCI9gMPGmCeBOzmxBXoOsCQ+lRVFURoW6rFQlBDGmBIRuRi4R0TaYv8/7gaqd8x9G3gixtMPAe4UkQqgFPgBgIh0BY6E2qQriqI0ejTdVFHiQEReBn5hjFmd4Hl+ChwwxvzLG80URVHcolshihIfv8IGcSbKPuAxD86jKIrSIFCPhaIoiqIonqEeC0VRFEVRPEMNC0VRFEVRPEMNC0VRFEVRPEMNC0VRFEVRPEMNC0VRFEVRPOP/A74o1RTM4EF9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # Clear out the existing circuitry from the previous example.\n",
    "\n",
    "# Create a pulsed voltage source, a resistor, and a capacitor.\n",
    "vs = PULSEV(initial_value=0, pulsed_value=5@u_V, pulse_width=1@u_ms, period=2@u_ms)  # 1ms ON, 1ms OFF pulses.\n",
    "r = R(value=1@u_kOhm)    # 1 Kohm resistor.\n",
    "c = C(value=1@u_uF)      # 1 uF capacitor.\n",
    "r['+', '-'] += vs['p'], c['+']  # Connect the resistor between the positive source terminal and one of the capacitor terminals.\n",
    "gnd += vs['n'], c['-']   # Connect the negative battery terminal and the other capacitor terminal to ground.\n",
    "\n",
    "# Simulate the circuit.\n",
    "circ = generate_netlist()            # Create the PySpice Circuit object from the SKiDL code.\n",
    "sim = circ.simulator()               # Get a simulator for the Circuit object.\n",
    "waveforms = sim.transient(step_time=0.01@u_ms, end_time=10@u_ms)  # Run a transient simulation from 0 to 10 msec.\n",
    "\n",
    "# Get the simulation data.\n",
    "time = waveforms.time                  # Time values for each point on the waveforms.\n",
    "pulses = waveforms[node(vs['p'])]      # Voltage on the positive terminal of the pulsed voltage source.\n",
    "cap_voltage = waveforms[node(c['+'])]  # Voltage on the capacitor.\n",
    "\n",
    "# Plot the pulsed source and capacitor voltage values versus time.\n",
    "figure = plt.figure(1)\n",
    "plt.title('Capacitor Voltage vs. Source Pulses')\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Voltage (V)')\n",
    "plt.plot(time*1000, pulses)       # Plot pulsed source waveform.\n",
    "plt.plot(time*1000, cap_voltage)  # Plot capacitor charging waveform.\n",
    "plt.legend(('Source Pulses', 'Capacitor Voltage'), loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Voltage-Controlled Voltage Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A voltage source whose output is controlled by another voltage source is demonstrated in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:00.921764Z",
     "start_time": "2021-04-29T13:33:00.695713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title \n",
      "V1 N_1 0 DC 0V AC 1V SIN(0V 1V 100Hz 0s 0Hz)\n",
      "E1 N_2 0 N_1 0 2.5\n",
      "R1 N_2 0 1kOhm\n",
      "R2 N_1 0 1kOhm\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # Clear out the existing circuitry from the previous example.\n",
    "\n",
    "# Connect a sine wave to the control input of a voltage-controlled voltage source.\n",
    "vs = SINEV(amplitude=1@u_V, frequency=100@u_Hz)  # 1V sine wave source at 100 Hz.\n",
    "vs['n'] += gnd  # Connect the negative terminal of the sine wave to ground.\n",
    "vc = VCVS(gain=2.5)  # Voltage-controlled voltage source with a gain of 2.5.\n",
    "vc['ip', 'in'] += vs['p'], gnd  # Connect the sine wave to the input port of the controlled source.\n",
    "vc['op', 'on'] += Net(), gnd    # Connect the output port of the controlled source to a net and ground.\n",
    "rl = R(value=1@u_kOhm)\n",
    "rl[1,2] += vc['op'], gnd\n",
    "r = R(value=1@u_kOhm)\n",
    "r[1,2] += vs['p'], gnd\n",
    "\n",
    "# Simulate the circuit.\n",
    "circ = generate_netlist()  # Create the PySpice Circuit object from the SKiDL code.\n",
    "print(circ)\n",
    "sim = circ.simulator()  # Get a simulator for the Circuit object.\n",
    "waveforms = sim.transient(step_time=0.01@u_ms, end_time=20@u_ms) # Run a transient simulation from 0 to 20 msec.\n",
    "\n",
    "# Get the time-varying waveforms of the sine wave source and the voltage-controlled source.\n",
    "time = waveforms.time\n",
    "vin = waveforms[node(vs['p'])]\n",
    "vout = waveforms[node(vc['op'])]\n",
    "\n",
    "# Plot the input and output waveforms. Note that the output voltage is 2.5x the input voltage.\n",
    "figure = plt.figure(1)\n",
    "plt.title('Input and Output Sine Waves')\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Voltage (V)')\n",
    "plt.plot(time*1000, vin)\n",
    "plt.plot(time*1000, vout)\n",
    "plt.legend(('Input Voltage', 'Output Voltage'), loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Current-Controlled Current Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows a current source controlled by the current driven through a resistor by a voltage source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:01.146341Z",
     "start_time": "2021-04-29T13:33:00.923244Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # Clear out the existing circuitry from the previous example.\n",
    "\n",
    "# Use the current driven through a resistor to control another current source.\n",
    "vs = SINEV(amplitude=1@u_V, frequency=100@u_Hz) # 100 Hz sine wave voltage source.\n",
    "rs = R(value=1@u_kOhm)  # Resistor connected to the voltage source.\n",
    "rs[1,2] += vs['p'], gnd  # Connect resistor from positive terminal of voltage source to ground.\n",
    "vs['n'] += gnd  # Connect the negative terminal of the voltage source to ground.\n",
    "vc = CCCS(control=vs, gain=2.5)  # Current source controlled by the current entering the vs voltage source.\n",
    "rc = R(value=1@u_Ohm)  # Resistor connected to the current source.\n",
    "rc[1,2] += vc['p'], gnd  # Connect resistor from the positive terminal of the current source to ground.\n",
    "vc['n'] += gnd  # Connect the negative terminal of the current source to ground.\n",
    "\n",
    "# Simulate the circuit.\n",
    "circ = generate_netlist()\n",
    "sim = circ.simulator()\n",
    "waveforms = sim.transient(step_time=0.01@u_ms, end_time=20@u_ms)\n",
    "\n",
    "# Get the time-varying waveforms of the currents from the sine wave source and the current-controlled current source.\n",
    "time = waveforms.time\n",
    "i_vs = waveforms[vs.ref]\n",
    "i_vc = waveforms[node(rc[1])] / rc.value  # Current-source current is the voltage across the resistor / resistance.\n",
    "\n",
    "# Plot the waveforms. Note the input and output currents are out of phase since the output current is calculated\n",
    "# based on the current *leaving* the positive terminal of the controlled current source and entering the resistor, \n",
    "# whereas the current in the controlling voltage source is calculated based on what is *entering* the positive terminal.\n",
    "figure = plt.figure(1)\n",
    "plt.title('Control Current vs. Output Current')\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Current (mA)')\n",
    "plt.plot(time*1000, i_vs)\n",
    "plt.plot(time*1000, i_vc)\n",
    "plt.legend(('Control Current', 'Output Current'), loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Transmission Line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The voltages at the beginning and end of an ideal transmission line are shown in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:01.372574Z",
     "start_time": "2021-04-29T13:33:01.149012Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # Clear out the existing circuitry from the previous example.\n",
    "\n",
    "# Create a 1 GHz sine wave source, drive it through a 70 ohm ideal transmission line, and terminate it with a 140 ohm resistor.\n",
    "vs = SINEV(amplitude=1@u_V, frequency=1@u_GHz)\n",
    "t1 = T(impedance=70@u_Ohm, frequency=1@u_GHz, normalized_length=10.0) # Trans. line is 10 wavelengths long.\n",
    "rload = R(value=140@u_Ohm)\n",
    "vs['p'] += t1['ip']  # Connect source to positive input terminal of trans. line.\n",
    "rload[1] += t1['op'] # Connect resistor to positive output terminal of trans. line.\n",
    "gnd += vs['n'], t1['in','on'], rload[2] # Connect remaining terminals to ground.\n",
    "\n",
    "# Simulate the transmission line.\n",
    "circ = generate_netlist()\n",
    "sim = circ.simulator()\n",
    "waveforms = sim.transient(step_time=0.01@u_ns, end_time=20@u_ns)\n",
    "\n",
    "# Get the waveforms at the beginning and end of the trans. line.\n",
    "time = waveforms.time * 10**9\n",
    "vin = waveforms[node(vs['p'])]      # Input voltage at the beginning of the trans. line.\n",
    "vout = waveforms[node(rload['1'])]  # Output voltage at the terminating resistor of the trans. line.\n",
    "\n",
    "# Plot the input and output waveforms. Note that it takes 10 nsec for the input to reach the end of the\n",
    "# transmission line, and there is a 33% \"bump\" in the output voltage due to the mismatch between the\n",
    "# 140 ohm load resistor and the 70 ohm transmission line impedances.\n",
    "figure = plt.figure(1)\n",
    "plt.title('Output Voltage vs. Input Voltage')\n",
    "plt.xlabel('Time (ns)')\n",
    "plt.ylabel('Voltage (V)')\n",
    "plt.plot(time, vin)\n",
    "plt.plot(time, vout)\n",
    "plt.legend(('Input Voltage', 'Output Voltage'), loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example demonstrates a transformer composed of two coupled inductors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:01.593626Z",
     "start_time": "2021-04-29T13:33:01.373934Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # Clear out existing circuitry from previous example.\n",
    "\n",
    "turns_ratio = 10  # Voltage gain from primary to secondary.\n",
    "primary_inductance = 1 @ u_uH\n",
    "secondary_inductance = primary_inductance * turns_ratio**2\n",
    "\n",
    "# Create a transformer from two coupled inductors.\n",
    "vs = SINEV(amplitude=1@u_V, frequency=100@u_Hz)  # AC input voltage.\n",
    "rs = R(value=0 @ u_Ohm)                    # Source resistor.\n",
    "primary   = L(value=primary_inductance)    # Inductor for transformer primary.\n",
    "secondary = L(value=secondary_inductance)  # Inductor for transformer secondary.\n",
    "rload = R(value=100 @ u_Ohm)               # Load resistor.\n",
    "\n",
    "# This is the coupler between the inductors that transfers the\n",
    "# voltage from the primary to the secondary.\n",
    "coupler_prim_sec = K(ind1=primary, ind2=secondary, coupling=0.99)\n",
    "\n",
    "# Connect the voltage source to the primary through the source resistor.\n",
    "gnd & vs['n,p'] & rs & primary[1,2] & gnd\n",
    "\n",
    "# Connect the secondary to the load resistor.\n",
    "gnd & secondary[2,1] & rload & gnd\n",
    "\n",
    "# Simulate the transformer.\n",
    "sim=generate_netlist().simulator()\n",
    "waveforms = sim.transient(step_time=0.1 @ u_ms, end_time=100@u_ms)\n",
    "\n",
    "# Get the waveforms from the primary and secondary.\n",
    "time = waveforms.time * 10**3\n",
    "v_pri = waveforms[node(primary[1])]    # Input voltage at the transformer primary.\n",
    "v_sec = waveforms[node(secondary[1])]  # Output voltage at transformer secondary.\n",
    "\n",
    "# Plot the input and output waveforms.\n",
    "figure = plt.figure(1)\n",
    "plt.title('Primary Voltage vs. Secondary Voltage')\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Voltage (V)')\n",
    "plt.plot(time, v_pri)\n",
    "plt.plot(time, v_sec)\n",
    "plt.legend(('Primary Voltage', 'Secondary Voltage'), loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Transistor Amplifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The use of SPICE models is demonstrated in this example of a common-emitter transistor amplifier.\n",
    "For this example, a subdirectory called `SpiceLib` was created with a single file `2N2222A.lib` that holds\n",
    "the `.MODEL` statement for that particular type of transistor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:01.826513Z",
     "start_time": "2021-04-29T13:33:01.594772Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title \n",
      ".include /media/devb/Main/xesscorp/KiCad/tools/skidl/examples/spice-sim-intro/SpiceLib/2N2222A.lib\n",
      "Q1 N_1 N_2 0 2n2222a\n",
      "V1 N_3 0 5V\n",
      "R1 N_4 N_2 5kOhm\n",
      "R2 N_2 N_3 25kOhm\n",
      "R3 N_1 N_3 1kOhm\n",
      "V2 N_4 0 DC 0V AC 1V SIN(0V 0.01V 1kHz 0s 0Hz)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # Clear out the existing circuitry from the previous example.\n",
    "\n",
    "# Create a transistor, power supply, bias resistors, collector resistor, and an input sine wave source.\n",
    "q = BJT(model='2n2222a')    # 2N2222A NPN transistor. The model is stored in a directory of SPICE .lib files.\n",
    "vdc = V(dc_value=5@u_V)     # 5V power supply.\n",
    "rs = R(value=5@u_kOhm)      # Source resistor in series with sine wave input voltage.\n",
    "rb = R(value=25@u_kOhm)     # Bias resistor from 5V to base of transistor.\n",
    "rc = R(value=1@u_kOhm)      # Load resistor connected to collector of transistor.\n",
    "vs = SINEV(amplitude=0.01@u_V, frequency=1@u_kHz)  # 1 KHz sine wave input source.\n",
    "q['c', 'b', 'e'] += rc[1], rb[1], gnd  # Connect transistor CBE pins to load & bias resistors and ground.\n",
    "vdc['p'] += rc[2], rb[2]    # Connect other end of load and bias resistors to power supply's positive terminal.\n",
    "vdc['n'] += gnd             # Connect negative terminal of power supply to ground.\n",
    "rs[1,2] += vs['p'], q['b']  # Connect source resistor from input source to base of transistor.\n",
    "vs['n'] += gnd              # Connect negative terminal of input source to ground.\n",
    "\n",
    "# Simulate the transistor amplifier. This requires a SPICE library containing a model of the 2N2222A transistor.\n",
    "circ = generate_netlist()  # Pass the directory to the SPICE model library when creating circuit.\n",
    "print(circ)\n",
    "sim = circ.simulator() \n",
    "waveforms = sim.transient(step_time=0.01@u_ms, end_time=5@u_ms)\n",
    "\n",
    "# Get the input source and amplified output waveforms.\n",
    "time = waveforms.time\n",
    "vin = waveforms[node(vs['p'])]  # Input source voltage.\n",
    "vout = waveforms[node(q['c'])]  # Amplified output voltage at collector of the transistor.\n",
    "\n",
    "# Plot the input and output waveforms.\n",
    "figure = plt.figure(1)\n",
    "plt.title('Output Voltage vs. Input Voltage')\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Voltage (V)')\n",
    "plt.plot(time*1000, vin)\n",
    "plt.plot(time*1000, vout)\n",
    "plt.legend(('Input Voltage', 'Output Voltage'), loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XSPICE Parts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XSPICE parts can model a variety of functions (ADCs, DACs, etc.) having different I/O requirements, so SKiDL handles them a bit differently:\n",
    "1. An `io` parameter is needed to specify the input and output pins. This parameter is either a comma-separated string or an array of strings listing the pin names in the order required for the particular XSPICE part. XSPICE I/O ports can be *scalar* (indicated by names which are simple strings) or *vectors* (indicated by names ending with \"[]\").\n",
    "2. A `model` parameter is required that specifies the parameters affecting the behavior of the given XSPICE part. This is passed as an `XspiceModel` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:01.955509Z",
     "start_time": "2021-04-29T13:33:01.827908Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title \n",
      "V1 N_1 0 DC 0V AC 1V SIN(1.65V 1.65V 100000000.0Hz 0s 0Hz)\n",
      "A1 [N_1] [N_2] adc\n",
      "A2 N_2 N_3 buf\n",
      "A3 [N_3] [N_4] dac\n",
      "R1 N_4 0 1kOhm\n",
      ".model adc adc_bridge (fall_delay=1e-09s in_high=0.1V in_low=0.05V rise_delay=1e-09s)\n",
      ".model buf d_buffer (fall_delay=1e-09s input_load=1e-12s rise_delay=1e-09s)\n",
      ".model dac dac_bridge (out_high=3.3V out_low=1.0V)\n",
      "\n",
      "  vin   vout  \n",
      "===============\n",
      "  1.65   3.30\n",
      "  1.65   3.30\n",
      "  1.65   3.30\n",
      "  1.65   3.30\n",
      "  1.66   3.30\n",
      "  1.67   3.30\n",
      "  1.68   3.30\n",
      "  1.72   3.30\n",
      "  1.78   3.30\n",
      "  1.89   3.30\n",
      "  1.99   3.30\n",
      "  2.09   3.30\n",
      "  2.19   3.30\n",
      "  2.28   3.30\n",
      "  2.38   3.30\n",
      "  2.47   3.30\n",
      "  2.56   3.30\n",
      "  2.64   3.30\n",
      "  2.72   3.30\n",
      "  2.80   3.30\n",
      "  2.87   3.30\n",
      "  2.94   3.30\n",
      "  3.00   3.30\n",
      "  3.06   3.30\n",
      "  3.11   3.30\n",
      "  3.16   3.30\n",
      "  3.19   3.30\n",
      "  3.23   3.30\n",
      "  3.26   3.30\n",
      "  3.28   3.30\n",
      "  3.29   3.30\n",
      "  3.30   3.30\n",
      "  3.30   3.30\n",
      "  3.29   3.30\n",
      "  3.28   3.30\n",
      "  3.27   3.30\n",
      "  3.24   3.30\n",
      "  3.21   3.30\n",
      "  3.17   3.30\n",
      "  3.13   3.30\n",
      "  3.08   3.30\n",
      "  3.03   3.30\n",
      "  2.97   3.30\n",
      "  2.90   3.30\n",
      "  2.83   3.30\n",
      "  2.76   3.30\n",
      "  2.68   3.30\n",
      "  2.60   3.30\n",
      "  2.51   3.30\n",
      "  2.42   3.30\n",
      "  2.33   3.30\n",
      "  2.23   3.30\n",
      "  2.13   3.30\n",
      "  2.03   3.30\n",
      "  1.93   3.30\n",
      "  1.83   3.30\n",
      "  1.72   3.30\n",
      "  1.62   3.30\n",
      "  1.52   3.30\n",
      "  1.41   3.30\n",
      "  1.31   3.30\n",
      "  1.21   3.30\n",
      "  1.11   3.30\n",
      "  1.02   3.30\n",
      "  0.92   3.30\n",
      "  0.83   3.30\n",
      "  0.74   3.30\n",
      "  0.66   3.30\n",
      "  0.58   3.30\n",
      "  0.50   3.30\n",
      "  0.43   3.30\n",
      "  0.36   3.30\n",
      "  0.30   3.30\n",
      "  0.24   3.30\n",
      "  0.19   3.30\n",
      "  0.14   3.30\n",
      "  0.11   3.30\n",
      "  0.07   3.30\n",
      "  0.04   3.30\n",
      "  0.02   3.30\n",
      "  0.01   3.30\n",
      "  0.00   3.30\n",
      "  0.00   3.30\n",
      "  0.01   3.30\n",
      "  0.02   3.30\n",
      "  0.03   3.30\n",
      "  0.06   3.30\n",
      "  0.09   3.30\n",
      "  0.13   3.30\n",
      "  0.17   3.30\n",
      "  0.22   3.30\n",
      "  0.27   3.30\n",
      "  0.33   3.30\n",
      "  0.40   3.30\n",
      "  0.47   3.30\n",
      "  0.54   3.30\n",
      "  0.62   3.30\n",
      "  0.70   3.30\n",
      "  0.79   3.30\n",
      "  0.88   3.07\n",
      "  0.97   2.84\n",
      "  1.07   2.61\n",
      "  1.17   2.38\n",
      "  1.27   2.15\n",
      "  1.32   2.04\n",
      "  1.42   1.81\n",
      "  1.52   1.58\n",
      "  1.58   1.46\n",
      "  1.59   1.48\n",
      "  1.61   1.53\n",
      "  1.65   1.62\n",
      "  1.73   1.80\n",
      "  1.78   1.92\n",
      "  1.79   1.94\n",
      "  1.81   1.99\n",
      "  1.85   2.08\n",
      "  1.89   2.15\n",
      "  1.89   2.17\n",
      "  1.91   2.21\n",
      "  1.94   2.28\n",
      "  2.01   2.43\n",
      "  2.11   2.66\n",
      "  2.21   2.89\n",
      "  2.30   3.12\n",
      "  2.38   3.30\n",
      "  2.39   3.30\n",
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      "  3.03   3.30\n",
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      "  1.98   3.30\n",
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      "  1.06   3.30\n",
      "  0.97   3.30\n",
      "  0.88   3.30\n",
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      "  0.39   3.30\n",
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      "  0.06   3.30\n",
      "  0.03   3.30\n",
      "  0.02   3.30\n",
      "  0.00   3.30\n",
      "  0.00   3.30\n",
      "  0.00   3.30\n",
      "  0.01   3.30\n",
      "  0.03   3.30\n",
      "  0.05   3.30\n",
      "  0.07   3.30\n",
      "  0.11   3.30\n",
      "  0.15   3.30\n",
      "  0.19   3.30\n",
      "  0.24   3.30\n",
      "  0.30   3.30\n",
      "  0.36   3.30\n",
      "  0.43   3.30\n",
      "  0.50   3.30\n",
      "  0.58   3.30\n",
      "  0.66   3.30\n",
      "  0.67   3.28\n",
      "  0.69   3.23\n",
      "  0.72   3.14\n",
      "  0.79   2.95\n",
      "  0.84   2.84\n",
      "  0.84   2.82\n",
      "  0.86   2.77\n",
      "  0.90   2.68\n",
      "  0.97   2.49\n",
      "  1.07   2.26\n",
      "  1.12   2.15\n",
      "  1.13   2.13\n",
      "  1.15   2.08\n",
      "  1.19   1.99\n",
      "  1.27   1.80\n",
      "  1.37   1.57\n",
      "  1.47   1.34\n",
      "  1.58   1.11\n",
      "  1.63   1.00\n",
      "  1.64   1.00\n",
      "  1.66   1.05\n",
      "  1.70   1.14\n",
      "  1.73   1.21\n",
      "  1.74   1.23\n",
      "  1.76   1.26\n",
      "  1.79   1.34\n",
      "  1.85   1.48\n",
      "  1.96   1.71\n",
      "  2.06   1.94\n",
      "  2.15   2.15\n",
      "  2.16   2.17\n",
      "  2.18   2.22\n",
      "  2.22   2.31\n",
      "  2.29   2.50\n",
      "  2.39   2.73\n",
      "  2.48   2.96\n",
      "  2.57   3.19\n",
      "  2.61   3.30\n",
      "  2.62   3.30\n",
      "  2.63   3.30\n",
      "  2.67   3.30\n",
      "  2.73   3.30\n",
      "  2.81   3.30\n",
      "  2.88   3.30\n",
      "  2.95   3.30\n",
      "  3.01   3.30\n",
      "  3.06   3.30\n",
      "  3.11   3.30\n",
      "  3.16   3.30\n",
      "  3.20   3.30\n",
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      "  1.92   3.30\n",
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      "  1.01   3.30\n",
      "  0.91   3.30\n",
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      "  0.10   3.30\n",
      "  0.07   3.30\n",
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      "  0.02   3.30\n",
      "  0.01   3.30\n",
      "  0.00   3.30\n",
      "  0.00   3.30\n",
      "  0.01   3.30\n",
      "  0.02   3.30\n",
      "  0.04   3.30\n",
      "  0.06   3.30\n",
      "  0.09   3.30\n",
      "  0.13   3.30\n",
      "  0.17   3.30\n",
      "  0.22   3.30\n",
      "  0.28   3.30\n",
      "  0.34   3.30\n",
      "  0.40   3.30\n",
      "  0.47   3.30\n",
      "  0.55   3.30\n",
      "  0.63   3.30\n",
      "  0.71   3.30\n",
      "  0.72   3.28\n",
      "  0.74   3.23\n",
      "  0.77   3.14\n",
      "  0.80   3.07\n",
      "  0.81   3.05\n",
      "  0.82   3.01\n",
      "  0.85   2.94\n",
      "  0.91   2.79\n",
      "  1.00   2.56\n",
      "  1.10   2.33\n",
      "  1.18   2.15\n",
      "  1.19   2.13\n",
      "  1.21   2.08\n",
      "  1.25   1.99\n",
      "  1.33   1.80\n",
      "  1.43   1.57\n",
      "  1.53   1.34\n",
      "  1.59   1.23\n",
      "  1.60   1.25\n",
      "  1.62   1.30\n",
      "  1.66   1.39\n",
      "  1.69   1.46\n",
      "  1.70   1.48\n",
      "  1.71   1.52\n",
      "  1.75   1.59\n",
      "  1.79   1.69\n",
      "  1.80   1.70\n",
      "  1.81   1.73\n",
      "  1.84   1.79\n",
      "  1.89   1.91\n",
      "  1.99   2.14\n",
      "  2.00   2.15\n",
      "  2.01   2.17\n",
      "  2.02   2.21\n",
      "  2.05   2.28\n",
      "  2.12   2.43\n",
      "  2.22   2.66\n",
      "  2.31   2.89\n",
      "  2.41   3.12\n",
      "  2.48   3.30\n",
      "  2.49   3.30\n",
      "  2.51   3.30\n",
      "  2.54   3.30\n",
      "  2.61   3.30\n",
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      "  2.98   3.30\n",
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      "  2.99   3.30\n",
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      "  2.36   3.30\n",
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      "  1.97   3.30\n",
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      "  0.96   3.30\n",
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      "  0.26   3.30\n",
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      "  0.01   3.30\n",
      "  0.00   3.30\n",
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      "  0.67   3.30\n",
      "  0.68   3.28\n",
      "  0.70   3.23\n",
      "  0.73   3.14\n",
      "  0.80   2.96\n",
      "  0.84   2.84\n",
      "  0.85   2.82\n",
      "  0.87   2.77\n",
      "  0.91   2.68\n",
      "  0.98   2.50\n",
      "  1.08   2.26\n",
      "  1.13   2.15\n",
      "  1.14   2.13\n",
      "  1.16   2.08\n",
      "  1.20   1.99\n",
      "  1.28   1.81\n",
      "  1.38   1.58\n",
      "  1.48   1.35\n",
      "  1.59   1.12\n",
      "  1.64   1.00\n",
      "  1.65   1.00\n",
      "  1.67   1.05\n",
      "  1.71   1.14\n",
      "  1.74   1.21\n",
      "  1.75   1.23\n",
      "  1.77   1.26\n",
      "  1.80   1.34\n",
      "  1.87   1.48\n",
      "  1.97   1.71\n",
      "  2.07   1.94\n",
      "  2.16   2.15\n",
      "  2.17   2.17\n",
      "  2.19   2.22\n",
      "  2.23   2.31\n",
      "  2.30   2.49\n",
      "  2.40   2.72\n",
      "  2.49   2.95\n",
      "  2.58   3.18\n",
      "  2.62   3.30\n",
      "  2.63   3.30\n",
      "  2.64   3.30\n",
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      "  3.01   3.30\n",
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      "  3.23   3.30\n",
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      "  3.30   3.30\n",
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      "  3.17   3.30\n",
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      "  3.07   3.30\n",
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      "  2.96   3.30\n",
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      "  2.21   3.30\n",
      "  2.11   3.30\n",
      "  2.01   3.30\n",
      "  1.91   3.30\n",
      "  1.81   3.30\n",
      "  1.70   3.30\n",
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      "  1.39   3.30\n",
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      "  1.19   3.30\n",
      "  1.09   3.30\n",
      "  1.00   3.30\n",
      "  0.90   3.30\n",
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      "  0.72   3.30\n",
      "  0.64   3.30\n",
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      "  0.07   3.30\n",
      "  0.04   3.30\n",
      "  0.02   3.30\n",
      "  0.01   3.30\n",
      "  0.00   3.30\n",
      "  0.00   3.30\n",
      "  0.01   3.30\n",
      "  0.02   3.30\n",
      "  0.04   3.30\n",
      "  0.06   3.30\n",
      "  0.10   3.30\n",
      "  0.13   3.30\n",
      "  0.18   3.30\n",
      "  0.23   3.30\n",
      "  0.28   3.30\n",
      "  0.34   3.30\n",
      "  0.41   3.30\n",
      "  0.48   3.30\n",
      "  0.56   3.30\n",
      "  0.64   3.30\n",
      "  0.65   3.28\n",
      "  0.66   3.23\n",
      "  0.70   3.14\n",
      "  0.76   2.96\n",
      "  0.81   2.84\n",
      "  0.82   2.82\n",
      "  0.84   2.77\n",
      "  0.87   2.68\n",
      "  0.95   2.50\n",
      "  1.04   2.26\n",
      "  1.09   2.15\n",
      "  1.10   2.13\n",
      "  1.12   2.08\n",
      "  1.16   1.99\n",
      "  1.24   1.81\n",
      "  1.34   1.58\n",
      "  1.44   1.35\n",
      "  1.54   1.12\n",
      "  1.60   1.00\n",
      "  1.60   1.00\n",
      "  1.61   1.02\n",
      "  1.63   1.07\n",
      "  1.65   1.11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reset()  # Clear out the existing circuitry from the previous example.\n",
    "\n",
    "# Sinusoidal voltage source.\n",
    "vin = sinev(offset=1.65 @ u_V, amplitude=1.65 @ u_V, frequency=100e6)\n",
    "\n",
    "# Component declarations showing various XSPICE styles.\n",
    "\n",
    "# Creating an XSPICE part from the pyspice library.\n",
    "adc = Part(\n",
    "    \"pyspice\",\n",
    "    \"A\",\n",
    "    io=\"anlg_in[], dig_out[]\",  # Two vector I/O ports in a string.\n",
    "    model=XspiceModel(\n",
    "        \"adc\",  # The name assigned to this particular model instance.\n",
    "        \"adc_bridge\",  # The name of the XSPICE part associated with this model.\n",
    "        # The rest of the arguments are keyword parameters for the model.\n",
    "        in_low=0.05 @ u_V,\n",
    "        in_high=0.1 @ u_V,\n",
    "        rise_delay=1e-9 @ u_s,\n",
    "        fall_delay=1e-9 @ u_s,\n",
    "    ),\n",
    "    tool=SKIDL\n",
    ")\n",
    "\n",
    "# Creating an XSPICE part using the SPICE abbreviation 'A'.\n",
    "buf = A(\n",
    "    io=\"buf_in, buf_out\",  # Two scalar I/O ports in a string.\n",
    "    model=XspiceModel(\n",
    "        \"buf\",\n",
    "        \"d_buffer\",\n",
    "        rise_delay=1e-9 @ u_s,\n",
    "        fall_delay=1e-9 @ u_s,\n",
    "        input_load=1e-12 @ u_s,\n",
    "    ),\n",
    ")\n",
    "\n",
    "# Creating an XSPICE part using the XSPICE alias.\n",
    "dac = XSPICE(\n",
    "    io=[\"dig_in[]\", \"anlg_out[]\"],  # Two vector ports in an array.\n",
    "    model=XspiceModel(\"dac\", \"dac_bridge\", out_low=1.0 @ u_V, out_high=3.3 @ u_V),\n",
    ")\n",
    "\n",
    "r = R(value=1 @ u_kOhm)  # Load resistor.\n",
    "\n",
    "# Connections: sine wave -> ADC -> buffer -> DAC.\n",
    "vin[\"p, n\"] += adc[\"anlg_in\"][0], gnd  # Attach to first pin in ADC anlg_in vector of pins.\n",
    "adc[\"dig_out\"][0] += buf[\"buf_in\"]  # Attach first pin of ADC dig_out vector to buffer.\n",
    "buf[\"buf_out\"] += dac[\"dig_in\"][0]  # Attach buffer output to first pin of DAC dig_in vector of pins.\n",
    "r[\"p,n\"] += dac[\"anlg_out\"][0], gnd  # Attach first pin of DAC anlg_out vector to load resistor.\n",
    "\n",
    "circ = generate_netlist(libs=\"SpiceLib\")\n",
    "print(circ)\n",
    "sim = circ.simulator()\n",
    "waveforms = sim.transient(step_time=0.1 @ u_ns, end_time=50 @ u_ns)\n",
    "time = waveforms.time\n",
    "vin = waveforms[node(vin[\"p\"])]\n",
    "vout = waveforms[node(r[\"p\"])]\n",
    "\n",
    "print('{:^7s}{:^7s}'.format('vin', 'vout'))\n",
    "print('='*15)\n",
    "for v1, v2 in zip(vin.as_ndarray(), vout.as_ndarray()):\n",
    "    print('{:6.2f} {:6.2f}'.format(v1, v2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Hierarchical Circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SKiDL lets you describe a circuit inside a function, and then call that function to create hierarchical designs that can be analyzed with SPICE. This example defines a simple transistor inverter and then cascades several of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:02.387267Z",
     "start_time": "2021-04-29T13:33:01.987718Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()  # You know what this does by now, right?\n",
    "\n",
    "# Create a power supply for all the following circuitry.\n",
    "pwr = V(dc_value=5@u_V)\n",
    "pwr['n'] += gnd\n",
    "vcc = pwr['p']\n",
    "\n",
    "# Create a logic inverter using a transistor and a few resistors.\n",
    "@subcircuit\n",
    "def inverter(inp, outp):\n",
    "    '''When inp is driven high, outp is pulled low by transistor. When inp is driven low, outp is pulled high by resistor.'''\n",
    "    q = BJT(model='2n2222a')  # NPN transistor.\n",
    "    rc = R(value=1@u_kOhm)    # Resistor attached between transistor collector and VCC.\n",
    "    rc[1,2] += vcc, q['c']    \n",
    "    rb = R(value=10@u_kOhm)   # Resistor attached between transistor base and input.\n",
    "    rb[1,2] += inp, q['b']\n",
    "    q['e'] += gnd             # Transistor emitter attached to ground.\n",
    "    outp += q['c']            # Inverted output comes from junction of the transistor collector and collector resistor.\n",
    "    \n",
    "# Create a pulsed voltage source to drive the input of the inverters. I set the rise and fall times to make\n",
    "# it easier to distinguish the input and output waveforms in the plot.\n",
    "vs = PULSEV(initial_value=0, pulsed_value=5@u_V, pulse_width=0.8@u_ms, period=2@u_ms, rise_time=0.2@u_ms, fall_time=0.2@u_ms)  # 1ms ON, 1ms OFF pulses.\n",
    "vs['n'] += gnd\n",
    "\n",
    "# Create three inverters and cascade the output of one to the input of the next.\n",
    "outp = Net() * 3  # Create three nets to attach to the outputs of each inverter.\n",
    "inverter(vs['p'], outp[0])  # First inverter is driven by the pulsed voltage source.\n",
    "inverter(outp[0], outp[1])  # Second inverter is driven by the output of the first.\n",
    "inverter(outp[1], outp[2])  # Third inverter is driven by the output of the second.\n",
    "\n",
    "# Simulate the cascaded inverters.\n",
    "circ = generate_netlist() # Pass-in the library where the transistor model is stored.\n",
    "sim = circ.simulator()\n",
    "waveforms = sim.transient(step_time=0.01@u_ms, end_time=5@u_ms)\n",
    "\n",
    "# Get the waveforms for the input and output.\n",
    "time = waveforms.time\n",
    "inp = waveforms[node(vs['p'])]\n",
    "outp = waveforms[node(outp[2])]  # Get the output waveform for the final inverter in the cascade.\n",
    "\n",
    "# Plot the input and output waveforms. The output will be the inverse of the input since it passed\n",
    "# through three inverters.\n",
    "figure = plt.figure(1)\n",
    "plt.title('Output Voltage vs. Input Voltage')\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Voltage (V)')\n",
    "plt.plot(time*1000, inp)\n",
    "plt.plot(time*1000, outp)\n",
    "plt.legend(('Input Voltage', 'Output Voltage'), loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using SPICE Subcircuits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `@subcircuit` lets you do hierarchical design directly in SKiDL, but SPICE has long had another option: *subcircuits*. These are encapsulations of device behavior stored in SPICE library files.\n",
    "Many thousands of these have been created over the years, both by SPICE users and semiconductor companies.\n",
    "A simulation of the [NCP1117 voltage regulator](http://www.onsemi.com/pub/Collateral/NCP1117%20PSPICE%20MODELS.ZIP)\n",
    "from [ON Semiconductor](http://www.onsemi.com/) is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:02.621663Z",
     "start_time": "2021-04-29T13:33:02.389806Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ncp1117_33-x (): 1.0a low-dropout positive fixed and adjustable voltage regulators\n",
      "    Pin X1/3/in/UNSPECIFIED\n",
      "    Pin X1/1/out/UNSPECIFIED\n",
      "    Pin X1/2/gnd/UNSPECIFIED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reset()\n",
    "\n",
    "lib_search_paths[SPICE].append('SpiceLib')\n",
    "\n",
    "vin = V(ref='VIN', dc_value=8@u_V)      # Input power supply.\n",
    "vreg = Part('NCP1117', 'ncp1117_33-x')  # Voltage regulator from ON Semi part lib.\n",
    "print(vreg)                             # Print vreg pin names.\n",
    "r = R(value=470 @ u_Ohm)                # Load resistor on regulator output.\n",
    "vreg['IN', 'OUT'] += vin['p'], r[1]     # Connect vreg input to vin and output to load resistor.\n",
    "gnd += vin['n'], r[2], vreg['GND']      # Ground connections for everybody.\n",
    "\n",
    "# Simulate the voltage regulator subcircuit.\n",
    "circ = generate_netlist() # Pass-in the library where the voltage regulator subcircuit is stored.\n",
    "sim = circ.simulator()\n",
    "dc_vals = sim.dc(VIN=slice(0,10,0.1)) # Ramp vin from 0->10V and observe regulator output voltage.\n",
    "\n",
    "# Get the input and output voltages.\n",
    "inp = dc_vals[node(vin['p'])]\n",
    "outp = dc_vals[node(vreg['OUT'])]\n",
    "\n",
    "# Plot the regulator output voltage vs. the input supply voltage. Note that the regulator\n",
    "# starts to operate once the input exceeds 4V and the output voltage clamps at 3.3V.\n",
    "figure = plt.figure(1)\n",
    "plt.title('NCP1117-3.3 Regulator Output Voltage vs. Input Voltage')\n",
    "plt.xlabel('Input Voltage (V)')\n",
    "plt.ylabel('Output Voltage (V)')\n",
    "plt.plot(inp, outp)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SKiDL can work with SPICE subcircuits intended for PSPICE and LTSpice. All you need to do is add the top-level directories where the subcircuit libraries are stored and SKiDL will recursively search for the library files. When it reads a subcircuit library file (indicated by a `.lib` file extension), SKiDL will also look for a *symbol file* that provides names for the subcircuit I/O signals. For PSPICE, the symbol file has a `.slb` extension while the `.asy` extension is used for LTSpice.\n",
    "\n",
    "***WARNING***: Even though SKiDL will read the PSPICE and LTSpice library files, that doesn't mean that ngspice can process them. Each SPICE simulator seems to support a different set of optional parameters for the various circuit elements (the nonlinear current source, `G`, for example). You will probably have to modify the library file to satisfy ngspice. PSPICE libraries seem to need the least modification. I wish it was easier, but it's not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The examples section gave you some idea of what the combination of SKiDL, PySpice, ngspice, and matplotlib can do.\n",
    "You should read the stand-alone documentation for each of those packages to get the full extent of their capabilities.\n",
    "This section will address the features and functions that come in to play at their intersection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed the use of units in the examples above, such as `10 @ u_kOhm` to denote a resistance of 10 K$\\Omega$.\n",
    "This is a feature of the PySpice package. If you want to see all the available units, just do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:02.639703Z",
     "start_time": "2021-04-29T13:33:02.624052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Frequency, FrequencyValue, FrequencyValues, Period, PeriodValue, PeriodValues, SiUnits, U_A, U_Bq, U_C, U_Degree, U_F, U_GA, U_GBq, U_GC, U_GDegree, U_GF, U_GGy, U_GH, U_GHz, U_GJ, U_GK, U_GN, U_GOhm, U_GPa, U_GS, U_GSv, U_GV, U_GW, U_GWb, U_Gcd, U_Gkat, U_Glm, U_Glx, U_Gm, U_Gmol, U_Grad, U_Gs, U_Gsr, U_Gy, U_GΩ, U_H, U_Hz, U_J, U_K, U_MA, U_MBq, U_MC, U_MDegree, U_MF, U_MGy, U_MH, U_MHz, U_MJ, U_MK, U_MN, U_MOhm, U_MPa, U_MS, U_MSv, U_MV, U_MW, U_MWb, U_Mcd, U_Mkat, U_Mlm, U_Mlx, U_Mm, U_Mmol, U_Mrad, U_Ms, U_Msr, U_MΩ, U_N, U_Ohm, U_Pa, U_S, U_Sv, U_TA, U_TBq, U_TC, U_TDegree, U_TF, U_TGy, U_TH, U_THz, U_TJ, U_TK, U_TN, U_TOhm, U_TPa, U_TS, U_TSv, U_TV, U_TW, U_TWb, U_Tcd, U_Tkat, U_Tlm, U_Tlx, U_Tm, U_Tmol, U_Trad, U_Ts, U_Tsr, U_TΩ, U_V, U_W, U_Wb, U_cd, U_kA, U_kBq, U_kC, U_kDegree, U_kF, U_kGy, U_kH, U_kHz, U_kJ, U_kK, U_kN, U_kOhm, U_kPa, U_kS, U_kSv, U_kV, U_kW, U_kWb, U_kat, U_kcd, U_kkat, U_klm, U_klx, U_km, U_kmol, U_krad, U_ks, U_ksr, U_kΩ, U_lm, U_lx, U_m, U_mA, U_mBq, U_mC, U_mDegree, U_mF, U_mGy, U_mH, U_mHz, U_mJ, U_mK, U_mN, U_mOhm, U_mPa, U_mS, U_mSv, U_mV, U_mW, U_mWb, U_mcd, U_mkat, U_mlm, U_mlx, U_mm, U_mmol, U_mol, U_mrad, U_ms, U_msr, U_mΩ, U_nA, U_nBq, U_nC, U_nDegree, U_nF, U_nGy, U_nH, U_nHz, U_nJ, U_nK, U_nN, U_nOhm, U_nPa, U_nS, U_nSv, U_nV, U_nW, U_nWb, U_ncd, U_nkat, U_nlm, U_nlx, U_nm, U_nmol, U_nrad, U_ns, U_nsr, U_nΩ, U_pA, U_pBq, U_pC, U_pDegree, U_pF, U_pGy, U_pH, U_pHz, U_pJ, U_pK, U_pN, U_pOhm, U_pPa, U_pS, U_pSv, U_pV, U_pW, U_pWb, U_pcd, U_pkat, U_plm, U_plx, U_pm, U_pmol, U_prad, U_ps, U_psr, U_pΩ, U_rad, U_s, U_sr, U_uA, U_uBq, U_uC, U_uDegree, U_uF, U_uGy, U_uH, U_uHz, U_uJ, U_uK, U_uN, U_uOhm, U_uPa, U_uS, U_uSv, U_uV, U_uW, U_uWb, U_ucd, U_ukat, U_ulm, U_ulx, U_um, U_umol, U_urad, U_us, U_usr, U_Ω, U_μA, U_μBq, U_μC, U_μF, U_μGy, U_μH, U_μHz, U_μJ, U_μK, U_μN, U_μPa, U_μS, U_μSv, U_μV, U_μW, U_μWb, U_μcd, U_μkat, U_μlm, U_μlx, U_μm, U_μmol, U_μrad, U_μs, U_μsr, U_μΩ, Unit, UnitValueShorcut, _SiUnits, _Unit, __builtins__, __cached__, __doc__, __file__, __loader__, __name__, __package__, __path__, __spec__, _build_as_unit_shortcut, _build_prefix_shortcut, _build_unit_prefix_shortcut, _build_unit_shortcut, _build_unit_type_shortcut, _exec_body, _has_matmul, _module_logger, _to_ascii, _version_info, as_A, as_Bq, as_C, as_Degree, as_F, as_Gy, as_H, as_Hz, as_J, as_K, as_N, as_Ohm, as_Pa, as_S, as_Sv, as_V, as_W, as_Wb, as_cd, as_kat, as_lm, as_lx, as_m, as_mol, as_rad, as_s, as_sr, as_Ω, atto, deca, define_shortcut, exa, femto, giga, hecto, kilo, logging, mega, micro, milli, nano, peta, pico, sys, tera, u_A, u_Bq, u_C, u_Degree, u_F, u_GA, u_GBq, u_GC, u_GDegree, u_GF, u_GGy, u_GH, u_GHz, u_GJ, u_GK, u_GN, u_GOhm, u_GPa, u_GS, u_GSv, u_GV, u_GW, u_GWb, u_Gcd, u_Gkat, u_Glm, u_Glx, u_Gm, u_Gmol, u_Grad, u_Gs, u_Gsr, u_Gy, u_GΩ, u_H, u_Hz, u_J, u_K, u_MA, u_MBq, u_MC, u_MDegree, u_MF, u_MGy, u_MH, u_MHz, u_MJ, u_MK, u_MN, u_MOhm, u_MPa, u_MS, u_MSv, u_MV, u_MW, u_MWb, u_Mcd, u_Mkat, u_Mlm, u_Mlx, u_Mm, u_Mmol, u_Mrad, u_Ms, u_Msr, u_MΩ, u_N, u_Ohm, u_Pa, u_S, u_Sv, u_TA, u_TBq, u_TC, u_TDegree, u_TF, u_TGy, u_TH, u_THz, u_TJ, u_TK, u_TN, u_TOhm, u_TPa, u_TS, u_TSv, u_TV, u_TW, u_TWb, u_Tcd, u_Tkat, u_Tlm, u_Tlx, u_Tm, u_Tmol, u_Trad, u_Ts, u_Tsr, u_TΩ, u_V, u_W, u_Wb, u_cd, u_kA, u_kBq, u_kC, u_kDegree, u_kF, u_kGy, u_kH, u_kHz, u_kJ, u_kK, u_kN, u_kOhm, u_kPa, u_kS, u_kSv, u_kV, u_kW, u_kWb, u_kat, u_kcd, u_kkat, u_klm, u_klx, u_km, u_kmol, u_krad, u_ks, u_ksr, u_kΩ, u_lm, u_lx, u_m, u_mA, u_mBq, u_mC, u_mDegree, u_mF, u_mGy, u_mH, u_mHz, u_mJ, u_mK, u_mN, u_mOhm, u_mPa, u_mS, u_mSv, u_mV, u_mW, u_mWb, u_mcd, u_mkat, u_mlm, u_mlx, u_mm, u_mmol, u_mol, u_mrad, u_ms, u_msr, u_mΩ, u_nA, u_nBq, u_nC, u_nDegree, u_nF, u_nGy, u_nH, u_nHz, u_nJ, u_nK, u_nN, u_nOhm, u_nPa, u_nS, u_nSv, u_nV, u_nW, u_nWb, u_ncd, u_nkat, u_nlm, u_nlx, u_nm, u_nmol, u_nrad, u_ns, u_nsr, u_nΩ, u_pA, u_pBq, u_pC, u_pDegree, u_pF, u_pGy, u_pH, u_pHz, u_pJ, u_pK, u_pN, u_pOhm, u_pPa, u_pS, u_pSv, u_pV, u_pW, u_pWb, u_pcd, u_pkat, u_plm, u_plx, u_pm, u_pmol, u_prad, u_ps, u_psr, u_pΩ, u_rad, u_s, u_sr, u_uA, u_uBq, u_uC, u_uDegree, u_uF, u_uGy, u_uH, u_uHz, u_uJ, u_uK, u_uN, u_uOhm, u_uPa, u_uS, u_uSv, u_uV, u_uW, u_uWb, u_ucd, u_ukat, u_ulm, u_ulx, u_um, u_umol, u_urad, u_us, u_usr, u_Ω, u_μA, u_μBq, u_μC, u_μF, u_μGy, u_μH, u_μHz, u_μJ, u_μK, u_μN, u_μPa, u_μS, u_μSv, u_μV, u_μW, u_μWb, u_μcd, u_μkat, u_μlm, u_μlx, u_μm, u_μmol, u_μrad, u_μs, u_μsr, u_μΩ, unit, unit_prefix, unit_value, yocto, yotta, zepto, zetta'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import PySpice.Unit\n",
    "', '.join(dir(PySpice.Unit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following units are the ones you'll probably use most:\n",
    "\n",
    "* Potential: `u_TV` (terravolt), `u_GV` (gigavolt), `u_MV` (megavolt), `u_kV` (kilovolt), `u_V` (volt), `u_mV` (millivolt), `u_uV` (microvolt), `u_nV` (nanovolt), `u_pV` (picovolt).\n",
    "* Current: `u_TA` (terraamp), `u_GA` (gigaamp), `u_MA` (megaamp), `u_kA` (kiloamp), `u_A` (amp), `u_mA` (milliamp), `u_uA` (microamp), `u_nA` (nanoamp), `u_pA` (picoamp).\n",
    "* Resistance: `u_TOhm` (terraohm), `u_GOhm` (gigaohm), `u_MOhm` (megaohm), `u_kOhm` (kiloohm), `u_Ohm` (ohm), `u_mOhm` (milliohm), `u_uOhm` (microohm), `u_nOhm` (nanoohm), `u_pOhm` (picoohm).\n",
    "* Capacitance: `u_TF` (terrafarad) `u_GF` (gigafarad), `u_MF` (megafarad), `u_kF` (kilofarad), `u_F` (farad), `u_mF` (millifarad), `u_uF` (microfarad), `u_nF` (nanofarad), `u_pF` (picofarad).\n",
    "* Inductance: `u_TH` (terrahenry), `u_GH` (gigahenry), `u_MH` (megahenry), `u_kH` (kilohenry), `u_H` (henry), `u_mH` (millihenry), `u_uH` (microhenry), `u_nH` (nanohenry), `u_pH` (picohenry).\n",
    "* Time: `u_Ts` (terrasecond), `u_Gs` (gigasecond), `u_Ms` (megasecond), `u_ks` (kilosecond), `u_s` (second), `u_ms` (millisecond), `u_us` (microsecond), `u_ns` (nanosecond), `u_ps` (picosecond).\n",
    "* Frequency: `u_THz` (terrahertz), `u_GHz` (gigahertz), `u_MHz` (megahertz), `u_kHz` (kilohertz), `u_Hz` (hertz), `u_mHz` (millihertz), `u_uHz` (microhertz), `u_nHz` (nanohertz), `u_pHz` (picohertz)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Available Parts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is a list of parts (and their aliases) that are available for use in a SPICE simulation.\n",
    "Many parts (like resistors) have only two pins denoted `p` and `n`, while some parts (like transmission lines)\n",
    "have two *ports* composed of pins `ip`, `in` (the input port) and `op`, `on` (the output port).\n",
    "The parts also have *attributes* that modify their characteristics.\n",
    "These attributes can be set when a part is instantiated:\n",
    "\n",
    "    r = R(value=1@u_kOhm)\n",
    "    \n",
    "or they can be set after instantiation like this:\n",
    "\n",
    "    r.value = 1 @ u_kOhm\n",
    "\n",
    "You can go [here](https://bwrcs.eecs.berkeley.edu/Classes/IcBook/SPICE/UserGuide/elements_fr.html)\n",
    "for more information about what device characteristics the attributes control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:02.662351Z",
     "start_time": "2021-04-29T13:33:02.641518Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A (XSPICE, xspice): XSPICE code module\n",
      "\tPins: \n",
      "\tAttributes: model\n",
      "\n",
      "B (BEHAVIORALSOURCE, behavioralsource, behavsrc, BEHAVSRC): Behavioral (arbitrary) source\n",
      "\tPins: p, n\n",
      "\tAttributes: i, i_expression, v, v_expression, tc1, tc2, temp, temperature, dtemp, device_temperature, p, n\n",
      "\n",
      "C (CAP, cap): Capacitor\n",
      "\tPins: p, n\n",
      "\tAttributes: value, capacitance, model, multiplier, m, scale, temp, temperature, dtemp, device_temperature, ic, initial_condition, p, n\n",
      "\n",
      "BEHAVCAP (behavioralcap, BEHAVIORALCAP, behavcap): Behavioral capacitor\n",
      "\tPins: p, n\n",
      "\tAttributes: expression, tc1, tc2, p, n\n",
      "\n",
      "SEMICAP (semicap, SEMICONDUCTORCAP, semiconductorcap): Semiconductor capacitor\n",
      "\tPins: p, n\n",
      "\tAttributes: value, model, length, l, width, w, multiplier, m, scale, temp, temperature, dtemp, device_temperature, ic, initial_condition, p, n\n",
      "\n",
      "D (diode, DIODE): Diode\n",
      "\tPins: p, n\n",
      "\tAttributes: model, area, multiplier, m, pj, off, ic, initial_condition, temp, temperature, dtemp, device_temperature, p, n\n",
      "\n",
      "E (vcvs, VCVS): Voltage-controlled voltage source\n",
      "\tPins: ip, in, op, on\n",
      "\tAttributes: gain, voltage_gain, op, on, ip, in\n",
      "\n",
      "NONLINV (NONLINEARVOLTAGESOURCE, nonlinv, nonlinearvoltagesource): Nonlinear voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: expression, table, p, n\n",
      "\n",
      "F (cccs, CCCS): Current-controlled current source\n",
      "\tPins: p, n\n",
      "\tAttributes: control, source, gain, current_gain, multiplier, m, p, n\n",
      "\n",
      "G (): Voltage-controlled current source\n",
      "\tPins: ip, in, op, on\n",
      "\tAttributes: gain, current_gain, multiplier, m, op, on, ip, in\n",
      "\n",
      "NONLINI (nonlinearcurrentsource, NONLINEARCURRENTSOURCE, nonlinvi): Nonlinear current source\n",
      "\tPins: p, n\n",
      "\tAttributes: expression, table, p, n\n",
      "\n",
      "H (ccvs, CCVS): Current-controlled voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: control, source, transresistance, p, n\n",
      "\n",
      "I (CS, cs, i): Current source\n",
      "\tPins: p, n\n",
      "\tAttributes: value, dc_value, p, n\n",
      "\n",
      "J (jfet, JFET): Junction field-effect transistor\n",
      "\tPins: d, g, s\n",
      "\tAttributes: model, area, multiplier, m, off, ic, initial_condition, temp, temperature, d, g, s\n",
      "\n",
      "K (): Coupled (mutual) inductors\n",
      "\tPins: \n",
      "\tAttributes: ind1, ind2, coupling\n",
      "\n",
      "L (): Inductor\n",
      "\tPins: p, n\n",
      "\tAttributes: value, inductance, model, nt, multiplier, m, scale, temp, temperature, dtemp, device_temperature, ic, initial_condition, p, n\n",
      "\n",
      "BEHAVIND (behavind, behavioralind, BEHAVIORALIND): Behavioral inductor\n",
      "\tPins: p, n\n",
      "\tAttributes: expression, tc1, tc2, p, n\n",
      "\n",
      "M (fet, MOSFET, mosfet, FET): Metal-oxide field-effect transistor\n",
      "\tPins: d, g, s, b\n",
      "\tAttributes: model, multiplier, m, l, length, w, width, drain_area, source_area, drain_perimeter, source_perimeter, drain_number_square, source_number_square, off, ic, initial_condition, temp, temperature, d, g, s, b\n",
      "\n",
      "N (): Numerical device for GSS\n",
      "\tPins: \n",
      "\tAttributes: \n",
      "\n",
      "O (): Lossy transmission line\n",
      "\tPins: ip, in, op, on\n",
      "\tAttributes: model, op, on, ip, in\n",
      "\n",
      "P (): Coupled multiconductor line\n",
      "\tPins: \n",
      "\tAttributes: model, length, l, op, on, ip, in\n",
      "\n",
      "Q (bjt, BJT): Bipolar Junction Transistor\n",
      "\tPins: c, b, e, s\n",
      "\tAttributes: model, area, areab, areac, multiplier, m, off, ic, initial_condition, temp, temperature, dtemp, device_temperature, c, b, e, s\n",
      "\n",
      "R (): Resistor\n",
      "\tPins: p, n\n",
      "\tAttributes: value, resistance, ac, multiplier, m, scale, temp, temperature, dtemp, device_temperature, noisy, p, n\n",
      "\n",
      "BEHAVRES (behavioralresistor, BEHAVIORALRESISTOR, behavres): Behavioral resistor\n",
      "\tPins: p, n\n",
      "\tAttributes: expression, tc1, tc2, p, n\n",
      "\n",
      "SEMIRES (semires, semiconductorresistor, SEMICONDUCTORRESISTOR): Semiconductor resistor\n",
      "\tPins: p, n\n",
      "\tAttributes: value, capacitance, model, ac, length, l, width, w, multiplier, m, scale, temp, temperature, dtemp, device_temperature, noisy, p, n\n",
      "\n",
      "S (VCS, vcs): Voltage-controlled switch\n",
      "\tPins: ip, in, op, on\n",
      "\tAttributes: model, initial_state, op, on, ip, in\n",
      "\n",
      "T (TRANSMISSIONLINE, transmissionline): Transmission line\n",
      "\tPins: ip, in, op, on\n",
      "\tAttributes: impedance, Z0, time_delay, TD, frequency, F, normalized_length, NL, op, on, ip, in\n",
      "\n",
      "U (): Uniformly-distributed RC line\n",
      "\tPins: o, i, cn\n",
      "\tAttributes: model, length, l, number_of_lumps, m, o, i, cn\n",
      "\n",
      "V (ammeter, vs, VS, AMMETER, v): Voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: value, dc_value, p, n\n",
      "\n",
      "W (CCS, ccs): Current-controlled switch\n",
      "\tPins: p, n\n",
      "\tAttributes: control, source, model, initial_state, p, n\n",
      "\n",
      "Y (): Single lossy transmission line\n",
      "\tPins: ip, in, op, on\n",
      "\tAttributes: model, length, l, op, on, ip, in\n",
      "\n",
      "Z (MESFET, mesfet): Metal-semiconductor field-effect transistor\n",
      "\tPins: d, g, s\n",
      "\tAttributes: model, area, multiplier, m, off, ic, initial_condition, d, g, s\n",
      "\n",
      "SINEV (sinev, SINUSOIDALVOLTAGE, sinusoidalvoltage): Sinusoidal voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: dc_offset, ac_magnitude, ac_phase, offset, amplitude, frequency, delay, damping_factor, p, n\n",
      "\n",
      "SINEI (SINUSOIDALCURRENT, sinei, sinusoidalcurrent): Sinusoidal current source\n",
      "\tPins: p, n\n",
      "\tAttributes: dc_offset, ac_magnitude, ac_phase, offset, amplitude, frequency, delay, damping_factor, p, n\n",
      "\n",
      "PULSEV (pulsevoltage, PULSEVOLTAGE, pulsev): Pulsed voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: initial_value, pulsed_value, delay_time, rise_time, fall_time, pulse_width, period, p, n\n",
      "\n",
      "PULSEI (pulsecurrent, PULSECURRENT, pulsei): Pulsed current source\n",
      "\tPins: p, n\n",
      "\tAttributes: initial_value, pulsed_value, delay_time, rise_time, fall_time, pulse_width, period, p, n\n",
      "\n",
      "EXPV (expv, exponentialvoltage, EXPONENTIALVOLTAGE): Exponential voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: initial_value, pulsed_value, rise_delay_time, rise_time_constant, fall_delay_time, fall_time_constant, p, n\n",
      "\n",
      "EXPI (exponentialcurrent, EXPONENTIALCURRENT, expi): Exponential current source\n",
      "\tPins: p, n\n",
      "\tAttributes: initial_value, pulsed_value, rise_delay_time, rise_time_constant, fall_delay_time, fall_time_constant, p, n\n",
      "\n",
      "PWLV (PIECEWISELINEARVOLTAGE, pwlv, piecewiselinearvoltage): Piecewise linear voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: values, repeate_time, delay_time, p, n\n",
      "\n",
      "PWLI (pwli, piecewiselinearcurrent, PIECEWISELINEARCURRENT): Piecewise linear current source\n",
      "\tPins: p, n\n",
      "\tAttributes: values, repeate_time, delay_time, p, n\n",
      "\n",
      "FMV (SFFMV, SINGLEFREQUENCYFMVOLTAGE, sffmv, singlefrequencyfmvoltage, fmv): Single-frequency FM-modulated voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: offset, amplitude, carrier_frequency, modulation_index, signal_frequency, p, n\n",
      "\n",
      "FMI (singlefrequencyfmcurrent, fmi, sffmi, SFFMI, SINGLEFREQUENCYFMCURRENT): Single-frequency FM-modulated current source\n",
      "\tPins: p, n\n",
      "\tAttributes: offset, amplitude, carrier_frequency, modulation_index, signal_frequency, p, n\n",
      "\n",
      "AMV (AMPLITUDEMODULATEDVOLTAGE, amplitudemodulatedvoltage, amv): Amplitude-modulated voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: offset, amplitude, carrier_frequency, modulating_frequency, signal_delay, p, n\n",
      "\n",
      "AMI (AMPLITUDEMODULATEDCURRENT, ami, amplitudemodulatedcurrent): Amplitude-modulated current source\n",
      "\tPins: p, n\n",
      "\tAttributes: offset, amplitude, carrier_frequency, modulating_frequency, signal_delay, p, n\n",
      "\n",
      "RNDV (randomvoltage, rndv, RANDOMVOLTAGE): Random voltage source\n",
      "\tPins: p, n\n",
      "\tAttributes: random_type, duration, time_delay, parameter1, parameter2, p, n\n",
      "\n",
      "RNDI (RANDOMCURRENT, randomcurrent, rndi): Random current source\n",
      "\tPins: p, n\n",
      "\tAttributes: random_type, duration, time_delay, parameter1, parameter2, p, n\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from skidl.libs.pyspice_sklib import pyspice_lib\n",
    "\n",
    "for part in pyspice_lib.parts:\n",
    "    print('{name} ({aliases}): {desc}\\n\\tPins: {pins}\\n\\tAttributes: {attributes}\\n'.format(\n",
    "        name=getattr(part, 'name', ''),\n",
    "        aliases=', '.join(getattr(part, 'aliases','')), \n",
    "        desc=getattr(part, 'description'),\n",
    "        pins=', '.join([p.name for p in part.pins]),\n",
    "        attributes=', '.join([a for a in list(part.pyspice.get('pos',[])) + list(part.pyspice.get('kw',[]))]),\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Startup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you import the PySpice functions into SKiDL:\n",
    "\n",
    "    from skidl.pyspice import *\n",
    "    \n",
    "several things occur:\n",
    "\n",
    "* The parts and utilities defined in `skidl.libs.pyspice.py` are imported.\n",
    "* The default CAD tool is set to `SKIDL`.\n",
    "* A ground net named `gnd` or `GND` is created.\n",
    "\n",
    "In addition, when the parts are imported, their names and aliases are instantiated in the namespace of the\n",
    "calling module to make it easier to create parts. So, rather than using the following standard SKiDL method\n",
    "of creating a part:\n",
    "\n",
    "    c = Part('pyspice', 'C', value=1@u_uF)\n",
    "    \n",
    "you can just do:\n",
    "\n",
    "    c = C(value=1@u_uF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Miscellaneous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the `node()` function if you need the name of a circuit node in order to extract its data\n",
    "from the results returned by a simulation. The argument to `node()` can be either a pin of a part or a net:\n",
    "\n",
    "If you need the actual SPICE deck for a circuit so you can simulate it outside of Python, just print the\n",
    "Circuit object returned by `generate_netlist()` or store it in a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T13:33:02.707104Z",
     "start_time": "2021-04-29T13:33:02.663759Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title \n",
      "VS N_1 0 1V\n",
      "R1 N_1 0 1kOhm\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reset()\n",
    "\n",
    "# Create and interconnect the components.\n",
    "vs = V(ref='VS', dc_value = 1 @ u_V)  # Create a voltage source named \"VS\" with an initial value of 1 volt.\n",
    "r1 = R(value = 1 @ u_kOhm)            # Create a 1 Kohm resistor.\n",
    "vs['p'] += r1[1]       # Connect one end of the resistor to the positive terminal of the voltage source.\n",
    "gnd += vs['n'], r1[2]  # Connect the other end of the resistor and the negative terminal of the source to ground.\n",
    "\n",
    "# Output the SPICE deck for the circuit.\n",
    "circ = generate_netlist()      # Translate the SKiDL code into a PyCircuit Circuit object.\n",
    "print(circ)                            # Print the SPICE deck for the circuit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two immediate features that need to be added:\n",
    "\n",
    "1. ~~Instantiation of subcircuits defined by the `.SUBCKT` command.~~\n",
    "2. Creation of schematics from the SKiDL code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to [Fabrice Salvaire](https://github.com/FabriceSalvaire) for inventing PySpice,\n",
    "and to [Steve Armour](https://github.com/PyLCARS)\n",
    "whose [Jupyter notebook of PySpice examples](https://github.com/PyLCARS/PySpiceExsamples) led the way."
   ]
  }
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