sim_nmos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "POSIX ON\n",
      "PYDDL'd\n",
      "cmd imported\n",
      "welcome to gnucap-python\n",
      "loading default plugins\n",
      "making session\n"
     ]
    }
   ],
   "source": [
    "import paths\n",
    "import gnucap\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gnucap import command as cmd\n",
    "from env import show_ckt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** simulation nmos\n",
      "Vds 1 0 DC +10V\n",
      "Vgs 2 0 DC +3V\n",
      "M1 1 2 0 0 nmos_enhance L=10u W=400u\n",
      "\n",
      "* model statement (Level 1 by default)\n",
      ".MODEL nmos_enhance nmos (kp=20u Vto=+2 lambda=0)\n",
      "\n",
      "** output\n",
      ".print DC V(1) I(Vds)\n",
      "\n",
      "** analysis\n",
      ".DC Vds 0V 3V 100mV\n"
     ]
    }
   ],
   "source": [
    "cmd(\"get ckts/sim_nmos.ckt\")\n",
    "show_ckt(\"ckts/sim_nmos.ckt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "setup simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmd(\"list\")  # lists loaded circuit in console\n",
    "cmd(\"store dc V(1) I(Vds)\")\n",
    "cmd(\"dc vds 0V 3V 100mV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fetch output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = gnucap.CKT_BASE_find_wave(\"I(Vds)\")\n",
    "\n",
    "xs=[]; ys = []\n",
    "for x,y in w:\n",
    "    xs.append(x)\n",
    "    ys.append(y*(-1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8dcd9dcac8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# xs, ys\n",
    "plt.plot(xs, ys)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}