{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\title{Diode Connected MOSFET Tobology}\n",
    "\\author{Steven K Armour}\n",
    "\\maketitle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\"><li><span><a href=\"#Background\" data-toc-modified-id=\"Background-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Background</a></span></li><li><span><a href=\"#References--(Need-to-Finish)\" data-toc-modified-id=\"References--(Need-to-Finish)-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>References  (Need to Finish)</a></span></li><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Imports</a></span></li><li><span><a href=\"#Diode-Connected-NMOS\" data-toc-modified-id=\"Diode-Connected-NMOS-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Diode Connected NMOS</a></span><ul class=\"toc-item\"><li><span><a href=\"#Square-Law-Theory\" data-toc-modified-id=\"Square-Law-Theory-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Square Law Theory</a></span><ul class=\"toc-item\"><li><span><a href=\"#Simplified-Analysis\" data-toc-modified-id=\"Simplified-Analysis-4.1.1\"><span class=\"toc-item-num\">4.1.1&nbsp;&nbsp;</span>Simplified Analysis</a></span></li><li><span><a href=\"#Full-Analysis\" data-toc-modified-id=\"Full-Analysis-4.1.2\"><span class=\"toc-item-num\">4.1.2&nbsp;&nbsp;</span>Full Analysis</a></span></li><li><span><a href=\"#Difference-Between-Simplified-and-Full-analysis\" data-toc-modified-id=\"Difference-Between-Simplified-and-Full-analysis-4.1.3\"><span class=\"toc-item-num\">4.1.3&nbsp;&nbsp;</span>Difference Between Simplified and Full analysis</a></span></li><li><span><a href=\"#Diode-Connected-as-a-Topology-Slice\" data-toc-modified-id=\"Diode-Connected-as-a-Topology-Slice-4.1.4\"><span class=\"toc-item-num\">4.1.4&nbsp;&nbsp;</span>Diode Connected as a Topology Slice</a></span></li></ul></li><li><span><a href=\"#SPICE-Diode-Connected-NMOS\" data-toc-modified-id=\"SPICE-Diode-Connected-NMOS-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>SPICE Diode Connected NMOS</a></span><ul class=\"toc-item\"><li><span><a href=\"#SPICE-analysis-of-TSMC-180nm-Diode-Connected-NMOS\" data-toc-modified-id=\"SPICE-analysis-of-TSMC-180nm-Diode-Connected-NMOS-4.2.1\"><span class=\"toc-item-num\">4.2.1&nbsp;&nbsp;</span>SPICE analysis of TSMC 180nm Diode Connected NMOS</a></span></li><li><span><a href=\"#SPICE-Confirmation-of-Topology-Interpretation\" data-toc-modified-id=\"SPICE-Confirmation-of-Topology-Interpretation-4.2.2\"><span class=\"toc-item-num\">4.2.2&nbsp;&nbsp;</span>SPICE Confirmation of Topology Interpretation</a></span></li><li><span><a href=\"#Testing-the-frequency-dependence-of-the-Bias-point\" data-toc-modified-id=\"Testing-the-frequency-dependence-of-the-Bias-point-4.2.3\"><span class=\"toc-item-num\">4.2.3&nbsp;&nbsp;</span>Testing the frequency dependence of the Bias point</a></span></li></ul></li></ul></li><li><span><a href=\"#Diode-Connected-PMOS\" data-toc-modified-id=\"Diode-Connected-PMOS-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Diode Connected PMOS</a></span><ul class=\"toc-item\"><li><span><a href=\"#Square-Law-Theory\" data-toc-modified-id=\"Square-Law-Theory-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Square Law Theory</a></span><ul class=\"toc-item\"><li><span><a href=\"#Simplifed\" data-toc-modified-id=\"Simplifed-5.1.1\"><span class=\"toc-item-num\">5.1.1&nbsp;&nbsp;</span>Simplifed</a></span></li></ul></li></ul></li><li><span><a href=\"#I-will-finish-this-later\" data-toc-modified-id=\"I-will-finish-this-later-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>I will finish this later</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "The Diode connected Mosfet is one of the four single Mosfet topologies (the others being CS, CD, CG) where the Mosfets gate is connected to its drain (for both NMOS and PMOS) thereby forcing it to behave much like a Diode till certain compliance voltage is reached. This compliance voltage is similar to the Diodes threshold voltage and where beyond this point the MOSFET will no longer conduct current at meaningful amounts. \n",
    "\n",
    "The usage of diode-connected MOSFETs is important not only in creating a pseudo Diode to then recreate notable diode topologies such as Half and Full wave rectifiers. But are also used like diodes in waveform shaping and biasing to eliminate resistors where possible at the ASIC level. The two most common first exposure to diode-connected MOSFETs come by way of the  Saturated Enhancement loaded inverters in Digital MOSFETs topologies and in biasing and the current reference of current mirrors in Analog Mosfet topologies.    \n",
    "\n",
    "(insert images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References  (Need to Finish)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import *\n",
    "init_printing()\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from itertools import product as IProd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Jupyter notebook from SympyMOSModels.ipynb\n"
     ]
    }
   ],
   "source": [
    "import nbimporter\n",
    "nbimporter.options['only_defs'] = False\n",
    "from SympyMOSModels import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PySpice.Probe.Plot import plot\n",
    "from PySpice.Spice.Library import SpiceLibrary\n",
    "from PySpice.Spice.Netlist import Circuit\n",
    "from PySpice.Unit import *\n",
    "\n",
    "libraries_path = 'SpiceLib/'\n",
    "spice_library = SpiceLibrary(libraries_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diode Connected NMOS\n",
    "(Insert Image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Square Law Theory "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Diode Connected NMOS is defined as the $V_{GS}=V_{DS}$ made possible by the connection of the gate to the drain. And in this analysis we will assume that the body voltage is not in play."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "VD, VS=symbols('V_D, V_S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simplified Analysis\n",
    "In the simplified analysis, we forgo the channel length modulation effect. And we proceed with the analysis of the NMOS via expanding $V_{DS}=V_D-V_S$ where we affix the $V_D$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the simplified analysis, we forgo the channel length modulation effect. And we proceed with the analysis of the NMOS via expanding $V_{DS}=V_D-V_S$ where we affix the $V_D$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/latex": [
       "$$\\begin{cases} 0 & \\text{for}\\: V_{DS} < V_{thn} \\\\\\frac{V_{DS} W k_{n'}}{2 L} \\left(V_{DS} - 2 V_{thn}\\right) & \\text{for}\\: V_{DS} < V_{DS} - V_{thn} \\\\\\frac{W k_{n'}}{2 L} \\left(V_{DS} - V_{thn}\\right)^{2} & \\text{otherwise} \\end{cases}$$"
      ],
      "text/plain": [
       "⎧             0                  for V_DS < Vₜₕₙ    \n",
       "⎪                                                   \n",
       "⎪V_DS⋅W⋅k_n'⋅(V_DS - 2⋅Vₜₕₙ)                        \n",
       "⎪───────────────────────────  for V_DS < V_DS - Vₜₕₙ\n",
       "⎪            2⋅L                                    \n",
       "⎨                                                   \n",
       "⎪                       2                           \n",
       "⎪   W⋅k_n'⋅(V_DS - Vₜₕₙ)                            \n",
       "⎪   ─────────────────────           otherwise       \n",
       "⎪            2⋅L                                    \n",
       "⎩                                                   "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIdSimpDiodConected=simplify(NMOS.IdSimp(subs={VGS:VDS}))\n",
    "NIdSimpDiodConected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4okZbqSV0uYHF1TR/QLw043IhhVaojUtb54Jz2QKmrq7018R/qiL6u+gj5iycMbGn7LyXg9Y5gXRjiaR4KpYgDadSRBGPjpr6VF5XTtOou+1xBJ+BaEtjvbwUbSX2YzlPfQFLScr8EdWMuNH0ocv2w7TOKy7pAeQqlX5K1C/yBTcVr02AdHOEraQHxb2bVcRrME9O46mk4VQ+g3hkhMvfn9RAElxJgHRjdgyZTBfgVHykG8YrmkzDqXwG8ShaeX5wRwPB311yViKkmx3WyxU4FR/phssQSyfhVEy0Uow45/9JDcxRLGDTfaWRbnbYR3+yMVvdu1/BD5Api2A1MEbWemw44xukm5+HfcSizm0rc+IHaaCieXookA1nzEg3oWry/V0NTEcBziacMSPd3FVvpt9rgP6pfJ+dc7IGPqmB/wG4TZ1u9oGnSAAAAABJRU5ErkJggg==\n",
      "text/latex": [
       "$$\\begin{cases} 0 & \\text{for}\\: V_{thn} > V_{D} - V_{S} \\\\\\frac{W k_{n'}}{2 L} \\left(V_{D} - V_{S}\\right) \\left(V_{D} - V_{S} - 2 V_{thn}\\right) & \\text{for}\\: V_{D} - V_{S} < V_{D} - V_{S} - V_{thn} \\\\\\frac{W k_{n'}}{2 L} \\left(V_{D} - V_{S} - V_{thn}\\right)^{2} & \\text{otherwise} \\end{cases}$$"
      ],
      "text/plain": [
       "⎧                   0                           for Vₜₕₙ > V_D - V_S      \n",
       "⎪                                                                         \n",
       "⎪W⋅k_n'⋅(V_D - V_S)⋅(V_D - V_S - 2⋅Vₜₕₙ)                                  \n",
       "⎪───────────────────────────────────────  for V_D - V_S < V_D - V_S - Vₜₕₙ\n",
       "⎪                  2⋅L                                                    \n",
       "⎨                                                                         \n",
       "⎪                               2                                         \n",
       "⎪      W⋅k_n'⋅(V_D - V_S - Vₜₕₙ)                                          \n",
       "⎪      ──────────────────────────                    otherwise            \n",
       "⎪                 2⋅L                                                     \n",
       "⎩                                                                         "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIdSimpDiodConected=NIdSimpDiodConected.subs(VDS, VD-VS)\n",
    "NIdSimpDiodConected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example values for a greater than $1\\mu m$ device are based on example 19.1 load NMOS from chapter 19 of reference DeMassa "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "subsN={W:5e6,L:15e6, nTechConst:20e-6, Vnth:1.1, VnA:1/.02, VD:5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(1.199999999999999e-06)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIdSimpDiodConectedN=lambdify(VS, NIdSimpDiodConected.subs(subsN), dummify=False)\n",
    "NIdSimpDiodConectedN(3.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "VSN=np.linspace(0, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vs[V]</th>\n",
       "      <th>Id[A]</th>\n",
       "      <th>DIdDVs[OhmInv]</th>\n",
       "      <th>DDIdDDVs</th>\n",
       "      <th>DDDIdDDDVs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.342977e-05</td>\n",
       "      <td>-1.019380e-05</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-4.900000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.487488</td>\n",
       "      <td>1.560910e-05</td>\n",
       "      <td>8.736476e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>5.995389e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.565986e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.410473e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>8.150771e-08</td>\n",
       "      <td>-1.766667e-05</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-4.201283e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>6.542010e-06</td>\n",
       "      <td>-9.333333e-06</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.341484e-05</td>\n",
       "      <td>-1.000000e-06</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>3.320369e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.070000e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1.633333e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Vs[V]         Id[A]  DIdDVs[OhmInv]   DDIdDDVs    DDDIdDDDVs\n",
       "count  50.000000  5.000000e+01    5.000000e+01  50.000000  5.000000e+01\n",
       "mean    2.500000  1.342977e-05   -1.019380e-05   0.000005 -4.900000e-07\n",
       "std     1.487488  1.560910e-05    8.736476e-06   0.000003  5.995389e-06\n",
       "min     0.000000  0.000000e+00   -2.565986e-05   0.000000 -2.410473e-05\n",
       "25%     1.250000  8.150771e-08   -1.766667e-05   0.000004 -4.201283e-19\n",
       "50%     2.500000  6.542010e-06   -9.333333e-06   0.000007  0.000000e+00\n",
       "75%     3.750000  2.341484e-05   -1.000000e-06   0.000007  3.320369e-19\n",
       "max     5.000000  5.070000e-05    0.000000e+00   0.000007  1.633333e-05"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VSN=np.linspace(0, 5)\n",
    "NIdSimpDiodData=pd.DataFrame()\n",
    "NIdSimpDiodData['Vs[V]']=VSN\n",
    "NIdSimpDiodData['Id[A]']=NIdSimpDiodConectedN(VSN)\n",
    "NIdSimpDiodData['DIdDVs[OhmInv]']=np.gradient(NIdSimpDiodData['Id[A]'], NIdSimpDiodData['Vs[V]'])\n",
    "NIdSimpDiodData['DDIdDDVs']=np.gradient(NIdSimpDiodData['DIdDVs[OhmInv]'], NIdSimpDiodData['Vs[V]'])\n",
    "NIdSimpDiodData['DDDIdDDDVs']=np.gradient(NIdSimpDiodData['DDIdDDVs'], NIdSimpDiodData['Vs[V]'])\n",
    "NIdSimpDiodData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMOS Simp Square Law Complince Voltage =3.878[V]\n"
     ]
    }
   ],
   "source": [
    "NVCompSimpLoc=np.where(NIdSimpDiodData['DDDIdDDDVs']==NIdSimpDiodData['DDDIdDDDVs'].min())[0][0]\n",
    "NVCompSimp=NIdSimpDiodData['Vs[V]'][NVCompSimpLoc]\n",
    "print(f'NMOS Simp Square Law Complince Voltage ={NVCompSimp:.3f}[V]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fiKSoakV3ZUzfxEY5OTk3nZNz0wHaE5IPdpexencpy9fs4/H39wCQm+lmTk4as3LSmJ2TSnZafFgvqGeMCX9nnHFG9aOPPpr5zW9+s7SgoCBq1apViYsXLy7rWG7Dhg0xdXV1zqKioo1t2/7rv/5r+LJly9Luu+++Qx3Lm2MLmQQEQETcwGTfY4rveSoQD6QGMbSIcWRCMo6mFg+bD1ayelcZa/aU8dKmQyxf420xHZIYw+ycNGaOTmVWTioThyURZfOQmEFs4sQngx2CCbCrrrqq4l//+ldSXl7e5DFjxjTMmTOn0y6YJ554Iv3cc88t99922WWXlV9++eW5loD0TcgkICKyB4gCtgKf4F2fZTEwXVU7XcjGDLxol4OTslM5KTuVmxiLx6N8WlTDmj1lrN1Txpo95fxjk/dnLy7KybRRycwcnUpUZQvT65rsThszqMTGjjp2IRMW6urq1gM4HA6WLVu271jl77///oMdt82dO7d+586dWwYivkgQMgkI8HfgDOBRVX0WQERus+QjtDgcQt7QRPKGJnLlyaMBOFhRz4f7ylm3t5wP95bzyFu7aPEoD374GrmZbmaMSmVGdgozslPIy0q02VpNyCoqegaAIUMuDXIkJhSkpqa2zJ8/f/yjjz665/TTT6/rruzcuXPH79+/PzoqKspmgOyhkElAVPVbIpID/FhEbgd+ANg/5CAwPCWO4SlxnHficADqm1p54u/5tKaOZv2+CvK3FfHCh965SOKinJw4Mpnp2SnMGJXCtFEpDE2KtbEkJiQUFPwOsATEeG3evLnHK6WvXr16+0DGEo5CJgEBUNU9wDUiMhn4KTBUROaran5QAzO9EhftZEKak/nzTwBAVTlQ7m0lWb+vgg/3lfPHd3fT3OrNL4ckxjDdl4xMG5nC1BHJdguwMcaEuZBKQNqo6hbgAhGZC9wjIner6unBjsv0jYgwKi2eUWnxnD99BOC92+bjQ1Vs2F/BhgOVbNhfwatbD99OPzo9nqkjkjlxZDJTR6QwZUQSibbyrzHGhI2QTEDaqOpq4PMi8vlgx2L6V2yUkxnZqczIPnxzU2VdM5sKKtlYUMGmA5Ws31fB3zceHlw+JsPN5OFJTB2RzJQRyUwenmSDXI0xZpAKmQRERD5U1ZM626eqrx+rjBn8kuOjmDcug3njDi8yWVrTyMaCSjYfqGTzwaOTkpGpcUwalsSk4UntzyNS4mxMiTGmS3PmzMm74447Dl100UVVbdvuvvvuIdu3b4+9/fbbP7v55ptH7d69O9blcumECRPqH3nkkX2jRo1q6e6cA6Wurk7mzp07oampSVpbW2XhwoXlDzzwwFF35Hz66afRV155ZU5VVZWrtbWVn/zkJwWXXnpp5e9+97u0//mf/xnaVm779u1x77777tZTTz21/pFHHkn79a9/PRQgKyur+dlnn909bNiwljlz5uQVFRVF3XPPPftLS0tdr732WtLKlSt3t53j0KFDrkmTJk0+ePDgxksvvTTnrbfeSn7wwQf3fu1rXyvvGFd3QiYBASaKyMZu9guQHKhgTGhIT4hhQd4QFuQNad9WXtvE5oOVbC6oYvPBSj4+VMVrH39G2xpSyXFRTByWyMRhSUwcmsSEYYmMz0okNsoZpFqYwWLy5OeDHYIJgIsvvrj06aefTvNPQF544YW0e++998DChQvH/fznP99/+eWXVwKsXLkysbCw0BWsBCQ2NlbffffdbcnJyZ7GxkaZPXt23r/+9a/Ks846q9a/3F133TXswgsvLL/jjjuK161bF7to0aJxl1566aabbrqp7KabbioD+OCDD+IuvPDCE0499dT65uZm7rzzzlFbtmzZMmzYsJYbb7xx5H333Tek7XbjZcuW7Tr99NPrysrKHD/84Q9HVldXOxITEz0ATz75ZOrZZ59dERcXpytWrNh90UUX5fSlbqGUgEzoQZnWAY/ChLxUdzSfG5fJ58Zltm+ra2rhk8Jqth6sYuuhKrYcrGL5B/upb/b+l3EI5GS4mTg0ibyh3oQkb2gi2WnxOB3WWmK8oqMzjl3IDHpXXXVV+c9+9rMR9fX1EhcXp9u2bYsuKiqK+vjjj2NPOumkmrbkA2DhwoXV4G2JuPrqq0dv3Lgx3ul08stf/nL/woULq//3f/83fcWKFSkej0e2bdsW981vfrOwqanJ8cwzz6RHR0d7Xn311U+zsrJa58yZkzdlypS69evXu2tqapxLly7dvWDBgm5v7QXvPCXJyckegKamJmlpaZHOWnhFhKqqKidAeXm5c8iQIUet0rts2bK0Cy64oAzA4/GIqlJdXe3IysqiqqrKccIJJxw123haWppn9uzZNcuXL0++/vrrywGef/75tO9973tHtcL0VsgkIKq61/+9iFwBrFFVu7XJHFN8tKt9wrQ2Ho+yr6yOTwqr+PhQNZ8UVrGpoLJ94jSAGJeDcVkJjM/yJiXjhiQwbkgiI1PjcFhiEnEOHXocgGHDlgQ1jkizbt2cvI7bMjIuLBs9+rvFLS3Vjg0bzhrXcX9W1pUlI0feUtrYeMi1efP5Y/33zZz5wbburjd06NDWadOm1b7wwgvJV155ZUXbInNbtmyJPemkkzpNCu69994hANu3b9+6fv362HPPPXfczp07N/u2xW3YsGFrfX29Iy8vb8oPfvCDgo8//njrtddeO+qRRx5Jv+uuu4oA6urqHOvXr//kn//8Z8INN9ww5tNPP92ycuXKxNtuu+2oGfDi4uI869ev/wSgpaWFKVOmTNq3b1/MNddcU3TmmWfWdiz/85///ODZZ5897rHHHhtSX1/v+Mc//nHUd+ff/va31BdffHEHQExMjN5///37TjrppMlxcXGto0ePbuxqQrbLLrus7Omnn067/vrry/fs2RO1Z8+emPPOO6/LRft6KmQSkE4UAw+LSDRQAmxX1e8GOSYziDgcQk6Gm5wMN+dMGda+va6phU8/q2HbZ9VsL6xm22fVvPtpCS9+WNBeJjbKwdjMBG9CkpXI2Ew3YzMTGJ3uJtplE6mFq8LCxwFLQCLBJZdcUvbMM8+kXnnllRUvvvhi2mOPPbbn8ccfT+uq/Pvvv59w8803FwHMmDGjYfjw4U2bNm2KBTj11FOrU1NTPampqZ6EhITWiy++uAJg6tSpdRs3boxvO8fll19eBvClL32ppqamxlFSUuJcuHBh9cKFC7d2F6vL5eKTTz7ZWlJS4vzyl788ds2aNbGzZ88+orXiT3/6U9rixYtLf/zjH3/2+uuvu5csWTJm+/btW5xOb9fzG2+84Y6Li/O0HdfY2ChLly7NXL169daJEyc2LlmyJPt73/vesF/+8pdHTSt/ySWXVHznO9/JLisrcyxbtiz13HPPLXe5jj99CNkERFVfFZEzVPX7IpII/DzYMZnwEB/t8s45MirliO2V9c3sKKphR1E1n35W45tyvpy/fnS4pdHpELLT4hmb6SY3M4ExGW5yM9yMyXSjavPmGdMX3bVYuFyJnu72x8QMazlWi0dnrrjiior//u//HvXuu+/GNzQ0OObNm1e3Zs2a+Lfffjuhs/Ld/XxHR0e373Q4HMTGxmrb65aWlvam1I5dJyJCT1pA2mRkZLTOmzeveuXKlckdE5Cnnnoq4+WXX94O8PnPf762sbHRUVhY6BoxYkQLwJ///Oe0Cy+8sH2RvVWrVsUBTJ48uRFg8eLFZb/4xS+G0omEhAQ944wzqv785z+nvvDCC2m//vWv93f5YfRCyCYgPkkiMhPYBLiDHYwJb8lxUcwcncrM0Ueue1jb2MLuklp2Ftews6iGHcU17Cyq5e3tJTS1etrLxTph3OZ3yclwMyY9ntHpbnIyvM/p7mi7M8eYEJKcnOw5+eSTq6+77rqcti/m66+/vvSBBx4Yunz58uTLLrusEuD5559Pys7Obp43b17NU089lbZo0aLqjRs3xhw6dCj6xBNPbFi9enV891c67Omnn05duHBh9SuvvJKQmJjYmp6e3nqsFpCDBw+6oqOjNSMjo7Wmpkby8/OTbr311sKO5YYPH9700ksvJd1yyy2lH374YWxTU5MMGzasBaC1tZW///3vqW+++WZ7QjN69OjmHTt2xB48eNA1fPjwlpdffjlp/PjxXa44v3jx4rK77rprRE1NjbOzLqC+CJkEREQexptobAQ2qWoV8P+Am4BvAi8HMTwTwdwxLqb45h7x1+pRDlbUs7uklt0ltbzz0TaaYqP5aH85/9h4EI/fH0wJMS6y0+IZnR5Ptm9StrbnESlx1q1jTBBcdtllZddcc83Yp59+ehd4/9L/29/+tuOWW24Zdccdd4xyuVw6ceLE+t/97nf7br/99qKrrrpq9Pjx4yc5nU4eeeSRPXFxcb1q9kxNTW2dMWPGhLZBqD05Zv/+/VFLliwZ09raiqrK+eefX7Z48eJKgG9/+9vDZ8+eXXvFFVdUPvDAA/uvv/76nIceeihLRPj973+/x+Hw/l755z//mTh06NCmSZMmNbWdNycnp/m22247NG/evDyXy6UjR45s+stf/tJlTBdeeGHljTfemLN48eKStvMer5BJQICPgBOBy4ApIlLN4YTkVVV9JpjBGdOR03F4htfTx2cyumkP8+fPAaCpxUNBRT17SmvZW1LLntI69pbWsv2zav71SRFNLYdbThwCQ5NiGZkaz8jUON/D+3pEahxDk2OJcdktxMb0t6uvvrri6quvXue/bcaMGQ3vvPPOp52Vf+GFF/Z03HbLLbeUAqVt7wsKCjZ1te/SSy8tf+ihhwrohblz59Z//PHHnbaQPPjgg+39wzNnzmz48MMPP+ms3HnnnVd93nnnHbXv9ttvL7799tuLexJHVFQU5eXlG3oad0+ETAKiqkv934vISLwJyVTgy8DyYMRlTF9EuxyMyXAzJsMNHcb3ezxKUXUj+8rqvI/SWg6U13OgvJ5Vu0oprGo4ovUEIDMxhuEpcYxIiWV4snfxv2HJsWQlxzIsOZbMhBhbZbgfnHjiS8EOwZigS0lJabn22mvH/OhHPzpwxRVXVHZXdtGiRWPWrVuX8NWvfrVXk5BBCCUgHanqAeAAYL8RTFhxOIShybEMTY5lzpijB903tXgorGzgQHkdByrqOVTRwMGKeg5W1vNJYTVvfFJEQ7PnyHMKDEn0JiRZiTFkJcWSlRTDkKRYspJiGZIYw5DEGFLjo+324m44nT3uzjemVz74oPcDZYPl1Vdf3dnTsitWrOhRV1JnQjYBMSZSRbscZKfHk53e+ZehqlJe10xhZQOFVfUUVjZSWFnPocoGCqsa2FNay+rdZVTWHzUPEU6HkJEQTWZiDBkJMWQmxJCRGEO6O5qMhBjS3NGkJ3hfp8ZHR9zYlIKChwEYMeIbQY7EmPAXlgmIiJwD/A/gBB5T1V902B8DLANm4u2fu1RV9wQ6TmP6QkRIc0eT5o5m0vCkLss1NLdSXN3IZ1XexKS4upGSmkbfcxPF1Y18cqia0tpGmls7H0uXGOMi1R1NanwUqe5o0uKjSYmPJiU+ipT4KAoOtsC2IlLio0mOiyI5LorEWBdRg7Q7qKjoWcASkADweDwecTgcdu96GPN4PAJ4utofdgmIiDiBh4Cz8XbhrBGRFarqP4jnWqBcVU8QkcuAe4FLAx+tMQMnNsrZPki2O6pKVUMLpTWNlNU2UVLTRGltI6U1TZTXNVFe20R5XTNltU3sKKqhvLaJ2qbDqyI8snHNUeeMi3KSFOciMTaKpFgXCbFRJMa4SIhx4Y5xkRDrItH3Oj7a6Xu4iI9x4o72bouJchAX5SQ2yjloExrTpc3FxcWTMjMzKy0JCU8ej0eKi4uTgc1dlQm7BASYA+xQ1V0AIrIcOB/wT0DOB37ke/088FsREbWZpEwEEpH21ovczGOXB2hu9VBZ38yr+e+RN3UGlfVNVNQ1U1XfTHVDC1UNh5+r6luorG/mYEU9NQ0t1DR6H73hcgixvmQkxuUgxuUg2vcc43IS7XIQ5RSinA7fQ3D5XrscgtP3cDkER9uzCCJ4n/HNnEs9CKzK77oLfNeuJj6mx13kYSG2spX5/Xi+lpaW6woLCx8rLCycAlh2GZ48wOaWlpbruioQjgnICMB/lrYDwNwxrKaTAAAgAElEQVSuyqhqi4hUAul4p3xvJyI3ADcAZGVlkZ+f36eAampq+nzsYGV1jgxJ1FG9ewMOIM33wIl32sBOpw50Ak48Gk1jKzS0KI2t0NiqNLR4n9u2N3ugqRWaPOp9blWaWltp9rTS7PHub2mAKo/S3AqtCi0e9T3T/uxRxaO0P1p9z539tfHdOd5lQO79oNO7GQ/bfoz9YeaiMdqv/7dnzpxZBCzqtxOaQSkcE5DOhvh3/F3TkzJttwYvBZg1a5bOnz+/TwHl5+fT12MHK6tzZBjMdVZV1JeIeHyvN238JQCfLDyny+PefvttTj/99ABFGRree+ftQfvvbEJXOCYgBwD/efVHAh2XDW4rc0BEXEAyUEY31q1bVyIie7sr041soNNVBsOY1TkyhGmdu/3VGKZ17tbx1Hl0fwZiwoeE27AHX0KxHTgLKADWAJer6ha/Mt8Epqrqjb5BqBeq6iUDGFOxqvawdz08WJ0jg9U5MkRinc3AC7sWEN+Yjm8Br+DtcP6jqm4RkbuBtaq6AvgD8KSI7MDb8nHZAIdVMcDnD0VW58hgdY4MkVhnM8DCLgEBUNWX6DCDqqre5fe6Abg4gCF1O5VtmLI6Rwarc2SIxDqbAWa3PwXG0mMXCTtW58hgdY4MkVhnM8DCbgyIMcYYY0KftYAYY4wxJuAsATHGGGNMwFkCYowxxpiAswSkF0TkjyJSJCJdLq7Ty/O1ishHvseK/jinMcYYMxjYINReEJHTgRpgmapO6Yfz1ahqwvFHZowxxgwu1gLSC6r6Nh2mbBeRsSLysoisE5F3RGRCkMIzxhhjBg1LQI7fUuBmVZ0J3Ao83ItjY0VkrYisEpGvDEx4xhhjTOgJy5lQA0VEEoBTgedE2hfYjfHtuxC4u5PDClT1i77X2ap6UERygTdEZJOq7hzouI0xxphgswTk+DiAClWd3nGHqr4IvNjdwap60Pe8S0TygRmAJSDGGGPCnnXBHAdVrQJ2i8jFAOI1rSfHikiqiLS1lmQApwFbByxYY4wxJoRYAtILIvI08G8gT0QOiMi1wBXAtSKyAdgCnN/D000E1vqOexP4hapaAmKMMSYi2G24xhhjjAm4sGwBEZFzRGSbiOwQke92sj9GRJ7x7V8tIjmBj9IYY4yJXGGXgIiIE3gI+BIwCVgsIpM6FLsWKFfVE4AHgHsDG6UxxhgT2cLxLpg5wA5V3QUgIsvxjsvwH19xPvAj3+vngd+KiGg3/VEZGRmak5PTp4Bqa2txu919OnawsjpHhkips0fBowoKtXW1xMeHf539NdTXkpjQt0mb161bV6Kqmf0ckgkDAUlARCStB8U8qlrRD5cbAez3e38AmNtVGVVtEZFKIB0o6eqkOTk5rF27tk8B5efnM3/+/D4dO1hZnSPDYK5zS6uHQ5UN7C+rY395HfvL6qmveZPy2ia2lk2nprHF+2hoocVz5N8mlUGKOVgunxDNz5ac3adjRWRvP4djwkSgWkAO+h7STRknkN0P1+rsGh1bNnpSBhG5AbgBICsri/z8/D4FVFNT0+djByurc2QI9TqrKlVNUFjr4VCtx/esFNZ6KKlXWv1+6h0C35/7MNkpsLvkl6TFCXGJEOdyEeeCGKfgFGhsbCQ2NiZ4lQqC4dGNIf3vbAanQCUgH6vqjO4KiMj6frrWAWCU3/uReJOfzsocEBEXkEyHNV4AVHUp3qnWmTVrlvb1L73B/FdiX1mdI0Oo1LmxpZW9pXXsKq5hZ3EtO4tr2FVcy67iGqoaWtrLxbgcjMlwM2tsAjkZ8WSnxTMqNZ5RafEMS45l08b7ALhm0TldXitU6hxIkVhnM/AClYCc0k9lemINME5ExgAFwGXA5R3KrACuwTunx1eBN7ob/2GMCT5VpbimkV0dEoxdJbXsL6vDv5ckKymG3IwEFk0fTm5GArmZbsZmJjAiJQ6Ho7uGWGNMoAQkAVHVhv4o08NrtYjIt4BX8Hbr/FFVt4jI3cBaVV0B/AF4UkR24G35uKw/rm2MOX4Nzd7WDG+S4U00dpbUsquohurGo1szpoxI5vxpw8nNTGBsZgJjMt0kxITj+HpjwkvAf0pF5A5VHdDbXlX1JeClDtvu8nvdAFw8kDEYY7qmqhRXN7KjvSXD16pRUsOB8nr82yOHJceSm+nmKzNGtLdk5Ga6GZ5srRnGDGYDnoCIyLP+b4Hp2LwbxkSEhuZWdpfUHtFd0tZ9UuPXmhEX5WRMhptpI1O4YMZIxvoSjTEZbtwBbM3Iy3skYNcyJtIF4ie7SlWva3sjIr8LwDWNMQGiqnxW1egdAFpS2z4QdFdxDQUVR7ZmjEiJIzfTzUUnjWDskIT28RlDk2JDojUjPj4v2CEYEzECkYDcA94VX1W1BPh+AK5pjOln9U2+1oySmvYukw276il+4xVqm1rby8VHO8nNdHNSdioXzxxFbqab3Ew3YzLcxEeH9tiMkpKVAGRkLAxyJMaEvwH/baCqu30v/wgsUtWjbnc1xoQGVaWwqoGdRUcmGruKaymoqG8vJwLDk+NIjRLmTxnF2Ex3+yDQrKQYRILfmtEX+/f/GrAExJhACOSfI4PzN5IxYaiuqeXI21l9XSe7S2qp82vNcEc7yc1MYHZOKpdm+lozfN0msVFO3/wQk4NYE2PMYBXIBMTm2TAmgDwe5VBVAzuLatoHgLYlHYcqD9/1LgIjU+PIzUhgzpg0X0uGdxDokMTB25phjAlt1gJizCBX29jia8U4PPhzZ3Etu0tqaGj2tJdLjHGRm+nm5Nx0cjPc3kGgmW5y0r2tGcYYE0iBTEDuDOC1jAkrHo9SUFHf3lXi331SWHW4NcMhMDI1ntxMN6fkpjN2iLfLZGymm0xrzTDGhJCAJSCquhlARFyq2j4BgIhkq+q+QMVhTCiraWxpTzD81zXZU1p7ZGtGrIvczAROHZvuu53VOwh0dHq8tWYch4kTnwx2CMZEjIAlICJyPXArkCQiccCHwPeAh4GTAhWHMcHW6lEOVtSz02++jLYulM+qGtvLOQRGpcWTm+Fm3gkZ5GYeXtMkIyHaWjMGQGzsqGMXMsb0i4AkICJyBzALOENVC33bvgA8CmQHIgZjAq26obnDnSaH7zhpajncmpHka82Yd0KmL8HwJhnZ6fHEuKw1I5CKip4BYMiQS4MciTHhL1AtIF8Dpqpqc9sGVX1VRD6PtwXEmEGp1aMUlHtbM17d08yr/7epveukuPpwa4bTIYxKjWNsZgKfG5fRPmdGbqabdLe1ZoSKggLvRM2WgBgz8AI5BqS5k22fichvAhWDMX1VWd98RFfJrvaxGXVHtGakxB8iN8PNGeMz27tLxma6yU5zE+1yBLEGxhgTWgKVgOwUkS+r6j/8N4rI3XinZrd2ZhN0La0eDpTX+yUYh7tPSmoOt2a4HEJ2ejy5GQksyBvim2o8gcLtG1j4hQVBrIExxgwegUpAvgG8ICJLgA1AAnAu8BGwLUAxGANAZV0zO0v87zTxvt5bWkdT6+HWjDR3NLkZbs6ckOnrLvF2mWSnxRPlPLo1I3+PdaMYY0xPBSQBUdW9IjIb+CIwEagCrlTVjb4Bqsb0q5ZWD/vL64+aM2NXSQ0lNU3t5aKcQnZaPLmZCZw5cUh7l0luRgKp7ugg1sAYY8JbIMeAKPCy7+G//d5AxWDCT3ltk98MoIdbNPaV1dHcenj2/3R3NLmZbs6akHV4cq4hCYxKjcPVSWuGiUyTJz8f7BCMiRiBug33Q1Xtdq6PnpQxkam51cO+sjrvmiYlhyfo2lVcQ3nd4bHNUU5hdLqbE4Yk8IXJQ9unGx+bkUByfFQQa2AGi+jojGCHYEzECFQLyEQR2djNfgGSAxSLCVFltU2+rpIju032ldXR4jncmpGREENupptzpgz1tWR4WzRGWmuGOU6HDj0OwLBhS4IahzGRIFAJyIQelGk9dhEz2DW1eNhXVntUl8mukloq/Fozop0OcjLiGZ+V6E002sZmZCaQHGetGWZgFBY+DlgCYkwgBGwQamfbRWQSMBrYpKoHAhGLGXiqSlWj8sHusiPuMtlV4m3NaPVrzchMjCE3w82XpgxrnwE0N9PNyNR4nA67q8QYY8JVIFfD7cyPgeeAG0RktKpeE+R4TC80trSyr7TuiPky2ubQqKxvhjf/DUC0y8GYdDcThiby5anD2ufNyM10kxRrrRnGGBOJgp2AvKaqzwLPBjkO0wVVpaSmyTsuo6T2iIGg+8rq8GvMYEiid2zGeScOw1NZyBdOmcYJmQkMT4mz1gxjjDFHCHYCcqqInAOUAh+r6v1BjidiNba0srf08J0m/iu1Vje0tJeLcTkYk+Fm8vBkFk4b7m3NyPC2ZiT6tWbk55cyP29IMKpijDFmEAh2ArJZVX8lIi5gcpBjCXuqSnFNIzuLjlzPZFdxLQfKj2zNyEqKYWxmAudPH94+Z0ZuhpsRKXE4rDXDhKkTT3wp2CEYEzGCnYCcJyKNwCuquiHIsYSNhuZW9pT632VS235ra3Xj4daM2CgHYzISOHFkMl+ZMaJ9BtAxmW4SYoL9X8OYwHM644MdgjERI9jfMpcCM4ALRWSsql5/PCcTkfuAhUATsBP4mqpWdFJuD1CN99bfFlWddTzXDQZVpai68YiukrYWjYKKetSvNWN4ciy5mQlccNKI9rtMcjMTGJYUa60ZxvgpKHgYgBEjvhHkSIwJfwFNQETkU2AT3gXpPgI2qOpR07Mfh9eAO1W1RUTuBe4EulprZoGqlvTTdQdMQ3Mru0tq/bpL2gaB1lLj15oRF+UkN9PNjOxULjppZHuXSW6mm/joYOeZxgwORUXe8fCWgBgz8AL9zfQIkIt30OmXgD+LyG7g/4CfqGpzdwcfi6q+6vd2FfDV4zlfoKgqhVUNR3aZ+O406diaMSIljtxMNxedNMKXZHhbNIZaa4YxxphBJNAJyJWqOr3tjYj8Hvga3tVx7wdu7sdrfR14pot9CrwqIgo8oqpL+/G6R6isb2ZfVSvv7SihvK6J8tomymqbKa9roqSmkT2ltewurqW26fBEsPHR3taMmaNTuXjmKHJ9E3SNyXATF+0cqFCNMcaYgBH1//N6oC8m8hZws6pu9Nu2WlXn9nQxOhF5HRjaya7vq+rffGW+D8wCLtROKigiw1X1oIgMwdttc7Oqvt1JuRuAGwCysrJmLl++vGcV9fPKnmae/qTpqO1xLkiMFobEOxjmFoa6HQxze1+nxAgig7s1o6amhoSEhGCHEVBW53Dwbd/zg12WCL86H9vx1HnBggXrBuM4OzPwAt0C8h94u10+wjsGJA/w+PZF9+QEqvr57vaLyDXAecBZnSUfvnMc9D0Xicj/AXOAoxIQX8vIUoBZs2bp/PnzexLiEbKLa0iLXcXn5swgzR1NSnwUKXHRRLvCe9G0/Px8+vJ5DWZW58Fv/foUAGbMmN9lmXCrc09EYp3NwAtoCwiAiDiBC4ET8Y4FeRJoAP5LVX96nOc+B29XzhmqWtxFGTfgUNVq3+vXgLt9g2G7O3cx0OmaNj2QDezr47GDldU5MlidI8Px1Hm0qmb2ZzAmPAQ8ARlIIrIDiMGb2ACsUtUbRWQ48JiqnisiuXgHvYK3BegvqnrPAMdVHGk/gFbnyGB1jgyRWGcz8MLq/kxVPaGL7QeBc32vdwHTAhkXcNRcJBHA6hwZrM6RIRLrbAZYeA9ECB2VwQ4gCKzOkcHqHBkisc5mgFkCEhgDdptvCLM6Rwarc2SIxDqbARZWY0CMMcYYMzhYC4gxxhhjAs4SEGOMMcYEnCUgxhhjjAk4S0CMMcYYE3CWgPSCiPxRRIpEZHM/na9VRD7yPVb0xzmNMcaYwcDugukFETkdqAGWqeqUfjhfjapG1qpWxhhjDNYC0iu+FXPL/LeJyFgReVlE1onIOyIyIUjhGWOMMYOGJSDHbylws6rOBG4FHu7FsbEislZEVonIVwYmPGOMMSb0hNVaMIEmIgnAqcBzItK2Oca370Lg7k4OK1DVL/peZ6vqQd8CeW+IyCZV3TnQcRtjjDHBZgnI8XEAFao6veMOVX0ReLG7g32L5KGqu0QkH5gBWAJijDEm7FkXzHFQ1Spgt4hcDCBePVppV0RSRaSttSQDOA3YOmDBGmOMMSHEEpBeEJGngX8DeSJyQESuBa4ArhWRDcAW4Pwenm4isNZ33JvAL1TVEhBjjDERwW7DNcYYY0zAWQuIMcYYYwLOBqH2UEZGhubk5PTp2NraWtxud/8GFOKszpEh3Ors8dQB4HDEd1km3OrcE8dT53Xr1pWoamY/h2TCgCUgPZSTk8PatWv7dGx+fj7z58/vUdmG5lZue34ju0tq+nStUFFdXUNiYmRN8mp1Dk0ThybxtdPGMGl4Ur+crzc/z+HieOosInv7NxoTLiwBCTH3vbKNlRsOcsb4TFwOOfYBIcrZVEtGYmywwwgoq3PoaVXl7xsP8dy6A5ySm87X543hrAlDcHTxs1VW9joAaWmfD2SYxkQkS0BCyPs7S/jDu7u5+pTR3H3+cS81E1Tev5hmBzuMgLI6h6bKumaeXrOPJ97fw/XL1pKTHs/XThvDV2eOxB1z5K/AvXt/ClgCYkwg2CDUEFHV0Mytz24gN8PNnV+aGOxwjAkbyfFR3HjGWN6+fQG/WTyDlPhofrhiCwt/+y6NLa3BDs+YiGUJSIj40YotfFbdyP2XTicu2hnscIwJO1FOBwunDeev3zyNhy4/iV3FtTy1al+wwzImYlkCEgJe3nyIFz8s4JvzxzJ9VEqwwzEm7H35xGHMOyGD377xKVUNzcEOx5iIFJYJiIicIyLbRGSHiHy3k/1LRKRYRD7yPa4LRpwARdUN3PniJqaOSObms8YFKwxjIs4d50ygvK6ZR96y5ZeMCYawG4QqIk7gIeBs4ACwRkRWdDLN+TOq+q2AB+hHVfnuC5uoa2rlgUunEeUMy3zQmJA0dWQyi6YN9w38ziErKZa8vEeCHZYxESMcv/HmADtUdZeqNgHL6fn6LAG1fM1+3vikiDvOmcAJQxKDHY4xEefWL+TR6lEefP1TAOLj84iPzwtyVMZEhnBMQEYA+/3eH/Bt6+giEdkoIs+LyKjAhHbY3tJafvL3rZx2QjpLTs0J9OWNMUB2ejxXzB3Ns2v3s6OohpKSlZSUrAx2WMZEhKAsRicirao6ILd6iMjFwBdV9Trf+6uAOap6s1+ZdKBGVRtF5EbgElU9s5Nz3QDcAJCVlTVz+fLlfYqppqaGhITDs0V6VPnZ6gYKajz89LQ40uPCLw/sWOdIYHUenKoaldvfrmNyhpObZ7QNGXuwy/LhUOfeOp46L1iwYJ2qzurnkEwYCNYYkIGc4vMA4N+iMRI46F9AVUv93j4K3NvZiVR1KbAUYNasWdrXqYg7TmP80Js72FGxjQcuncYFM0b26ZyhzqarjgzhUuc9rk+5/7XtOGMTSIhxMWPG/C7LhkudeyMS62wG3oD96S0iV4jI+C52H7PZRUSG9fHSa4BxIjJGRKKBy4AV3Zx7EfBxH6/Va1sOVvLg69s5d+pQvjK9s54hY0ygXTtvDBkJMewrqwt2KMZEjIFs+y8GHhaRt0XkRRH5RccCInK2iDwqItN972/w232Pb9sVIvKeiHy5JxdV1RbgW8AreBOLZ1V1i4jcLSKLfMVuEZEtIrIBuAVY0tdK9kZDcyv/75kNpMRHc89XpiIyeNd6MSacuGNc/Ofnx1Hd0ExFXVOwwzEmIgxYAqKqrwKrVfV04Bqgsw7EbwC3AVeKyJnAdL99Fb7nLwDzgAt6ce2XVHW8qo5V1Xt82+5S1RW+13eq6mRVnaaqC1T1k15XsA/uf2072z6r5pdfPZFUd3QgLmmM6aHLZo8iNsrJvrI6Wj2BHxtnTKQZ6NGPSSIyE2gE3J3sL1bVClW9FW+i4b+qlUtE/hvYp96RsrUDHOuAWrWrlEff2cXlc7NZkDck2OEYYzqIcjpIH/ko/7Pu2+RvKwp2OMaEvX5JQETkYRG5SUROE5Ekv13/DzgN+D3eLpGO/iEiEwBU9bvAMr993wHeA37qez9oJ02rb1G+8+wGstPi+f65ttCcMaHqnBNn4ooayZOr9gY7FGPCXn99qX8EnIh3wOcUEakGNgEbgU2q+vXODlLVv4nILhHJB36oqr/x29cMvOn3/pv9FGvA/eXjJg5VtvDcjacetfy3MSZ0lJc+x01zD3H3v8axr7SO7PT4YIdkTNjqlxYQVV2qqt9S1TNUNR3vmI3fAVXAsQaPTgDWA2+LyIMiktkfMYWKV7cU8k5BCzfNH8vM0anBDscY042Cgt8xKeVFHCL8+QNrBTFmIA3IGBBVPeAbCHqvql51jLJNvpaPiXjn8Fjtu2MlLOYmT4hxMS3TyX+e1dUdycaYUBLtdPCFSVk8u2Y/Dc2twQ7HmLB13AnIMW6l7TFVbVDVXwFTgQbgQxG59XjjC7ZTT8jgv2bGEu0Kv9lOjQlXV508mvK6Zl7adCjYoRgTtvrjW7G7W2l7TERyROQc4DogG6gGftYP8RljTK+cMjad3Ey3DUY1ZgD1RwLS3a20x+RbEK4M+CveCcFSgDfoeu4QY4wZUCLClXNHs35fBZsLKoMdjjFhqT9uyfhH2wtV/a6I3Nxd4U5cAOzSYKyKZ4wxfiZPfr799UUzR/LLVz7hqVV7+cVFJwYxKmPC03G3gKjq3wBEJEZEfgCcKyLPicidIjK2B8fvBKJF5Aci8lJvjjXGmP4UHZ1BdHQGAMlxUXxl+gj++lEBlfXNQY7MmPDTnyMj7wNGA9/FezdLOvAnEbmyF8fe2YdjjTGmXxw69DiHDj3e/v7Kk0fT0OzhhXUHgheUMWGqP2fFmgacr6oVIjJHVU8TkRggH3hqAI81xph+UVj4OADDhi0BYMqIZGZkp/DUqr187bQcW0DSmH7U3y0gbecrEZH78Q4k9QzwscYYM2CuOnk0u0pqeX9nabBDMSas9FsCoqp/V9Uy39uLgbV472K5uJPiR/wZ0ctjjTEmYM6dOozU+CiesltyjelXA7Iwiao2AX/pZn+Xic+xjjXGmECKjXJyyexRPPbObg6U1wU7HGPChk3PaYwxx3Dl3NFEOx1c98RaappsxgBj+oMlIMYY43PiiS9x4okvHbV9VFo8j10zi10ltfxqbQNVDXZbrjHHyxIQY4zxcTrjcTrjO9132gkZ/P7Kk9hf7eFrf1pDbWNLgKMzJryEZQIiIueIyDYR2SEi3+1kf4yIPOPbv1pEcgIfpTEm1BQUPExBwcNd7j9zQhY3Toth/b5yrntira2Wa8xxCLsEREScwEPAl4BJwGIRmdSh2LVAuaqeADwA3BvYKI0xoaio6FmKip7ttszsoS5+fck0Vu0u5can1tHYYkmIMX0RdgkIMAfYoaq7fHfULAfO71DmfOAJ3+vngbPEZhgyxvTQBTNG8rMLppK/rZhbnl5PZX0zTS0ebEkrY3puQG7DDbIRwH6/9weAuV2VUdUWEanEO/17SUAiNMYMeovnZNPQ3MqPV27llS2vAuAQ7227sVFOYl0OnE5BGPx/25w9vIX5wQ7ChJ1wTEA6+2nv+GdJT8ogIjcANwBkZWWRn5/fp4Bqamr6fOxgZXWODOFX5wqAbuvkX+cxwP+bGUNBjdLsUZpbocmjNLd6aPJ4aA2TFpEYbQmzf2cTCsIxATkAjPJ7PxI42EWZAyLiApKBsg5lUNWlwFKAWbNm6fz58/sUUH5+Pn09drCyOkeGcKvz+vUpAMyYMb/LMh3r3HXJ8BFu/84mNIRjArIGGCciY4AC4DLg8g5lVuBda+bfwFeBN/QYnbfr1q0rEZG+zsWcDezr47GDldU5MoRpnbvtNgnTOnfreOo8uj8DMeFDwnHQlIicCzwIOIE/quo9InI3sFZVV4hILPAkMANvy8dlqrprAOMpVtXMgTp/KLI6Rwarc2SIxDqbgReOLSCo6kvASx223eX3uoHALnRXEcBrhQqrc2SwOkeGSKyzGWDheBtuKKoMdgBBYHWODFbnyBCJdTYDzBKQwFga7ACCwOocGazOkSES62wGWFiOATHGGGNMaLMWEGOMMcYEnCUgxhhjjAk4S0CMMcYYE3CWgPSCiPxRRIpEZHM/na9VRD7yPVb0xzmNMcaYwcAGofaCiJwO1ADLVHVKP5yvRlUTjj8yY4wxZnCxFpBeUNW36bBmjIiMFZGXRWSdiLwjIhOCFJ4xxhgzaFgCcvyWAjer6kzgVuDhXhwbKyJrRWSViHxlYMIzxhhjQk9YTsUeKCKSAJwKPCfSvnhVjG/fhcDdnRxWoKpf9L3OVtWDIpILvCEim1R150DHbYwxxgSbJSDHxwFUqOr0jjtU9UXgxe4OVtWDvuddIpKPd3E8S0CMMcaEPeuCOQ6qWgXsFpGLAcRrWk+OFZFUEWlrLckATgO2DliwxhhjTAixBKQXRORp4N9AnogcEJFrgSuAa0VkA7AFOL+Hp5sIrPUd9ybwC1W1BMQYY0xEsNtwjTHGGBNw1gJijDHGmICzQag9lJGRoTk5OX06tra2Frfb3b8BhTirc2QItzp7PHUAOBzxXZYJtzr3xPHUed26dSWqmtnPIZkwYAlID+Xk5LB27dpeH/fhvnKeeGUNnSUv7hgnl88dTUJM+P0z5OfnM3/+/GCHEVBW58hgde4dEdnbv9GYcBF+33wh5qN9FfxtZzPs/LTT/e/tKOUP18zC5bTeMGOCrazsdQDS0j4f5EiMCX+WgAywr88bQ27L3k7/elj+wT6+++ImfrRyCz85fwp+k5kZY4Jg796fApaAGBMIloAE0WVzstldWssjb+0iJ93NdZ/LDXZIxhhjTEBYAhJkd3xxAvtK67jnpY/JTovnC5OHBjskY4wxZsDZwIMgcziE+y+ZzokjU/jP5R+x6UBlsEMyxhhjBpwlICEgLtrJoxNioJgAAA7xSURBVFfPJM0dzbVPrOFgRX2wQzLGGGMGlCUgIWJIYix/XDKb+qZWvv74GmoaW4IdkjERJy/vEfLyHgl2GMZEBEtAQkje0EQeuuIkPi2q4Vt/+ZCWVk+wQzImosTH5xEfnxfsMIyJCJaAhJjTx2fyk/OnkL+tmLv/vhVbq8eYwCkpWUlJycpgh2FMRLC7YELQ5XOz2VNay9K3vbfnfn3emGCHZExE2L//1/+/vXuPkrq87zj+/uyyy/2iXFYEFCtyB+USEw9pAUMVFUMT48FobFJtrTmaao0nlRKNSRoTT6jFaDBiYqHGFO9KlUawuphENKiICMhtg8jNRVBgVW673/4xQ84Ku+z1N7PMfF7n7NmZ+T3P7/d9zp7Z+c7zey4AdOt2YZYjMct9iSQgkiojojCJc+eLmyYO5J0dH/HDZ1Zy0vHtmDC4JNshmZmZNZukbsFkdUlPSRMlrZa0TtJNNRxvLemh9PFXJPXNfJRHV1AgZkwZwbBenfmnuUt5a7On55qZWe5odAIi6TJJ/Ws5XOfABUk9G3vtOs5bCPwcOA8YDHxV0uDDil0JfBAR/YD/AG5PIpamaltcyC//djRd2hZx5ZwlbN3l6blmZpYbmtIDsh2YKelFSY9L+snhBSRdLek+SZdIelrSN6sd/lG6zGWS/iDpgibEUt2ZwLqIKIuI/cBcYPJhZSYDc9KPHwW+oBa6EUuPTm24/+8+w0f7Krly9qt85Om5ZmaWAxo9BiQiFkgaGxHTJHUEflxDsbOBKcDvIuLzkn5R7diH6d/nAJ8H7gOeaWw81fQC3q32fBPw2drKRMRBSbuArsD7zXD9ZjfwhE7cfekIrpzzKkNvfZaClpkrfUpEoAXzsx1GRrnN2SNg5EnHMXnEiVwwrCdd2hU36jyDBj3QLPHsO1jJbc+sYnHZDvYfrEr9VFax70AV+yqrqKw6tma3XdK/iHHZDsJyTlMHoXaSNApYDrSv4fiOiAhJh25x7Kt+bUnfBTamy3zUxFgOqenT+fB3e33KIOkq4CqAkpISSktLGxVQRUVFo+tW988ji1m989hYG2T/gf0UFxVlO4yMcpuz50BVsGz7B0x7Yie3PPkWw7sXctaJrTijeyHFhY1J2NfXeqSu9/Pu/cHdS/ey5oMqhncv5PhiaNVGtCqAogIoKiikQFkeKNdAJcX7muV/mFl19UpAJM0klWS8CSyPiN3pQzcA3wSuAZ6toeqdkgZGxKGJ9Y9XO/ZtUj0fLzUklnrYBPSp9rw3sKWWMpsktQI6AzsPP1FEzAJmAYwePTrGjRvXqIBKS0tpbN3qmn6GzGmuNh9L3ObsighWbNnNk0s3M2/ZFma+sY8OrVtx4zn9+caY+k1lLy9/CIAePabUWuZobV773h5unrOE8j1w96UjmDT8xAa3oyVqSX9nyx31/dB/AxgOXAIMlbSHTyckV9RUKSLellQmqRS4NSIWVTt2AHih2vNrGteEIywBTpN0CrA5HfOlh5WZB3wdWAx8BXg+vOKX2TFNEkN7dWZor85MPX8QL5ftYGbpOn74zCpGnnwcw3t3qfMcmzffAxw9AalN6epyvvWbpbQpLuShfzyLM/rUfT2zfFavQagRMSsiro2IsRHRlVTPxT3AbqCuwaMDgaXAIkkzJHVvUsR1x3oQuJZUj8wq4OGIWCHpB5K+mC72K6CrpHWkenGOmKprZseuwgIxpl83Zl42im4dirnxkWXsO1iZ2PXmvLSBK2Yvoffx7XjqmjFOPszqoVGzYCJiU0TMj4jbI+LyOsruj4i7gEGkbn28kk4GOjbm2vWMb35E9I+IUyPiR+nXbomIeenHeyPi4ojoFxFnRkRZUrGYWfZ0blvETy4azpr3KvjZ/61N5Bo/+J+VfG/eCs4eWMKjV5/FiV3aJnIds1xTZwJSx1Taekt/6E8HhgF7gdcl3diYc5mZ1df4AT24eFRv7ildz7J3P6y7QgO8UraD+//wJy7/3Mnce/ko2rf27hZm9VWfHpCzSc0EuTYiJgGnN+ZCkvpKmgj8PXASsAe4rTHnMjNriO9OGkyPjm248ZFl7D3QPLdiIoLpC1ZT0qk10y4YRGHBsTSvxSz76pOA7EgP0KxpKm2dJL0paSfwJPANoAvwPKlBoB0aci4zs8ZI3YoZxtryCu48yq2YIUMeZciQR+t1zkVrtrNkwwdce/ZptCny1ldmDVWf/sI7AapNpX2sgdf4ElDmWSZmlk3jBvRgyug+3LtoPecOOaHGgaLFxd3qda6I4N8XrKH3cW2ZMrpP3RXM7Ah19oBExNvw5w3cbgamSnpE0lRJp9aj/nqgWNLNkuY3pK6ZWXOaNmkQJZ1qvxWzdetstm6dXed5nl2xjeWbd3H9hP4Ut0pqT0+z3NaQd85PgZNJTVndRGrp8v+U9LUG1J3aiLpmZs2iU5vUrJh15RXMeO7IWzHbts1m27bZRz1HVbr349Tu7fnSiF4JRWqW+xoyZPt0YHJEfCjpzIgYI6k1UAr8OsG6ZmbNZmz/7lzymT7c97syJg3vydBenRtU/+Wtlawt/5ifXzrSA0/NmqChPSCHyr8v6Q5SA0nrszlJU+qamTWrqecN4rh2xfzrE8sbtDHcgcoqnli7n8E9O3He0BMSjNAs99U7AYmIpyPi0H4pFwOvkprFcnENxT/1taCBdc3MEtW5XRG3XDiYNzft4oHFG+pd75FXN7H9k+DGc/tT4N4PsyZp1Ko5EbEf+M1Rjtea2NRV18wsEy4c3pNHX9vE9AVrOHfoCfTsfPQVTPceqOSu59fSr0sB4wf0yFCUZrnLw7fNLC9J4t8mD+VAZRXfn7cSgOHD5zN8+Pwayz/4yka27trLRacVI7n3w6ypnICYWd46qWs7rptwGr9dsY2FK9+jsLAdhYXtjij37s6Puad0HWP6dWVQVy86ZtYcnICYWV77h7/8CwaUdOSWp96i7J272Lx55p+PVVUFc17awLkzXuST/ZXcNHFQFiM1yy05lYBI+qmkt9PLvz8hqcY9sSVtkLRc0huSXs10nGbWchQVFnDbl4exddde3lz3AOXlDwNQtr2CKbMW8715Kxjd93gW3DCWYb0bNmXXzGqXa1s3LgSmRsRBSbeTWvjsX2opOz4i3s9caGbWUo06+Tgu++xJbNv1Cd3aF3PvovXcsXANrVsVMP3i07loZC+P+zBrZjnVAxIRCyLiYPrpy0DvbMZjZseO70wcSFFhASu27ObH//s24wZ057kbxvKVUb2dfJglINd6QKq7AniolmMBLJAUwL0RMStzYZlZS9S5bRGndGvPuztTq5yeP+wEJx5mCdKxtkmtpOeAmpYgnBYRT6XLTANGA1+uaRdeSSdGxBZJPUjdtvlWRLxYQ7mrgKsASkpKRs2dO7dRMVdUVNChQ4dG1T1Wuc35IffafH3694xaS+Rem+vWlDaPHz/+tYgY3cwhWQ445hKQukj6OnA18IWI+Lge5W8FKiJieh3ltgPvNDKsk4CNjax7rHKb84PbnB+a0uaTI6J7cwZjuSGnEhBJE4E7gLERsb2WMu2BgojYk368EPhBRPw2wbi259sb0G3OD25zfsjHNlvycmoQKnA30BFYmJ5i+wtI3XKRdGh5wxLg95KWAX8Enkky+Uj7MOHzt0Ruc35wm/NDPrbZEpZTg1Ajol8tr28Bzk8/LgNOz2RcwK4MX68lcJvzg9ucH/KxzZawXOsBaanycZaN25wf3Ob8kI9ttoTl1BgQMzMzOza4B8TMzMwyzglIwiRNlLRa0jpJN2U7nqRJul9SuaS3sh1LpkjqI+kFSaskrZB0XbZjSpqkNpL+KGlZus3fz3ZMmSCpUNJSSU9nO5ZM8L5ZliTfgkmQpEJgDfDXwCZgCfDViFiZ1cASJOmvgArgvyJiaLbjyQRJPYGeEfG6pI7Aa8Df5PjfWUD7iKiQVAT8HrguIl7OcmiJknQDqUUOO0XEpGzHkzRJG4DR3jfLkuAekGSdCayLiLKI2A/MBSZnOaZEpVeU3ZntODIpIrZGxOvpx3uAVUCv7EaVrEipSD8tSv/k9LcZSb2BC4BfZjsWs1zgBCRZvYB3qz3fRI5/MOU7SX2BEcAr2Y0keenbEW8A5cDCiMj1Ns8AvgNUZTuQDDq0b9Zr6a0pzJqNE5Bk1bSTVU5/S8xnkjoAjwHXR8TubMeTtIiojIgzSO06faaknL3lJmkSUB4Rr2U7lgwbExEjgfOAa9K3WM2ahROQZG0C+lR73hvYkqVYLEHpcRCPAQ9GxOPZjieTIuJDoBSYmOVQkjQG+GJ6TMRc4GxJv85uSMlLL+JIRJQDT5C6rWzWLJyAJGsJcJqkUyQVA5cA87IckzWz9IDMXwGrIuKObMeTCZK6S+qSftwWmAC8nd2okhMRUyOid0T0JfU+fj4ivpblsBIlqX16UPWhPbTOAfJmdpslzwlIgiLiIHAt8CypgYkPR8SK7EaVLEn/DSwGBkjaJOnKbMeUAWOAy0l9K34j/XN+toNKWE/gBUlvkkq0F0ZEXkxNzSPZ2DfL8oin4ZqZmVnGuQfEzMzMMs4JiJmZmWWcExAzMzPLOCcgZmZmlnFOQMzMzCzjnICYmZlZxjkBMTMzs4xzAmLWwkjqJ2n5Ya+1lvQnSYOrvdZX0ifpDeHqqjckvUDafkndMtMSM7PaOQExa3nKgD6Sqr8/rwIWRcTKw8quT28IV1e9Fely3ovIzFqEVtkOwMw+LSKqJG0E+gJl6b1Wvg2MS6KemVk2uAfErGVaBQxMP74GmBcRGxKsZ2aWUe4BMWuZVpHa0O9FUonE5xKuZ2aWUe4BMWuZDvVkXAc8GBHvSeoj6T5J0yVNqG+9DMVrZtYg7gExa5lWATcBE4CR6dcGAvuBn0XERkl961nPzKzFcQ+IWcu0GhgGzIqIXQARsRC4C7hbUq/61jMza4ncA2LWAkXEPg57f0q6HSgENgLlwBFJSE31zMxaIkVEtmMws0aQ1Ad4CdhRbS2Q2sq2BRYD3YFhEbEzAyGamdXKCYiZmZllnMeAmJmZWcY5ATEzM7OMcwJiZmZmGecExMzMzDLOCYiZmZllnBMQMzMzyzgnIGZmZpZxTkDMzMws4/4fK7QbzZBS/1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2725f4fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, [axId, axDId, axDDId, axDDDId]=plt.subplots(ncols=1, nrows=4, sharex=True)\n",
    "NIdSimpDiodData.plot(ax=axId, x='Vs[V]', y='Id[A]', legend=False)\n",
    "axId.axvline(x=NVCompSimp, linestyle='--', color='y', \n",
    "             label=f'VComp={NVCompSimp:.3f}[V]')\n",
    "\n",
    "axId.set_ylabel('$I_d[A]$')\n",
    "axId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axId.grid()\n",
    "\n",
    "box = axId.get_position()\n",
    "axId.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "axId.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "NIdSimpDiodData.plot(ax=axDId, x='Vs[V]', y='DIdDVs[OhmInv]', legend=False)\n",
    "axDId.axvline(x=NVCompSimp, linestyle='--', color='y')\n",
    "\n",
    "axDId.set_ylabel(r'$g_m[\\Omega^{-1}]$')\n",
    "axDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDId.grid()\n",
    "\n",
    "\n",
    "NIdSimpDiodData.plot(ax=axDDId, x='Vs[V]', y='DDIdDDVs', legend=False)\n",
    "axDDId.axvline(x=NVCompSimp, linestyle='--', color='y')\n",
    "\n",
    "axDDId.set_ylabel('$\\dfrac{\\partial^2 I_d}{\\partial V_{S}^2}$')\n",
    "axDDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDDId.grid()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "NIdSimpDiodData.plot(ax=axDDDId, x='Vs[V]', y='DDDIdDDDVs', legend=False)\n",
    "axDDDId.axvline(x=NVCompSimp, linestyle='--', color='y')\n",
    "\n",
    "axDDDId.set_ylabel('$\\dfrac{\\partial^3 I_d}{\\partial V_{S}^3}$')\n",
    "axDDDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDDDId.set_xlabel('$V_s[V]$')\n",
    "axDDDId.grid()\n",
    "\n",
    "fig.subplots_adjust(hspace=.5)\n",
    "plt.suptitle('NMOS Diode $I_d$ Simplifed ');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above graph, the substitutions were made to our simple model and `lambdify` was utilized to create a numerical version of the symbolic model of a greater than $1\\mu m$ Diode connected NMOS device. As well be seen later the transconductance ($g_m$) dictates the behavior of the Diode Connected NMOS. And as the differential voltage $V_{DS}$ becomes less, remember $V_D$ is fixed the conductance of the Diode Connect NMOS drops before shutting off much like that of an ordinary rectifying diode as the voltage differential between its terminals approaches the Diodes threshold voltage. Unlike the Diode, this voltage is typically referred to as the compliance voltage. Where we by examining up to the third derivative of the $I_d$ we respect to the changing $V_s$ we can ascertain the compliance voltage exactly (within the scope of the model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Full Analysis\n",
    "In this full analysis, the Channel Length Modulation effect $\\dfrac{V_{DS}}{V_A}$ is not ignored and proceed through the analysis as was done in the Simplified Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/latex": [
       "$$\\begin{cases} 0 & \\text{for}\\: V_{DS} < V_{thn} \\\\\\frac{V_{DS} W k_{n'}}{2 L V_{An}} \\left(V_{An} + V_{DS}\\right) \\left(V_{DS} - 2 V_{thn}\\right) & \\text{for}\\: V_{DS} < V_{DS} - V_{thn} \\\\\\frac{W k_{n'} \\left(V_{DS} - V_{thn}\\right)^{2}}{2 L V_{An}} \\left(V_{An} + V_{DS}\\right) & \\text{otherwise} \\end{cases}$$"
      ],
      "text/plain": [
       "⎧                    0                         for V_DS < Vₜₕₙ    \n",
       "⎪                                                                 \n",
       "⎪V_DS⋅W⋅k_n'⋅(V_An + V_DS)⋅(V_DS - 2⋅Vₜₕₙ)                        \n",
       "⎪─────────────────────────────────────────  for V_DS < V_DS - Vₜₕₙ\n",
       "⎪                 2⋅L⋅V_An                                        \n",
       "⎨                                                                 \n",
       "⎪                                     2                           \n",
       "⎪   W⋅k_n'⋅(V_An + V_DS)⋅(V_DS - Vₜₕₙ)                            \n",
       "⎪   ───────────────────────────────────           otherwise       \n",
       "⎪                 2⋅L⋅V_An                                        \n",
       "⎩                                                                 "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIdFullDiodConected=simplify(NMOS.IdFull(subs={VGS:VDS}))\n",
    "NIdFullDiodConected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/latex": [
       "$$\\begin{cases} 0 & \\text{for}\\: V_{thn} > V_{D} - V_{S} \\\\\\frac{W k_{n'}}{2 L V_{An}} \\left(V_{D} - V_{S}\\right) \\left(V_{An} + V_{D} - V_{S}\\right) \\left(V_{D} - V_{S} - 2 V_{thn}\\right) & \\text{for}\\: V_{D} - V_{S} < V_{D} - V_{S} - V_{thn} \\\\\\frac{W k_{n'}}{2 L V_{An}} \\left(V_{An} + V_{D} - V_{S}\\right) \\left(V_{D} - V_{S} - V_{thn}\\right)^{2} & \\text{otherwise} \\end{cases}$$"
      ],
      "text/plain": [
       "⎧                            0                                     for Vₜₕₙ > \n",
       "⎪                                                                             \n",
       "⎪W⋅k_n'⋅(V_D - V_S)⋅(V_An + V_D - V_S)⋅(V_D - V_S - 2⋅Vₜₕₙ)                   \n",
       "⎪──────────────────────────────────────────────────────────  for V_D - V_S < V\n",
       "⎪                         2⋅L⋅V_An                                            \n",
       "⎨                                                                             \n",
       "⎪                                                  2                          \n",
       "⎪      W⋅k_n'⋅(V_An + V_D - V_S)⋅(V_D - V_S - Vₜₕₙ)                           \n",
       "⎪      ─────────────────────────────────────────────                    otherw\n",
       "⎪                         2⋅L⋅V_An                                            \n",
       "⎩                                                                             \n",
       "\n",
       "V_D - V_S      \n",
       "               \n",
       "               \n",
       "_D - V_S - Vₜₕₙ\n",
       "               \n",
       "               \n",
       "               \n",
       "               \n",
       "ise            \n",
       "               \n",
       "               "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIdFullDiodConected=NIdFullDiodConected.subs(VDS, VD-VS)\n",
    "NIdFullDiodConected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(1.2408000000000009e-06)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIdFullDiodConectedN=lambdify(VS, NIdFullDiodConected.subs(subsN), dummify=False)\n",
    "NIdFullDiodConectedN(3.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vs[V]</th>\n",
       "      <th>Id[A]</th>\n",
       "      <th>DIdDVs[OhmInv]</th>\n",
       "      <th>DDIdDDVs</th>\n",
       "      <th>DDDIdDDDVs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.452101e-05</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>-6.134400e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.487488</td>\n",
       "      <td>1.705876e-05</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>6.635026e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.486786e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>8.359108e-08</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-4.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>6.869597e-06</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>-4.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.517164e-05</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>-4.000000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.577000e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>2.031467e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Vs[V]         Id[A]  DIdDVs[OhmInv]   DDIdDDVs    DDDIdDDDVs\n",
       "count  50.000000  5.000000e+01       50.000000  50.000000  5.000000e+01\n",
       "mean    2.500000  1.452101e-05       -0.000011   0.000006 -6.134400e-07\n",
       "std     1.487488  1.705876e-05        0.000010   0.000003  6.635026e-06\n",
       "min     0.000000  0.000000e+00       -0.000029   0.000000 -2.486786e-05\n",
       "25%     1.250000  8.359108e-08       -0.000019   0.000005 -4.000000e-07\n",
       "50%     2.500000  6.869597e-06       -0.000010   0.000007 -4.000000e-07\n",
       "75%     3.750000  2.517164e-05       -0.000001   0.000008 -4.000000e-07\n",
       "max     5.000000  5.577000e-05        0.000000   0.000008  2.031467e-05"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VSN=np.linspace(0, 5)\n",
    "NIdFullDiodData=pd.DataFrame()\n",
    "NIdFullDiodData['Vs[V]']=VSN\n",
    "NIdFullDiodData['Id[A]']=NIdFullDiodConectedN(VSN)\n",
    "NIdFullDiodData['DIdDVs[OhmInv]']=np.gradient(NIdFullDiodData['Id[A]'], NIdFullDiodData['Vs[V]'])\n",
    "NIdFullDiodData['DDIdDDVs']=np.gradient(NIdFullDiodData['DIdDVs[OhmInv]'], NIdFullDiodData['Vs[V]'])\n",
    "NIdFullDiodData['DDDIdDDDVs']=np.gradient(NIdFullDiodData['DDIdDDVs'], NIdFullDiodData['Vs[V]'])\n",
    "NIdFullDiodData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMOS Full Square Law Complince Voltage =3.878[V]\n"
     ]
    }
   ],
   "source": [
    "NVCompFullLoc=np.where(NIdFullDiodData['DDDIdDDDVs']==NIdFullDiodData['DDDIdDDDVs'].min())[0][0]\n",
    "NVCompFull=NIdFullDiodData['Vs[V]'][NVCompFullLoc]\n",
    "print(f'NMOS Full Square Law Complince Voltage ={NVCompFull:.3f}[V]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271f215128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, [axId, axDId, axDDId, axDDDId]=plt.subplots(ncols=1, nrows=4, sharex=True)\n",
    "NIdFullDiodData.plot(ax=axId, x='Vs[V]', y='Id[A]', legend=False)\n",
    "axId.axvline(x=NVCompFull, linestyle='--', color='y', \n",
    "             label=f'VComp={NVCompFull:.3f}[V]')\n",
    "\n",
    "axId.set_ylabel('$I_d[A]$')\n",
    "axId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axId.grid()\n",
    "\n",
    "box = axId.get_position()\n",
    "axId.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "axId.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "NIdFullDiodData.plot(ax=axDId, x='Vs[V]', y='DIdDVs[OhmInv]', legend=False)\n",
    "axDId.axvline(x=NVCompFull, linestyle='--', color='y')\n",
    "\n",
    "axDId.set_ylabel(r'$g_m[\\Omega^{-1}]$')\n",
    "axDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDId.grid()\n",
    "\n",
    "\n",
    "NIdFullDiodData.plot(ax=axDDId, x='Vs[V]', y='DDIdDDVs', legend=False)\n",
    "axDDId.axvline(x=NVCompFull, linestyle='--', color='y')\n",
    "\n",
    "axDDId.set_ylabel('$\\dfrac{\\partial^2 I_d}{\\partial V_{S}^2}$')\n",
    "axDDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDDId.grid()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "NIdFullDiodData.plot(ax=axDDDId, x='Vs[V]', y='DDDIdDDDVs', legend=False)\n",
    "axDDDId.axvline(x=NVCompFull, linestyle='--', color='y')\n",
    "\n",
    "axDDDId.set_ylabel('$\\dfrac{\\partial^3 I_d}{\\partial V_{S}^3}$')\n",
    "axDDDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDDDId.set_xlabel('$V_s[V]$')\n",
    "axDDDId.grid()\n",
    "\n",
    "fig.subplots_adjust(hspace=.5)\n",
    "plt.suptitle('NMOS Diode $I_d$ Full ');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference Between Simplified and Full analysis\n",
    "We can compare not only the $I_d vs. V_S$ graphs for the two analysis but also the difference in the found Compliance Voltage to see if there are any difference in ignoring the Channel Length Modulation effect in a greater then $1\\mu m$ Diode connected NMOS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABoAAAAOBAMAAADDIxFwAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJZjLNVN0i77urRHZ72Yd1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAdUlEQVQIHWNgEDIxZQABCJ3OIDEBxAPTLAsYmBOAHAjNfYCB/SOQB6H5DzDwfgfyILS8AgPvPyAPQs83YGD9DORB6PkKUB6YRlUJ1M0ONQVIc29gYAHbAKaBtjIlAE2B0u0MUgGM3xgYQDQDg/C7owwMPRAaABkSKGrnHpT1AAAAAElFTkSuQmCC\n",
      "text/latex": [
       "$$0.0$$"
      ],
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NVCompFull-NVCompSimp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271f0805f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax=plt.subplots(nrows=1, ncols=1)\n",
    "ax.plot(VSN, NIdSimpDiodConectedN(VSN), label='Simp Model')\n",
    "ax.plot(VSN, NIdFullDiodConectedN(VSN), label='Full Model')\n",
    "ax.set_xlabel('$V_S[V]$'); ax.set_ylabel('$I_d[A]$')\n",
    "ax.grid()\n",
    "ax.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "fig.subplots_adjust(hspace=.5)\n",
    "plt.suptitle('NMOS Diode Id Compersion between Simp and Full');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As is shown the differences between the Simplified and the Full analysis are minute for a diode connected NMOS ( be in mind a large channel NMOS)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diode Connected as a Topology Slice\n",
    "In the following, we will examine the what a Diode Connected Transistor means in terms of the global view of $I_d$. This is maybe not a new viewpoint but a less common than the declaration that a Diode Connected MOSFET is always in the saturated state (This is true-ish up to the compliance voltage). But given the ease of data collection and analyses with `PySpice` and *Python* we can do better in predicting the Diode-Connected MOSFET from the transistors $I_d$ surface that shows the MOSFETS behaver for all combinations of $V_{GS}, V_{DS}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "NIdFullN=lambdify((VGS, VDS), NMOS.IdFull(subs=subsN), dummify=False)\n",
    "VGSN, VDSN=np.meshgrid(np.linspace(0, 5), np.linspace(0, 5))\n",
    "NIdFullNDate=NIdFullN(VGSN, VDSN)\n",
    "NIdFullNDateDiag=NIdFullNDate.diagonal()\n",
    "VdiagN=VGSN.diagonal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        return MozWebSocket;\n",
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       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
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       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
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       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "%matplotlib notebook\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "fig = plt.figure()\n",
    "axId = fig.gca(projection='3d')\n",
    "axId.set_xlabel('$V_{gs}$'); axId.set_ylabel('$V_{ds}$'); axId.set_zlabel('$I_d[mA]$')\n",
    "\n",
    "\n",
    "surf = axId.plot_surface(VGSN, VDSN, NIdFullNDate*1000, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=True)\n",
    "axId.plot(VdiagN, VdiagN, NIdFullNDateDiag*1000, label='$V_{GS}=V_{DS}$')\n",
    "box = ax.get_position()\n",
    "axId.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "axId.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "plt.suptitle('NMOS $I_d$ Full Surface')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(if the 3D Graph is not showing up try rerunning the cell twice, at the time of this writing the nbmagic `notebook` is still a bit temperamental)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above surface plot of the current for (this cant be stressed enough) large channel Full modeled NMOS shows every combination of $V_{GS}$ and $V_{DS}$ that effects the NMOS's $I_d$. Since we are interested in the Diode-Connected NMOS in this analysis we know that the regions of interest will be where $$V_{GS}=V_{DS}$$.\n",
    "Thus we can find this ROI and overlay it on the $I_D$ surface and plot it individually below. The result is identical to what we predicted from modify the governing equations to the Diode-Connected topology. Save for the vertical mirroring that arises from the choice of fixing the $V_D$ and not the $V_S$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271e2fa4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "Xdiag=VDSN.diagonal(); Zdiag=NIdFullNDate.diagonal()\n",
    "fig, ax=plt.subplots(ncols=1, nrows=1)\n",
    "ax.plot(Xdiag, Zdiag*1000)\n",
    "ax.set_xlabel('$V_{DS}[V]$'); ax.set_ylabel('$I_d[mA]$')\n",
    "plt.suptitle('NMOS $I_d$ Full Surface Cut along $V_{DS}=V_{GS}$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SPICE Diode Connected NMOS\n",
    "Here we proceed with the same analysis but instead utilizing an older submicron NMOS technology. The technology that was chosen was the readily available Taiwan Semiconductor Manufacturing Company (TSMC) $180nm$ NMOS with a channel width and length set in the next cell. While the $180nm$ is not even close to there current cutting-edge $7nm$ process it does display non-square law effects without getting too much deviation from greater $1\\mu m$ technology where the Square Law holds better. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "NMOSR_W=200; NMOSR_L=200 #nm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPICE analysis of TSMC 180nm Diode Connected NMOS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "setup the \"NetList\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title DCBench\n",
      ".include /home/iridium/Documents/ClonedGitReps/PySpiceExsamples/SpiceLib/TSMC180N.lib\n",
      "Vbs base 0 0V\n",
      "Vds drain 0 1.8V\n",
      "Vsgnd source 0 0V\n",
      "M0 drain drain source base TSMC180nmN l=200nm w=200nm\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TestCirN=Circuit('DCBench')\n",
    "Vbs=TestCirN.V('bs', 'base', TestCirN.gnd, 0@u_V)\n",
    "#VDD\n",
    "Vds=TestCirN.V('ds', 'drain', TestCirN.gnd, 1.8@u_V)\n",
    "#Vload\n",
    "Vsgnd=TestCirN.V('sgnd', 'source', TestCirN.gnd, 0@u_V)\n",
    "\n",
    "TestCirN.include(spice_library['TSMC180nmN'])\n",
    "M0=TestCirN.MOSFET('0', 'drain', 'drain', 'source', 'base',\n",
    "                          model='TSMC180nmN', \n",
    "                   length=NMOSR_L@u_nm, width=NMOSR_W@u_nm)\n",
    "print(TestCirN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "setup and run the Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "TempC=27\n",
    "TestCirNSim=TestCirN.simulator(temperature=TempC, nominal_temperature=TempC)\n",
    "TestCirNSimData=TestCirNSim.dc(Vsgnd=slice(0, 1.8, .01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vsgnd[V]    181\n",
       "Id[A]       181\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NDiodeConctedData=pd.DataFrame()\n",
    "NDiodeConctedData['Vsgnd[V]']=pd.Series(TestCirNSimData.sweep)\n",
    "NDiodeConctedData['Id[A]']=-TestCirNSimData.Vds\n",
    "NDiodeConctedData.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vsgnd[V]</th>\n",
       "      <th>Id[A]</th>\n",
       "      <th>DIdDVs[OhmInv]</th>\n",
       "      <th>DDIdDDVs</th>\n",
       "      <th>DDDIdDDDVs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>181.000000</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>3.845577e-05</td>\n",
       "      <td>-8.825913e-05</td>\n",
       "      <td>1.163705e-04</td>\n",
       "      <td>-1.101192e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.523943</td>\n",
       "      <td>4.931266e-05</td>\n",
       "      <td>8.334699e-05</td>\n",
       "      <td>1.504332e-04</td>\n",
       "      <td>1.701090e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.809886e-12</td>\n",
       "      <td>-2.096984e-04</td>\n",
       "      <td>-1.387779e-13</td>\n",
       "      <td>-7.176422e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>3.238798e-11</td>\n",
       "      <td>-1.727597e-04</td>\n",
       "      <td>3.938239e-08</td>\n",
       "      <td>-1.725217e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>8.786765e-06</td>\n",
       "      <td>-9.506566e-05</td>\n",
       "      <td>7.793643e-05</td>\n",
       "      <td>9.746982e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.350000</td>\n",
       "      <td>7.199544e-05</td>\n",
       "      <td>-1.099948e-09</td>\n",
       "      <td>1.514092e-04</td>\n",
       "      <td>1.931309e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.587005e-04</td>\n",
       "      <td>1.110223e-14</td>\n",
       "      <td>6.400539e-04</td>\n",
       "      <td>4.088288e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Vsgnd[V]         Id[A]  DIdDVs[OhmInv]      DDIdDDVs    DDDIdDDDVs\n",
       "count  181.000000  1.810000e+02    1.810000e+02  1.810000e+02  1.810000e+02\n",
       "mean     0.900000  3.845577e-05   -8.825913e-05  1.163705e-04 -1.101192e-04\n",
       "std      0.523943  4.931266e-05    8.334699e-05  1.504332e-04  1.701090e-03\n",
       "min      0.000000  1.809886e-12   -2.096984e-04 -1.387779e-13 -7.176422e-03\n",
       "25%      0.450000  3.238798e-11   -1.727597e-04  3.938239e-08 -1.725217e-07\n",
       "50%      0.900000  8.786765e-06   -9.506566e-05  7.793643e-05  9.746982e-05\n",
       "75%      1.350000  7.199544e-05   -1.099948e-09  1.514092e-04  1.931309e-04\n",
       "max      1.800000  1.587005e-04    1.110223e-14  6.400539e-04  4.088288e-03"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NDiodeConctedData['DIdDVs[OhmInv]']=np.gradient(NDiodeConctedData['Id[A]'], NDiodeConctedData['Vsgnd[V]'])\n",
    "NDiodeConctedData['DDIdDDVs']=np.gradient(NDiodeConctedData['DIdDVs[OhmInv]'], NDiodeConctedData['Vsgnd[V]'])\n",
    "NDiodeConctedData['DDDIdDDDVs']=np.gradient(NDiodeConctedData['DDIdDDVs'], NDiodeConctedData['Vsgnd[V]'])\n",
    "NDiodeConctedData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$0.92$$"
      ],
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NVCompReal=NDiodeConctedData['Vsgnd[V]'][np.where(NDiodeConctedData['DDDIdDDDVs']==NDiodeConctedData['DDDIdDDDVs'].max())[0]]\n",
    "NVCompReal=np.around(float(NVCompReal), 4); NVCompReal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$0.46$$"
      ],
      "text/plain": [
       "0.46"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NVbiasTarget=NVCompReal/2; NVbiasTarget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271c027f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, [axId, axDId, axDDId, axDDDId]=plt.subplots(ncols=1, nrows=4, sharex=True)\n",
    "NDiodeConctedData.plot(ax=axId, x='Vsgnd[V]', y='Id[A]', legend=False)\n",
    "axId.axvline(x=NVCompReal, linestyle='--', color='y', \n",
    "             label=f'VComp={NVCompReal:.3f}[V]')\n",
    "axId.axvline(x=NVbiasTarget, linestyle='--', color='g',\n",
    "            label=f'VBiasTar={NVbiasTarget:.3f}[V]')\n",
    "\n",
    "axId.set_ylabel('$I_d[A]$')\n",
    "axId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axId.grid()\n",
    "\n",
    "box = axId.get_position()\n",
    "axId.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "axId.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "###############\n",
    "\n",
    "NDiodeConctedData.plot(ax=axDId, x='Vsgnd[V]', y='DIdDVs[OhmInv]', legend=False)\n",
    "axDId.axvline(x=NVCompReal, linestyle='--', color='y')\n",
    "axDId.axvline(x=NVbiasTarget, linestyle='--', color='g')\n",
    "\n",
    "axDId.set_ylabel(r'$g_m[\\Omega^{-1}]$')\n",
    "axDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDId.grid()\n",
    "\n",
    "#######################################\n",
    "NDiodeConctedData.plot(ax=axDDId, x='Vsgnd[V]', y='DDIdDDVs', legend=False)\n",
    "axDDId.axvline(x=NVCompReal, linestyle='--', color='y')\n",
    "axDDId.axvline(x=NVbiasTarget, linestyle='--', color='g')\n",
    "\n",
    "\n",
    "axDDId.set_ylabel('$\\dfrac{\\partial^2 I_d}{\\partial V_{S}^2}$')\n",
    "axDDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDDId.grid()\n",
    "\n",
    "\n",
    "#############################################\n",
    "\n",
    "NDiodeConctedData.plot(ax=axDDDId, x='Vsgnd[V]', y='DDDIdDDDVs', legend=False)\n",
    "axDDDId.axvline(x=NVCompReal, linestyle='--', color='y')\n",
    "axDDDId.axvline(x=NVbiasTarget, linestyle='--', color='g')\n",
    "\n",
    "\n",
    "axDDDId.set_ylabel('$\\dfrac{\\partial^3 I_d}{\\partial V_{S}^3}$')\n",
    "axDDDId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "axDDDId.set_xlabel('$V_s[V]$')\n",
    "axDDDId.grid()\n",
    "\n",
    "fig.subplots_adjust(hspace=.5)\n",
    "plt.suptitle(f'TSMC180nm NMOS $I_d$ Diode Connnected W={NMOSR_W}[nm], L={NMOSR_L}[nm]');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Finish later)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPICE Confirmation of Topology Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test Circuit for extracting NMOS characteristics curves "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title DCBench\n",
      ".include /home/iridium/Documents/ClonedGitReps/PySpiceExsamples/SpiceLib/TSMC180N.lib\n",
      "Vgs gate 0 1.8V\n",
      "Vbs base 0 0V\n",
      "Vds drain 0 1.8V\n",
      "M0 drain gate 0 base TSMC180nmN l=200nm w=200nm\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TestCir=Circuit('DCBench')\n",
    "Vgs=TestCir.V('gs', 'gate', TestCir.gnd, 1.8@u_V)\n",
    "Vbs=TestCir.V('bs', 'base', TestCir.gnd, 0@u_V)\n",
    "Vds=TestCir.V('ds', 'drain', TestCir.gnd, 1.8@u_V)\n",
    "\n",
    "TestCir.include(spice_library['TSMC180nmN'])\n",
    "\n",
    "M0=TestCir.MOSFET('0', 'drain', 'gate', TestCir.gnd, 'base',\n",
    "                          model='TSMC180nmN', length=200@u_nm, width=200@u_nm)\n",
    "print(TestCir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create simulation object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "TempC=27\n",
    "TestCirSim=TestCir.simulator(temperature=TempC, nominal_temperature=TempC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 50, 2500)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.linspace(0, 1.8)\n",
    "y=np.linspace(0, 1.8)\n",
    "X, Y=np.meshgrid(x, y)\n",
    "len(x),len(y), np.prod([*X.shape])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "perform a DC operating point analyses at every combination of $V_{GS}$ and $V_{DS}$ specified and bind the results to a 2D array for use in plotting a 3D surface of $I_D$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z=np.zeros(X.shape)\n",
    "M0.length=200@u_nm\n",
    "for i in IProd(x, y):\n",
    "    Vgs.dc_value=i[0]@u_V; Vds.dc_value=i[1]@u_V\n",
    "    TestCirSimData=TestCirSim.operating_point()    \n",
    "\n",
    "    \n",
    "    Z[np.where(x==i[0])[0], np.where(y==i[1])[0]]=np.array(-TestCirSimData.Vds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIdDVgs, DIdDVds=np.gradient(Z, x, y)\n",
    "Av0=DIdDVgs/DIdDVds\n",
    "DIdDVgsDiag=DIdDVgs.diagonal()\n",
    "DIdDVdsDiag=DIdDVds.diagonal()\n",
    "Av0Diag=Av0.diagonal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xdiag=X.diagonal(); ZDiag=1000*Z.diagonal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "%matplotlib notebook\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "fig = plt.figure()\n",
    "###############\n",
    "axId = fig.add_subplot(3, 1, 1, projection='3d')\n",
    "axId.set_xlabel('$V_{gs}[V]$'); ax.set_ylabel('$V_{ds}[V]$'); \n",
    "axId.set_zlabel('$I_d[mA]$')\n",
    "\n",
    "\n",
    "axId.plot_surface(X, Y, Z*1000, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=True)\n",
    "axId.ticklabel_format(axis='z', style='sci', scilimits=(-1,1))\n",
    "\n",
    "axId.plot(Xdiag, Xdiag, ZDiag, label='Diode Line')\n",
    "axId.legend()\n",
    "\n",
    "##################\n",
    "\n",
    "axDIdDVgs = fig.add_subplot(3, 2, 3, projection='3d')\n",
    "\n",
    "axDIdDVgs.set_xlabel('$V_{gs}[V]$'); axDIdDVgs.set_ylabel('$V_{ds}[V]$'); \n",
    "axDIdDVgs.set_zlabel('$\\dfrac{\\partial I_d}{\\partial V_{gs}}$')\n",
    "\n",
    "\n",
    "axDIdDVgs.plot_surface(X, Y, DIdDVgs, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=True)\n",
    "axDIdDVgs.ticklabel_format(axis='z', style='sci', scilimits=(-1,1))\n",
    "\n",
    "axDIdDVgs.plot(Xdiag, Xdiag, DIdDVgsDiag)\n",
    "\n",
    "##############################\n",
    "\n",
    "axDIdDVds = fig.add_subplot(3, 2, 4, projection='3d')\n",
    "axDIdDVds.set_xlabel('$V_{gs}[V]$'); axDIdDVds.set_ylabel('$V_{ds}[V]$'); \n",
    "axDIdDVds.set_zlabel('$\\dfrac{\\partial I_d}{\\partial V_{ds}}$')\n",
    "\n",
    "\n",
    "axDIdDVds.plot_surface(X, Y, DIdDVds, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=True)\n",
    "axDIdDVds.plot(Xdiag, Xdiag, DIdDVdsDiag)\n",
    "axDIdDVds.ticklabel_format(axis='z', style='sci', scilimits=(-1,1))\n",
    "\n",
    "############################\n",
    "\n",
    "axAv0 = fig.add_subplot(3, 2, 5, projection='3d')\n",
    "axAv0.set_xlabel('$V_{gs}[V]$'); axAv0.set_ylabel('$V_{ds}[V]$'); \n",
    "axAv0.set_zlabel('$A_{V0}$')\n",
    "\n",
    "\n",
    "axAv0.plot_surface(X, Y, Av0, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=True)\n",
    "axAv0.plot(Xdiag, Xdiag, Av0Diag)\n",
    "axAv0.ticklabel_format(axis='z', style='sci', scilimits=(-1,1))\n",
    "\n",
    "\n",
    "plt.suptitle(f'TSMC180nm NMOS $I_d$ Surface W={NMOSR_W}[nm], L={NMOSR_L}[nm]')\n",
    "fig.subplots_adjust(hspace=.5, wspace=0.5);\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Need to Get a hold of the subplot numbering arghh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271f464978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, [axId, axDIdDVgs, axDIdDVds, axAv0]=plt.subplots(nrows=4, ncols=1, sharex=True)\n",
    "\n",
    "axId.plot(Xdiag, ZDiag)\n",
    "axId.set_ylabel('$I_d[mA]$')\n",
    "axId.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "\n",
    "\n",
    "#########################\n",
    "\n",
    "axDIdDVgs.plot(Xdiag, DIdDVgsDiag)\n",
    "axDIdDVgs.set_ylabel('$\\dfrac{\\partial I_d}{\\partial V_{gs}}$')\n",
    "axDIdDVgs.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "\n",
    "\n",
    "\n",
    "########################\n",
    "\n",
    "axDIdDVds.plot(Xdiag, DIdDVdsDiag)\n",
    "axDIdDVds.set_ylabel('$\\dfrac{\\partial I_d}{\\partial V_{ds}}$')\n",
    "axDIdDVds.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
    "\n",
    "\n",
    "#######################\n",
    "\n",
    "axAv0.plot(Xdiag, Av0Diag)\n",
    "axAv0.set_ylabel('$A_{V0}$')\n",
    "axAv0.set_xlabel('$V_{ds}[V]$')\n",
    "\n",
    "fig.subplots_adjust(hspace=.5)\n",
    "plt.suptitle(f'TSMC180nm NMOS $I_d$ Diode Connnected Counters W={NMOSR_W}[nm], L={NMOSR_L}[nm]');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above graph there is a $A_{vo}$ where\n",
    "$$A_{vo}=g_m r_{DS}=\\dfrac{\\partial I_d}{\\partial V_{gs}}/\\dfrac{\\partial I_d}{\\partial V_{ds}}$$\n",
    "this is not a true intrensit gain. It is however a meassure of the influsince between $\\dfrac{\\partial I_d}{\\partial V_{gs}}$ and $\\dfrac{\\partial I_d}{\\partial V_{ds}}$ that shows that the transcondutance domonated the output resistince for a Diode connected NMOS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the frequency dependence of the Bias point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title DCBench\n",
      ".include /home/iridium/Documents/ClonedGitReps/PySpiceExsamples/SpiceLib/TSMC180N.lib\n",
      "Vbs base 0 0V\n",
      "Vds drain 0 1.8V\n",
      "Vsgnd source 0 DC 0.46V AC SIN(0V 0.1V 1kHz 0s 0Hz)\n",
      "M0 drain drain source base TSMC180nmN l=200nm w=200nm\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TestCirSinN=Circuit('DCBench')\n",
    "Vbs=TestCirSinN.V('bs', 'base', TestCirSinN.gnd, 0@u_V)\n",
    "#VDD\n",
    "Vds=TestCirSinN.V('ds', 'drain', TestCirSinN.gnd, 1.8@u_V)\n",
    "#Vload\n",
    "Vsgnd=TestCirSinN.SinusoidalVoltageSource('sgnd', 'source', TestCirSinN.gnd, \n",
    "                             dc_offset=NVbiasTarget@u_V, \n",
    "                          amplitude=.1@u_V, frequency=1@u_kHz)\n",
    "\n",
    "TestCirSinN.include(spice_library['TSMC180nmN'])\n",
    "M0=TestCirSinN.MOSFET('0', 'drain', 'drain', 'source', 'base',\n",
    "                          model='TSMC180nmN', length=200@u_nm, width=200@u_nm)\n",
    "print(TestCirSinN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "TempC=27\n",
    "TestCirSinNSim=TestCirSinN.simulator(temperature=TempC, nominal_temperature=TempC)\n",
    "TestCirSinNData=TestCirSinNSim.ac(start_frequency=1@u_Hz, stop_frequency=100@u_MHz, number_of_points=100,  variation='dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PySpice.Plot.BodeDiagram import bode_diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/KX+fWmd9hvw+NcY5vMrvB/NqMW9tXnZNH/v3qU2oz255DV1HFj1XzvbfftW/I/PO63l5LfvvwV7XekheU/57vWx9Vv29cpH/7THvd/ydzuGd9nvru67J0y94VLbue49skkV7IWdU1VXdfd5CczWYhs0bq8F01Z/8Zb7yRW/Krfu/HAAAAHDo3fH25+Rnvu7RG9VkmrLBdM6qSTGu373ug5pLAAAAcEB8/JZb87vXfXDdaewZDaYN8ej7fUo+6Xa7PeoKAAAA2A8qye2PnJNH3+9T1p3Knpn3kO+/VVX3SXLf7ft09xumSOow2rrvPXL5JV/oGUxLxvIMpun/jngG0/L3Z3sG0zj3yo/xzIDdzsVlnxkwL5ZnMC1fi2VjeQbTdOv8oufBqsfzDKbpf59aZ32G/D41xjm8yu8HnsE0vD675eUZTKv/3rJX//Z7BtPyv+MflGcwTaq7576S/Jck1yd5TZJXz16vWmTfvXhtbW31yZMnexmLzp83b9XxZbev25R5DYm9yr5qv5yp8hoad1Nrv8qY2k+7/1h1nzdH7aePe1Bqv6l171b7scad8+PE3tS1frdxtR8n7qbW3lo/fdyDstYvesx1WDavJFf2gr2ZRa9genKSB3X3RyfocQEAAACwjy36DKbrktx+ykQAAAAA2J8WvYLpI0murqrXJ/nbq5i6+99OkhUAAAAA+8aiDaZXzV4AAAAA8Pcs1GDq7pdMnQgAAAAA+9OuDaaq+rnuvrCq3pmkbzve3Y+YLDMAAAAA9oV5VzD937M/v3TqRAAAAADYn3ZtMHX3n83+/JO9SQcAAACA/eacRSZV1aOr6i1VdaqqPlZVn6iqm6ZODgAAAIDNt1CDKckLkjw9yXuS3CnJ1yV5/lRJAQAAALB/LPQtcknS3ddW1e26+xNJXlxVb5owLwAAAAD2iUUbTB+pqk9KcnVV/dckf5bkztOlBQAAAMB+segtcs+YzX1WkpuTfHqSr5gqKQAAAAD2j4WuYNr2LXJ/k+T7hx60ql6W5EGzj3dP8uHufuRs7BFJfjTJXZPcmuRR3f03Q48JAAAAwDR2bTBV1ZOSnNvdPzL7/HtJ7jUb/s7ufvkqB+3up207xqVJbpy9P5LkpUme0d1vr6pPSfLxVY4BAAAAwN6Yd4vcdyZ51bbPd0jyqCTnJ/mmoQevqkpyYZLLZ5uekOQd3f32JOnuD84eKg4AAADAhprXYPqk7v5f2z7/1qzp86cZ5yHfj0nyvu5+z+zzA5N0Vb22qt5aVd85wjEAAAAAmFB1986DVdd29/13GPvj7v4/dtn3dUnufZahZ3f3K2dzXpjk2u6+dPb525P8m5y+SuojSV6f5Hu6+/VniX9JkkuS5NixY1snTpzI0aNHd/xZbuvUqVMLzZ83b9XxZbev25R5DYm9yr5qv5yp8hoad1Nrv8qY2k+7/1h1nzdH7aePe1Bqv6l1T9R+rHHn/DixN3Wt321c7ceJu6m1t9ZPH/egrPXL5LbXls3r+PHjV3X3eQtN7u4dX0l+JsnXn2X7NyS5fLd9571y+vlP78vpZzyd2XZRksu2ff7eJN8xL9bW1lafPHmyl7Ho/HnzVh1fdvu6TZnXkNir7Kv2y5kqr6FxN7X2q4yp/bT7j1X3eXPUfvq4B6X2m1r3brUfa9w5P07sTV3rdxtX+3HibmrtrfXTxz0oa/2ix1yHZfNKcmUv2OeZ9y1y35rkFVX1VUneOtu2ldPPYnryoh2vHVyQ5JruvmHbttcm+c6q+uQkH0vyT5P88MDjAAAAADChXRtM3f0XSf5JVT0uycNmm3+5u39jhGNflL97uPeZ4/1lVf1Qkrck6SSv6e5fHuFYAAAAAExk3hVMSZJZQ2mMptL2mBfvsP2lSV465rEAAAAAmM68b5EDAAAAgF1pMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAIMcWcdBq+plSR40+3j3JB/u7kdW1Vcn+Y5tUx+R5PO6++q9zhEAAACAxaylwdTdTzvzvqouTXLjbPvPJPmZ2faHJ3ml5hIAAADAZltLg+mMqqokFyZ53FmGn57k8r3NCAAAAIBlrbXBlOQxSd7X3e85y9jTkjxpj/MBAAAAYEnV3dMErnpdknufZejZ3f3K2ZwXJrm2uy+9zb5fkOREdz98l/iXJLkkSY4dO7Z14sSJHD16dOH8Tp06tdD8efNWHV92+7pNmdeQ2Kvsq/bLmSqvoXE3tfarjKn9tPuPVfd5c9R++rgHpfabWvdE7ccad86PE3tT1/rdxtV+nLibWntr/fRxD8pav0xue23ZvI4fP35Vd5+30OTuXssrp6+eel+Sc88y9sNJ/v2isba2tvrkyZO9jEXnz5u36viy29dtyryGxF5lX7VfzlR5DY27qbVfZUztp91/rLrPm6P208c9KLXf1Lp3q/1Y4875cWJv6lq/27jajxN3U2tvrZ8+7kFZ6xc95josm1eSK3vB3sw6b5G7IMk13X3D9o1VdU6Sr0zy2LVkBQAAAMBSzlnjsS/K2R/i/dgkN3T3dXucDwAAAAArWNsVTN198Q7br0jy6D1NBgAAAICVrfMKJgAAAAAOAA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgkCPrOGhVvSzJg2Yf757kw939yKq6fZITST5vlttPdfcPriNHAAAAABazlgZTdz/tzPuqujTJjbOPX5nkDt398Kr65CR/UFWXd/f1a0gTAAAAgAWspcF0RlVVkguTPG62qZPcuaqOJLlTko8luWlN6QEAAACwgHU/g+kxSd7X3e+ZfX55kpuT/FmSP03y3O7+0LqSAwAAAGC+6u5pAle9Lsm9zzL07O5+5WzOC5Nc292Xzj5/UZJ/neTiJPdI8sYkT+zu684S/5IklyTJsWPHtk6cOJGjR48unN+pU6cWmj9v3qrjy25ftynzGhJ7lX3VfjlT5TU07qbWfpUxtZ92/7HqPm+O2k8f96DUflPrnqj9WOPO+XFib+pav9u42o8Td1Nrb62fPu5BWeuXyW2vLZvX8ePHr+ru8xaa3N1reeX07XnvS3Lutm0/kuQZ2z7/ZJIL58Xa2trqkydP9jIWnT9v3qrjy25ftynzGhJ7lX3VfjlT5TU07qbWfpUxtZ92/7HqPm+O2k8f96DUflPr3q32Y40758eJvalr/W7jaj9O3E2tvbV++rgHZa1f9JjrsGxeSa7sBfs867xF7oIk13T3Ddu2/WmSx9Vpd07y6CTXrCU7AAAAABayzgbTRUkuv822H0lyNMm7krwlyYu7+x17nRgAAAAAi1vbt8h198Vn2XYqyVfufTYAAAAArGrd3yIHAAAAwD6nwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyylgZTVb2sqq6eva6vqqtn2z+pql5cVe+sqrdX1fnryA8AAACAxR1Zx0G7+2ln3lfVpUlunH38+tn4w6vqU5P8SlU9qrtvXUOaAAAAACxgrbfIVVUluTDJ5bNND03y+iTp7r9I8uEk560nOwAAAAAWUd29voNXPTbJD3X3ebPPlyR5fJKnJ/n0JG9L8szu/oWz7HtJkkuS5NixY1snTpzI0aNHFz72qVOnFpo/b96q48tuX7cp8xoSe5V91X45U+U1NO6m1n6VMbWfdv+x6j5vjtpPH/eg1H5T656o/VjjzvlxYm/qWr/buNqPE3dTa2+tnz7uQVnrl8ltry2b1/Hjx68607OZq7sneSV5XZJ3neX1pG1zXpjk27Z9PpLkh5NcneSVSV6zff5Or62trT558mQvY9H58+atOr7s9nWbMq8hsVfZV+2XM1VeQ+Nuau1XGVP7afcfq+7z5qj99HEPSu03te7daj/WuHN+nNibutbvNq7248Td1Npb66ePe1DW+kWPuQ7L5pXkyl6wDzTZM5i6+4LdxqvqSJKnJNnats8tSb5125w3JXnPVDkCAAAAMNw6n8F0QZJruvuGMxuq6pOr6s6z949Pckt3/8G6EgQAAABgvrV8i9zMRfm7h3uf8alJXltVtyb530mesedZAQAAALCUtTWYuvvis2y7PsmD9jwZAAAAAFa2zlvkAAAAADgANJgAAAAAGESDCQAAAIBBNJgAAAAAGESDCQAAAIBBNJgAAAAAGESDCQAAAIBBqrvXncNgVfX+JB9OcuMSu91twfnz5q06vtP2eyb5wAJ57bVF/3vtdexV9lX75UxV+6FxN7X2q4yp/bT7j1X3eXPUfvq4B6X2m1r3RO3HGnfOjxN7U9f63cbVfpy4m1p7a/30cQ/KWp8cnNrft7vvtdDM7j4QryQ/NsX8efNWHd9l+5Xr/m85xn/fvYq9yr5qvxm1Hxp3U2u/ypjaT7v/WHVX+/XHPSi139S6q/144875cWJv6lqv9tPH3dTaW+s3r/abutYfxtp394G6Re7VE82fN2/V8WXzXbcp8x0Se5V91X45U+U7NO6m1n7VsU10UGo/Vt3nzVH76eOq/fTUfpxxdR8n9qau9buNq/04cTe19v69nz6utX56k+V7IG6RO0iq6sruPm/debD31P7wUvvDS+0PJ3U/vNT+8FL7w0vtD6/DWPuDdAXTQfFj606AtVH7w0vtDy+1P5zU/fBS+8NL7Q8vtT+8Dl3tXcEEAAAAwCCuYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYNpwVXXnqnpJVf14VX31uvNh71TV/arqJ6rq5evOhb1TVU+ene+vrKonrDsf9k5VPaSqXlRVL6+qb1p3Puyt2Xp/VVV96bpzYe9U1flV9cbZuX/+uvNh71TVOVX1n6vq+VX1r9adD3ujqh4zO99PVNWb1p0Pe6eqPqOqXlVVP1lV37XufKaiwbQGs79Uf1FV77rN9i+uqj+sqmu3/aV7SpKXd/fXJ/myPU+WUS1T++6+rrufuZ5MGdOSdX/F7Hy/OMnT1pAuI1qy9u/u7m9McmGS89aRL+NZcq1Pkn+X5Of2NkumsGTtO8mpJHdMcsNe58q4lqz9k5LcJ8nHo/b72pJr/Rtna/0vJXnJOvJlPEue8w9M8svd/bVJHrrnye4RDab1uCzJF2/fUFW3S/IjSZ6Y03/hnl5VD01ybpL/NZv2iT3MkWlclsVrz8FxWZav+/fMxtnfLssSta+qL0vyW0lev7dpMoHLsmDtq+qCJH+Q5H17nSSTuCyLn/dv7O4n5nSD8fv3OE/Gd1kWr/2DkvxOd/8/SVy1ur9dluV/z/uqJJfvVYJM5rIsXvu3Jbmoqn4jyck9znPPaDCtQXe/IcmHbrP585NcO7tq5WNJfjan/5+NG3K6yZSo1763ZO05IJape532X5L8Sne/da9zZVzLnvPd/aru/idJ3BK9zy1Z++NJHp3T/4Pj66vKer+PLVP77r51Nv6XSe6wh2kygRV+x//L2Rz/J/I+tuxaX1WfkeTG7r5pbzNlbEvW/muSfF93Py7Jl+xtpnvnyLoT4G/dJ393pVJyetH5giTPS/KCqvqSJK9eR2JM7qy1r6pPSfKfk3xuVX13d//gWrJjKjud89+c5IIkd6uq+3f3i9aRHJPa6Zw/P6dvi75DktesIS+md9bad/ezkqSqLk7ygW1NBw6Onc77pyT5Z0nunuQF60iMye3qKvthAAAgAElEQVS03v/3JM+vqsckecM6EmNSO9U9SZ6Z5MV7nhF7ZafavyjJc6rqq5Jcv4a89oQG0+aos2zr7r45p7udHFw71f6DSb5xr5Nhz+xU9+fldGOZg2un2l+R5Iq9TYU9dtba/+2b7sv2LhX22E7n/f9I8j/2Ohn21E61/0hONxo4mHb89767v2+Pc2Fv7XTOvyvJU/c6mb3mEuzNcUOST9/2+dwk711TLuwttT+c1P3wUvvDS+0PL7U/vNT+cFL3w+tQ116DaXO8JckDquqzquqTklyU5FVrzom9ofaHk7ofXmp/eKn94aX2h5faH07qfngd6tprMK1BVV2e5HeSPKiqbqiqZ3b3LUmeleS1Sd6d5Oe6+/fXmSfjU/vDSd0PL7U/vNT+8FL7w0vtDyd1P7zU/h+q7p4/CwAAAAB24AomAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAAAAAAbRYAIAAABgEA0mAODAqqpPVNXV216fue6cxlJVF1fV+6vqxOzz+VX1S7eZc1lVPXWXGP+tqv68qr596nwBgIPtyLoTAACY0F939yN3GqyqI919y14mNLKXdfezVt25u7+jqm4eMyEA4HByBRMAcKjMrvz5+ap6dZJfm237jqp6S1W9o6q+f9vcZ1fVH1bV66rq8jNX+lTVFVV13uz9Pavq+tn7282uCjoT6xtm28+f7fPyqrqmqn6mqmo29qiqelNVvb2q3lxVd6mqN1bVI7fl8dtV9YgBP/N5267iemdV9aqxAADOxhVMAMBBdqequnr2/n9295fP3n9hkkd094eq6glJHpDk85NUkldV1WOT3JzkoiSfm9O/M701yVVzjvfMJDd296Oq6g5Jfruqfm029rlJHpbkvUl+O8kXVdWbk7wsydO6+y1Vddckf53kRJKLk3xLVT0wyR26+x0L/LyP2fbzJslnJPml7r4yySOT07fFJfnVBWIBACxMgwkAOMh2ukXu17v7Q7P3T5i93jb7fDSnG053SfKL3f2RJKmqVy1wvCckecS25x7dbRbrY0ne3N03zGJdneQzk9yY5M+6+y1J0t03zcZ/Psn3VtV3JPnaJJct+PO+sbu/9MyHqvp7+1XVhUk+b5YnAMBoNJgAgMNo+3OHKskPdvePbp9QVd+SZKdbyW7J3z1q4I63ifXN3f3a28Q6P8lHt236RE7/HlZnO0Z3f6Sqfj3Jk5JcmOS8OT/PXFX1sCTfn+Sx3f2JofEAALbzDCYA4LB7bZKvraqjSVJV96mqT03yhiRfXlV3qqq7JPnn2/a5PsnW7P1TbxPrm6rq9rNYD6yqO+9y7GuSfFpVPWo2/y5Vdeb/ADyR5HlJ3rLtaquVVNXdkvxskn/Z3e8fEgsA4GxcwQQAHGrd/WtV9ZAkvzN77vapJP+iu99aVS9LcnWSP0nyxm27PTfJz1XVM5L8xrbtJ3L61re3zh7i/f4kT97l2B+rqqcleX5V3Smnn790QZJT3X1VVd2U5MUj/JhPTnLfJD8++xmz27frAQAsq7p9iQgAwDxV9Zycbvw8d4+O92lJrkjy4O6+9SzjFyc5r7ufNfA4z8ke/lwAwMHkFjkAgA1TVf8yye8lefbZmkszf53kiVV1YsBx/luSf5G//0wqAICluYIJAAAAgEFcwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBvVYKqq+1fV+6vq+qq6uqo+VFV/XFV3XXduAAAAAJzdRjWYuvvaJL+V5Bnd/cgk70jy5O6+ab2ZAQAAALCTI+tO4CweluRds/cPTvKHZ5tUVZckuSRJ7nSnO23d5z73yTnnLN4vu/XWWxeaP2/equPLbl+3KfMaEnuVfdV+OVPlNTTuptZ+lTG1n3b/seo+b47aTx/3oNR+U+ueqP1Y4875cWJv6lq/27jajxN3U2tvrZ8+7kFZ65fJba8tm9cf/dEffaC777XQ5O7emFeSOyX509n7T0/y+4vst7W11SdPnuxlLDp/3rxVx5fdvm5T5jUk9ir7qv1ypspraNxNrf0qY2o/7f5j1X3eHLWfPu5Bqf2m1r1b7ccad86PE3tT1/rdxtV+nLibWntr/fRxD8pav+gx12HZvJJc2Qv2dDatnfbQJO+evX/ItvcAAAAAbKhNazBtvz3ur5N8XlU9eI35AAAAADDHRj2Dqbt/atv7Nya53xrTAQAAAGABm3YFEwAAAAD7jAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwiAYTAAAAAINoMAEAAAAwyEY2mKrqi6vqj6vqpVX1P6vqwevOCQAAAICz28gGU5Lrk7wkyXcleXl3X7PedAAAAADYyaY2mD4nyduTfN7sTwAAAAA21KY2mB6R5Ook/yjJPdacCwAAAAC7qO5edw4rqapLklySJMeOHds6ceJEjh49uvD+p06dWmj+vHmrji+7fd2mzGtI7FX2VfvlTJXX0LibWvtVxtR+2v3Hqvu8OWo/fdyDUvtNrXui9mONO+fHib2pa/1u42o/TtxNrb21fvq4B2WtXya3vbZsXsePH7+qu89baHJ3b9wrSZ/ttdP8ra2tPnnyZC9j0fnz5q06vuz2dZsyryGxV9lX7ZczVV5D425q7VcZU/tp9x+r7vPmqP30cQ9K7Te17t1qP9a4c36c2Ju61u82rvbjxN3U2lvrp497UNb6RY+5DsvmleTKXrCXc2TZbtde6O5adw4AAAAALGYjn8FUVU+uqh+vqldW1RPWnQ8AAAAAO9vUK5hekeQVVXWPJM9N8mtrTgkAAACAHWzkFUzbfE+SH1l3EgAAAADsbCOvYKqqSvL/JvmV7n7ruvMBAAAAYGcb2WBK8s1JLkhyt6q6f3e/aN0JAQAAAHB2G9lg6u7nJXneuvMAAAAAYL5NfwYTAAAAABtOgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABhEgwkAAACAQTSYAAAAABjkyLI7VNVN86Yk+bPufuBqKQEAAACwnyzdYEryx939ubtNqKq3rZgPAAAAAPvMKrfIfcVIcwAAAAA4AJZuMHX3dUlSVXeuqnNm7x9YVV9WVbffPgcAAACAg2/IQ77fkOSOVXWfJK9P8jVJLhsjKQAAAAD2jyENpurujyR5SpLnd/eXJ3noOGkBAAAAsF8MajBV1Rcm+eokvzzbtspDwwEAAADYx4Y0mL4lyXcn+cXu/v2qul+Sk+OkBQAAAMB+sfIVR939m0l+c9vn65L82zGSAgAAAGD/WLrBVFWvTtI7jXf3lw3KCAAAAIB9ZZUrmJ47+/MpSe6d5KWzz09Pcv0IOQEAAACwjyzdYJrdGpeq+o/d/dhtQ6+uqjeMlhkAAAAA+8KQh3zfa/Zg7yRJVX1WknsNTwkAAACA/WTlh3wn+dYkV1TVdbPPn5nkGwZnBAAAAMC+MuRb5H61qh6Q5MGzTdd090fHSQsAAACA/WLIFUxJ8oAkD0pyxySfU1Xp7p8anhYAAAAA+8XKDaaq+r4k5yd5aJLXJHlikt9KosEEAAAAcIgMecj3U5P8X0n+vLu/JsnnJLnDKFkBAAAAsG8MaTD9dXffmuSWqrprkr9Icr85+wAAAABwwAx5BtOVVXX3JD+e5Kokp5K8eZSsAAAAANg3VmowVVUl+cHu/nCSF1XVrya5a3e/Y9TsAAAAANh4K90i192d5BXbPl+vuQQAAABwOA15BtPvVtWjRssEAAAAgH1pyDOYjif5hqr6kyQ3J6mcvrjpEaNkBgAAAMC+MKTB9MTRsgAAAABg31q5wdTdfzJmIgAAAADsT0s/g6mq3jrGnAVifHFV/WFVXVtV3zU0HgAAAADTWOUKpodU1W7fGFdJ7rZiPqcDVN0uyY8keXySG5K8pape1d1/MCQuAAAAAONbpcH04AXmfGKFuNt9fpJru/u6JKmqn03ypCQaTAAAAAAbprp73Tn8A1X11CRf3N1fN/v8jCRf0N3P2jbnkiSXJMmxY8e2Tpw4kaNHjy58jFOnTi00f968VceX3b5uU+Y1JPYq+6r9cqbKa2jcTa39KmNqP+3+Y9V93hy1nz7uQan9ptY9Ufuxxp3z48Te1LV+t3G1HyfuptbeWj993IOy1i+T215bNq/jx49f1d3nLTS5uzfuleQrk5zY9vkZSZ6/0/ytra0+efJkL2PR+fPmrTq+7PZ1mzKvIbFX2VftlzNVXkPjbmrtVxlT+2n3H6vu8+ao/fRxD0rtN7Xu3Wo/1rhzfpzYm7rW7zau9uPE3dTaW+unj3tQ1vpFj7kOy+aV5MpesJez9EO+98gNST592+dzk7x3TbkAAAAAsItBDaaqum9VXTB7f6equss4aeUtSR5QVZ9VVZ+U5KIkrxopNgAAAAAjWrnBVFVfn+TlSX50tuncJK8YI6nuviXJs5K8Nsm7k/xcd//+GLEBAAAAGNcq3yJ3xr/J6W97+70k6e73VNWnjpLV6XivSfKaseIBAAAAMI0ht8h9tLs/duZDVR1JsnlfSQcAAADApIY0mH6zqv59kjtV1eOT/HySV4+TFgAAAAD7xZAG03cleX+Sdyb5hpy+ne17xkgKAAAAgP1j5WcwdfetSX48yY9X1T9Kcm53u0UOAAAA4JAZ8i1yV1TVXWfNpauTvLiqfmi81AAAAADYD4bcIne37r4pyVOSvLi7t5JcME5aAAAAAOwXQxpMR6rqHye5MMkvjZQPAAAAAPvMkAbTDyR5bZJru/stVXW/JO8ZJy0AAAAA9oshD/n++SQ/v+3zdUm+YoykAAAAANg/Vm4wVdUdkzwzycOS3PHM9u7+2hHyAgAAAGCfGHKL3E8nuXeSf5bkN5Ocm+SvxkgKAAAAgP1jSIPp/t39vUlu7u6XJPmSJA8fJy0AAAAA9oshDaaPz/78cFV9dpK7JfnMwRkBAAAAsK+s/AymJD9WVfdI8r1JXpXkaJL/MEpWAAAAAOwbQ75F7sTs7W8mud846QAAAACw3wz5Frk7JPmKnL4t7m/jdPcPDE8LAAAAgP1iyC1yr0xyY5Krknx0nHQAAAAA2G+GNJjO7e4vHi0TAAAAAPalId8i96aqevhomQAAAACwLy19BVNVvTNJz/b9mqq6Lqdvkask3d2PGDdFAAAAADbZKrfIfenoWQAAAACwb63SYHpfkm9Mcv8k70zyE919y6hZAQAAALBvrPIMppckOS+nm0tPTHLpqBkBAAAAsK+scgXTQ7v74UlSVT+R5M3jpgQAAADAfrLKFUwfP/PGrXEAAAAArHIF0+dU1U2z95XkTrPPZ75F7q6jZQcAAADAxlu6wdTdt5siEQAAAAD2p1VukQMAAACAv6XBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLB9P+3d68xtp1lHcD/j1RLQ0s1ohguBQl3Eii0gJeUtKapIAiFEEAErDRUSEpCVCIGDPCBoIEvtpBUOMop0XBrEAqKFKQNh0ukFwqCFANStUG0WNKmQKyUxw+zC0Od256191r7zP79ksnZs953vevZ5z/rrDnPrL0HAAAAgEE0mAAAAAAYRIMJAAAAgEFWrsFUVU+sqq9W1V9W1deq6qFT1wQAAADA9lauwZTk+iQXJ3lFkku6+7ppywEAAABgJ6vYYHpUks8leczsTwAAAABWWHX31DX8iKp6XZK3JDkjyQndfeE2885Lcl6S3POe9zzl0KFDOf744/d8nFtvvXVP83ebt9/xebdPbZl1DVl7P/vKfj7Lqmvouqua/X7GZL/c/ReV+25zZL/8dQ9K9quaeyL7RY075xez9qpe63cal/1i1l3V7F3rl7/uQbnWz1Pb2Oat64wzzri6u0/d0+TuHv0jyUeTfGGLj6ftZ71TTjmlL7/88p7HXufvNm+/4/Nun9oy6xqy9n72lf18llXX0HVXNfv9jMl+ufsvKvfd5sh++eselOxXNfdu2S9q3Dm/mLVX9Vq/07jsF7PuqmbvWr/8dQ/KtX6vx5zCvHUluar32Js5Zs5m10J095nbjVXVlrdUdXctryIAAAAA9muSBtNONJIAAAAAji4r9ybfVXV2Vb21qt5fVWdNXQ8AAAAAO1vFO5jel+R9VfVTSd6Y5LKJSwIAAABgByt3B9Mmr0ry5qmLAAAAAGBnK3cHU1VVkj9O8qHuvmbqegAAAADY2co1mJK8NMmZSU6sqgd290VTFwQAAADA9lauwdTdFyS5YOo6AAAAANibVX4PJgAAAACOAhpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyiwQQAAADAIBpMAAAAAAyyUg2mqnpgVd1YVddX1bVVdVNVfbWq7j51bQAAAABsbaUaTN39lSSfSPL87j45yeeTnN3dt0xbGQAAAADbqe4e94BVH03yc1sMvbK7319V/5zk8d39rar6RpKTuvu2LdY5L8l5s08fkuQbSW6eo5QT9zh/t3n7Hd9u+z2SfHMPdY1tr39fY6+9n31lP59lZT903VXNfj9jsl/u/ovKfbc5sl/+ugcl+1XNPZH9osad84tZe1Wv9TuNy34x665q9q71y1/3oFzrk4OT/f26+2f2NLO7V+YjyXFJ/m32+L5JvjjHvm+Z81h7mr/bvP2O77D9qqlzWMTf71hr72df2a9G9kPXXdXs9zMm++Xuv6jcZT/9ugcl+1XNXfaLG3fOL2btVb3Wy375665q9q71q5f9ql7r1zH77l6tl8gleXiSL80eP2zT4734wJzH2uv83ebtd3zeeqe2zHqHrL2ffWU/n2XVO3TdVc1+v2Or6KBkv6jcd5sj++WvK/vlk/1ixuW+mLVX9Vq/07jsF7Puqmbv3/vlr+tav3xLq3f0l8jtpKpekORR3f17VXVakouT/Fp3XzdxaaOpqqu6+9Sp62B8sl9fsl9fsl9Pcl9fsl9fsl9fsl9f65j9MVMXsFl3v33T4yNJHjBhOVN5y9QFMBnZry/Zry/Zrye5ry/Zry/Zry/Zr6+1y36l7mACAAAA4Oizau/BBAAAAMBRRoMJAAAAgEE0mAAAAAAYRINpxVXV3arq4qp6a1X95tT1MJ6qekBV/XlVXTJ1LYynqs6ene/vr6qzpq6H8VTVw6rqoqq6pKpeMnU9jGt2vb+6qp4ydS2Mp6pOr6ojs3P/9KnrYTxV9WNV9bqqurCqfmvqehhHVZ02O98PVdWnpq6H8VTVSVV1aVX9RVW9Yup6lkWDaQKzL6r/qqov3Gn7E6vqy1X1lU1fdM9Ickl3vyjJU0cvloWaJ/vu/pfuPneaSlmkOXN/3+x8PyfJsycolwWaM/svdfeLkzwryVr9StuDaM5rfZL8QZJ3j1slyzBn9p3k1iR3TXLD2LWyWHNm/7Qk907yv5H9UW3Oa/2R2bX+g0kunqJeFmfOc/7BSf6mu1+Y5OGjFzsSDaZpHE7yxM0bquouSd6c5EnZ+IL7jap6eJL7JPn32bTbR6yR5TicvWfPwXE48+f+qtk4R7fDmSP7qnpqkk8k+ftxy2QJDmeP2VfVmUn+Kcl/jl0kS3E4ez/vj3T3k7LRYHztyHWyeIez9+wfkuTT3f27Sdy1enQ7nPm/z3tukneMVSBLczh7z/6zSZ5TVR9LcvnIdY5Gg2kC3f3xJDfdafPjknxldtfKbUnemY2fbNyQjSZTIq+j3pzZc0DMk3tt+JMkH+rua8aulcWa95zv7ku7+5eSeEn0UW7O7M9I8gvZ+A/Hi6rK9f4oNk/23f392fi3khw7YpkswT6+x//WbI4fIh/F5r3WV9VJSW7u7lvGrZRFmzP7307y6u7+lSRPHrfS8RwzdQH8wL3zwzuVko2LzuOTXJDkTVX15CQfmKIwlm7L7Kvqp5O8Lsmjq+oPu/v1k1THsmx3zr80yZlJTqyqB3b3RVMUx1Jtd86fno2XRR+b5G8nqIvl2zL77j4/SarqnCTf3NR04ODY7rx/RpJfTfKTSd40RWEs3XbX+z9NcmFVnZbk41MUxlJtl3uSnJvkbaNXxFi2y/6iJK+pqucmuX6CukahwbQ6aott3d3fzka3k4Nru+z/O8mLxy6G0WyX+wXZaCxzcG2X/RVJrhi3FEa2ZfY/eNB9eLxSGNl25/17k7x37GIY1XbZfycbjQYOpm3/ve/uV49cC+Pa7pz/QpJnjl3M2NyCvTpuSHLfTZ/fJ8nXJ6qFccl+Pcl9fcl+fcl+fcl+fcl+Pcl9fa119hpMq+PKJA+qqp+vqp9I8pwkl05cE+OQ/XqS+/qS/fqS/fqS/fqS/XqS+/pa6+w1mCZQVe9I8ukkD6mqG6rq3O7+XpLzk3w4yZeSvLu7vzhlnSye7NeT3NeX7NeX7NeX7NeX7NeT3NeX7P+/6u7dZwEAAADANtzBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAAAMAgGkwAAAAADKLBBAAcWFV1e1Vdu+nj/lPXtChVdU5V3VhVh2afn15VH7zTnMNV9cwd1nhDVX2jqn5/2fUCAAfbMVMXAACwRN/t7pO3G6yqY7r7e2MWtGDv6u7z97tzd7+8qr69yIIAgPXkDiYAYK3M7vx5T1V9IMlls20vr6orq+rzVfXaTXNfWVVfrqqPVtU77rjTp6quqKpTZ4/vUVXXzx7fZXZX0B1r/c5s++mzfS6pquuq6q+qqmZjj62qT1XV56rqM1V1QlUdqaqTN9Xxyap65IDnfOqmu7j+sap6v2sBAGzFHUwAwEF2XFVdO3v8te5++uzxLyZ5ZHffVFVnJXlQksclqSSXVtUTknw7yXOSPDob3zNdk+TqXY53bpKbu/uxVXVskk9W1WWzsUcneUSSryf5ZJJfrqrPJHlXkmd395VVdfck301yKMk5SV5WVQ9Ocmx3f34Pz/e0Tc83SU5K8sHuvirJycnGy+KS/N0e1gIA2DMNJgDgINvuJXIf6e6bZo/Pmn18dvb58dloOJ2Q5K+7+ztJUlWX7uF4ZyV55Kb3PTpxttZtST7T3TfM1ro2yf2T3JzkP7r7yiTp7ltm4+9J8kdV9fIkL0xyeI/P90h3P+WOT6rqR/arqmclecysTgCAhdFgAgDW0eb3Haokr+/uP9s8oapelmS7l5J9Lz98q4G73mmtl3b3h++01ulJ/mfTptuz8X1YbXWM7v5OVX0kydOSPCvJqbs8n11V1SOSvDbJE7r79qHrAQBs5j2YAIB19+EkL6yq45Okqu5dVT+b5ONJnl5Vx1XVCUl+fdM+1yc5Zfb4mXda6yVV9eOztR5cVXfb4djXJblXVT12Nv+EqrrjB4CHklyQ5MpNd1vtS1WdmOSdSV7Q3TcOWQsAYCvuYAIA1lp3X1ZVD0vy6dn7bt+a5HndfU1VvSvJtUn+NcmRTbu9Mcm7q+r5ST62afuhbLz07ZrZm3jfmOTsHY59W1U9O8mFVXVcNt5/6cwkt3b31VV1S5K3LeBpnp3kfkneOnuO2em36wEAzKu6/RIRAIDdVNVrstH4eeNIx7tXkiuSPLS7v7/F+DlJTu3u8wce5zUZ8XkBAAeTl8gBAKyYqnpBkn9I8sqtmksz303ypKo6NOA4b0jyvPzoe1IBAMzNHUwAAAAADOIOJgAAAAAG0WACAAAAYBANJgAAAAAG0WACAAAAYBANJgAAAAAG0WACACNx86MAAAAKSURBVAAAYJD/A64jW7S+er1sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2715b93518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Id_Freq=pd.Series(-TestCirSinNData.Vds)\n",
    "figure = plt.figure(1, (20, 10))\n",
    "axes = (plt.subplot(211), plt.subplot(212))\n",
    "bode_diagram(axes=axes,\n",
    "             frequency=TestCirSinNData.frequency,\n",
    "             gain=20*np.log10(np.absolute(Id_Freq)),\n",
    "             phase=np.angle(Id_Freq, deg=True),\n",
    "             marker='.',\n",
    "             linestyle='-',\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diode Connected PMOS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Square Law Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simplifed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A6wDP+aTiF3V3BSzKzG9CSsNrteyTKE2NvoVeaJP7Yzvz/is4cvikG1aYz6J8AYmSIZXFtT+GxV3mDHtj7yw+6YZs6LEoX0GiZEg3D9JdhfFH9JTp985Jm3rCJ72DbCg0ut0C9ca3KOsFquze8tkX4BMT2wKwAQMX+aRbTTeccS6NG5R9hAr6BEFoqQ0YeAc+aYJEGeb2OcooAQkYePikGYX1LiL/BwVJlQdZUP+6AAAAAElFTkSuQmCC\n",
      "text/latex": [
       "$$\\begin{cases} 0 & \\text{for}\\: V_{SD} < \\left|{V_{thp}}\\right| \\\\\\frac{V_{SD} W k_{p'}}{2 L} \\left(V_{SD} - 2 \\left|{V_{thp}}\\right|\\right) & \\text{for}\\: V_{SD} < V_{SD} - \\left|{V_{thp}}\\right| \\\\\\frac{W k_{p'}}{2 L} \\left(V_{SD} - \\left|{V_{thp}}\\right|\\right)^{2} & \\text{otherwise} \\end{cases}$$"
      ],
      "text/plain": [
       "⎧              0                   for V_SD < │Vₜₕₚ│    \n",
       "⎪                                                       \n",
       "⎪V_SD⋅W⋅k_p'⋅(V_SD - 2⋅│Vₜₕₚ│)                          \n",
       "⎪─────────────────────────────  for V_SD < V_SD - │Vₜₕₚ│\n",
       "⎪             2⋅L                                       \n",
       "⎨                                                       \n",
       "⎪                         2                             \n",
       "⎪   W⋅k_p'⋅(V_SD - │Vₜₕₚ│)                              \n",
       "⎪   ───────────────────────            otherwise        \n",
       "⎪             2⋅L                                       \n",
       "⎩                                                       "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PIdSimpDiodConected=simplify(PMOS.IdSimp(subs={VSG:VSD}))\n",
    "PIdSimpDiodConected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/latex": [
       "$$\\begin{cases} 0 & \\text{for}\\: \\left|{V_{thp}}\\right| > - V_{D} + V_{S} \\\\\\frac{W k_{p'}}{2 L} \\left(- V_{D} + V_{S}\\right) \\left(- V_{D} + V_{S} - 2 \\left|{V_{thp}}\\right|\\right) & \\text{for}\\: - V_{D} + V_{S} < - V_{D} + V_{S} - \\left|{V_{thp}}\\right| \\\\\\frac{W k_{p'}}{2 L} \\left(- V_{D} + V_{S} - \\left|{V_{thp}}\\right|\\right)^{2} & \\text{otherwise} \\end{cases}$$"
      ],
      "text/plain": [
       "⎧                     0                             for │Vₜₕₚ│ > -V_D + V_S   \n",
       "⎪                                                                             \n",
       "⎪W⋅k_p'⋅(-V_D + V_S)⋅(-V_D + V_S - 2⋅│Vₜₕₚ│)                                  \n",
       "⎪───────────────────────────────────────────  for -V_D + V_S < -V_D + V_S - │V\n",
       "⎪                    2⋅L                                                      \n",
       "⎨                                                                             \n",
       "⎪                                   2                                         \n",
       "⎪       W⋅k_p'⋅(-V_D + V_S - │Vₜₕₚ│)                                          \n",
       "⎪       ─────────────────────────────                      otherwise          \n",
       "⎪                    2⋅L                                                      \n",
       "⎩                                                                             \n",
       "\n",
       "    \n",
       "    \n",
       "    \n",
       "ₜₕₚ│\n",
       "    \n",
       "    \n",
       "    \n",
       "    \n",
       "    \n",
       "    \n",
       "    "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PIdSimpDiodConected=PIdSimpDiodConected.subs(VSD, VS-VD)\n",
    "PIdSimpDiodConected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "subsP={W:8e6,L:2e6, nTechConst:2e-6, Vnth:1.1, VnA:1/.02, VD:5}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I will finish this later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "author": "Diode Connected MOSFET Tobology",
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   "display_name": "Python 3",
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    "equation": "Ctrl-E",
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   "user_envs_cfg": false
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  "toc": {
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    "height": "calc(100% - 180px)",
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    "top": "150px",
    "width": "190px"
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