{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO:\n",
    "\n",
    "\n",
    "R1\n",
    "-\tget the Nyquist plot axis dimensions issue when $k=1$ fixed\n",
    "-\tfigure out the failing of .pz with active elements\n",
    "\n",
    "\n",
    "R2\n",
    "-\tmake the frequency analysis stuff happen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: KICAD_SYMBOL_DIR environment variable is missing, so the default KiCad symbol libraries won't be searched.\n"
     ]
    }
   ],
   "source": [
    "from skidl.pyspice import *\n",
    "#can you say cheeky \n",
    "import PySpice as pspice\n",
    "#becouse it's written by a kiwi you know\n",
    "import lcapy as kiwi\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sympy as sym\n",
    "\n",
    "from scipy.signal import zpk2tf as scipy_zpk2tf\n",
    "\n",
    "\n",
    "from IPython.display import YouTubeVideo, display\n",
    "\n",
    "import traceback\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.7.6 64bit [GCC 7.3.0]"
        },
        {
         "module": "IPython",
         "version": "7.12.0"
        },
        {
         "module": "OS",
         "version": "Linux 4.19.104 microsoft standard x86_64 with debian bullseye sid"
        },
        {
         "module": "skidl",
         "version": "0.0.31.dev0"
        },
        {
         "module": "PySpice",
         "version": "1.4.3"
        },
        {
         "module": "lcapy",
         "version": "0.75.dev0"
        },
        {
         "module": "sympy",
         "version": "1.6.2"
        },
        {
         "module": "numpy",
         "version": "1.18.1"
        },
        {
         "module": "matplotlib",
         "version": "3.3.0"
        },
        {
         "module": "pandas",
         "version": "1.1.4"
        },
        {
         "module": "scipy",
         "version": "1.4.1"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.7.6 64bit [GCC 7.3.0]</td></tr><tr><td>IPython</td><td>7.12.0</td></tr><tr><td>OS</td><td>Linux 4.19.104 microsoft standard x86_64 with debian bullseye sid</td></tr><tr><td>skidl</td><td>0.0.31.dev0</td></tr><tr><td>PySpice</td><td>1.4.3</td></tr><tr><td>lcapy</td><td>0.75.dev0</td></tr><tr><td>sympy</td><td>1.6.2</td></tr><tr><td>numpy</td><td>1.18.1</td></tr><tr><td>matplotlib</td><td>3.3.0</td></tr><tr><td>pandas</td><td>1.1.4</td></tr><tr><td>scipy</td><td>1.4.1</td></tr><tr><td colspan='2'>Mon Jan 18 15:33:46 2021 MST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.7.6 64bit [GCC 7.3.0] \\\\ \\hline\n",
       "IPython & 7.12.0 \\\\ \\hline\n",
       "OS & Linux 4.19.104 microsoft standard x86\\_64 with debian bullseye sid \\\\ \\hline\n",
       "skidl & 0.0.31.dev0 \\\\ \\hline\n",
       "PySpice & 1.4.3 \\\\ \\hline\n",
       "lcapy & 0.75.dev0 \\\\ \\hline\n",
       "sympy & 1.6.2 \\\\ \\hline\n",
       "numpy & 1.18.1 \\\\ \\hline\n",
       "matplotlib & 3.3.0 \\\\ \\hline\n",
       "pandas & 1.1.4 \\\\ \\hline\n",
       "scipy & 1.4.1 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Mon Jan 18 15:33:46 2021 MST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.7.6 64bit [GCC 7.3.0]\n",
       "IPython 7.12.0\n",
       "OS Linux 4.19.104 microsoft standard x86_64 with debian bullseye sid\n",
       "skidl 0.0.31.dev0\n",
       "PySpice 1.4.3\n",
       "lcapy 0.75.dev0\n",
       "sympy 1.6.2\n",
       "numpy 1.18.1\n",
       "matplotlib 3.3.0\n",
       "pandas 1.1.4\n",
       "scipy 1.4.1\n",
       "Mon Jan 18 15:33:46 2021 MST"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import dc code from parral folder\n",
    "import sys\n",
    "sys.path.insert(1, '../DC_1/')\n",
    "from DC_1_Codes import get_skidl_spice_ref, easy_tf\n",
    "\n",
    "from AC_2_Codes import *\n",
    "\n",
    "sym.init_printing()\n",
    "\n",
    "#notebook specific loading control statements \n",
    "%matplotlib inline\n",
    "#tool to log notebook internals\n",
    "#https://github.com/jrjohansson/version_information\n",
    "%load_ext version_information\n",
    "%version_information skidl, PySpice,lcapy, sympy, numpy, matplotlib, pandas, scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is PZ anylsis\n",
    "\n",
    "Pole-Zero analysis (.pz)does exactly what it says it does. It analysis the circuit between two ports and will return all the poles and or zero between the ports and that's it. That means with the resulting poles and zeros we can reconstruct the transfer function between the ports up to the gain term that is $$H(s)_{true}\\propto \\dfrac{\\prod_n (s-a_n)}{\\prod_m (s-b_m)}$$ where $a_n$ are the zeros and $b_n$ are the poles. Again this can't be stressed enough .pz does not recover the \"gain\" term $K$ that would turn the proportionality into an equality.\n",
    "\n",
    "So what use is it? While that depends on what stage of the design cycle you're in and what is being analyzed. So for RLC elements, it's just going to tell us the poles and zeros where we know that the poles and zero do not move. But for active devices such as BJTs, FETs, etc we could cycle the .pz analysis and see how the .pz locations move with the bias. And while .pz is limited from a verification standpoint seeing as it will just confirm the pole-zero locations that should have been set in the design stage it can be of use when performing reverse engineering on an unknown circuit. Further during the design stage or when analyzing an unknown circuit the lack of $K$ is not a total handicap. Since even without $K$ we can perform root-locus analysis or we can sweep $K$ while comparing the resulting transfer function response to an .ac simulation to then determine $K$ when reverse engineering an unknown design.\n",
    "\n",
    "So then let's go ahead and start looking at .pz and what we can do with that data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PZ analysis of an RC lowpass filter\n",
    "\n",
    "For this first use case, we will use the RC lowpass filter that we developed in the last section bassed on the work of ALL ABOUT ELECTRONICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "height": 150,
       "width": 442
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#instatate the rc_lowpass filter to \n",
    "lowpassF=rc_lowpass(C_value=.1@u_uF, R_value=1@u_kOhm)\n",
    "lowpassF.lcapy_self()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets then get the voltage transfer function for this filter topology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{1}{C R \\left(s + \\frac{1}{C R}\\right)}$$"
      ],
      "text/plain": [
       "      1      \n",
       "─────────────\n",
       "    ⎛     1 ⎞\n",
       "C⋅R⋅⎜s + ───⎟\n",
       "    ⎝    C⋅R⎠"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_rcl_gen=lowpassF.get_tf(with_values=False); H_rcl_gen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The voltage transfer function for this topology shows that it has a single pole and the following gain term $K$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{1}{C R}$$"
      ],
      "text/plain": [
       " 1 \n",
       "───\n",
       "C⋅R"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_rcl_gen.K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Real quickly how lcapy is getting the full voltage-transfer function is based on\n",
    "\n",
    "1.\tGenerating the symbolic Modified nodal analysis in the Laplace domain\n",
    "\n",
    "2.\textracting the so-called Two-Port admittance parameters $Y$ from the Modified nodal analysis matrix\n",
    "\n",
    "3.\tfinding the port 1 to port 2 voltage transfer function via $$H_v(s)=\\dfrac{Y_{21}}{Y_{22}}$$ Which is just one way of doing it. Where more on two-port network theory and SPICE acquisition will be shown in the remaining sections of this chapter.\n",
    "\n",
    "Lets now get the transfer function for this instinacs of the rc lowpass topology and isolate its $K$ term\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{10000}{s + 10000}$$"
      ],
      "text/plain": [
       "  10000  \n",
       "─────────\n",
       "s + 10000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_rcl=lowpassF.get_tf(with_values=True, ZPK=True); H_rcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 10000.0$"
      ],
      "text/plain": [
       "10000.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#K should always be real or where in trouble\n",
    "K_rcl=np.real(H_rcl.K.cval); K_rcl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with any SPICE simulation we have to instantiate our DUT in a circuit. However unlike DC's .tf we do not actually have to have any supplies but since we are also going be comparing the .ac simulation to what .pz we need a full circuit with a source to perform the .ac simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title \n",
      "V1 In 0 DC 5V AC 1V 0.0rad SIN(0V 1V 50Hz 0s 0Hz)\n",
      "C1 Out 0 0.1uF\n",
      "R1 In Out 1kOhm\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reset()\n",
    "#create the nets\n",
    "net_in=Net('In'); net_out=Net('Out'); \n",
    "\n",
    "#create a 1V AC test source and attache to nets\n",
    "vs=SINEV(ac_magnitude=1@u_V, dc_offset=5@u_V); vs['p', 'n']+=net_in, gnd\n",
    "\n",
    "#attaceh term_0 to net_in and term_2 to net_out per scikit-rf convention all \n",
    "#other terminals are grounded\n",
    "lowpassF.SKiDl(net_in, gnd, net_out, gnd)\n",
    "\n",
    "circ=generate_netlist()\n",
    "print(circ)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_freq</th>\n",
       "      <th>Stop_Freq</th>\n",
       "      <th>SamplingInc</th>\n",
       "      <th>StepType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1 Hz</td>\n",
       "      <td>1 GHz</td>\n",
       "      <td>20</td>\n",
       "      <td>decade</td>\n",
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      "text/plain": [
       "  Start_freq Stop_Freq SamplingInc StepType\n",
       "0     0.1 Hz     1 GHz          20   decade"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_responce=qfilter_explorer(circ, 'RC Low Pass Filter Responce');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this is the full filter tf response in comparison to the .ac sim\n",
    "filter_responce.symbolic_tf(lowpassF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .tf does not get $K$\n",
    "\n",
    "Lets be clear about this .tf will not yield $K$ in the generic case. It might get lucky but recalling that in DC simulations capcitors are treated as non existing elements there is no way that .tf will recover the $K$ for this topology where $K=\\dfrac{1}{RC}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf=easy_tf(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>loc</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Output_resistance</th>\n",
       "      <td>(Out-0)</td>\n",
       "      <td>1000</td>\n",
       "      <td>[Ohm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Input_resistance</th>\n",
       "      <td>V1</td>\n",
       "      <td>1e+20</td>\n",
       "      <td>[Ohm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC_Vgain</th>\n",
       "      <td>(Out-0)/V1</td>\n",
       "      <td>1</td>\n",
       "      <td>[V/V]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          loc  value  units\n",
       "Output_resistance     (Out-0)   1000  [Ohm]\n",
       "Input_resistance           V1  1e+20  [Ohm]\n",
       "DC_Vgain           (Out-0)/V1      1  [V/V]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.dc_voltage_gain(vs, node(net_out), node(gnd))\n",
    "tf.vg_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PZ ease\n",
    "\n",
    "The following class like the rest in this book makes using the SPICE analysis easier and enhance it with the power of Python. But real quick let's look at the ngspice call for .pz (typically found in chapter 15 section 3)\n",
    "\n",
    "```\n",
    ".pz node1 node2 node3 node4 <transfer_type('vol'/'cur')> <analysis_type('pz'/'zer'/'pol')>\n",
    "```\n",
    "\n",
    "This differs from .tf in DC analysis where we had to specify a source for the input, where instead the input port terminals are specified by `node1` & `node2` which are the positive and negative terminals respectively. And similarly, the output port terminals are specified by `node3` & `node4`. Since .pz only requires the specification of the terminals to define the two-port network we can take advantage of this to look just at sat the feedback (aka $\\beta$) network in circuits containing feedback structures.\n",
    "\n",
    "Following the node arguments are the transfer type argument where if `vol` is used we are acquiring the poles and or zeros of voltage transfer function \n",
    "\n",
    "$$H_v(s)=\\dfrac{V_o}{V_i}$$\n",
    "\n",
    "else, if `cur` is used we are acquiring the Transimpedance (aka Transfer Impedance) \n",
    "$$H_F(s)=\\dfrac{V_o}{I_i}$$\n",
    ", were again when using .pz we are only acquiring the poles and or zeros that make up the respective transfer function not the transfer function as a whole.\n",
    "\n",
    "Finlay the last argument `analysis_type` controls what we are acquiring from the .pz analysis. While typically we leave it as `pz` to get both the poles and zeros there are times it might not be possible to get both or the poles and zero have to be acquired separately. Where in that case we can use `pol` to get just the poles and `zer` to get just the zeros\n",
    "\n",
    "Below the class, `pz_ease` is designed to perform the .pz analysis with additional methods to analyze the results. And in both it's instantiation and in serval of its methods, the value of $K$ can be feed into it if known.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": [
     1,
     25,
     66,
     96,
     178,
     201,
     245,
     287,
     387,
     428,
     498,
     528
    ]
   },
   "outputs": [],
   "source": [
    "#%%writefile -a AC_2_Codes.py\n",
    "#chapteer 2 section 4 pz_ease class\n",
    "#class to perform .pz simulations with a bit more grace \n",
    "#with some additional built-in analysis tools\n",
    "\n",
    "class pz_ease(ac_representation_tool, eecomplex_plot_templets):\n",
    "    def __init__(self, circ_netlist_obj, K=1.0):\n",
    "        \"\"\"\n",
    "        Class to perform Pole Zero (.pz) SPICE simulation with grace\n",
    "        \n",
    "        Args:\n",
    "            circ_netlist_obj (pyspice.Spice.Netlist.Circuit): the Netlist circuit produced \n",
    "                from SKiDl's `generate_netlist()`\n",
    "            \n",
    "            K (float/int; 1): the gain; must be manually put in or found from .tf analysis\n",
    "        \n",
    "        Returns: \n",
    "            \n",
    "        \"\"\"\n",
    "        self.circ_netlist_obj=circ_netlist_obj\n",
    "        \n",
    "        assert (type(K)==float) or (type(K)==int), 'K must be a float or int'\n",
    "        self.K=K\n",
    "        \n",
    "        \n",
    "        #dic of allowed pz control statements\n",
    "        self.allowed_control_statments={'voltage':'vol', 'current':'cur',\n",
    "                                       'pole-zero':'pz', 'zeros':'zer', 'poles':'pol'}\n",
    "\n",
    "    \n",
    "    def pz_def_ports(self, port_0_pos_term, port_0_neg_term, port_1_pos_term, port_1_neg_term, display_table=False):\n",
    "        \"\"\"\n",
    "        Method to set the Port terminals for the two-port section of the circuit under test\n",
    "        where all inputs must be nodes in the circuit under test\n",
    "        \n",
    "        Terminals:\n",
    "        port_0_pos_term, port_0_neg_term, port_1_pos_term, port_1_neg_term\n",
    "        \n",
    "        Port & Terminals are defined via:\n",
    "        ```\n",
    "                        Left_Port - Two-Port Section under Test - Right_Port\n",
    "                                        +-------------+  \n",
    "        Postive Port0   port_0_pos_term-| DUT Section |-port_1_pos_term     Postive Port1\n",
    "        Negtive Port0   port_0_neg_term-|             |-port_1_neg_term     Negtive Port1\n",
    "                                        +-------------+\n",
    "        ```\n",
    "        Args:\n",
    "            display_table (bool; False): when true will display the generated `self.control_df` below\n",
    "                this method call in a jupyter notebook like environment\n",
    "            \n",
    "        \n",
    "        Returns:\n",
    "            Settings are recoded in `self.control_df` rows: `'port_0_terms+-'` & `'port_1_terms+-'`\n",
    "        \"\"\"\n",
    "        \n",
    "        assert port_0_pos_term in self.circ_netlist_obj.node_names, f'`{port_0_pos_term}` is not a node in the circuit under test'\n",
    "        self.port_0_pos_term=port_0_pos_term\n",
    "        \n",
    "        assert port_0_neg_term in self.circ_netlist_obj.node_names, f'`{port_0_neg_term}` is not a node in the circuit under test'\n",
    "        self.port_0_neg_term=port_0_neg_term\n",
    "        \n",
    "        assert port_1_pos_term in self.circ_netlist_obj.node_names, f'`{port_1_pos_term}` is not a node in the circuit under test'\n",
    "        self.port_1_pos_term=port_1_pos_term\n",
    "        \n",
    "        assert port_1_neg_term in self.circ_netlist_obj.node_names, f'`{port_1_neg_term}` is not a node in the circuit under test'\n",
    "        self.port_1_neg_term=port_1_neg_term\n",
    "        \n",
    "        #record the results in table\n",
    "        self._build_control_table(display_table)\n",
    "        \n",
    "    \n",
    "    def pz_mode_set(self, tf_type='voltage', pz_acu='pole-zero', display_table=False):\n",
    "        \"\"\"\n",
    "        Method to set the pole-zero analysis controls\n",
    "        \n",
    "        Args:\n",
    "            tf_type (str; 'voltage'): the tf for wich the poles and zeros fit to\n",
    "                if `voltage` the tf is of the form V_o/V_i else if `current` in the form of\n",
    "                V_o/I_i\n",
    "            \n",
    "            pz_acu (str; 'pole-zero'): if `pole-zero` will attempt to get all the poles and zeros for the\n",
    "                specfied transfer function; else if `zeros` or `poles` will get just the respective zeros\n",
    "                or poles \n",
    "            \n",
    "            display_table (bool; False): when true will display the generated `self.control_df` below\n",
    "                this method call in a jupyter notebook like environment\n",
    "        \n",
    "        Returns:\n",
    "            Settings are recoded in `self.control_df` rows: `'tf_type'` & `'acqui_mode'`\n",
    "        \n",
    "        \n",
    "        \"\"\"\n",
    "        assert tf_type in self.allowed_control_statments.keys(), f'`{tf_type}` is not `voltage` or `current`'\n",
    "        self.tf_type=tf_type\n",
    "        \n",
    "        assert pz_acu in self.allowed_control_statments.keys(), f'`{pz_acu}` is not `pole-zero` or `poles` or `zeros`'\n",
    "        self.pz_acu=pz_acu\n",
    "        \n",
    "        #record the results in table\n",
    "        self._build_control_table(display_table)\n",
    "        \n",
    "    def _build_control_table(self, display_table=True):\n",
    "        \"\"\"\n",
    "        Internal method to build a pz control table to display pz simulation settings\n",
    "        \n",
    "        Args:\n",
    "            display_table (bool; True): when true will display the generated `self.control_df` below\n",
    "                this method call in a jupyter notebook like environment\n",
    "        \n",
    "        Returns:\n",
    "            creates dataframe table `self.control_df` that records pz simulation controls\n",
    "            if `display_table` is true will force showing under jupyter notebook cell\n",
    "            \n",
    "        \"\"\"\n",
    "        \n",
    "        self.control_df=pd.DataFrame(columns=['value'], \n",
    "                                     index=['tf_type', \n",
    "                                            'acqui_mode',\n",
    "                                            'port_0_terms+-',\n",
    "                                            'port_1_terms+-'\n",
    "                                           ])\n",
    "        if hasattr(self, 'tf_type'):\n",
    "            self.control_df.at['tf_type']=self.tf_type\n",
    "        \n",
    "        if hasattr(self, 'pz_acu'):\n",
    "            self.control_df.at['acqui_mode']=self.pz_acu\n",
    "        \n",
    "        if hasattr(self, 'port_0_pos_term') and hasattr(self, 'port_0_neg_term') :\n",
    "            self.control_df.at['port_0_terms+-', 'value']=[self.port_0_pos_term,  self.port_0_neg_term]\n",
    "        \n",
    "        if hasattr(self, 'port_1_pos_term') and hasattr(self, 'port_1_neg_term') :\n",
    "            self.control_df.at['port_1_terms+-', 'value']=[self.port_1_pos_term,  self.port_1_neg_term]\n",
    "        \n",
    "        self.control_df.index.name='pz_sim_control'\n",
    "        \n",
    "        if display_table:\n",
    "            display(self.control_df)\n",
    "        \n",
    "    \n",
    "    def do_pz_sim(self, display_table=False):\n",
    "        \"\"\"\n",
    "        Method to perform the pole-zero simulation based on values stored in self.control_df\n",
    "        If the simulation does not converge will give a warning with a basic debug action\n",
    "        but will set `self.pz_values` to empty dict.\n",
    "        \n",
    "        TODO:\n",
    "            - add simulation kwargs\n",
    "            - flush out exception handling\n",
    "        \"\"\"\n",
    "        \n",
    "        attriputs_to_check=['port_0_pos_term', 'port_0_neg_term', 'port_1_pos_term', 'port_1_neg_term', \n",
    "                           'tf_type', 'pz_acu']\n",
    "        \n",
    "        for i in attriputs_to_check:\n",
    "            if hasattr(self, i):\n",
    "                pz_is_go=True\n",
    "            else:\n",
    "                pz_is_go=False\n",
    "                warnings.warn(f'{i} has not been set; pole-zero simulation will not procdede till set')\n",
    "                \n",
    "        if pz_is_go:\n",
    "            self.sim=self.circ_netlist_obj.simulator()\n",
    "            #I cant catch the warning when it hangs so going to have to do this\n",
    "            self.pz_values={}\n",
    "\n",
    "            try:\n",
    "                self.pz_values=self.sim.polezero(\n",
    "                    node1=self.port_0_pos_term, \n",
    "                    node2=self.port_0_neg_term, \n",
    "                    node3=self.port_1_pos_term, \n",
    "                    node4=self.port_1_neg_term, \n",
    "                    tf_type=self.allowed_control_statments[self.tf_type], \n",
    "                    pz_type=self.allowed_control_statments[self.pz_acu]\n",
    "                )\n",
    "                \n",
    "                self._record_pz_results(display_table)\n",
    "            \n",
    "            except pspice.Spice.NgSpice.Shared.NgSpiceCommandError:\n",
    "                self.pz_values={}\n",
    "                warnings.warn(\"\"\"PZ analysis did not converge with the current setting:\n",
    "                start by changing the tf type (self.tf_type) and pz acusisiton type (self.pz_acu) \"\"\")\n",
    "            \n",
    "    \n",
    "    def _record_pz_results(self, display_table=True):\n",
    "        \"\"\"\n",
    "        Internal method to record the PZ results to a dataframe\n",
    "        \n",
    "        Args:\n",
    "            display_table (bool; True): when true will display the generated `self.control_df` below\n",
    "                this method call in a jupyter notebook like environment\n",
    "        \n",
    "        Returns:\n",
    "            creates dataframe table `self.pz_results_DF` that records pz simulation results\n",
    "            if `display_table` is true will force showing under jupyter notebook cell\n",
    "            \n",
    "        \"\"\"\n",
    "        self.pz_results_DF=pd.DataFrame(columns=['Type', 'Values'])\n",
    "        \n",
    "        if hasattr(self.pz_values, 'nodes'):\n",
    "            for k, v in self.pz_values.nodes.items():\n",
    "                self.pz_results_DF.at[len(self.pz_results_DF)]=k, v.as_ndarray()[0]\n",
    "        \n",
    "        if display_table:\n",
    "            display(self.pz_results_DF)\n",
    "            \n",
    "        \n",
    "    def get_pz_sym_tf(self, dec_round=None, overload_K=None):\n",
    "        \"\"\"\n",
    "        Method to get the symbolic transfer function via lacpy\n",
    "        \n",
    "        Args:\n",
    "            dec_round (int; None): contorl to `np.around`'s `decimals` argument\n",
    "                if left `None` np.around will not be used\n",
    "            \n",
    "            overload_K (float/int; None): if not `None` will overload the DC\n",
    "                gain constant stored in `self.K`\n",
    "        \n",
    "        Returns:\n",
    "            if `self.pz_results_DF` exists return the symbolic transfer function in the s\n",
    "            dominan in `self.sym_tf`\n",
    "        \"\"\"\n",
    "        if overload_K!=None:\n",
    "            assert (type(overload_K)==float) or (type(overload_K)==int), 'K must be a float or int'\n",
    "            self.K=overload_K\n",
    "        \n",
    "        if hasattr(self, 'pz_results_DF')!=True:\n",
    "            warnings.warn('no poles/zero recorded run `self.do_pz_sim`')\n",
    "        else:\n",
    "            zeros_B=np.empty(0)\n",
    "            poles_A=np.empty(0)\n",
    "            for index, row in self.pz_results_DF.iterrows():\n",
    "                if 'zero' in row['Type']:\n",
    "                    zeros_B=np.hstack((zeros_B, row['Values']))\n",
    "                elif 'pole' in row['Type']:\n",
    "                    poles_A=np.hstack((poles_A, row['Values']))\n",
    "            \n",
    "            if dec_round!=None:\n",
    "                zeros_B=np.around(zeros_B, dec_round)\n",
    "                poles_A=np.around(poles_A, dec_round)\n",
    "            \n",
    "            self.zeros_B=zeros_B; self.poles_A=poles_A\n",
    "            #wish I didn't have to do this\n",
    "            zeros_B=zeros_B.tolist(); poles_A=poles_A.tolist()  \n",
    "            \n",
    "            #use lcapy to get the symbolic tf \n",
    "            self.sym_tf=kiwi.zp2tf(zeros_B, poles_A, K=self.K)\n",
    "            #use simplify because if in pzk it does weird things with j that\n",
    "            #lambdfy has issues with\n",
    "            self.sym_tf=self.sym_tf.simplify()\n",
    "            \n",
    "    def plot_pz_loc(self, ax=None, title='', unitcircle=False):\n",
    "        \"\"\"\n",
    "        uses lcapy's `plot_pole_zero` in https://github.com/mph-/lcapy/blob/6e42983d6b77954e694057d61045bd73d17b4616/lcapy/plot.py#L12\n",
    "        to plot the poles and zero locations on a Nyquist chart\n",
    "        \n",
    "        Args:\n",
    "            axs (list of matplotlib axis; None): If left None will create a new plot, else must \n",
    "                be a list of matplotlib subplots axis to be added to where the first entry\n",
    "                will be the magnitude axis, and the second will be the phase axis\n",
    "            \n",
    "            title (str; ''): Subplot title string\n",
    "            \n",
    "            unitcircle (bool; False): when True will plot the unit circle on the resulting plot\n",
    "        \n",
    "        Returns:\n",
    "            Returns a real-imag with Poles and Zero map, and if an axis was passed to `ax` will be modified\n",
    "            with the pole-zero map\n",
    "        \n",
    "        \n",
    "        \"\"\"\n",
    "        axs=ax or plt.gca()\n",
    "        if hasattr(self, 'sym_tf')==False:\n",
    "            warnings.warn(\"\"\"Trying to get symbolic transfer function from `self.get_pz_sym_tf`\n",
    "            thus you will get what you get\"\"\")\n",
    "            self.get_pz_sym_tf()\n",
    "        \n",
    "        self.sym_tf.plot(axes=axs, unitcircle=unitcircle, \n",
    "                         #wish there be a better way to do this\n",
    "                        label=\"'X'=pole; 'O'=zero\")\n",
    "        \n",
    "        axs.axhline(0, linestyle='--', linewidth=2.0, color='black')\n",
    "        axs.axvline(0, linestyle='--', linewidth=2.0, color='black')\n",
    "        axs.set_xlabel('Real'); axs.set_ylabel('Imag')\n",
    "        \n",
    "        axs.legend()\n",
    "        \n",
    "        if title!='':\n",
    "            title=' of '+title\n",
    "        axs.set_title(f'Pole-Zero locations plot{title}');\n",
    "\n",
    "    \n",
    "    \n",
    "    def plot_3d_laplce(self, title=''):\n",
    "        \"\"\"\n",
    "        Creates 3d plots of the Laplace space of the transfer function, one for the mag\n",
    "        the other for the phase in degrees unwrapped\n",
    "        \n",
    "        Args:\n",
    "                title (str; ''): Subplot title string\n",
    "        \n",
    "        Returns:\n",
    "            returns a 3d plot with the left subplot being the  mag and the right being the phase\n",
    "        \n",
    "        TODO:\n",
    "            - get the freaking color bar into a clean location when working with 3d plots\n",
    "            - merge phase as color into mag see the physics video by eugene on Laplace \n",
    "        \"\"\"\n",
    "        if hasattr(self, 'sym_tf')==False:\n",
    "            warnings.warn(\"\"\"Trying to get symbolic transfer function from `self.get_pz_sym_tf`\n",
    "            thus you will get what you get\"\"\")\n",
    "            self.get_pz_sym_tf()\n",
    "        \n",
    "        #import the additnal matplotlib featuers for 3d\n",
    "        from mpl_toolkits.mplot3d import Axes3D\n",
    "        from matplotlib import cm\n",
    "        \n",
    "        #stole this off lcapy's plot_pole_zero\n",
    "        #https://github.com/mph-/lcapy/blob/7c4225f2159aa33398dac481041ed538169b7058/lcapy/plot.py\n",
    "        \n",
    "        \n",
    "        #check self.sys_tf is good to be used\n",
    "        sys_tf_syms=self.sym_tf.symbols\n",
    "        assert len(sys_tf_syms)==1 and ('s' in sys_tf_syms.keys()), 'trasfer function must be laplce form and only have `s` as a free symbol'\n",
    "\n",
    "        #lambdfy the tf\n",
    "        sys_tf_lam=sym.lambdify(kiwi.s, self.sym_tf.canonical(), 'numpy', dummify=False)\n",
    "\n",
    "        #get the plot bounds\n",
    "        #stole this off lcapy's plot_pole_zero\n",
    "        #https://github.com/mph-/lcapy/blob/7c4225f2159aa33398dac481041ed538169b7058/lcapy/plot.py\n",
    "\n",
    "        poles = self.sym_tf.poles()\n",
    "        zeros = self.sym_tf.zeros()\n",
    "        try:\n",
    "            p = np.array([p.cval for p in poles.keys()])\n",
    "            z = np.array([z.cval for z in zeros.keys()])\n",
    "        except ValueError:\n",
    "            raise TypeError('Cannot get poles and zeros of `self.sym_tf')\n",
    "        a = np.hstack((p, z))\n",
    "        x_min = a.real.min()\n",
    "        x_max = a.real.max()\n",
    "        y_min = a.imag.min()\n",
    "        y_max = a.imag.max()\n",
    "\n",
    "        x_extra, y_extra = 3.0, 3.0\n",
    "\n",
    "        # This needs tweaking for better bounds.\n",
    "        if len(a) >= 2:\n",
    "            x_extra, y_extra = 0.1 * (x_max - x_min), 0.1 * (y_max - y_min)\n",
    "        if x_extra == 0:\n",
    "            x_extra += 1.0\n",
    "        if y_extra == 0:\n",
    "            y_extra += 1.0\n",
    "\n",
    "        x_min -= 0.5 * x_extra\n",
    "        x_max += 0.5 * x_extra\n",
    "        y_min -= 0.5 * y_extra\n",
    "        y_max += 0.5 * y_extra\n",
    "\n",
    "        #the input domain\n",
    "        RealRange=np.linspace(x_min, x_max, 100); ImagRange=np.linspace(y_min, y_max, 100)\n",
    "        sr, si=np.meshgrid(RealRange, ImagRange)\n",
    "        s_num=sr+1j*si\n",
    "\n",
    "        #plot this\n",
    "        fig = plt.figure()\n",
    "        \n",
    "        #mag 3d plot\n",
    "        ax3d_mag = fig.add_subplot(121, projection='3d')\n",
    "\n",
    "        XmagPlot=ax3d_mag.plot_surface(sr, si, np.abs(sys_tf_lam(s_num)), alpha=0.5,\n",
    "                                 cmap=cm.coolwarm, antialiased=False)\n",
    "        ax3d_mag.set_xlabel(r'$\\sigma$'); ax3d_mag.set_ylabel(r'$j\\omega$'), ax3d_mag.set_zlabel(r'$|X|$')\n",
    "        fig.colorbar(XmagPlot, shrink=0.5, aspect=5)\n",
    "\n",
    "        #phase 3d plot\n",
    "        ax3d_phase = fig.add_subplot(122, projection='3d')\n",
    "\n",
    "        XphasePlot=ax3d_phase.plot_surface(sr, si, angle_phase_unwrap(sys_tf_lam(s_num)), alpha=0.5,\n",
    "                                 cmap=cm.coolwarm, antialiased=False)\n",
    "        ax3d_phase.set_xlabel(r'$\\sigma$'); ax3d_phase.set_ylabel(r'$j\\omega$'), ax3d_phase.set_zlabel(r'$ang(X)$')\n",
    "        fig.colorbar(XphasePlot, shrink=0.5, aspect=5)\n",
    "\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        if title!='':\n",
    "            title=' of '+title\n",
    "        ax3d_mag.set_title(f'3D Mag Laplace plot{title}');\n",
    "        ax3d_phase.set_title(f'3D Phase_deg Laplace plot{title}');\n",
    "\n",
    "    \n",
    "    \n",
    "    def scipy_pzk(self, dec_round=None, overload_K=None):\n",
    "        \"\"\"\n",
    "        Method to create to generate the Numerator (a) and Denominator (b)\n",
    "        arrays to use within the scipy signal framework see \n",
    "        https://docs.scipy.org/doc/scipy/reference/signal.html\n",
    "        &\n",
    "        https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.zpk2tf.html#scipy.signal.zpk2tf\n",
    "        \n",
    "        Args:\n",
    "            dec_round (int; None): contorl to `np.around`'s `decimals` argument\n",
    "                if left `None` np.around will not be used\n",
    "            \n",
    "            overload_K (float/int; None): if not `None` will overload the DC\n",
    "                gain constant stored in `self.K`\n",
    "        \n",
    "        Returns:\n",
    "            if `self.pz_results_DF` exists tries to return the coefficients for an lti\n",
    "            filter for use in scipy's signal library in a dictionary in `self.scipy_tfcoef`\n",
    "        \"\"\"\n",
    "        if overload_K!=None:\n",
    "            assert (type(overload_K)==float) or (type(overload_K)==int), 'K must be a float or int'\n",
    "            self.K=overload_K\n",
    "        \n",
    "        if hasattr(self, 'pz_results_DF')!=True:\n",
    "            warnings.warn('no poles/zero recorded run `self.do_pz_sim`')\n",
    "        else:\n",
    "            zeros_B=np.empty(0)\n",
    "            poles_A=np.empty(0)\n",
    "            for index, row in self.pz_results_DF.iterrows():\n",
    "                if 'zero' in row['Type']:\n",
    "                    zeros_B=np.hstack((zeros_B, row['Values']))\n",
    "                elif 'pole' in row['Type']:\n",
    "                    poles_A=np.hstack((poles_A, row['Values']))\n",
    "            \n",
    "            if dec_round!=None:\n",
    "                zeros_B=np.around(zeros_B, dec_round)\n",
    "                poles_A=np.around(poles_A, dec_round)\n",
    "                \n",
    "            b, a=scipy_zpk2tf(zeros_B, poles_A, self.K)\n",
    "            self.scipy_tfcoef={'b':b, 'a':a}\n",
    "    \n",
    "    def get_sym_freq_resp(self, freq_vec=None):\n",
    "        \"\"\"\n",
    "        method to get the symbolic transfer function  response \n",
    "        \n",
    "        Args:\n",
    "            freq_vec (numpy array or pandas series; None): frequencies\n",
    "            to generate results from the generated symbolic transfer function\n",
    "            if `None` will come up with a freancy vector inside via `np.logspace(-1, 12, 12*10*10)`\n",
    "        \n",
    "        Returns:\n",
    "            will store frequency vector as the index in `self.pz_symresults_DF`\n",
    "            where the response of the symbolic transfer function will be stored\n",
    "            in `self.pz_symresults_DF['pzk_symres_[V]']` from there will\n",
    "            use the inheritance from `ac_representation_tool` to then \n",
    "            generate `self.ac_sim_real_DF['pzk_symres_[V]'], self.ac_sim_imag_DF['pzk_symres_[V]']`, ect\n",
    "            \n",
    "        \"\"\"\n",
    "        if hasattr(self, 'sym_tf')==False:\n",
    "            warnings.warn(\"\"\"Trying to get symbolic transfer function from `self.get_pz_sym_tf`\n",
    "            thus you will get what you get\"\"\")\n",
    "            self.get_pz_sym_tf()\n",
    "            \n",
    "        if freq_vec==None:\n",
    "            self.freq_vec=np.logspace(-1, 12, 12*10*10)\n",
    "        \n",
    "        self.pz_symresults_DF=pd.DataFrame(index=self.freq_vec)\n",
    "        self.pz_symresults_DF.index.name='freq[Hz]'\n",
    "        \n",
    "        self.pz_symresults_DF['pzk_symres_[V]']=self.sym_tf.frequency_response(self.pz_symresults_DF.index.values).astype('complex')\n",
    "        \n",
    "        ac_representation_tool.__init__(self, self.pz_symresults_DF)\n",
    "        \n",
    "        #just make the reps\n",
    "        self.make_real_imag()\n",
    "        self.make_mag_phase()\n",
    "    \n",
    "    def plot_nyquist_with_pz(self, ax=None, title='', unitcircle=False):\n",
    "        \"\"\"\n",
    "        plotting utility to plot the pole-zero location on top of a Nyquist\n",
    "        a plot of the response of the symbolic transfer function\n",
    "        \n",
    "        Args:\n",
    "            ax (matplotlib axis; None): If left None will create a new plot, else must\n",
    "                be a matplotlib subplot axis to be added to\n",
    "            \n",
    "            title (str; ''): Subplot title string\n",
    "            \n",
    "        Returns:\n",
    "            Returns a Nyquist plot with the pole-zero locations superimposed on top of, \n",
    "            and if an axis was passed to `ax` will be modified\n",
    "            \n",
    "            \n",
    "        \n",
    "        \"\"\"\n",
    "        axs=ax or plt.gca()\n",
    "        if hasattr(self, 'ac_sim_real_DF')==False:\n",
    "            warnings.warn(\"\"\"Trying to get the real imag data from `self.get_sym_freq_resp`, \n",
    "            you get what you get\"\"\")\n",
    "            self.get_sym_freq_resp()\n",
    "        \n",
    "        \n",
    "        self.nyquist_plot_templet(self.ac_sim_real_DF['pzk_symres_[V]'], self.ac_sim_imag_DF['pzk_symres_[V]'], \n",
    "                           ax=axs)\n",
    "        self.plot_pz_loc(ax=axs, unitcircle=unitcircle)\n",
    "\n",
    "        \n",
    "        if title!='':\n",
    "            title=' of '+title\n",
    "        axs.set_title(f'Nyquist with Pole-Zero locations plot{title}');\n",
    "    \n",
    "    def plot_bode_from_pz(self, ax=None, title=''):\n",
    "        \"\"\"\n",
    "        plotting utility to plot single mag and phase on a single bode plot \n",
    "        for the response of the symbolic transfer function\n",
    "        \n",
    "        Args:\n",
    "            ax (matplotlib axis; None): If left None will create a new plot, else must\n",
    "                be a matplotlib subplot axis to be added to\n",
    "            \n",
    "            title (str; ''): Subplot title string\n",
    "            \n",
    "        Returns:\n",
    "            Returns a bode plot from the symbolic transfer function, \n",
    "            and if an axis was passed to `ax` will be modified\n",
    "        \"\"\"\n",
    "        axs=ax or plt.gca()\n",
    "        if hasattr(self, 'ac_sim_real_DF')==False:\n",
    "            warnings.warn(\"\"\"Trying to get the mag phase data from `self.get_sym_freq_resp`, \n",
    "            you get what you get\"\"\")\n",
    "            self.get_sym_freq_resp()\n",
    "        \n",
    "        \n",
    "        \n",
    "        self.bode_plot_one_templet(self.ac_sim_mag_DF.index, self.ac_sim_mag_DF['pzk_symres_[V][dB]'], self.ac_sim_phase_DF['pzk_symres_[V][deg]'], \n",
    "                           ax=axs)\n",
    "        \n",
    "        if title!='':\n",
    "            title=' of '+title\n",
    "        axs.set_title(f'Bode plot from Pole-Zero anylsis plot{title}');\n",
    "    \n",
    "    def plot_nichols_from_pz(self, ax=None, title=''):\n",
    "        \"\"\"\n",
    "        plotting utility to plot the Nichols chart\n",
    "        for the response of the symbolic transfer function\n",
    "        \n",
    "        Args:\n",
    "            ax (matplotlib axis; None): If left None will create a new plot, else must\n",
    "                be a matplotlib subplot axis to be added to\n",
    "            \n",
    "            title (str; ''): Subplot title string\n",
    "            \n",
    "        Returns:\n",
    "            Returns a Nichols chart plot from the symbolic transfer function, \n",
    "            and if an axis was passed to `ax` will be modified\n",
    "        \"\"\"\n",
    "        axs=ax or plt.gca()\n",
    "        \n",
    "        if hasattr(self, 'ac_sim_real_DF')==False:\n",
    "            warnings.warn(\"\"\"Trying to get the mag phase data from `self.get_sym_freq_resp`, \n",
    "            you get what you get\"\"\")\n",
    "            self.get_sym_freq_resp()\n",
    "        \n",
    "        \n",
    "        \n",
    "        self.nichols_plot_templet(self.ac_sim_mag_DF['pzk_symres_[V][dB]'], self.ac_sim_phase_DF['pzk_symres_[V][deg]'], \n",
    "                           ax=axs)\n",
    "        \n",
    "        if title!='':\n",
    "            title=' of '+title\n",
    "        axs.set_title(f'Nichols plot from Pole-Zero anylsis plot{title}');\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use `pz_ease` we instantiate it by passing in the circuit under test at the time of creation. The instantiation has another argument to pass in $K$ if known *a prior* if just the moment we will leave $K=1$ to demonstrate what .pz and `pz_ease` can do when $K$ is not known"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz=pz_ease(circ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "next, we define the port terminals and set the control for the voltage transfer function and to get both poles and zero which is the default case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pz_sim_control</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tf_type</th>\n",
       "      <td>voltage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acqui_mode</th>\n",
       "      <td>pole-zero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_0_terms+-</th>\n",
       "      <td>[In, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_1_terms+-</th>\n",
       "      <td>[Out, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    value\n",
       "pz_sim_control           \n",
       "tf_type           voltage\n",
       "acqui_mode      pole-zero\n",
       "port_0_terms+-    [In, 0]\n",
       "port_1_terms+-   [Out, 0]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.pz_def_ports('In', '0', 'Out', '0')\n",
    "pz.pz_mode_set( display_table=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And so now we run the .pz simulation"
   ]
  },
  {
   "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>Type</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pole(1)</td>\n",
       "      <td>(-10000+0j)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Type       Values\n",
       "0  pole(1)  (-10000+0j)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.do_pz_sim(display_table=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results when $K=1$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we see that we get the pole that we know exists with the value we expect. So now then we can feed the results into Lcapy's zp2tf to generate the symbolic transfer function. Where we will keep $K=1$ for the moment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{1}{s + 10000.0}$$"
      ],
      "text/plain": [
       "     1     \n",
       "───────────\n",
       "s + 10000.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pz.get_pz_sym_tf()\n",
    "H_pzk1=pz.sym_tf; H_pzk1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the pole zero locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_pz_loc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the 3d plot of the Laplace domain response of this filter. Where the Laplace variable $s$ is equal to \n",
    "$$s=\\sigma +j\\omega$$\n",
    "where $j\\omega$ is the angular frequency response and when $\\sigma$ is zero $s$ gives yields the Fourier transform response of the system. Otherwise $\\sigma=1/\\tau$ the decay response of the system. For more info review the section [\"Transfer Function Analysis\"](https://control.com/textbook/ac-electricity/transfer-function-analysis/) from the controls book at https://control.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_3d_laplce()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then can get the symbolic transfer function’s frequency response and view the Nyquist interpretation over the poles and zeros. Though when $K=1$ this can have issues in the plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pz.plot_nyquist_with_pz()\n",
    "#dont get why this is not working with K=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as plotting the symbolic transfer function’s Bode and Nichols plot interpretation of its response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iridium/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:522: UserWarning: Trying to get the mag phase data from `self.get_sym_freq_resp`, \n",
      "            you get what you get\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_bode_from_pz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_nichols_from_pz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, besides generating the symbolic transfer function, pz_ease can also use scipy's signal module to generate a dictionary containing the transfer functions numerator and denominator terms from the pole zeros and $K$ to the use within the scipy signal module or in the python controls library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': array([1.]), 'a': array([1.e+00, 1.e+04])}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pz.scipy_pzk(); pz.scipy_tfcoef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results with Proper $K$\n",
    "\n",
    "Now giggle and grin lets see what our lowpass filtes full transfer function responce looks like when we know $K$ using the SPICE estracted poles and zeros from .pz anylsis. Where after overriding the $K$ in `pz_ease` we need to rerun `pz_ease.get_sym_freq_resp()` to regenerate the symploic transfer functions respnonce for the new sympolic transfer function found after $K$ what overridden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{10000}{s + 10000.0}$$"
      ],
      "text/plain": [
       "   10000   \n",
       "───────────\n",
       "s + 10000.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pz.get_pz_sym_tf(overload_K=K_rcl)\n",
    "pz.get_sym_freq_resp()\n",
    "H_pzk1=pz.sym_tf; H_pzk1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wr+MPxxVJDRRdZBgGruses30ymWTjxo00NjayaNEibNtmyhRdmd62bTo6OmhtbWX37t2YpkltbS11dXVUVlaWxPbuj9lxnBy5Hv/mCznpzREnCgYKPhoohWOywfdZZTKZnKUkm8320bT6J+6X5Wvy47ghqcGiiwoJ0dGjR9m1axdLly6lpqbmGBKzLIspU6bkSCuTydDe3s4rr7zC9u3bCYVCOS2r2AKa/xI43s0XMsF5HCIyG7gXmAoo4LtKqW+IyJ3Ae4Fmb9NPKaUe8/b5JHAT4AAfUUr92lv+ZuAbgAl8Tyn1ZW/5POB+oB54AXiXUiojIiHv3OcCrcD1Sql9Jb/oIiPfOgEcM0EbaBI4WeHLq09a+cszmUwucd+vNuNrWpOx9uBEy9dkwKQnqeHkPuU/WK7rsn37dnp6eli5ciXBYHBY5wkGg0ydOpWpU6cC0NPTQ3t7OwcOHCAej7Nx48acP6uiomJMD3N+ln7/6zjuSEsEKYGpdASwgduUUi+KSCXwgoj81lv3/5RSX83fWEROB94GLAVmAL8TkcXe6v8A3gQcBJ4TkYeVUluAf/WOdb+I/Bea4L7t/W1XSi0Ukbd5211f0qstMnwTmeM4A76kh9KkXNdl9+7dJJNJ6urqqK2tJRQKlXLYQ2Kg6xiItPx3S755cFKQ1sTL14RjUpPUSJMHk8kkGzZsYNq0aZx22mljesAikQiRSISpU6fy0ksvMX/+fNrb29mzZw89PT3EYrEcaYXD4RFf13DGNpDNvT9pTbTNfSLNEUqpV4BXvO/dIrIVmDnILquA+5VSaWCviOwCzvPW7VJK7QEQkfuBVd7xLgbe4W3zI+BONEmt8r4DrAG+JSKijgfbGMPPfRrMJ5VKpdiwYQO1tbXMmDGDjo4OtmzZgm3bVFdXU1dXR01NTUkClQbCcGUgn7T86+tf13MyFKMum/smIfqbH4ZDUL7D1Dfv9cdYXt4iQjQaJRqNMmvWLJRSxONx2tvb2bZtG5lMhqqqqhxpDaW9DZekCo2j/0zQJ6329naCwWAuctGvO1hy0hJBSi9EU0Tk+bz/v6uU+u6xQ5G5wHLgGeA1wIdE5AbgebS21Y4msKfzdjtIL6kd6Lf8fLSJr0MpZRfYfqa/j1LKFpFOb/uWUV7nuGCklVlEpKC5r7W1lW3btnHaaadRU1NDNpulpqaGuXPn4jgOnZ2dtLe3s2/fPkSEmpoa6urqSp5vNRrkp4zAsaR15MgRGhoaiEQi40ta4yNfkxqTjqR8AhiuZuC6Ltu2bSObzfLqV7+aQCBQ8jGKCJWVlVRWVjJnzhxc16Wrq4v29nYOHTqE4zi5yMFCs8jRklShcfhC1dXVRWVl5bjb3MfJZt6ilFox+DgkBqwFblVKdYnIt4HPo/1Unwe+Bryn1AOd7FBK0dnZSSgUGvbz0N/cp5Ri9+7dtLe3s2LFCkKhEI7j9NnHNE3q6uqoq6sD9CSyo6OD5uZmdu3ahWVZ1NXVYVnWpAzK6E9afrGA8e6gUPZJTTKS8qOL1q9fz7Jly4a0a+eb9/wZzkTAMAxqamqoqalh3rx5uVlkW1tbn1lkfiBGscnCdV1M0xyRzb04QjXxMz0RCaAJ6idKqQcBlFJH89b/N/CI9+8hYHbe7rO8ZQywvBWoERHL06byt/ePdVBELKDa235SwteeXnzxRV796lePyCzmE0k6nWbDhg3U1NSwYsWKYR8jEAjQ0NCQS7hPp9O0tbVx9OhRuru7SafTRfP5lgKu6+bICAp3UCgNaU28fE00JgVJFYouGmp2deTIEXbv3s0ZZ5xBdXU1hw8fHo+hDgsDzSJbWlrYvXs3qVSKQ4cO0dDQULRwd9d1jznOSGzuvn9rxFAuZNNjG/wYIPpt9n1gq1Lq63nLp3v+KoCrgE3e94eBn4rI19GBE4uAZwEBFnmRfIfQwRXvUEopEfkj8LfoCL8bgV/mHetG4K/e+j9MRn9UodynkZCAH93X1tbG1q1bOfXUU3NRsaNFKBRi+vTphMNhmpubmTVrFm1tbcf4fOvq6iY8CAOOla9C/uKB2v74cjYqOZ9g+QIQkR8AbwWalFJneMvqgJ8Dc4F9wHVKqXZPHr8BXAYkgb9XSr04lvNPOEkVii4aLOTVcRy2b99OOp3mvPPOG5H2VAoNZjjoP4t86aWXCIfDHD58mO7u7ly4e11dHdFodFRjLERS+RjK5n7RRRexbt26EZ9XTBOzsmrE+xURrwHeBWwUkXXesk8BbxeRs9Hmvn3A+wGUUptF5BfAFnRk4AeVUg6AiHwI+DU6BP0HSqnN3vFuB+4XkS8AL6FJEe/vfV7wRRua2CYVhlN4edcnPs+8v38D5mmvxj20HWPmqcdsk0gk2LVrF+eee27BQKGx+nwrKiqoqKjI+Xy7u7tpb2/PBWFUVVXlgjAmwmIy1LujEGn5xaj37dvH5z73OR566KERn3cSyBfAD4FvodMtfHwC+L1S6ssi8gnv/9uBt6AnfovQPt1ve39HjQklqYGiiwYiqUQiwYYNG5gxYwZLliwZsWBMFhOCiNDY2MisWbOA3nD3/fv3E4/HiUajOdNHJBIZ1riHIqlCYwBy1adHDWFCzRFKqaf0KI7BY4Psczdwd4HljxXaz4v4O6/A8hRw7UjGO14YKvfJx/OXvYeePftp/8s6cL9C7coltL+wg3O+exvmaa8mk8mwceNGXNdlxYoV4xIsICJUVVVRVVXFKaecguu6uSCMl19+GaVUTj5KVS1moHGNZFt/+2QySTQaHeVJJ1a+AJRST3pBSflYBVzkff8R8ASapFYB93oWhadFpKafVWPEmBCSGiq6qBBJvfLKK+zdu5elS5dSXX18lwnx6w368MPdZ8yYgVKKZDJJW1sbu3btIpVKDcv04fukRgPbtscg6IJxkudxTDYMJ/cJ4MXVN2MEDex4gowFynE5tPaPVMydyYvv+xpKfRXXcahdcSqL3nXxxIVgG0aOlKC3WoxvPrcsK7e+srJyQsY4GBKJxOhJavLK19Q84jmCTqaHvIhXD3407PFDUsPJfTJNM0dSjuPkovdWrlw5YcERxcRgpoP8cPfZs2f3MX1s3bqVTCZDdXV1Tij9++GXWhoNxiJEUk42nFQYbu7Tvnu+gxESElt3EGqoxO5OIobCCArJfYdQKMxIGBGha91u2l/YjhX4b2pWnMrCr34RZ9tfME979ThfnUahajFtbW0583kqleLAgQPU1taO2nzeH2OxNsTjcWKx2Kj2HQf5GlZ6x2Dw/LYl88WOK0n1r54w2IvacZyceW/mzJnMnj17yIdtonxOI8VIxjmY6ePgwYO4rkt1dTXpdHrUpWt8E+OocZJHH00GjCj36Zf/S+eBI/Ts2YVVVYnd0YkVNTAjYbJtCQL1EXC1P0RlHZIHDoMonGgFHS/u4PmLrqJmxRI6Xvw6y//zn5DKetzuVph7zvhdcB6CwSDTpk1j2rRpKKV49tlnMQyDffv2kUgk+lgiRpp4Xwz4Yxg1SitfQ6Z3DICjvhlPRKYDTd7ywaJnR4VxIalC0UWDwTAMWlpaaG1t5YwzzqCqamjHoR8me6KRVH/0N304jkNHRwdHjx5l06ZNfdZXVVUNy4yXTCZHL0RlTWrCMdLKLLJrF6lXjmBYoDIJgg0xcEFlbazaCG4qjRWN4DoGbjpJIGYi4RDZtiTZzi7srjg9h49SMWcG6z709ZxHsPrsxZzynsshVoeaumAcrrwwDMNg5syZzJw5s0/ivR9wlR+EMdyyaWPBmEhq8sqXH9n6ZY6NeP2Q6Iot5wOdY/FHwTiQ1EjbujuOQ2trK4ZhcN555w07LLp/wuFkRjHHaZom9fX1hEIhVqxYkbPXNzU15ZIm/VlkLBYrOMMekyYlIJOsesDJBD84Yijzno/NN/8DxtHDBGqrQYFS4HR1IsEwrp1CRAjWR1G2gkwPgboohinYKQcraoJKEWqM4fS49Bx6BTHBikaxqqro3LCTDbfeg2EK1WcvYva7LgNALVg5HrdCn6vfBHCwxHvfEpGfeF9oUue/u0aLMfmkJoF8icjP0EESU0TkIHAHmpx+ISI3AfuB67zNH0OHn+9Ch6C/e6znLxlJ+c7bw4cPM3Xq1GH5S/xCruFwmGnTpo0ob+d4IikoTaShiBRMmvQrYXR3dxMOh3Oalm+vH5u5T2ByzvROaPi+ykQiQW1t7bDka+sH/hG7+QiGaSACTiKBCJiVEdx4D1bYxIxEUK5CAgZmpBJRDq4LQgYrZhKZM4f4zn2IKYQaogTr60g3dZA+cgRMiMyYAUDn+p10b/kmVcsWMesdAghq/rnI0T2oqfNLel8Gk63+ife2becS7/fu3XuMJcIP4hpL0EgikRhD1/CJly+l1NsHWPWGAtsq4IPFPH9JSCq/ptz+/fuZPn36kPscPnyYffv2ccYZZ9DW1jZiwillO4HY17+Fe9W5GH/7oZIcv5QIhUJ97PWpVCpXCcOf4f3xj3/EcZycgDuOw4oVK5g5cyaPPPIIMkiriuXzZiJOdmIv8iSDb52Ix+M0NTXlksYHw+Hvfx8xBJVKYlZFcZNJzKCBEwgiPT2E6qpQjoNyFUbAwEn2YEajqKwL6RSB6gpCU+pIvnyQqtNmkzpwmPCsKST2HkJMg9DUKEZVFemXXyFQE0U5glGtzfRbP/PvGJZQecYiZlzzFtj7EsxbXpJ7M1JTumVZ1NfXU19fD/S27Dly5Ag7duwgFApRVVWF67qjNtOPLbpPlVq+6kTkUnRE3l6l1KRrA1x0kurf1n0o4nAchy1btuC6bs6819HRMWLCOd40qYmAiBCJRPrY6xOJBC0tLTzzzDPcdtttfP3rX+cb3/gGS5Ysoaury9914FYVhoFUjMEpXMaw0T/3yTTNY2rmFcKuWz+C6yoyL+8hWFtFuq0DIyC4wRBWOoM1pRanswuzIkS2O4k4QrA6ilKCk8oSqIpi1FTidHZhhU2cri4ip8wkdfAVQnUxVHU19oHDGJkkoYYYZk012Ywic+QorU91UP+680js2k9yxy6OPvwbGldditq/ATllWUnu0VisFIVa9jQ1NZHJZHj22WdzOYx1dXVEIpFhHXNMPqnSy1c98FngKPAbEXlUKXVgiH3GFUUzdvraU34dq6FU5Hg8zrPPPktNTQ3Lli3LmfdGoxUdb43ZJgNEhFgsxsqVK3nve9/L17/+dQ4ePMijjz7KzTff7G8j6FYVa7zdfgSs9r6vypkjSvkpo09vsfw6jEOR1O6P3Uq2+SiZ/bsJVFXi9iSRyggSrSAcixCsrYJUkkBNFDeVwgqbBKr0dzNkEqiuIDy1HqezC3EyRBecon1UzU3EFswCA8xUnHB9JaHGOsJT61FtLYRUiujCWRhulva/Pkc6k8FV4LoO+77+7ZLep2Ka0iORCA0NDVRXV3Peeecxd+5cXNdl586dPPvss2zdupUjR47kSo0Vwtii+0osX7BTKfUa4APAdOCDohuJThoURZMaaXQRwKFDh9i/fz9nnnnmMQl4hmHkZovDRVmTGj3i8XguWvDWW2/lK1/5Ct3d3f7qQVtVyCRw7J7oGCj3KT+fsBD2/PNHEUMQQ7CqqnAScdxQEDOVwrBMDDOMm04QqKnGzWYwQxZmJIxyFdF5s0gdPkJ4xjR6DryCZUDdG15L6xN/RQQC1VHSR45QMWsGdlMTKhDAbmrCqIjQ8KYLaXvir5CE6IJZZNs7CNRFyRxpIrlrL0opDv3nd3A/eDsNdhtKVRTtXpWqeLPfkSEWixGLxZg9ezau69Ld3U1bWxubNm0asPvBWEhqHORLRBdPbkIHREw6jJmkRhpdZNs2W7duRSk1YPTeaLSiwUhKKcX+/fvZv38/0Wg0V/x1OCWHjpeQ9rEgmUwyZ84cHnnkERobGzn33HN54oknhrm3gFHWdkqBfPNeodynwcx9zT/9EYYhpPbsQj8eChUOEchkIGSiQhWoVBKrSk8QLTFxbINQQx2pg6+QbW6m/qLX0P7kX7DCJkb9FDqeeproglOwUymcrk4CIQsrYOJWVEA6Re0bXkvXcy/R/exzhOfOxmlvJRCxMIP1WBVB3KCuDF65dBHpl1+myukmk8lwtLkb27ZzcjmWsPBSklR/GIZBdXU11dXVA3Y/yGazdHd3D/uaji3mWnL5Ukr3QpuJnowmSnmy0WDUJDWi5EEP3d3dbNy4kTlz5jBz5swBH6Zimvscx2Hz5s25kHa/RYBfcqiqqor6+vpcs8DjEcWIPorFYvz5z3/m4Ycf5rHHHiOVSvk+qW8wWKsKYWnZJFd8DLcySyGSarn/PpKbN2C3Nuk8G3FR4QiBdBqztho7m8UQAzNchduTIHTKPFJ7dmFFK8gcPYoZsjAiFXT+9Wkip8zE6ezEScYJVEbJOllIdGMqh+qLXkPHU09jxaKYDdOJP/sc4RnTUK4Cw0LExQpbZNs6oCJM/evPp/nXf4Kli/Q1Pf0koVe9hqlTa5g2bVpOI/HDwv0GiSMp2VUKknIcZ1hjKNT94KWXXuLll1/muuuu47vf/S4XXHDBUIf5IfnFXIVSm7wjInINcA3wFWCdyOTqLj3qN1s2mx1Wcq4fLXbw4EE2btzImWeeyaxZs4YMEy2GJpVMJnn22Wepra1l6dKlGIaRq7S8bNkyVqxYwdSpU+nq6mLdunW88MIL7N27l87OzuPKdFgMkopGo3zpS1/i4MGD7Nu3j/vvv5+LL74YpdTfAX6rCjgmcc+b6ZXyMwhEZLaI/FFEtojIZhG5xVteJyK/FZGd3t9ab7mIyDdFZJeIbBCRc/KOdaO3/U4RuTFv+bkistHb55uen27Ac4wVfsuHocznhmEcQ1ItP9cElT2wF9WTAFEYsUpMO0ugphpSSW0+SvdApgczGiN7YB+ByihuOo0VMLFiEQxTCMQqcLu7CNTVYLg21FVjtDRj4RKaMZ34c89TMXemDmlPJQhUR3Hj3ahEN6apCE6fiupso2rlcqRLNysO1VXSs2lj/9+Qqqoq5s6dyznnnMPZZ59NTU0NLS0tvPDCC6xbt46XX36ZeDw+pFyOpyY1FAKBAOeddx4VFRU888wznH/+0MXAlVJPoivqeyixfEEjcDPwW2CbN4ZJ9fIbk+owrOx2ETZu3IhpmsNOzi0GSbW0tLB9+/ZcO/lC971/9QY//PTw4cNs27aNiooKvW5S/WTHolia1CAYuFWF8K0JLotko1vDvygilcALIvJb4O8ZQSsB0f1x7gBWoH/xF0TkYaVbzn8beC+6Lf1jwJuBxxm4XcGY4MvVSE3Rrb/4MT1bNuC0NSGRCuxslkA0AOkepKoS1ZPQ5r2uLiSoOzWrTBKzqhK3u4ugFzBhGAZuOo0RDoNSOK0tBKY34ra0YlTHUK7CbWvBjFVgNzdjhEJYtTV627ZWjClTcVuOEJg2FScdIbN9K8GZM8ls30L9686n7U9/pnLpYlTLkYLX1b82X/+0icrKygFNg5OJpHxkMhmCweDoxiWUuixSSin1llKeYKwYNUkNpzFhd3c38XicadOmMXfu3BEde7TmPqUU+/bto7m5OdfaerjIDz/1w7NbW1tRyiWdSXNgxw7q6uqora0dt/YAw0EpSOqiiy7ioosuAgZvVXHu4nkwjDDoUsErufKK971bRLaiAztG1ErA2/a3Sqk2AI/o3iwiTwBVSqmnveX3oqMbHx/kHGPGaAKBup/8HQBOvBsEgmIidgaJxnB7kliVOnjCCJgQqdDPsAIxhOCc2WRfOYQZrUClUliVMZRto+wMRiSMamsjNGcOdmsLgbpqnNZmJGCgXItAYz3ZI0cxKiIY4RBGvI3o+efRs/4lomctJ7VhHVY4COEATtMrVDTW4Bw5hDFMP004HGbGjBm5LgH5wQr9TYOTkaRg4FYpQ0JRavmqFJF3ortaT8rpeMmSeQ8dOsSBAweorq6msbFxRPsPN/8jHyKCbdusX78+VyKoUKfakRwvFotRUVHBTsMgFAwxZcqUXGa6ZVm52VwsFpvQAItx0KQGhmFAuHjRWQNgWJWaRfe8WY7WeEbaSmCw5QcLLGeQc4w7Dn/+EyDgdHdjBkxAR/UZFVHcRILw/IVkX96LVVXpkZhO7BVDIBzFbjpCaPo07ZOKRlAComzMaIXe1jIwTAPDMlCdbQSmTsVpbSH22teRePoprOoYKIWIiUJI79hKoLGR7PbNWFO1/JtVusWO5VkuMEyMnRth7jHznwHhmwZ982D/th0+uftVVIohl2ORrzG/90svX6+gG4LGROS/ldcAdDKh6CRl23afQIXNmzePinBGqknZts22bdtYsGABM7zSLMVGvlPUD8DwbeWxWCy3fjDtrRSTlWKQ1Jj68JReq3TRTQoTQAZoFZEKpVTS30BEYsBa4FalVFf+y6nUrQRKcY6RaFKv3P1JMs1HcBJxrJCFLvgGRoWucmBWxrAP7cesrMRNxr2/CayAhQqFId2jTXctzYRmTMdpacYNBLWvyTJR6TRGKITTfATTFAiHUZ1tmNEIqRefITRzOnZzM1ZDA05LM9a0GajOdoyghTQ06Dhqw8IIm2RbPXdLkRJU+5sGm5ubOXDgwLBMg8PFWNrg+BgTWZZWvhJof/P/AP9dyhONFkUlKT96b+7cuTmiGI1WNBxTYj6amppobm4uKUH1RygUYvr06UyfPj03c2ttbc2Rsp+VXig6aaKijwbCWMq2KAQlJSepSnT0UQCoQhe0/HdgsxfEYKEJ6idKqQe9fUbaSuAQvaY7f/kT3vJZBbYf7BzjhvqHf0K6vQXlugRra5D8JsXpJK6jMExBLIG0Djl3uruxAiZGLIZKJpBYJThZzIoIbnsrUl+P2dGOEY1ogopW4Pb0YIUCYFlgZ5FoBWQySCyKSia0xtWTwIxGIRlHHBtJ9aCyGSQYQmUzUFePFQ5i1daSOtKM290JixcU9X4Eg0FisRiLFy8+xjQ4lFwOBNd1R01wo+iY3aeY6/JTF5RcvpRSR0XkYmCqiMSVUl1D7jSOGDVJ9ZupcvDgQQ4ePMiyZcv6mI5GQ1LD3Ucpxe7du+no6GDGjBnDLlNSbEhepeX+Johdu3YRCoX6aFmTzWbuuu7om0mKoEqfJ7VfKfXOQdZ/H9iqlPp63rIRtRIQkV8DX8yL0LsE+KRSqk1EukTkArQZ8QY0QQ52jnHB4bs/SSDRhRGNYloWKpnQjnbPz0RFFDOVJDBnHtmX92JUalIyKnXwgxgChmBYBm4qjQgQDCLxLqxpU7FbWghMm6a1pMooYlronh6u1rBcA8MyUakMRjiEymSQmjro7tQaVLwLo34KdHdgzJgF8Q5UUD9n4ZoYwZkzsQe7wFEgv2L5UKbBQCCQk8vBTINjka9kMklFxfDNdf2LuZ6zZJEqsXxVi8hl6GoTFwK/AX5ayhOOFGPWpGzbZtOmTViWxXnnnXfM7GSorPhCGE7gRDabZePGjUSjUc4991x27949IeGphdDfBNHT00NbWxt79uwhmUySzWY5evQodXV1Rek0PFabeVHs5iWGiMwCepRSrf3yOF4DvAvYKCLrvGWfYoStBDwy+jzwnLfdXX4QBfCP6PyVCDpg4nFv+UDnKMb1Drr+8E+/T6b5MJaTxRQFdgbDMpCwTtKViqinJcWw9+/CrKpB9SQxojHcZByCYUj1YAYsFGjTXigE2QxSUYHT1kpg6lQdgj51Kk5bC0bdFFTLUYwpU3A72rGmNOB2dmA1NqI62jC9v8a06aj2FqS+Hkl044bCmghdB6uuHlI9uHZpiqYOJuPDjRqsra3tY7Ifi3yNuaEolFq+GoF/RlsNPqnG2PupFBgTSXV1dbFp06Y+5r3+KJTLMRSGIql4PM6GDRuYP38+06ZNAyZ3WaT8oq6ZTIb169eTTCY5ePAgSqncbM5vDTBSFCP6aNTkPT6aVC3weeAX9BIEAEqpp4CBBv+G/gsGayWglPoB8IMCy58HziiwvLXQOUoJ13XZsWMH0t1FzYzZJHuSWG3NiGkgkYocEfl/Sfdg1NSierwgibQmJieb1gQVDKN6EpgVFVqTsiKQzSKRCKqjDQkEUV3tSF09dLRgVESgu5NAYyNueyuBadNxW44ioSCqqwNCIehsxQiHkFQSgkGsWCWqq53gNJ3oq5RD8JQ5Jbk/I5mIDhQ1uGXLFmzbzpkGbdsetXyNuStv6eVrp1LqolKeYKwYNUm1t7ezefNmzjrrrEFnCqP1SQ1EUkeOHGHPnj3H1Pw7ngrMBgIB5s2bx7x588hms31aA4TDYerq6qivrx+2+XKsPqmxYhxIqh5YB/wFJl+y4Xihp6eHDRs20NjYyNz33YqIsPN736QiqsPJnbZmJBrVeVHeX7zEXd//ZJgmKhRGHAfXC1AKBAJgZwGlE33DEcimIRTSvqVwBBLdWtOys0g4rAMjwmFUohtCIV3bLhhETAOVzUIkCj1xiMaQTArxTV7Jbqzqav3yFQOjs7Wo92i01pL+pkHHcWhvb6e1tZWjR4/S1dVFPB4f0jTYH8XQpEosX9NE5Aa0KfuVyeaPgjGQVE1NTUHzXn+MNpy8P5RS7Nixg3g8zsqVK48xk01mTSof/YUoEAjQ2NhIY2MjSqmcaXDHjh2k02mqq6tzJoiBEqHHokn5iYajx7hoUruUUt8o9UkmE/rLQFNTEzt37uT000/PJZ8DpC+4mPDs2VRXV9N6z51gCE5rM6STSDSG6klgVFRqzSpWqU2BIkhGJ+2ahqHzpqwQZNIoBeJqIsLOIOGwjrY1wogJYoXBEJQr4DrgOpiGAa6DEQyi7CzGlEakqx1q63WFC+VihyuReJfW0EIRJJNGlIth20zf+zwsWlSU+1Ysk75pmjnToG3bNDY2kk6n2b9/P/F4fEDTYH+MrZcUjIN8mWiz9SvAz0XkHqXUwCXdJwBjSuYdbj0rv33HaJHJZNiwYQM1NTWcc845BR/CoTQpP4N/vPxSA2Gw84sIFRUVudJNruvmClbu378/VyGjvr6eysrK3HFc1x113cFEIjEix+4x14NClV6DnSoiYaVUqtQnmmzw20L4k7P+E4p8n2/9rXcC0PbvdxFcdDrZ3Vtx2lu0qS8aRfUkIVKBm4hjWCZmRSVkegBBbBtCYQwRMA1EKbDC4HUj8P1VhMKQ9apR2FmMSAUqk8aoqYN4J0ZdPRLvRAUCSCYFKIxwmJCTRoUjOviiJ65D5KtqIRgknI4XTS5LlcwbDoepr6/PmQb9aN5802ChFvRjNfeNg3wdQgf9vAfIontL/Us/v++EoijRfYNhNJpUPrq6uti4cSOLFi0aNCm4VJrU/ls/WdTjjUSICpVtamtr4+DBg3R3d+cquo9FG/JzvEYNMVCB8Oj3Hx4qgU+JyBdPJqJKpVJs2LCB+vr6ASdnpmli231j5Oo+/FkAEr9eS2b3Vp3ofmg/YghOTxIrFMQ2A9qPVRHVeUyODemUTrGyQtovZRkoQzBME1cEM2CBIToM3TA8zSmDWVeP6u5EauqQRDcSjmA42nxIdS3Sk9Dbx6o0QYXCSHUdoLQ2VV3Nc889V5Riz+NRcaJ/NK/jOHR0dOSCo/xuv3V1dWPPQSy9fJnoqL4G4D/ojVw1gEmR2Fvyst9jIanDhw+zf/9+zj777CFV5tEkAE8ExiJEwWCwTyv4RCJBW1sbTU1NNDU10dXVRV1d3TGzucGQTCbHRlLjZO4DLkBXethd6pNNBnR2drJu3TqWLFkyaHv4waJno5deQxSI/3otyaTOe47YaUAh2SyS6dEEBRiiUIYglqn9WJaJAGKZSDCIIYKIjgQUK4SYJtg2RlUtkujKhZ4jote5NlRWI8luCIZ1CaR0Uvu7QhGkR/crE8PACkVYsWIFXV1dtLa25qwGvm92JBVdJqIskmmafVrQ+4n+e/fu5e677yYWi/GqV72KN73pTaM4e8nlaxq6nuX/oPMHO0p5stFgUpKU67qkUimamppYuXLlsIvSjrRR4kSgWELkl22KxWJks9lcZGD+bM4X8sEcvWM19yHjEjjhR/fNF5EZwD41yVpcFxvRaHRYtSeHip7t6elhU/VMpv/tCqbseBFn33YUYB7ahxEMQCgCmZTOfTIUEgojKDC9fD5Bz+azGQgGMRwbMQyU7SKhCJLsRqprwQugEMPQvqxgEFIJCIYQ5erAjMpqHfGXTiCBEIRCKDOAgcIwDGpqaqipqQG01aC1tTVX0aWysjKnnQyWtlGqVh0j8fnmJ/rfdNNNdHZ2jn4iWHr5ygK3K6W2icj5wM+85ZNmxl9yc99Io+7S6TTr169HRDjrrLOGfZ7jNXCiGHAch0AgQE1NzTGzuaHKw4zV3DdOFSeq0DlIQaACeFhEvq96uwWfcBhu/txgk8DW1la2bdvWG2xxyikAJH73EIlkgmClDg2nIoo4NigXnKyuwWcICtG+qIwmKHEcJBQGJ6v9SYlegpJgEDw5l1AIHBuJVXvamgFWAPGjDQMhTX6Oo0lNCeb63+CcdUlu7MFgsE9Fl+7ublpbWzl4UJdR9Cdg+b5ZKJ0mNdro2Z6eHk4//XRe9apXjWr/cZAvF1gqIiml1DO5806il+mk0qQ6OjrYvHkzp556Kjt27BjReU5mkipkjuhftql/5Wg/B6Srq4tYLMaBAwe44YYbOHr0KCLC+973Pm655RZEt7D4OTAX2Adcp5Rq98oRfeOspUtwpeTJvPu9vlYnDUbi8+1vQVBKsXfvXlpaWgpqY5GLV7EhOo3lqWbs/TsQw8B5RfustLlOk4rhOFoDsgxM5WJEwtp3VV0HnW3IlOnQ2YwEveXBIBkVIOBkyIaqCfYkIBDCMQIE3ZT2RRmGp50BgRDK0EEakhw48jk/PNxP28j3zfp1M+vr60siX2M55tij+yi1fJnAR9A5iP8hvc1NJw3G3E9qKGIYLkkdOHCAgwcPsnz5cl15fOfOEQcZHC8+qbEm3vbHUDbzgcrDNDU1cffdd+M4DgsXLuRrX/sa55xzDt3d3Zx77rm+DX2gnkleX6Zx0aTwSFHQk7zJPxsZJ5imSSrVG0ti2zYbN24kHA4X7AQAvXIb/psrAUg/+QjYKZ2M6zpIwC9S6/mXDE8GvRwpEl1QOwW623XlCuXosHQMTJXBNQMEnSSuEcBSNsoF2wphOS5KCelgBQE3jakc3YrCNME0kRcf5wctl/PuSwaX40Ag0Keljh9pt2nTJlKpFNFolGg0Ourk+EIYLUkVxedbWvk6qpR6PWgZm2wEBZNAk3Jdly1btuC6bp+8K590hvuQlTWp4QtjfnmYD3zgAzQ1NbF8+XLOOUc3qa2srGTJkiUcOnQIhurLJLzZHYeySB4xTf4feJyRL19+JZbBKsDAsS/c0OveSmDBIjLPPYHb2qRD1bMZrfU4Cglov5UEQ2Db2sQX70J8ohLBNUxQDmJaGOKSCVYScntQYiKGhWEonbxrGoRUCtcwcbAwBZRpIa6Lgcsbal/ii/efS8ACwxAMQ3QRdf+vKRgiGIa2HAqCYcYwjGkel7pY3TabNluAwjSFOdUdJJwqLNNABBqrHD9ehCXTS5sSNGZzukCJ5avCK47cPBkJCiaYpFKpFOvXr2fatGnMmTOnj/CMtObfUCSllMJxnJx9udjazHCRXwCzmMccrc08Ho/n2nb72LdvHy+99JLf7nqIvkyCOw6aVBmF4cvXQJVYRoLgyovItB0hs20DgUQX9PRozcrOam3HdbS/qbtDh5B3tkAojMo6uIYOSxfXIROsJGzHcawQJoqsBFBmkAqVxBUhbVYQEBtTHBwJY/jTD9fllFAz/3DGc/zX5pUYLpiWJiZTvFxHdECii2CgO6C7Ck12eEqfBBDDIGDqqMVD3XWIgSY3UXT2GJrgBPa2BBABEYVpQMBQOEowUIhALOySqDiLvS0B5k0ZeWBWMZJ5SyxfU4Bbge8BO0t5otGi5Oa+gcxwbW1tbN26dcAQ25HW/BssBN0nKD+h13Gc3LENw0BExo20JkP0UT76JxvG43GuueYa7rnnHqqqqvpsW6hnkgKcyW9lPe4wEjN3W1sbPT09BSuxDBcK6Ghvx0yliC1djhgG9s4tqPYW7ZfKpBCvfJJEKiDZjdRNxe1qx7UCusitcnDFIOQkcMygXgZY2Jiug+0tC7k9dBq1VKs4pjgoLGwJYAFiZ6izWrjt7D/zrS0X4irBdcERrVXp140mKEQ0MRn6uyHeGvHJCjAES3ytSy8XQyEoran5AYze7XaVJjb9I0AibSCBKg62w6EOg6CZ97yLYmaNQ2ePwZkzCxPYWPOkxkG+jgI7GCSaT0TeDHwD7b/6nlLqyyUdUT+U/M3cX9iU1959586dnHvuuQPmgIzUxzRQDyqfoJRSBAIBQqEQwWCQQCCQ09YcxyGbzWLbdsn9WpPB3JePfJt5Npvlmmuu4e/+7u+4+uqr/U2OeuYACvdlEpQVLOlnKIjID0SkSUQ25S27U0QOicg673NZ3rpPisguEdkuIpfmLX+zt2yX53/zl88TkWe85T8XkaC3POT9v8tbP3dUP8LA1zXo+kwmw7Zt2wBYvnz5qAnKcRyajh7Ftm0qYzFt5gOsRacTOO91SEUMKqt1xF7Qq98XDOF0dZANVeZe6mIaiGngmiEscUhbUa2BGYJrBT1CEjBNKiXuMYMgKAJuGtcF5QUJBO0kt57+BJapCAUgYNHHBGiammB0Spdg6tQuAgEhYOl8Y8vM/ygClsKyFAETAqZgGQrLVFiGg2W4WKaLZShMQ+l1houZ++jljlIg+iPAoQ5zwOrGUCSfVGllK4Ku/P9ywbOLmOgk37cApwNvF5HTx3BBI0bJzX35cBwn17V35cqVg75YR0pShbQ637yXrynd+8AhfvNEMwBTG0KctbSKd14zPUdkjuPkMvhN0yy6F2SykZRvjlBKcdNNN7FkyRL+6Z/+KX+TQfsyIZPC3PdD4FvAvf2W/z+l1FfzF3gC9jZgKTAD+J2ILPZW/wfwJnSL+OdE5GGl1BbgX71j3S8i/wXcBHzb+9uulFooIm/ztru+FBfYH52dnWzatIlTTjmF5ubmUT9TPT09rF+/ntOqI1RVVaG6jy34GrjwMjIvPqkZIZNCQiFc29ZmvWwcxwxiKRtHAqBcDGVjW2HCThLHDAIKw83iBMKYysbBxBaLMBlcwDGCWOJiuFmwFQpNtoYYfHjJ/5E1Q3x326sAIRzAj+nQ/Gdo/5Rp6P8tU38XUTm/lSkK09OsDFE5UvXjQXQ6mKLXSKB613n/+/dX4WtkCoVQEVQsnTGwK6cYVdBLLF8zgWeBc4AtBcohnYeunblHD0fuR/ujt5RyUPkYs7lvuEgmk6xfv55Zs2Yxe/bsIbcfDUn52yulcF03R1D+OG+7cytbd8YJBgwyWZfm1iy79yV58NEjxKImUxtC/NtnT+3jv0plladuqqK0xChV7cCxhMjGYjH+/Oc/c99993HmmWdy9tlnA/DFL34Rhu7LlJv9ThSUUk+OQItZBdyvlEoDe0VkF1oQoYAwishW4GLgHd42PwLuRJPUKu87wBrgW+NR8yw/EtY0TY4cOTKq47S3t7NlyxZOP/10qpKDNxV2Zy8AEQJHXoaudrJuRpv1jCAmrg6KEAcXrU2ZysExApi4OoTaMDA8IjNwCCrtxxIUppvFNSxtzhPBVV5/YeViOBkCAv9w2p/JBiL8ZOcK7TuyBNPUhOFrTaaB1nhMRcAAw9CEJJJnwsMnKv+7q/PB0EQFaBL0fFJKaY0Jzyzob1sRhNOmDx1nkM1mh0zIHgollq+j6PzDn4qIDXzX+/jwfM85HEQ3Cx03jIsmZds2L730EkuXLs1llA+F0Zr7ChFUvvZUVxOguTVLJGxAAHpSLpGwQVuHtilf/Z6XAFgwt4LTF0dYqRSWqWd2xfBlTXSB2/7wzRErVqwYyFxasGeS35fpzGXL/tFVJSepKSLyfN7/31VKfXfArXvxIdFtCJ4HblNKtaOF7um8bQ56y6CwMNYDHXmRT/nb5wRYKWWLSKe3fcuwr2wQ9LcOOI7D1q1b+0TC2rY9qrJj2WyW7du3c+655xIOh3GHICnvjY1xyiL2H85QcWAj0WCYkJMgbcUIuUlcsUDQM3/DwMIhZVUSdpM4mGCYmDikjChhUihDNMmJg+BoDUopxLVRjgA2ovQkUUwXUS43zP8L2UAFthHkD68s1SY+SxG0XK0xeWa5fI3J/z8/IKLPpeX0I/1dIVqryi3uJS/XBdVzlAgp4vHBq7kUAwoosXy1AGFglVJqfylPNFqUlKT8pMJ0Os2FF15IODz8Qomj1aR8s51PULfduRWAeMLJEZJpQibrEgzoH9//3tJmEw7pB27Ttjg7dndzjgX1l55Le2eSKcFgHy0LeoMWhktYk42kxlxgFsEtvWuzRSm1YoT7fBtdSkl5f7+GrvR8XMI3y82YMYPZs2fnnqGRlh1zXZdt27bhOA7Lly/PzfKNmafiHtpecB/V3YqgPev7D2fI2IqtoVcxr6ab+sTLVNjdpAMxwm4PjhJMcfWE0TAIuUnSEiJouLqen2kQUmnSEiKEg+HaKMNERMioIAHXxjBcXOXiYmkNx9HzA0OBKBdRCstMc2njC2SsMI4EsCWAi0lWBXAwUWiNzFEmBgpHGYgoXGXkSClH/0pAFK7SARn+cjFAKXzaIhw0WNAI6XRNn2ou+YVx8/2CxVGoSy5fs4D1DFyzz/M999n+UCkH1B8lM/f5beVDoRCVlZUjrmo8Gk0qHo/T3t5OTU1NToMCTTiRsEEkbJC1IRQ06Um5iCFUVJj0pBSVsQCVMWhuyxIOQigoOK5+OBxH4dqKG2/ZRDzhsGBuBWedXsnfXT0tR1g+QQJ9cr36Y7KRVDGij8aBpEYMpdRR/7uI/DfwiPfvYEJXaHkrUJOXiZ+/vX+sgyJiAdXe9kVFS0sL27dvL2iJGMmzlMlkWLduHQ0NDcMu2iqV9Tk/VWuHSyartYlMVr+AW6NzkJ6DRJxu0laUgJPSPalMA+0EMgiSJS1RQpJBKR16F5QsDkFtjsMBCWCJjYirNTKlMOwMyrTANLXcuFmUgOFkNFmZLoarzYaOYencKyOAg4WD6VVqEFwEV5noSHfPt6SM3P9e2Idenlvf65SybYelc3on2P2rufiFcQ8c0Iq4X2MwFouNSt5F5Fq0GXnJ0jOXlVq+UsDfKaU6B1j/HLBIROahn/e30Wv6HheURJNKJBKsX78+l1T4/PPP4zjOiIhqJCTlH3vBggUcOXKE7du386MHA7zSpBAxsCxNTsGggeO6IEIkYuY0q0hYaG7TJkDDgEwWQiGDTMoLoQ0YpHq0NgawdWdc+7IeO5rzZX3lXxYdQ1aFtKzJRlLJZHJsBWZVnkBPIojI9Lz8rqsAP/LvYbT9/evowIlFaMexUEAYvbD7PwJ/C9zPscEjNwJ/9db/oZj+KKUUe/bsGbC80Ujgt7xZvHgxDQ0NtLW1DUu+lB4Ih5ohkxVcpbUQ13UxROG6Qnt0Ni0K5sS3sUfNYb71CqA1KpQu6xNSKdJGlDA92iSPiYGDqyxMUSTdEGFsMCBDCEtldT6UEpQyMFwHMU0M5aJEUK6t/UaG69MQyjUwDRtXTE1YYuGKR05iarOZkPvrBaJrbUl0opXydCklgJiELZeOjr3owLZjISJUV1dTXV0N9G2n81//9V90dHTwk5/8hHe84x0jkftNwNXAd1C8vsTy1aKUGtA87ZmxPwT8Gh2C/gOl1OZSDqg/ik5SfgfRM888M5dnU+wW8j7y/U+GYeQ63L7rQxtwlUvWtrEs5Zn3hIqIQSxq5nxSDfUB2jqyBAPaBJjOuFimJikQGuotukxdlNMAsrYiYOkHxv/e1pGlrSPLNTevB7Qva9mSGH939bTc2Hzi8k2SxSSpsb4TlVKj7t2jMS7mvsFHIPIzdFWMKSJyELgDuEhEzka/Z/cB7wdQSm0WkV+go5Ns4INKKcc7zkDCeDtwv4h8AXgJ+L63/PvAfV7wRRua2IqGHTt24DjOgOWNhgs/0Te/5c2wJ4FK0dSW9b7qUG//6XUcEHGxHR1u97vUShY3dNOVzlLhxhGV6fUNGSYhenDE6s1hAkxsXIIEJat1GjGxVEbb2gBD2Ri+duOilzs2GBbiuNqH5X2UYSKOgyEmrjIxDBdQeWTlkZJI7jv01sbLaVESIFZhURsL0tXVRdcI7n1+O50777yTjRs30tHRMSKZV0ptBV9Lnnj5Uko9hg6UmhAUzdynlGL37t20t7cf00F0pNUjYGghKhQgcd+aw6zf0k1bpyaeUMjXloSQKFrbbIJBRSgo2twnIIaQtRWWBZmM4CqoqDA8c6ALee6az1+wns88cxahkEkm65J1lP6e0cfKZBXbdiXYvS/JQ4839dGy/KCLjo4OampqyGazRUkkHotm5geajAUKyDgTq0kppd5eYPH3Cyzzt78buLvA8oLC6EX8nVdgeQq4dkSDHQEWjbGlulKKXbt20dXVdUyi73AngShFosclldbPieP2kpRWqvKqQCgX5Sqag9NAhDmpXbnwbgtADEwcHCOIoRxcsfCNblkJYaoeUipEiDSISYooFaRQeF2CbV2hHRFEOTqiULkoR5ORwkWH4bmeluZF7hngejlaSgwvpFty5GR4sX2uESAaCVEV6zXtjSWit6enh4aGBj74wQ+Oan+YHPI10SiKJpXNZtm4cWOuB07/l+ZIq0f4+wxVQSI/QOK+NYf5zZ9aiCecXIh5XU0gpzlVRAwaGizaO7IEdD1LrTlZQjqjsCwhEtH7AUTCBm9/+1ysxvmw/ie9me30Blpksm7OZJjJugSD+m8qo4iEDdo7bVraerWs167QhPThm2fkCBbIaVmGYYxYIIoRFj82zU6Qic+TOiExEgtE/8mKbdts2LCBWCxWsKvvcCaBAH94uovpEdcLw/ZNYhAwAVGYhoHtKJ3f6m2jyxS57A8tQAAz2cmscCdhlfKIxcbFwFCOVzVCCLsJMEwslcXBJGAIFSoBYmotKZ3UxW6VmxfRoLSJQwwMRyfiixlAlI3CzJWSEFdhiEeHYuQISy8TlBUiHIkQK1C+aKzVXAYqifTGN76RI0eOsHnz5k39Vn1aKfXL3n/L8jVmkuru7mbjxo3Mnz+fadOmFdxmtOa+/m2xoTBBfewuHZWUH8Gno/W0Wc8KCD1pBeKAQNbRPqdUSmHbinDIIJ2BYMAlYBlksxAMClOnmPixPuGwiUrroAvQWlMoaJLJ6PWhgEkmq6irDhBPOn3IKxYzyWSy/O7PEA4pXtys8+Auef2UXPCFf13+d9M0h6VlOY4z6rp9xXCfjEOI7EmLkZRGyq/f6PuE582bx/Tp0wfd5xgolYtu23swRSIRxoxqDco0wHW0JpPJaH+T46hclLbyaug5SuVMgwo4IDNxrVrmOPsBISAOoHANC0vZZLAIkqWHCAGVJWjYKCsG2TRYIR0VCGAFkWwKJKRNc7kT+xqTiek6KMMA5WIqhWv4fixDf5SLeOREKEogFCZSWT3gvR1rXcyBImd/97vf+V/PGOwYZfkaI0n5BDVUUcvRklT/l2h+UIL/8v7YXdvZvS+Z0250BJ8iGDSIRMxerccrTRUKmPSkXSoiBgHLJtljYJpCRUQLnmH4WpY+dyBk0RxbwIzurZCGVFqTmngZfkHPdBgJAwLtXQ7BgCB+eZeA0NKaJRQSTAuyjjaf9KRcHnzsKA8+dpQFc3Xgwlf+ZdExIe62bec0rEKENRZNKpPJjCgtYCA4kzBw4mSCL1+maeYiAfN9woXQn6R8068vcU2tGVCwsD4JCMpVOL0bc/r0pM4ZQmF6BfCU62qtyqsIYYiL7ZrMrkwgYvCyzMMwYJbzMj1OgBpJoEwDiyzdToQwaYKmC0YAyegK6jgZQHQ3YL9povJynZRCeaRpmAFwbZ346riIYeIaBoZLng/K0RpWMEigtndCnW/J6I9iVHMZK052+RoTSVVWVnLeeecN6XgfDUnl7zNQBYmP3bWdo81pQM86fXIiL7vcdfW2OuxcEQnr8PK2dodI2CQUEjJZRTCg/VOOq7UoVylCYdMbi0GqeiZTomCaQtZRHjkpptRZiEBPWhEOal8WAaGhzqK5LUvAAtMyMQ2hKmbQ3JaFgDYnZjIuAUtyIfL5wRf9Q9wHSiQeixDF4/GxRfahnekn+0xvouEn9R46dIjm5uYRt5335cufFDa1ZkgkHZ7fG2Tp9BRKSY4UAHa1RTm1sQdT8wGOo2vZKeWXJ1JkbBAx2NYU47TGOCI+eRk0Reaxubmav6nZrM17YhEzUxAM6QNaAa1FBYM6SEI5EIgi2TTKCiJ2RkfjGVbOXyWurUum59UxMxwbZQZ0zLwBRihKZMZCgNz19o/G7W92nwiSEpGrgH8HGsryVYTAieFEho0lum+wAAlfgwoF80KO6DW9BYNCJKzJpKHOIloB7Z1ZLBMqKkwytiJoCcGQQTarCIa0C7UnrYiE9IOR7IH2OW9kzr7fk569CMvqIJ3VL+dIWGhtd7wEYEXWVlREDDJZRXfCJmgpHFcIeeSVsZ1e/1XA8EyPiooKU88KPfnyQ9x/8ycdGeqbBQslEmez2VH7lMaeyKvhDlpis4zRYiTmvm3bthEKhYYdCZhfoSVnPkeLkW82V66uFO70lv0GgWjE0MTUuxTDNKipNHCVQtmazxzHq4en9HFcb5mrFEHT5YB5Cq4VZFr6ABnXIWb3aPNWugdlBTGzGU8lM8FvQ59JgWlpzci1tQZlpz0NykYMS/u5nCwYJuLYSCCIUd1IoG56n+sH/W4KBAJ9CgHkTwht2x5TybHR5CAqpR4CHgJYcsY56mSXr3EpizQWkipEUH6AhJ6ZadtCMCA64s4jJwRPczKwLGjpsAkGAARHgWnoDHTtf9I+qmxW6SrKAcH2opiCQZN0JjeJJBC0qK4S2rscLFGeZgV1NSZdcUXWgUBAB2WEAgaurcPVfXISEQTP7xXoDdTQUVK9RNWT0ssz2V6zYLTCYOqUIP/22VMBTVItLS2EQqFcC/GR9Moae4VmDfckN0dMJFKpFG1tbcyaNYvFixcPvYMHX5Pq79/dvT/pJeoqL+Y7Hzpqor0jy/SIV3LIFBxHEbCgszvL1LB/LK+qg+sToQ5yEC/azn/xC/CCnMPFF01D9m+CziaMdBLsFBkjQMB1sAlioSCgtSjXdbxAJiPnQ8uZ//Jf6YaJUTMVa8qsYd2PfO1JKUU2m6W1tZUZM2bkJoMjCW5KJpNFMfed7PI1LgVmTdMknU6P+NjxeJxUKkUoFOoTIJFIOrkIul6fkJELagCPtHDJelF36YwmiHDYpCetsB1dQRl0DlU45Gk8aUUkJLzrxnmYFjiO0B1X7J5zCXPcfVx0+Zn87pebCAe1vyscFjJZ6E7qNgBZG1xXYZgmtqsJ0A9Rz486jCdtlFK9y7yAi1TaJRwySKVcYlET6M3rau+wSSQcbvjwBuIJhxlThflzhI9+YFmfGeBw6wsWw2au6DWpljG+6OjoYPPmzdTU1NDQ0DCifQ3DoLOzs08pn/VbuohBnubU97tp6t5Olu5viOPoSVU4ZNDTo/1R4PmpRHBdhfK0flcpDCWIoQkrnXERP2jce3zUKTqGQDb9ESRGKNGJMkwCbhbHMFGZlGY1JZjitVV3HSQYQdkZbe5zbTADSLiSwNxlo7qv/gR527ZtNDQ0MHXq1FEFN8Xj8WHXKh0IZfkaJ01qpCWOlFJEo1Gqq6vZuHEjIsIPHwzQ0QUdXY4XWk4uHyoSNnIaEXi+qYBo7cgWHNclGDLJZvWPHo0YpDKapIJBAzG0X6q22sII6JwQK2DR1ClUVbja3xQVNqcWEY26iGWQdiHsl1kKKbJZhe0IwYCL7ZgETL0sk9XnyNq6j42I0N5lE7B6xylI7loAj0yNXKIxkAtxB4gntclv78tw+KjBui0bAbjkogbeec30YdnaQQvRmElKCWn75LaZlwqDTQIPHjzIgQMHOOecc3j55ZdHFKruui4NDQ0cOHCAF154gXA4TENDgyYVdCSrvy1K5bQl0AVv02kH21ZYXjvcnpQXSu6qHHkFPNN5daVBKCg5iwcibDoSZdGULlwC2sTYT2NTZ/yN/nJgC3S1QKYH08niKt2UUJTSvigno4nKzvgXh1RUY85bPrIb3Q+O47BhwwamTJmS69jQX8saTnBTIpFg1qyhtbjBUJavIpBUoT5O/TFcc1++/8k0TebPn8/8+fP5pzu30taRIpF0UQpaO7IEA74W5BIKmjiu8sLCdcSdDoYwMAyF6xrUVGqB6OhyydoQCQlZV0jbQjgEoZBJR1znfUQrhH3NYebUp7FdIRC06O5ySLsmlmkQCAYxHZesqwiGFJmMorpK0dHpYgUCmKarNaKw1qJCQS24PRndDyeVgdqoSTyhr7u2JkBLm02swsxdU211gPasLg2TC2WPmsQTDq7rVRsz+pkFHz3Sp+3I1+5cUtDW7s8Ci+WTmkxlnk50uK7L9u3bSafTrFy5EsuyRiVf4XA4Zx5MJBI0Nzdj1yjaqaShoYHQwX3UVgcIh/3juji21wrH1VaMnpRe5xmqUa6bq8iS8p7L9k6HGRWuNlmJQgwDy1CEg0LK0fuEAwO8P2Z7pYgObqWn6SBBO4VpWZrUAlp7UiKIkyUtJo4ESFfNoXYMwQ6O47B+/XoaGxsLEky+Lwt6I44LBTeV5as4mDQ+qcEi+Pa+nMoFSJiWDhV3XYVpaFNdJCxEK0w6uhw/oxDwAiDCBoGA0NpuEwmbOkjCBltpTcswtJZjGCaBoIFpGZy6fA6GYbD55TCOo5g7Q2ey2xmFIcKr3rCYp5/YQwCFY9uEgi6dcUU4ooM0QgHBsgwcB0IhIZlysQIWWUfb0oOWoq1DB3CkvUlgJCy0dzr4RQHaO7M6OdiLAAwGjNw+giCmEDSgxznWLNjcmiWecHjnB9cRTzhcffk0brh25jGzwKeeeoqurq4x/74nu818vJDJZFi/fj11dXWcdtppI6qEPpB8AUSjUaLRKHPnziWTydDc3MzRWA3V81Ko+tl07WmjOpDIPZu11RbJnjSG6Jm+QhGJWJw5K01PSm8jIl5elYPtuFiWiet6geCOS0/KIRQxcSyDKVUD5yEppdjWDVIxm1NjIPFWHTxhZ8hV2ovVEZp/Lu3t7TQ3N7Njxw4qKipoaGhgypQpfarfDAbHcVi3bh3Tpk1j5syZQ+/Asb6s/M9TTz3FmWeeOazjDIaTXb4mBUkNFMHnR7fFoibtnarXeC2QzUIkrB/8tg6XYNDFNHX1iKAnTJapW02LCBVhg6wDQVN0ZKtjkLENDFOIRCxcZRAIB6meUo1SmjzmNGpCCIWEdEoIB4WubhsQps1toP1oJ5I1sDM20ahLJqOoiLjadFhj0N6ZRbyWAYYhVER0CaWaaou2jgxiQCiohTb/MewTUAE5P5VyQbfl8cgroHO68s2C0Kt5xRP63t1wrRa4/Fng448/znPPPcfPf/7zMf22Otnw5BaiUiGfSPycxIULF9LY2Nhnu+HIV/8AiYEQDAaZOXMmM2fOxHEc2trayMzK0NzlUlVVxZQpU6h6ZgehABhmb+v053eZLJ3pYJheykdIxwrWVJmEg15eoaXNfdGIzk1MpV2CVn7QeF+4rsumTZuIRqPMnz+/77h3PK3dU4CxcCWgq4/X19ejlCKRSNDS0sL69etz6war/m7bdq4VykAJ0EPBJyylFF/5yleYNWsW11xzzaiO5aMsX5PA3DdQiaP1W7qJJ3QggQ6Q6A3dFgydEiHatJfNulRXBUgktfPWVdpebpiiq58HBAxBlHg+JJNAwEBMExFwlEkgHCBaEyMcCRC0oLFa5QQw3q0j9np6HMJGlq6kEA4HiFRGSCUE0zTIprMEQy6ZNIBDZ7eXRJxxiYR0WHrAu9vtnXaOiIKWV67FdTHRScSWqf1XuVJLGZdQQJsNO7rIJQ4LQl1NINewsU94O5qgfvwfZx9zz3/729/yr//6rzz++ONMmTJlhL94/x9QcCbYsSsiPwDeCjQppc7wltUBPwfmogvMXqeUahf9hvoGuqtwEvh7pdSL3j43Av/iHfYLSqkfecvPRbeoj6Br+93iVUcveI5iX9/Ro0fZvXs3y5YtK2g+Mk0zF93ZH0qpXOWWkZrATNOkoaGBhoYGlFK0t7ezdetW7GqXRNUUGhsb6dmyHUFrV+Fwb4WYnh4t761taaZFHEzT0Mm/4tLWkWVahcKwIGDB9IZj3x++2W3KlCnMmTPn2MEtvgDok3mSg4gQi8WIxWI57bC1tZW9e/eSSCSoqalhypQp1NXV5XLM1q1bx6xZswasmjNcKKW455572LJlC/fff3+feomjO+DEy9dEY0I1qUIC5Cfo+m0xDAOiUYtE0sFxIFZtEk86ZLO9NfSCQd2wMBiAQEBwXYOAH4Rg6hJG1ZXa/yQi2I4ujVRRAaZlYRgWpmURDoCyM9hZ4VCToBQ01vRGAfakFLEgdCcdTHEIhSyctEnGcQiHTdIZAzGymGLhOjpEnj4fHUHoRxqC5HK1xNCzTcdxcV0QU3cY1bXDoDuJRz5aw/JJTiV7axUGA0aOoOpqAgUJ6k9/+hOf//zneeyxx8ZOUEyamd4PgW8B9+Yt+wTwe6XUl0XkE97/twNvQbfnWITuvPtt4HyPcO4AVqAv6wURedgjnW8D7wWeQZPUm4HHBzlHUeAXiO3o6DimQGw+TNMklUods+9A5r3RIJvNsnv3bubOncvMmTNzfqz2Gp0HtHhRA207m6mSOKAj/gBqqyzCoSwg2qeaVFRGIGAq/JJ0+flL/rl80hitVpOPYDCY6//kui4dHR20tLSwe/dugsEgyWSSuXPnFoWg/vM//5PnnnuOX/ziF2MnKCaNfE0oJoykfL9IvgDdt+ZwH4ICCAV7a/BFwt73iOkFR7g5C6AhKqdum57JQQwDAx0R1BlXhEJefyhLiFUIiRQoAwKWEAiaOI6LMkyqwzamoUNkDx8VUllher0QVA5NbZBJO1QFU3Rl/eoUFuIKKp2lttqircOmpsqkvcOmttqivcPWIfEZrU0FgwbKVV5ul9IknNBkXVcToLlNHyeedHAcl4owdHQrAqaDG9Q+OT/STxCU9J2JDqRBPfXUU3z605/m0UcfPcZkNBaMsMB90aGUelJE5vZbvArdvgPgR8ATaAJZBdzr9X16WkRqRGS6t+1vlVJtACLyW+DNIvIEUKWUetpbfi+wGk1SA52jaAiHw5x77rmDkkyhEkfFJKhEIsHGjRtZtGgR9fX1QGE/ltEQoCUVoa6ujsbGRlqef4mpDUFQPfo4SZdY1IJBdM1UKsX69euZP3/+iMPqhwPDMKirq6Ouro5sNsvzzz9PbW0tR48e5fDhw9TX12uTZlXViO6bUorvfe97PPHEEzz44IPD9oMNBxMtXxONopj7hkJ+q47BAiR270vm9umtNK77QfUeS4hV6GG3deoafF1xm2BQ+6MiEe26SmcgHEJ3AfXMflWVAXpSOsw2mdLHVUrXGFNOFjurCAD7DxtURIRkj2LuzACVEWHnvgxzZwRobk4xrU5xtN0iErLJpl0SGQfTdAkGNBmC1roQSPbkykMDilQGwkE9Lq0UCYmkg1L6muNJ7WfyCUoEMrZBJKxIpHQQhWVpDStWIXQnFEHLI7fWLGecFuNrdy455jd45plnuP322/nVr35VlNmpD6UYD3PEFBF5Pu//7yqlvjvEPlPzmh4eAaZ632cCB/K2O+gtG2z5wQLLBztHUSAizJ49e0Tm9GITVEdHB1u3buWMM84YsHpCIT/WoUOH6IqGqaoKUdWwlCl2J3u3vUIi6XjEpTW/7kxv+SafDE899VRqa2vHNO6h4HcpXrhwYY4Ms9ksbW1tHDhwgO7ubqqqqmhoaKCurm7Iyjo/+tGPeOyxx/jlL385puaU/TFO8jWpMW55Ur7faagafD4cB2K1WsPI2L1CGgwauS66uumgTShkYDvikZk+Zn2tRUenQzAkoBSBgE6GDYUMDNMgFguQSmvicGxNEllbUVmRZVpdGNM0qKs02bU/RSQIIctg87YuqquDvHzYpjIKXd0OruOyYLbFjr1pKqMGyaRNXbVJW54WVVsdoL0jS021SXtnloqI1pKUUtRVW17Lep0oGaswURi0tmeojgntXbreoK5UQZ/7povgKtJZhdOVpbZauPO2Wce0bXjhhRf46Ec/yi9/+csx520Ugjv2YupDoUUptWK0O3v+o5KOslTnGInPdyQBEsPB0aNH2bdvH8uXLx92IeL+fqzOzk6am5vZ09pKuE6/9Gc3NByjaXR1dbF58+ZBybBYyGQyvPTSSyxYsKCPyTsQCDB16lSmTp2aG3tLSwt79+4lEAgwZcoUGhoaiEQifY73k5/8hLVr1/KrX/2qKAWb+2Mc5GtSY1xIyheW/gJ07wOHWL+5iz37e3LaRm9Ld4OW1kyu7UZ+QIBp6uABO+uSzggxz2clIrlAi0RStwPQAwDQJY+iFbriRDxhY4gmLEGYOyMAKFo6XPYcSHu5IMLUKQEONbnUVCSpCEdob01RWWmRSWdxHZeQpdh7MENVTOiO24RDQjxug6tIJHUEXiLpEAgIyR6tTSY9p3LW1hpTJKzLu4RDBm2dWUxDIQipjA6v96+9tjZIW3tGV6xIOFTG9M/X3Jpl0fwKPv6BWvbt20c8Hqe6uppAIEBraysf/ehHWbt2LaecckrRf1tXQSozKWd6R8VrIe+Z85q85YeA2XnbzfKWHaLXdOcvf8JbPqvA9oOdY1zhk9RoAyT6QynF/v37aWtr49xzzx1152YRoaamhpqaGhYtWpTzY/kRdz6ZZTIZtm/fzllnnTXmgsdDIZ1O5zQo33Q51NgXLlxIT08PLS0tbN26lUwmk4sifOGFF/jxj3/Mo48+WpKxT2L5GjeMOZV5OLM1f4a3e/duEgmdweoT1O59SS8p0M/56N3PNCAWtXJJrv6xlFJ0dDpkHcNrLtgb2ZSTT6Woqba85mjkCri2tWdxHYVytVYXDipClktTS4Z9BzPEEzZ2Jks4qOuA7d7Xg5tJ0tQeoDJsEwlBTRS6ujLMbNSkErJcmlszRIKKdMqhIiy4SpsiMxlFRVi88+uqFn5tNMeFaN61RStMlKu8moHH3td4QkcF+j67tg6dD9VQF+Drnzud6dOnc+aZZ3L++eczffp0Hn30Ua677jrq6upKZj4RtOWylJ9R4mHgRu/7jcAv85bfIBoXAJ2eye7XwCUiUisitcAlwK+9dV0icoEXGXhDv2MVOse4wjRN2tvbOXDgwIjLj/WHXw4okUhw9tlnj5qgCsH3Ya1cuZKzzjqLQCDApk2bePHFF6muriaTyRSlx9lA8Akq37c2XEQiEWbPns0555zDypUrqa6u5p577uG2225j/vz5RUnaLYRSy9fxgJJrUn6AxMqVK3NhoP/+wxRdcYNU2kv6M8VLSIW2jjwHMF4OULCXS10dx4oYOjouWmGS6bKJVuicjUyXk9s3kXS86uZ+PocWgGjEIJlyUUA8bmNYJpLqJbr5cyK8/EqWSFjIZrJEQgEyKYcjzS5KuWxvU8yeZrJ7f5KFs0PseTlF0FIkenRgRLzHJRDwfU0uiR7JKXWJhJPLCzFFaY1P6WoZtp3ROVVmXxNPXbUOM6+ttognnNy9am51WbCogq96BWd9GIbB0aNHuf/++/nTn/5EOBwuGUnp2mIlOfSwISI/Q2tBU0TkIDpK78vAL0TkJmA/cJ23+WPo8PNd6BD0dwMopdpE5PPAc952d/lBFMA/0huC/rj3YZBzFPPaBlznm88DgQArVqygubmZLVu24DhOTksZycvTLwdUXV3NvHnzSlrpIBgMerJv8prXvIbu7m4OHTrE1q1bc76g+vr6UTcc7I9UKsW6deuK4u8yTZPnnnuO7du3s3v3btrbi551kMNkkK+JRslIqr//KRAIMG3aNH7zfw5VlZ0cOpLAMsHFxXWFtnZNRvlJqd6BAKitDnjNA/Fyo8glrKLwzHu9u+T3v4lG/LYbLpYpdCeyuVpiIoCrEwx9oTzSnCabcUj1uCw8JcL+w7r9vIhBd1wn3u4/lKWhJsv2PVmtQdm6oGbGdnVrbaXvQdDShBOtMGnrzFBTaZHKZIlGTKJhg3jSIRo1SbZkqa02MD0/GZBL6I0nda5XPNmrQQUDBmecFjuGoAB27tzJ3//93/PjH/+YpUuXFuX3HBBKtwCaSCil3j7AqjcU2FYBHxzgOD8AflBg+fMU6KCqlGotdI7xQH/5CofDzJ49m9mzZ5PNZmlubmbXrl2kUinq6+tpbGwcNGItnU6zfv16Zs+eXdTAmoHgmxOXL1+OaZq5+oF9/Fh79hAKhWhsbGTKlCmjDkjo6elh/fr1nHbaaWMu+Arwm9/8hq9+9as89thjuQTikmESyNdEoyTRffkFTv1K3AC33bk1F8HXm5xr4njC5tiKSETo7Cbni8Ijm67uDKFQfsVhRTCgZ1l+vTBfm4r3OICXJKtUrq2Ht1su6x0BQ7RWFY/rJyEWs+juSuM4Cssy2XMgRUVI6O62qYiYRHw5UQYHXjE4dX7Q06RsMrZgoHBdyGZcgkHT40khntRFZRNJB1zVG2avFJ2dWQyDXCCHD98XF40Yw9KgAPbt28cNN9zAD3/4Q5YtG10V6JFAz/ROcs/uOGOoAIlAIMCMGTOYMWNGrp2LH7FWW1tLY2MjtbW1uf3i8TibNm1i8eLF1NXVlXzsu3fvpqenh7POOusY/9lAfqyNGzeilGLKFJ1EPNzCyD5BLVmyhOrq6jGP/w9/+ANf+MIXcgRVapTlqwSaVL4AFSIoPwggFjVp6/CLpkI2qxsEpjPilQrSfirH0RX4TbO3JTvopF3fV+VHyvnalDbx6arNmsB0M0LwgheUSzyhf/hY1Mq1IxCBrq4sAcshUhHIJfF2x7PEohbxuNaokj0urlLU11jsfTkFKF1iSbzeOVldo8zOZIlWGPSkocLznVmmzs5v68xSW2XR3Ko1qFTOlSBkMjrQAjRRJXp0cyuf2AbSoA4cOMA73vEO/vu//5tzzjmnaL/pUDjZzRHjif7yNRRM08xFrLmuS3t7O0eOHGH79u1UVVVRUVHBkSNHOPPMM0vmV8kf+9atWzEMgzPOOGNY5sRC+Vg7d+4klUpRV1dHQ0MDNTU1BY+VTCZZv349S5cupaqqaszj/7//+z/uuOOOoucZDoWTXb6KSlKFZnj3PnCI3zzRDJAjJuh94eYn7upggd6ZoVK6IGo45PWXSrrUVVv6pQ192lvoauOGbhmAIhoxQbQZMBYxPW1KvKaIBqBNcPGEndOqHEdXWc9khEzWQcTJNSHsjmdRCro9jStaYdAVz1ARNkgkXSrCmgT9AJC2jiwBE+JJ3eG0K6vD1TG1iS9oCZ1dWUyDPILS1xzwGjj6PaaUUsQqzFweVCGCOnz4MNdffz3f+ta3OO+884rxcw4LqmyOKCnyX775CfCjieAzDKNPfbvdu3fz8ssvEwgE2LNnT86sVsxgCR+D1uEbJgrlY73yyits27btGD9WIpFgw4YNRQtp/+tf/8onPvEJHnnkkTFXphgJyvJVRHPfUALkk5HjAF61kHz/k9aMLBD9gq+uNIgnFJZpkOzR6wN+9XDLzx+BWIU+WFunJhg/mi+e6K0j1p3IS3RUioqIJpO2jgx1NUHiCVu3ujZ0EIdSCsM0cB1NZMmUm2snnztmV1aTXNwGFImk1xXUz2tRWpMTUdguWKbCssjlZGWyUFslWJbOmYpVmLR1ZPpoUD5BZTIucTWwBnXkyBGuu+467rnnHl772teO4lccPRTgnOTmiFKj2Am6Sin27t1Ld3c3r3nNa3JtW5qamti/fz/BYJDGxkYaCuQzjQa2bef6MxWswzcKDJiPtWcPlmWRTCZZunRpUQjqueee47bbbuPhhx8ednX0YqEsX0UgqfzWD4UEyK/AvX6zbgnRv+yR3yMJ/Eg+wTIhnnAxTcML1yZXZaK90/Y6hOpWHR1daQwRyKtvle+jAk1oCS83KWBBd7yXtDRB6dJDGdv0NCIfBvF4lmiFlSMjv3IEQMLrrqtNmOJpZo4mtqRDdZWpc6QsTaiGoTU0w9AlkRI9LqgMYghdcQeFyvXB8scXqzBp7hnYB9XU1MS1117Lv/7rv3LRRReN5KcrDhS4w+u3V8YoUGyC8kPMRaSPT6iyspLKykoWLFjQJ5/JMAwaGhpobGwcVaKq316kWHX4CiHfjzV9+nTWr1/PtGnT2LNnD3v27BmxHysfL730Eh/5yEd46KGHikawI8Ikly8RuRa4E1gCnOcFGfnrPgncBDjAR5RSv/aWvxld5NkEvqeU+vJg5xgzSf34xz/mV7/6FatXr+aSSy4p+CDccO1MuLZ3BuLnSAG5QAo/ks1xdAJrZzdETMk1N8wnNpCcvwi0IJuGS8BUGIZOgo2FtZajywr1vviztsqFtCsFlmETtAy6vNp+WgPzyQjv3PkVprV21dKWpb7WIpHUEYPKhe64TazCIp7wknoTdp8+BDo3S1FVadKT8qtLKNraM5imDklHBKV0mL3vh2qoDxQkqNbWVq699lo+//nP86Y3vWnI36oUcBUk0yf3TK+U+PSnP01bWxurV6/mta997ZiKlvoaTV1dHaeccsqAhJfvB0qlUjQ3N7N58+ZcaPtwX/ilrsPXH93d3WzatImzzz47N75MJkNLS8uw/Vj52LhxI//wD//AmjVrmD9/fsnHXwjHgXxtAq4GvpO/UEROB94GLAVmAL8TkcXe6v8A3oQuMfacV8h5y0AnGDNJvfOd7+T0009nzZo1fPWrX2Xu3LlceeWVvOUtbxnQWZlPWrfduZWzllaxflMnBw4nvCg33Tm3EHxTWiyqywk11Gm/TdZVZGytsSil6OjK5HpJ+VoOaB+V8tRnA5dUxsAw+kbR+DlV0Qpdxqi2OkAi6ZD2Gi92x21CQfFynvQ+rqf1dCey2geWdHLnbG/XZjzHcQkGhZ6U9j3FE14bbAHT7PWtoRShoEtXt7B4foCv3nHaMfehvb2da6+9ls985jNcdtllo/npigJBYRmTWoiOa3zhC1/gj3/8I2vXruX2229n5cqVrF69mte//vUjMsWlUik2bNjAnDlzRuRTKRTa7r/wfQ2lsrLymBe+X4evWGHfQ8Evq9S/akUwGOwT6TiYHysfW7Zs4b3vfS/3339/roPxRGCyy5dSaisUjPJeBdyvlEoDe0VkF+A7y3cppfZ4+93vbVs6kjIMgxUrVrBixQq++MUvsmnTJh544AEuv/xypk2bxqpVq7j88ssHTKD72p1LyGQyLJl7mGc3xNhzQF/w7v19i83W1Xi+pw6XdMZFKRtTVC53yNeURMiVC4onbJSrsB2XzqyLYYDt6G0dx9UkhtZwUIpoROciZTIuwZAmnIAFcc+sF7A0AUYjBu2dujYfiA4PzzP1xZMOmbTjEapnV7bdXH+rgOVqH1Re4EfM2w/PH5a1ob7O4KM3V/YpI1NfX4/rulx33XV8/OMf58orrxzrTzgmKMB1Jq8QHe8IBAJccsklXHLJJdi2zVNPPcUDDzzApz/9ac4++2xWr17NxRdfPKgpztcwTjvttDElshYKbd+/fz/xeDxX+bympobu7u5xq8MH0NnZydatW4csqzSYHysUCuXk68CBA7znPe/hJz/5CaeffnrJxz8YxkG+RlO8eTiYCTyd939+Yeb+hZzPH+xARQ3jMQyDZcuWsWzZMu666y62bt3KmjVruOqqq6itrWXVqlW89a1v7VPUMb8NwMqVvXkHfuNDONaP5b/YAwEjL3O390WfHzQhBthZHXihlMJ1FGbQJhg2dAXxoH4AAmavSTEQMHLalgKtuXmkUlet6+YFLE1OfmWIbs+055v4ar3IvGjEoKfHobrGoCcFAUtvH89LPg5aoqMBA+JdEyyY2+uDmjdvHul0mpaWFu677z6+9rWvsXLlSl7zmteM/UcbK1TZsTtesCyLiy66iIsuugjHcfjLX/7C2rVr+dznPsfpp5/OqlWruOSSS/q8qFtbW9m5cyfLli0blU9mIPQPbfc1lE2bNuE4DgsXLizq+QZCR0cH27Zt46yzzjqm8OtgGCgf6/bbb+fhhx/m7W9/O6eeeqyJfdxRevkasniziPwOKKR+f1opVfJSYCWrOCEinH766Xz2s5/lM5/5DLt27WLNmjVcf/31RCIRrrzyShoaGgiHw7z2ta89Zsb1rr+dwbu87/0Jy4cmCXKVJfwXv+/fqqsO5C3XuUumqXOasNFNBW3Hi0jUpYtiUe1vmuKZEf1r6fa0su5+fqZYhdnH7Oiv84myszuDYejWIIXgV8NQedpYPkH5CIVC1NTU8Nhjj3HXXXcxderUopWMGQvKZVsmBqZpcuGFF3LhhRfiui7PP/88DzzwAF/5yldYsGABV155JclkksWLF3PuuecWtb9RfxiGwZQpU3Bdl+7ububNm0d7ezvPPPMM0WiUqVOnUl9fX/TQ9vb2drZv387ZZ5895urj0WiUlpYWnnvuOe677z7a2tqK0rRwrJgM8qWUeuModhuokDODLC8IGaKgY9EpXCnFvn37uOOOO3j88cc588wzectb3sLq1auZMWPGsKKXPnbX9tx3P/DCjxLM7Z53nExW92FyHVdXPffWZbOKmuoArqPbvfuh7YZXMqk//NwlIBc2nq8R4UX6BQK6okVXd5bKmNUnJL+tM0vQ8vPAyA8WBChIUKAz59/2trdx/fXXc/PNNw95j4qIQX+QWfPPVR+++y8lHcAn3hF+YSytOiYxii5fruuybt06PvKRj3Dw4EGWLVvGlVdeyWWXXVZS39Dhw4c5fPhwrnAseNGz8ThHjx6ltbW1qKHtbW1t7NixoygEBXDo0CGuvfZavv3tb/OqV71qzMcbASZUvoolW15z0I/50X0ishT4KdoPNQP4PbobtgA70OXEDqFrZb5DKbV5oGOPS6uOfIgI8+bN46abbuKb3/wm8XictWvX8t73vpdsNstb3/pWVq1aNWj0Uf5LPF/Lgl6zYDbb270WBUHLJVgRoL3LzkUM6iKwWuMJBQ0UCuW6pLIQqwDTELoSuvySoM1yvt8IPJ8XimxW5fxTKqlyBGWYhqdB9ZZAwlUoegvO+lUz2jqy1NUUjuJLp9O8853v5KqrruKmm24a3Y0fBt7znvfwyCOP0NjYyKZNm4a1j0LhTPRUr4wcDMPgnHPO4ZOf/CSXXnop27ZtY82aNaxatYr6+npWr17N5ZdfXtSSPv3r8PkQkVxo+8KFC0kkEjQ1NY05tL21tZVdu3axfPnyojQYPHLkCNdffz3f/OY3S0ZQo5EtmPzyJSJXAf8ONACPisg6pdSlSqnNIvILdECEDXxQKeV4+3wI3XXABH4wGEHBBGhSA55IKY4ePcqDDz7Igw8+SHd3N5dffjmrVq1i4cKFo9awlHJxFViW9vVksqpPVXUoYLLzx+S6ZLI6EVe8SEERGXB7/zpcLygja9ObP5XsNef5Wl9+jyyAh36w/JhrymQyvOtd7+KNb3wjH/nIR0pamfrJJ58kFotxww035AvSoCecOe8c9Q+fe6pkYwL4zI3RQWd7IrIP6EbPBmyl1AoRqQN+DswF9gHXKaXavXYb30BXQk8Cf6+UetE7zo3Av3iH/YJS6kfe8nPprYL+GHCLKk5PiXGVrx07drBmzRp+9atfUVlZyZVXXskVV1xBQ0PDqJ6r/Dp8S5cuHVEVDD+0vampaUSh7S0tLezevZvly5cXxYTZ1NTENddcw1e+8hXe8IbS1QoeQLZgguVrKNmaDJg0JNUfLS0t/M///A9r166lpaWFt7zlLVx55ZUsWbJkWAJ135rDPPtiM5msTaInkKvrlx8p2N6RJRga3K+TybrUVgdwXYeOTlcTlkiOtPJRETFoadX5U36Dw8Hgk1UsanLfvx9bDDabzfLud7+bCy64gI9//OMlJSgf+/bt461vfeuwSWrGvHPU++94sqRjuvPdlcMhqRVKqZa8ZV8B2pRSXxaRTwC1SqnbReQy4MNokjof+IZS6nyP1J4HVqCf+xeAcz1iexb4CPAMmqS+qZR6nLFjQuRLKcWePXtYu3Yt//M//0MwGOTKK69k1apVTJs2bdg94vw6fKeeeuqYnk0/l6mpqWnQ0Pbm5mb27t3L2WefXRSCamlp4eqrr+YLX/gCb37zm8d8vKFQQLZgguVrKNmaDBh3c99wMWXKFG6++WZuvvlm2tvbefjhh7nrrrs4ePAgl1xyCatXr+aMM84oOHtTSvGac1IsWwRnnHE2P33oaMFIwfwovmzWpTYX5t4bGh4whUTCRikIBQWFUBES2rscYhU6ebg7DjXVFl1dGcIh4xiC0jULszlyzK9bOBBB2bbN+973Ps4555xxI6jRwPU6EE9CrKK30+6P0F12b/eW3+tpQk+LSI3XVfci4Ld+DykR+S3wZs/WXqWUetpbfi+wmt6eUscdRIQFCxbwz//8z3z84x/nwIEDrF27lne/+924rstb3/pWrrrqKmbNmlXwuStGHb585Ocy2bZNa2vrMaHtmUyG/fv3s3z58qIENPh5hnfccce4ENRoMYnla9wwaUkqH7W1tdx4443ceOONdHV18cgjj/Bv//Zv7N69mze84Q2sXr2a5cuXYxhGzqxh2zZnnnkmhmEMGCm4e18yp80E8jre9ofyov6aW7M01OsQ9FBQ5zIppSMGu7qzuQK2tTUmIpIjpnjC6e2omyujZBAdgKAcx+Ef//EfWbx4MZ/+9KcnLUGBngYGzJIrBEPlcijgNyKigO9466Z6XXUBjgBTve8zOTZPY+YQyw8WWH5CQESYM2cOH/3oR7n11lt55ZVXePDBB/nHf/xHEolEzkfsk5FftaKhoYHZs2cPfYIRwrKsY0Lb9+zZQ0dHB1OnTqWzs5O6urpRFdj10dnZybXXXsvtt9/OFVdcUcTRFx/jJF+TGscFSeWjqqqKd7zjHbzjHe8gkUjw2GOP8a1vfYutW7dy4YUX0tbWxoc//GHOOuusgi/3QoQ1taFwTUHo1ariCYdI+Fgii1YYtLbZ1Ndpk6KIJizwaxDqkPVMpldTUwldzeLH3zrrmPG5rsstt9zCzJkz+dznPjepCcqHW/oyzUPlcrxWKXVIRBqB34rItvyVSinlEVgZg0BEmDFjBh/60If40Ic+RFNTEw899BAf+9jHaGtr4+KLL+bw4cPceeed49IY0TAMbNvGdV0uvPDCXODFzp07icViNDY2jji0vbu7m+uuu45bbrmFq6++uoSjLx7GQb4mNY47kspHNBrl2muv5dprr6Wnp4c3vvGNKKV43/vex4UXXsjq1at51ateNeBDPFgulk9GuYoQ/Ux2bR1ZApbQ3a3JK9mjO/b6HX8rIgYtbVmqYrrMk2n6NQA16Vxy0RT6w3VdbrvtNqqqqvjSl740ptnieMFPkJ7gMRzy/jaJyEPosNejIjJdKfWKZ85r8jYfKH/jEL3mQX/5E97yWQW2P+HR2NjI+9//ft7//vezf/9+LrroIubPn891113HpZdeylVXXcWSJUtK9py+8sorHDp0iOXLl2NZFsFgkNraWpRSdHd309TUxN69ewmHw7k2I4P5qhKJBNdffz3ve9/7uP7660sy5mJjMsjXRGPyvwWHiUgkwk9+8hP+8pe/8MILL3DFFVfw85//nFe/+tXccsst/OEPfyCbzQ64/7v+dgZf/eypfPWzp3LJ66ewYG4FC+bqzP1ohUE8busSSXGb7rjuFeU4DpUxk0xGd8+NVpikM70NGCNhg6xtYJp+OxNFwHL4m1eHuPot1blqFaAJ6pOf/CSmafL1r399Qgjq7W9/O6961avYvn07s2bN4vvf//7QOyk90yvlZzCISFREKv3vwCXoopcPAzd6m90I+JnxDwM3iMYFQKdnFvw1cImI1IpIrXecX3vrukTkAi8y8Ia8Y500OOWUU/jjH//I73//e37/+9+zZMkSvvjFL3LhhRdyxx138NJLL+EWMVTaz7vyCSofIkJVVRULFy7kggsuYNGiRaTTadatW8cLL7zAgQMHSKVSffbx8wxvuOEG3vWudzHeGJVsQcnl63jApI3uKxay2Sx/+tOfWLNmDU899RQrVqxg1apVXHTRRcPKsbhvzWHWb+7SIfKehlUREVrbbKbUBwZ0ahbSvIIBg9VvbuDS1wdoamqip6eH+vp6EokEa9eupbOzk+9+97uTTYMa1N44dfbZ6u8++puSDuD/3TZ1wAgkEZkPPOT9awE/VUrdLSL1wC+AOcB+dAh6m0c03wLejA5Bf3deAuJ7gE95x7pbKfX/ectX0BuC/jjw4eMtBL1U6O7u5rHHHmPNmjXs2LGDv/mbv2H16tWsWLFi1M/xwYMHaWpq4qyzzhpxVZVUKkVTUxPNzc24rkt9fT1dXV3cddddrF69mve///2TzYQ+ofI1mGxNFpzwJJUPv0DnmjVreOKJJ1i2bBmrV6/mDW94w7Dqft37wCFeWN9GMtlDMnUsQfUPruiPqy+fluuvBeSKdN50002sX7+eW2+9lc985jNjv9DiYnAhmnWWevstvy7pAL7xz9MnvSCNEieUfPX09PD444+zdu1aNmzYwOte9zpWr17NBRdcMGyyOXDgAC0tLSxbtmzMZb8ymQw7d+7kxhtvJB6P89WvfpXrrrtuTMcsASZUvo4H2TqufVIjRf8CnU8//TRr1qzhC1/4AqeeeuqgPbEA3vqGCItnJzj77LP59Jf36hYjm7tyGtZAwRWxqMklFzX0ISjQjuEf/ehHTJs2jUcffZSDBw9yPOJ4MRuUUVpEIhGuvvpqrr76atLpNL/97W/58Y9/zEc/+lFe/epXc9VVV/Ga17xmQB/xyy+/TGtra59mjGOBiPDFL36R97znPbzvfe+js7NzzMecCJzs8nVSaVIDwXVdXnjhBR544AF+85vfMG/evGN6YvmlWM4+++xjzIR+E8ezllbx4KNHjqko0V+DAu0Q/cY3vsGLL77Iz372s5IWs/zf//1fbrnlFhzH4eabb+YTn/jESHYfdKY3ZfoydcVNj4xtgEPgh3efMulne6PESSFfmUyGP/7xj6xZs4a//vWvnHfeeaxatapPT6z9+/fT0dGRSxsZK2zb5qabbmL58uV88pOfLJmJb4yyBRMsX8eDbJVJqh9c12XDhg2sWbOGxx57jBkzZrB48WLq6+v54Ac/OGSme37X4aPNaaY2hPjanUv6bKOU4tvf/jb/93//xwMPPFDSCtWO47B48WJ++9vfMmvWLFauXMnPfvazkfTJGVSIGmYsU9e8/1djH+gg+M6dcye9II0SJ5182bbNk08+yZo1a3jyySdzybmXXnopl19+eVEIynEcPvCBD7Bo0SLuuOOOkhFUEWQLJli+jgfZOqnMfcOBYRicffbZnH322Xz+85/nhz/8IXfccQczZ87kySefLNgTKx/5XYcLQSnF97//ff7whz/w0EMPlZSgAJ599lkWLlyYa3/9tre9jV/+8pdFbOamTnpzRBnDh2VZXHzxxVx88cU4jsOtt97KH/7wB55//nkefPBBVq9ezZve9KZBmxcOBsdx+PCHP8ycOXNKSlAwHrIFZfkqk9SgEBFWrVrF6tWrqampYefOnX16Yq1atYorrriCqVOnDlsY7r33Xh555BF++ctfFqWC81A4dOhQn8oAs2bN4plnnineCVTZZl7G6GCaJrfddhv33HMPIsJzzz3HAw88wJe//GUWLlzI6tWrufTSS4nFYsM6nuu6/NM//RP19fXcfffdJY/iK7lsQVm+KJPUkKirq8t9X7x4MZ/61Kf45Cc/yd69e1m7di3vete7sCyLK664gtWrVzN9+vQBheOnP/0pDzzwAI888siIuohOZigUrlO4nFQZZQyFuXPn5r6ff/75nH/++bmeWA888ABf//rXmT17NqtWreKyyy6jurq64HFc1+X2228nFArxb//2b5MtjWPUKMvXOCXzPvDAA7lS/s8///zQO0xyiAjz58/n4x//OE899RQ//vGPsSyLm266iUsuuYRvfvOb7Nu3r0+y7po1a7jvvvt4+OGHR23KGA1mzpzJgQO9JekOHjzIzJlFLD03wcm8JztONNmC3p5YX/rSl3jxxRe5++672b9/P1dccQXXXHMN9957L21tbbntXdfls5/9LNlslm9+85vjRlAlly0oJ/MyTprUGWecwYMPPsj73//+8TjduEJEmD17Nrfeeiu33HILR44c4cEHH+TDH/4w8Xicyy+/nMrKStauXcujjz46bNNFsbBy5Up27tzJ3r17mTlzJvfffz8//elPi3Z8VTZHTChOZNkCLV9nnnkmZ555Jp/73OdyTRyvueYaqqqquPLKK9m5cyfd3d1873vfG1cNqtSyBWX5gnEiqSVLlgy90QkAEWH69Ol88IMf5IMf/CDNzc08+OCD3HPPPTz11FMDmipKCcuy+Na3vsWll16K4zi85z3vYenSpUU8g8I5yc0RE4mTRbZAy9eSJUv4zGc+w7/8y7+we/du7r33Xp599ln+/Oc/jzn5d6QovWxBWb7KPqmSoqGhIVegcyJx2WWXcdlll5Xm4EqhTnIhKmP8ISIsXLiQu+66i7vuumvCxlFS2YKyfFFEknrjG9/IkSNHjll+9913s2rVqmKdpowJgIjElFLxQutsR9HZlRnvIZ1UKMvWiY2yfA2OopHU7373u2IdqoxJBBE5FbhNRH6mlPpj//WGAZHSR9Kf1CjL1omLsnwNjbK5r4yhUAHsAe4QEUMp9fv8lR0t23699nvnF85sLh5aSnz8MsqYKEy0fE1+2VJKDfYpCh588EE1c+ZMFQwGVWNjo7rkkktGdZzHH39cLV68WC1YsEB96UtfKtbwysiD67r9FwFEgY8C3wXq1eDPTPkz/M+YUSzZUqosX+OBsnyN/FNyISoWbNtW8+fPV7t371bpdFotW7ZMbd68eaKHNeH4xS9+oU4//XQlIuq5554b9XE6Ojpy+69bt069+OKLSimlgIVADbov0x+BM9UkeHBPkM+kQVm+jkWxZEupsnyN5XPcpGXn18kKBoO5OlknO/w8mde97nVjOs6zzz5Ld3c3AH/4wx9Yv369v+os4J+UUi8DzwHFLExWxiRBWb6ORbFkC8ryNRYMVQV90kBE/hZ4s1LqZu//dwHnK6U+NLEjmxwQkSeAjymvy+woj9EInAucgm6T/lpgPvDfwGHg1egOt8+NecBlTCqU5WtgFEO2vOOU5WsU6KNJiYghAxSeE5EKETnJ40xOeFwA3A48DDwLfFEptQv4X+AvwHvKAjQ6lGWrDMryNSr0ISmllKuUUqJh9hOqlcAvRWSdiJwxvsME4BAwO+//Wd6yEx4i8jsR2VTgU9QkGaXUw8Cj3udFICYiPwFuBB5XBUJkyxgeJrlswUkqX+MlW1CWr9EiF4IuIsuANwH3K6UOAU7eOhPYCNwBrAGqxnmcoO21i0RkHlp43ga8YwLGMe5QSr2x1OcQEfF8vP8mIi7wGuAxYAnwK6XUnlKP4UTFcSBbcJLK13jIFpTlayzIz5NygVOBX4lIB5rtH1JK7VFKOUCbiMxAR6Bs738gETFAzxhLMVCllC0iHwJ+DZjAD5RSm0txrpMR/izfizh6DJ2/4Sqlvuxvk7e+jJFhUsuWd+yyfJUQZfkaPQoGTojIhcB1wEXAl4C1Sqm0iHwXLUT3eMKFiESVUonxG3JxISI/AN4KNCmlJsrUMmqIyFXAvwMNQAewTil1aRGOexNwMfD3SqnsWI9XhsbJJFtwfMtXqWTLO3ZZvoaJHEl5szXxBSS3gUiNUqpDRKLAI8AnlFLPeOtOBa4E3gJ0AZ9VSm3wMqddb5tLgDOA70xGgROR1wFx4N7jTYhKgfzZnIj8L/DvSqlHJ3hYxzVOVtmCsnz1R1m+Ro5c4ITn2PVncIaIWN7yDm+T1wNtwAFvmwbgU+gM6YvRYZQ3iEggT4g+CtwKvBdt7ph0UEo9ib6uMsiZJUwRCQC16NDYMsaAk1W2oCxf/VGWr5GjYO0+TxB8YTgdqANWAf+rlPJv6mXAdLSz9TF0DaizlFIf82aG7wbOQ9tfFwNbS3khZRQP3gvVEZHLlFKtZVt58VCWrTLK8jUyDKfArABfRMf4nysiLUqph4AQ8LRS6rNe2Oy1wLMi8mpvWwP4OPBO4BWlVE/5xzju0AZ69jfRAzlBUZatkxtl+RoGhiyLpJTarJR6HXrG95/okEmAPwHne3b1TUqpO5RS30IL1CrgGSAMNAK/L3DoMiY5ysJTWpRl6+RGWb6Gh2G36lC6KdcP8hbtB/4P+IuIHATWAt8HvgXcBHwbqEZHxfywOMMto4wTD2XZKqOMgTHm2n2eE/itQEIp9du85RXAv6FrUz2glPrBAIeYUIjIz9DhwFOAo8AdSqnvT+igyiiD41+2oCxfZYwdRS8w64fIishK4B+B7yql/jrI9uJFvATQWdhT0BnY6aIOrIwyjnOMVLa8fcryVcZxjZJWQReRJcABpVR8KMeuiPwKeBmYCrwO2At8GfilKmGmfRllHI8YiWx525flq4zjEiXtJ6WU2urZ2wd0EopIVETeB6SADyml/hZtc7eAc9C29zLKKCMPw5EtKMtXGcc/hh04UUIs9T4/8swSU4B24GdKqa9O7NDKKOO4R1m+yjiuMRk683aiWye/6P2/2vt/C2ib+sQMq4wyTgiU5auM4xoT3plXRILoEFsDXQX674CPAE+V8wjKKGNsKMtXGcc7JpykfIjIFegZ3s+VUi0TPZ4yyjiRUJavMo5XTDhJlcu5lFFG6VCWrzKOd0y4TyqvbP2Ej6WMMk40lOWrjOMdE65JlVFGGWWUUcZAKM+uyiijjDLKmLQok1QZZZRRRhmTFmWSKqOMMsooY9KiTFJllFFGGWVMWpRJqowyyiijjEmLMkmVUUYZZZQxafH/A9kTexKobcpbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_3d_laplce()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# figure out this strange matplotlib value error\n",
    "#pz.plot_nyquist_with_pz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_bode_from_pz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_nichols_from_pz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that with an appropriate value of $K$ set we get the value that matches the .ac simulation results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asking too much from .pz\n",
    "\n",
    "Lets now see what happens when .pz does not work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pz_sim_control</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tf_type</th>\n",
       "      <td>voltage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acqui_mode</th>\n",
       "      <td>zeros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_0_terms+-</th>\n",
       "      <td>[In, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_1_terms+-</th>\n",
       "      <td>[Out, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   value\n",
       "pz_sim_control          \n",
       "tf_type          voltage\n",
       "acqui_mode         zeros\n",
       "port_0_terms+-   [In, 0]\n",
       "port_1_terms+-  [Out, 0]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error: There are no vectors currently active.\n",
      "/home/iridium/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:181: UserWarning: PZ analysis did not converge with the current setting:\n",
      "                start by changing the tf type (self.tf_type) and pz acusisiton type (self.pz_acu) \n"
     ]
    }
   ],
   "source": [
    "pz.pz_mode_set(pz_acu='zeros', display_table=True)\n",
    "pz.do_pz_sim(display_table=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first error case, the pyspice ngspice wrapper catches the ngspice error that in this case, we know is due from trying to calculate zeros from a circuit we know does not have any zeros. We can also run into cases where instead of throwing a blatant error ngspice will throw a warning when it cant converge to a solution. In which case at the moment `pz_ease` won't display the warning but will set `pz_ease.pz_values` to an empty dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pole-Zero analysis of a Series Band Reject filter \n",
    "\n",
    "Here we get the pole-zero analysis results of one of the filter examples from the previous section where we know that the transfer function has a zero coefficient term in its numerator to demonstrate the utility of `pz_ease` to formulate the transfer function form the .pz results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lz33uc2++/vrrEz9PCAvnocY8z3BKH5T0rKQfu/utIgdkRjAWmkBuZvsKFd3YEZW5kZmFpjJkRmyOHVGZG5l5alGGEcf8UOEhlMMeUDmL65LemNO5gXlpHh0dqVY722BoZnP5ohr6D7XX6+nSpUsPNLrfBcvhsqSfzencH9Bki4UfKPRzbWnMQ4BjsIxttkzcvW9mBwpV2pbC4gmjzp+WK+uPC7l4/p6ZtTLnHxe8G5JuSfqrkv7BuPNP4bck/Zmk/ziHcwPz0uz3+2eCrteLv5eWXUIq/Gbt7Xb7VAl5dHTkV65ceagCzVFsbJNsCtXWkUITYH3MvhM3Fab/T8etNmbf/bjfzgTnP25KLXD+tgo26075s2xPcu9sbIuwPfnkkz9fX1/3vPX1db9x48a7c7no2tra5yX51taWt9tt39vb8w996EPvXrt2rXA/BxvbJFuRAJskEEecf2gITBKIA44dG2CaoG9xhp8jQce2dJukT1+6dOlRs9n0/f19b7fbvr6+7o8//vgjSZ+e54WbN27c+N+S/Nq1aw/W1tY+f9E/DLbqbkVCRicDS/anOP/YqmuaajFz7NgBLNNUi1PcB0HHtpSbpF948sknf3b16tVHly5devTkk0/+TNKn3Z0lwFAd45YFm3TQyoDjhw4ymWbQyoj7u+e5uX3TDFqZBk8lQRWxBBiqJIXDmWXBMnPzutOEXO78gyZ4bymE3ME0IReNmqCertmaV8gBVUXQoTJigKVlwfKrpRwHxQznTxPIBz3hvMgE8XEOdDKB/HgVoRjahUeKAjiNoEPVnKm6sk/4zjc5TuFM1RWrxZpmqxblw5+FV0a1CKwsgg6VMqTqmrmay8hWXWlZsDLPfxx0mWXByqgWgZVF0KGKso/wSQ8MzlZLU8tVXeslV4uKFVs27EqpFoFVRtChio6rLoUh+VK5gziyzYs7udfKkG0eLbNaBFYSQYfKyVVdaVBHaUGRq7oaKqlazJ0/DaoprVoEVhVBh6rKBsOgJ3zPKjv6cR5D/k+dv+RzAytlHos6AxfOw2LJmwoVUelBEc+/rVB1zeP8HTNLYUfQATNgZRQAx1gZBVVE0yUAoNIIOgBApRF0AIBKI+gAAAvDzJpmtjN+z+IIOgBApTG9AAsprtjfGPJ2r2qLG8fP21T4zD2dPFLoYOSBAMYi6LCo0hd/XeERNT2dPJlA8TE2fYUV/ecWBma2r7BKSU9SVyGIGgphm384anrmXbJbZCK5mW0pLPfVSsP6Y/BtxYfJbhJ4wPQIOiykWLFtx0WT1xWC5dTcrhh2982s5e6bc7qVdZ19tt3ugJDby95j5t7ujAq7zFPRTz31PB6zbWY9SftmNvCp6QDGI+iwtNy9a2YthVX+u3NaD7KrUNElZ5YTi5Vc091v5+7tQGHR54EhHEN8Q6EqHfhkAndvxfNvmVmHJxgAkyPosOxStVQfudcM5y9QSTUVmjbzugrV2PaQqm4js98oHYXm0nWdDl1gaQ1o6k9qCs+THDjycpqWDYIOy64W/yx7UeVJrOv0IsxJuqeGBgdUuvdxQZfen1eYAxehq8G/IKZBaIP+zUz175ygw9KKAzbWJXXcfVDQlHWdNBqypvhInlShxXsYp6nRldi4AEv/8C8yzIFSufvAX/DMTJJqZTbTE3RYFrUYOEkKn+1hfXMjmkZGyjWNNCTVU5DGc943s7uxr27YFIgiOjoZWTpKClOaLYEpEHRYZnWNbvYb1jQySr5qupcbDZkGh+xJujvhufNaCk8Qb47ZrykevgpMjaDDsujnmjI6sdmwHYfwnwm0YU0jkxjSfNJRGAU5U5+Zu/fjM+d2zKw+6DPEaQp1SfdmuRawylgCDMvsQKFZb2PcjtOIa+6NqrbqBfsRhvatxSbRjs7O1UvWNedJ8Tlrkl4ws634+Yv0QQILjYoOVTCwn2zKPrp+ZmDLvkKQ2rhjdNKPNsiwTvdUxW3Gaw0aUNNUbCIdVvWVbE3SLUnPZe6zr/BLRauMKhkYo6/xI5EnQtChCo7DzMxqmTlrs/bR5SeLZ6+XbUo90OBAbepsk2vWhsJgmp6ZdfNBFpstO5nPsyFp3quj/GdJH5b0I50sd5aq5g0z6yr0LR4UWd4MmFT8ZYqgA6IUCvVMwG2Y2YG790qoPs4EXWbx5Wz11VIYiVnLffk34nvD1GPV2YtbWtA5aUrqxebTukZXjaVw93+Rfy32RW7EraEwEGfLzO5R4WEZ0EeHhWRmDTNrKyyhJUkNM2tnV0uI1U8KnI0YQrWymvfiNIN1M9uI/VXrktoKTXi7mf26is2PZlY3s1q8zzPrc+b0FEIjfc58E2wt8/6eJq9OSxF/adh29zWFz9lVCN52rDqBhWbuftH3AMwkVhxpMEer7Ca1+GWenqDQGRak8T5S9XVQ1Won/kKxr9g0q7AgdSU/K6qBoAMwlcyTF3rZBa2BRUPQAZiamR0qzvPjmXlYVPTRAZhF6qscNg8QuHAEHYBZpP5KJpZjYTG9AFhhmYE2g0ZP9hQGm3QkdYcM8uHRQVh4BB2wgmLA7atYUG1J6scnpu/mRp2mJkv657CwGIwCrJg4H3A//rWnEFIdhdGTvbhPmiaRpkxkK76OwkT4NM+vL+k2K6VgURF0wIrJjJRsuftmwWOyq6Pk++M2eYQQFhlBB6wYM3OFNTjXpjg23+TZcfdZn8sHzBWjLoHV01N4Ynu76GN4zGzdzPYl3Vdc1Dq+1cwuywYsIio6YMXEqqytkybIA4X1K7PLeNUU+uXqmT+lEHAHCvPnUnVHHx0WGkEHrKDY57aj4hO9ewoDUE6tJRqrvHWF0ZjzfoQQMBWCDlhhselyXScjLFOVl51D1xuxkHUawUlfHRYW8+iAFRars5Z0PKUgGRpuOUObKzMjNdPTJY6vpVAZzvWxQwOeMH/mmjHot3KHFr63WY8veI1hP8eeu7fGvV/WfSw1d2djY1vBTaGPbUfSoSQfsqVn4W0MOcdO3G9/xHWa6Vzn+Nl2JDUyf6/Fz5J/bUfhGYbpta14r+sFrjHT8VN8ppE/x4v4OS/LxqhLYAXFkZL3Fb6Y0yjKTmZLA1OaCtXCnpkdmdlOrCCyzZ7S6Cepn6v01HbPPCPPQ+W6qdPV11bcmpn9djPvjTPr8TgnNF0CKyaGXPoi3tWIh8TGEZoNhbBrxOO2zGxXJw+Z7bp7Z+43XtzAZc3cvZd7Inp/1P4FzHo8zglBB6yeFHJ3hgVcEt/vSmrFkNhQqOLSOVKltEj6CmHcz1RYqdI7DuT43m72wEwQjg3uWY+P+9ecaRlzR9MlsJqm+XLtZ7akp5NH9SyK1Iy6EyfF1zNPaRg3BWJLIdh3x+w38/Fx8M/RgNe34rQNlISKDlg9LYXKrG1m2wpNl6NGT67rpK8uSZVgmjRe2tSCAaMlC/E4j8/d+2Z2V2EQTVOhL7Ln7neGXK+pk4Wre5LuTlJlzXB8U6cn6SfrGl0RNsysPeB1ngk4BEEHrJ5thS/FdYUw2DOzjkKllqqztBpKPnBaCsHYiYNRDhWWAWuMawadQFeTV4n5YOnH83QUqqwUDvfyIRT7FzuZSfT7ZnZmv2FmOL6pXKDFn2lDoyvCrg+YsxgDd1AArjyCDlgx8Qv4XqycmgqB1xyyewqMrsLqJ8df3rFyaikEyYZm6Kszs7rHuWezBmZsptxRDLV4j6m6a0saWNnFwSqbCuE9dL9hJjl+RKCl/x0WaXDP0qOPDlhR7t5y93vubgpfyndz25q7r7n7XXffHlKhpC/kWUcebozfpbB9ZULZ3XuxAtpWqOyGLnsWj+mO26+E44cFWkPDn+aOKVHRASsuNnk1dLaPp2ZmvRKbJIvezzR9dNkRlvVB0x3cfTfzWQ/M7L7CZ74zJFgaGvHk9BmPHxZox82ZjMgsD0EHrKDYdJYWdR45iMHMpPhUcXfPf3GnqmXqMIx9W9n5bWX00Q2TfUpDumZtyPGnPlO8z34mfCY6PqeZP2ZAc+aWxo8SRQEEHbBiBjw8NQ3aSM1uUvgirsV90qjCppn1FKqUVnw/NTlOtTJKDI/9zHVn7qNTmPO3lZ1DF69VU1gCLIXHgXLrUsZ9mgrV1kHm9bpC31tP0u1Jjx90HzrbbJme69dNoTrBZ8YIPL0AWDFx9GEa2n7PxyxAnFnqKy0XJp18Cdc05BE9mUEhafRmNkiT1FdV2mN+4v3uKYRStg+xIem4rzFT1WYryJ14n6dGTcZ97ysE2L1Jj8/dX3riQ0+heusp/BwO4n0f6OTn2h/xc+y6+3bm/RSgip+7W9bPdNkRdMCKMTPXlA9LjX1cO8p8oQ4a6r4IYlWUmmbHLXPWHLffiOtMdHxcgq3p7nfSSirZY+LPmAEpJSLogBVjZkcKX8rb+ea9EcekZspBA0U2ncfBFBYHsXSots4PQQesmDiqcS/+tavQVNbJVRWpGSxNHM8O/+8pDJJIq6X03P22MFb8uR4pNG0OHdGJchF0wAqK/UR7mmzZqAOFprnsII37CmFIVVdAZvWSNZomzw9BB6ywzDqWaXRlkhZrTgMtOoMGrWSqw4Xtq1skaVQm1dz5IugATC1ToRB0WFjMowNWWK6iy66OUqiiEw8dxRKgogNW0Ax9dK3s8lr00WEZEHTAimHUJVYNQQesGObRYdUQdMCKKWFllD2dhB2DULDweB4dsHo6ChVdOy6TNZKZ1cxsI/bHtXWy3mJfYaHnnZEnAC4YFR2wYkY8vSCNtJTOPr0gyT+94H58/fa4xaGBi0LQAStokufRRR2FVVFO9cWZ2Z5Cv11pTx8AykbQASsu9rs1B7zVVXjQ6JmndWeOTY+coa8OC4sJ48CKi0E2NMzGYL1GLDwGowCYRaoE6Z/DwqLpEsBUYj/foUIf351JH1gKnBcqOgATiyHXVgi5DiGHRUbQAZhInJ5wqLA8WFfSvYu9I2A0gg5AIWbWiNMJUiXXlXSXB4hi0THqEsBQsXprKMy3y05BOFBY45KQw8Ij6AAcM7M/lHRT0s8UAi6rr7AiSotVULBMGHUJ4JiZ/VDSrcxL2Qev8oQCLCUqOgBZr0r6tqTfYiQlqoLBKACy3pb0I0IOVULQAQAqjaADAFQaQQcAqDSCDgBQaQQdAKDSCDoAQKURdACASmPCOLBAzOyWwrqSyYG7P7iQmwEqgooOWCw3Jf09Sf9I0r+VdGhmL+R3MrNvmdk7ZuZm9pqZ/d6AfW6Z2bfmf8vAYiPogAXi7t909+clfUnS6wr/Rr8+YL/n3f19kt5y97/m7r8qSWb2QgzBP5H0XUnPnePtAwuJoAMW1w8U1p28amZ/MGSfv8z9/UeS/pO7f1zSH8/z5oBlQdABi+0zkn4u6e8MasLMc/cH7v6b878tYHkQdMACiwNRXtaQJkwA4xF0wIKLFdq4JkwAQxB0wHL4jKS3FJowX7romwGWCUEHLIHYhLmp8G9272LvBlguBB2wJNz9KwpNmI8zPw4ojqADlkicY/eWpOdowgSKIeiA5bMZ/6QJEyiAoAOWTLYJ86LvBavLzGoDXqsP2i+776Dj5o2gAxbT9VFvZpowx4oLRQNlWzez/bje6pGZ7UhqDNivISnt15a0cb63ydMLgIUS+91+R9LV+PfXJP1hWssy5+9KGrSY82vxP9Nvzt81s7+U9IMYkMDM3L0lqWVmLqnr7ttD9uvEKq7n7puD9pk3c/eLuC6ABRR/4x76pQXkmdmRJLn72oh99i4q5CSaLgEAs+nqpPXgjNikeXB+t3MWQQcg62lJz170TWCp9CXJzM70z6XBKe7eOe+byiLoAMjM+mb2SNLfkHTXzB6Z2X+46PvCUujFPwdVdVuSds/xXgYi6IAVZ2Z9SVckWfZlSf/EzP7ZxdwVlkg//nmqojOzDUkdd++fPeR8EXQA8iGXmKR/f873guXTjX/m58o13P1C++YSgg5YYWb2Xy76HrD0UsWWnSy+EE2WCUEHrLYfXfQNYLm5+6mKzsyakvru3ht+1PliHh2w4uIglEFNl5L00N0fO8/7wfLJzqUzs313v3fR95RFRQfg+0Ned0l/+xzvA8urK6kW58yNbLI0s7qZbZjZ1qC1MeeBoANWnLt/TNLvKwTb8cuS/rm7//eLuSssmeORlZmmzDPiSMwNhQnkLUl7salzrljrEoDc/RMSS4Bhar24Da3mzGxd0pa738681lUIvrlOKCfoAACzOpB0MGbO3I5CFZc398f2EHQAgJmMaq7MqOtkzl3S1Dmsg0kfHQDgPJyq9mJTptx97vPtqOgAAOfhnqQNM5NCJVeTdPc8LkzQAQDmLj7BoGNmjfMe7ETTJQDg3BTszysVQQcAqDSCDgBQaQQdAKDSCDoAQKURdACASiPoAACVRtABACqNoAMAVBpBBwCoNIIOAFBpBB0AoNIIOgBApRF0AIBKI+gAAJVG0AEAKo2gAwBUGkEHAKg0gg4AUGkEHQCg0gg6AEClEXQAgEoj6AAAlUbQAQAqjaADAFQaQQcAqDSCDgBQaQQdAKDSCDoAQKURdACASiPoAACVRtABACqNoAMAVBpBBwCoNIIOAFBpBB0AoNIIOgBApRF0AIBKe+9F3wAAFGFm65Ia8a99SQfu3kvvufvBhd0cFpq5+0XfA4AxzGxHJ1/y8/SLkv5c0p+ew7UO3L01bicz25C0I6klqePuHTNrSFqX1FMIvQ13v1v2DcZr1zMv7bp7v+CxNUlbkmrxHF1JrRTOcZ9m3OdA4bOc4e6d6e7++Bp1SRuSmgr/H+or/CyzGnGrSbqdvcdKcHc2NrYF3yS1JXnFtp0Cn3tP0pGkxpD3N+L77Tn8zPckbWX+3pB0KKlW4NiapH1J9czfz3yWeP+jfkalfS6FoBt6zniP9yU1L/r/72VvNF0CS8DnUK0sOjPbUgiCTXfvDtrH3VuxKqqVfO0NhS/825lrdc3sQKG63Bxzih2F++7HY/tmtq1Qhe5LSuetS7qnUGXlbRW4TmniPe7qdAVbCQxGAbBwMs1+PR/fvDm2+XMKTQ1uSuxK2oj3N8q6pMPYbCgpBImkjqR6DOf0+oG7d7KbQtgc90Geo3TtSiHoACyiDYUqbewAE5+xD2uIdYVQy0uV17j+0mEBlX/9TEjHcGwUCPi0/0zVrJmtxz7PbBhXCkEHYBGliqdoRbNd1oULBkdz1Jvufsfd1wZUZKc+15CKbUsFP0+sDI8GvL5lZvtFzqGTQSiK91S5oKOPDsAiKxR02T68ASMlC3H3FC5zGd2aKjWFkZsDP1e8954XHNmpEJyDKs91Da/Mapmm06ZC9Vy5cMsi6ABUTVfFK8GkaLDMYk9SNxOog2xJujPBOZvKhVSsSBuSdgscX61pBEMQdAAWUbYvbGy1YWb1VCUNG6F5keI8yJqkoaNnYzXXL1rNjQi0VK0N+7n1s82T2QEzVUUfHYBFlL6Iiw602CjrwgX7qApXgDHAarHfbtRxzUnOq+GB1lCoHIueqzvhdZcOFR2ARZTmq61rzMCM/OCRKfvo+u6erYz6Gh2yharGtGyZu29mXqtLAweirKtYc2MyLNCOmzPNrDYu8HwFlk4j6AAsnDh5eVPSvplt5UIob0unh+mX0Ud3oMFh2dTgpr8zTY5xyP6pkIvWlZs2kZ1XN4EzFeCA5szCIzirjKADsJDc/SCG3Z6Z9QfNK4sB0c9WRyX10bUk3R9QETWUCdUYcocKwXo783pDYQWUg9g/l9QUVlyZpHI7IxNo+WbLdK1uCuBZrlMVBB2AhRWX+OophF1ToRJKzYoNnW1yLOu63UxFuRmvmVZqyVZIfYWQy4frvkJFuDXg9GUM5U8VYD1NSYivtRR+LusKP6PdGLo7OqlQm2bWjv+9vYiDd8rG0wsALIXUFKiTJwF0JhhwMe016woBkpbkmksoxAptQ+HpBmM/U6wSm+5+J7OqSXYuYVOTDUipNIIOAJaMmd1XCPqV738rgukFALBEMv1zlW9yLAtBBwDLJS1RVullu8rEYBRgCcTBA9MMQV9kuzS9TaUr6R79b8URdMByOK9mql+U9OeS/vQcrrUS6yyWLQZc5Sd5l4nBKACOxcpx3MLDwFKhjw4AUGkEHQCg0gg6AEClEXQAgEoj6AAAlUbQAQAqjaADAFQaQQcAqDSCDgBQaQQdAKDSCDoAQKURdACASiPoAACVRtABACqNoAMAVBpBBwCoNIIOAFBpBB0AoNIIOgBApRF0AIBKI+gAAJVG0AEAKu29F30DAIDVY2Z1SRsTHtZy996k1yLoAAAXoS+pM8UxEyPoAADnzt2nCbqp0EcHAKg0gg4AUGkTBZ2Z1cxsZ143AwCAmd0ys2/lthemPd/YoDOzupntmNmepLakrWkvBgDAKGZ2S9JXJX3G3Z939+cVBqF8w8x+e5pzFqno+pI67r6pc+o4BACsrK9Kek7SP00vuPuvxP/cnOaEY0ddnufIGADAyktTCD5W1gmZXgAAWBiZ6u2Ymb0U//OPpzknoy4BAIvu30h6S9JnpjmYoAMALBwz+7U42vI1SX8h6Rl3fzDNuQg6AMDCcfffjCMu/76kD0i6H0dkToygAwAsLHf/pqRfllST9N1pzkHQAQAWWmyy7Et6fJq5dAQdAGBhmNlfmNk7I5opf2nScxJ0AIBF8rikxyTdHPL+/5j0hFMFnZnVpjkOAIAx/kzSr8e+OUnHy4JdlfSWu//LSU9YaMK4mbXjfzbin20z60vquvv2pBcFAGCIX5b0B2b2C5L+T3ztX0t6XdKdaU5YKOjc/e40JwcAYBJx4MnH42ooLymMttx0969Me06WAAMALJwYbFOHWxaDUQAAlTa3oLt69ern19bWXjMzv3z58ts3btz4XQaxAADmwcxefuaZZx6amZuZP/XUU++a2cvSnILu6aef/tKjR4/+1c7OztWjoyO98sor779+/fo/vnbt2v+ax/UAAKsrBtpvfOITn/grh4eHOjw81IsvvvgeSb9hZi+bu5d9wYak+/fv31ej0Th+vd/v69lnn333wYMHL7v7bqkXBVCKOMKa0dRYKk899dS7L7744nt2dnZOvb69va0vf/nLPo+g22o2mzvtdvvMe7u7u/rCF77wf3/84x9/tdSLAijLS5L+n6RvjtsRWCBbh4eHqtfrp17s9/taW1s7/1GX77zzzvvO+5oACvu2pNcu+iaAMs0j6Drf+c53ft7tdh/LNl1K0te+9rWHjx492nP3z8/hugCAFXT9+vVfb7Valm+63N3d1ZWwVxp2AAAAkUlEQVQrV7z0wSju3v3whz/8lU996lPvdjodSVKv19Pdu3f1k5/85NWjo6PfKfuaAIDV9dOf/vSLu7u72t7eVr/fV7/f1/b2tnZ3d/XGG298sfQ+uuTGjRu/e3R09A/ffvvt90vS008//fvf//73f9Xd+3O5IABgZZnZ3pUrVz77xhtvmCTVajXv9/tfdPfN/w9qNAYZTuEJ2QAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "image/png": {
       "height": 268,
       "width": 442
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#instantiate the rlc_bandstop filter\n",
    "bandstopRLC_s=rlc_series_bandstop(L_value=8.33e-5@u_H, C_value=2.7e-7@u_F, R_value=50@u_Ohm)\n",
    "bandstopRLC_s.lcapy_self()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{s^{2} + 44462229336.1789}{s^{2} + 600240.096038415 s + 44462229336.1789}$$"
      ],
      "text/plain": [
       "           2                              \n",
       "          s  + 44462229336.1789           \n",
       "──────────────────────────────────────────\n",
       " 2                                        \n",
       "s  + 600240.096038415⋅s + 44462229336.1789"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_rlcs_bs=bandstopRLC_s.get_tf(with_values=True, ZPK=False).ratfloat(); H_rlcs_bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 1.0$"
      ],
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_rlcs_bs=np.real(H_rlcs_bs.K.cval); K_rlcs_bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title \n",
      "V1 In 0 DC 0V AC 1V 0.0rad SIN(0V 1V 50Hz 0s 0Hz)\n",
      "L1 Out N_1 8.33e-05H\n",
      "C1 N_1 0 2.7e-07\n",
      "R1 In Out 50Ohm\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reset()\n",
    "#create the nets\n",
    "net_in=Net('In'); net_out=Net('Out'); \n",
    "\n",
    "#create a 1V AC test source and attache to nets\n",
    "vs=SINEV(ac_magnitude=1@u_V); vs['p', 'n']+=net_in, gnd\n",
    "\n",
    "#attaceh term_0 to net_in and term_2 to net_out per scikit-rf convention all \n",
    "#other terminals are grounded\n",
    "bandstopRLC_s.SKiDl(net_in, gnd, net_out, gnd)\n",
    "\n",
    "circ=generate_netlist()\n",
    "print(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Start_freq</th>\n",
       "      <th>Stop_Freq</th>\n",
       "      <th>SamplingInc</th>\n",
       "      <th>StepType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1 Hz</td>\n",
       "      <td>1 GHz</td>\n",
       "      <td>20</td>\n",
       "      <td>decade</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Start_freq Stop_Freq SamplingInc StepType\n",
       "0     0.1 Hz     1 GHz          20   decade"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_responce=qfilter_explorer(circ, 'RLC Series Bandstop Filter Responce');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_responce.symbolic_tf(bandstopRLC_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where again we will invoke the .tf of this function to show that .tf will not give us $K$ reliably only in this case serendipitously. So don't rely on it for finding $K$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf=easy_tf(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>loc</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Output_resistance</th>\n",
       "      <td>(Out-0)</td>\n",
       "      <td>50</td>\n",
       "      <td>[Ohm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Input_resistance</th>\n",
       "      <td>V1</td>\n",
       "      <td>1e+20</td>\n",
       "      <td>[Ohm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC_Vgain</th>\n",
       "      <td>(Out-0)/V1</td>\n",
       "      <td>1</td>\n",
       "      <td>[V/V]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          loc  value  units\n",
       "Output_resistance     (Out-0)     50  [Ohm]\n",
       "Input_resistance           V1  1e+20  [Ohm]\n",
       "DC_Vgain           (Out-0)/V1      1  [V/V]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.dc_voltage_gain(vs, node(net_out), node(gnd))\n",
    "tf.vg_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then getting the .pz results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz=pz_ease(circ, K=K_rlcs_bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pz_sim_control</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tf_type</th>\n",
       "      <td>voltage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acqui_mode</th>\n",
       "      <td>pole-zero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_0_terms+-</th>\n",
       "      <td>[In, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_1_terms+-</th>\n",
       "      <td>[Out, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    value\n",
       "pz_sim_control           \n",
       "tf_type           voltage\n",
       "acqui_mode      pole-zero\n",
       "port_0_terms+-    [In, 0]\n",
       "port_1_terms+-   [Out, 0]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Type</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zero(2)</td>\n",
       "      <td>-210860.69j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>zero(1)</td>\n",
       "      <td>210860.69j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pole(2)</td>\n",
       "      <td>(-86555.51+0j)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pole(1)</td>\n",
       "      <td>(-513684.6+0j)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Type          Values\n",
       "0  zero(2)     -210860.69j\n",
       "1  zero(1)      210860.69j\n",
       "2  pole(2)  (-86555.51+0j)\n",
       "3  pole(1)  (-513684.6+0j)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.pz_def_ports('In', '0', 'Out', '0')\n",
    "pz.pz_mode_set( display_table=True)\n",
    "\n",
    "pz.do_pz_sim(display_table=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where the resulting transfer function the .pz anylsis is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{1.0 s^{2} + 44462229532.9727}{1.0 s^{2} + 600240.1015625 s + 44462230867.489}$$"
      ],
      "text/plain": [
       "              2                            \n",
       "         1.0⋅s  + 44462229532.9727         \n",
       "───────────────────────────────────────────\n",
       "     2                                     \n",
       "1.0⋅s  + 600240.1015625⋅s + 44462230867.489"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pz.get_pz_sym_tf()\n",
    "pz.sym_tf.canonical()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and recaling the anylsitical transfer function as the reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{s^{2} + 44462229336.1789}{s^{2} + 600240.096038415 s + 44462229336.1789}$$"
      ],
      "text/plain": [
       "           2                              \n",
       "          s  + 44462229336.1789           \n",
       "──────────────────────────────────────────\n",
       " 2                                        \n",
       "s  + 600240.096038415⋅s + 44462229336.1789"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_rlcs_bs.ratfloat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then look at the error, wich given the size of the coefficients is small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerator Delta: -196.793739318848\n",
      "Denomator Delta: -0.00552408467046916*s - 1531.31009674072\n"
     ]
    }
   ],
   "source": [
    "#numerator error\n",
    "N_error=H_rlcs_bs.N-pz.sym_tf.canonical().N\n",
    "D_error=H_rlcs_bs.D-pz.sym_tf.canonical().D\n",
    "print(f'Numerator Delta: {N_error}')\n",
    "print(f'Denomator Delta: {D_error}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The so the results for scipy signal the .pz aquared pole-zeros yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': array([1.00000000e+00, 0.00000000e+00, 4.44622295e+10]),\n",
       " 'a': array([1.00000000e+00, 6.00240102e+05, 4.44622309e+10])}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pz.scipy_pzk(); pz.scipy_tfcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_3d_laplce()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iridium/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:491: UserWarning: Trying to get the real imag data from `self.get_sym_freq_resp`, \n",
      "            you get what you get\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_nyquist_with_pz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that because of the larger space of the poles of zero for this system under test the results of the frequency response is concentrated around (0,0) and shows up as a blip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_bode_from_pz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_nichols_from_pz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pole-Zero analysis of a lattice All-Pass filter¶\n",
    "\n",
    "We also know that from the previous section that the lcapy generated transfer function was not correct to the .ac simulation results (properly due to this authors lcapy usage error) Thus let's take a look at what .pz gives us\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance conflict 0.2761423749153966 vs 1.0 in horizontal graph for WWanon2 between nodes (2_2) and (2), due to incompatible sizes\n"
     ]
    },
    {
     "data": {
      "image/png": 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21Z8z3B8uTDLFvy6o6iOqKhm3by37+Eg+UNRrjqq+oKof8U5tny37eEjruRPrrBU86g1ifZFY/3tgEXmYiw64SCci555vKxm5H2BSa/LXouVhGP9CVW8iA5v4mNM4g1fDs+sbqQMUdUJILrjQdgBcJQ1jEgRDmFj4w/2e3+8ACO1A42H8s2i9sK2Qc7+B5cG/1+//mUiIe4nXA3bxEB/PPVz8w2uaMARPGg5FvWWIyBdF5DURUf/7ubKPiTSGkMOeJejAREQf9WltcAe3MYBtj8gfwOxLbxLD+EGIk2L9W6q6E9nIXiXWC3/vji3yhU8tlosI7nKts54VkW8Xkd8Tkdf9nPG6iPxM2cdFsqGotwgR+T0A78Pkc38IwJMiktbtjZBliS1ZU/Hh8u/yh+/EdBR+5ev0APy0L/t6YhNBYINIB6H9+Yz14ogeKSMIN9FxZRFekxnRNxER+XYAvwbgLQAkLAbwPSLyP0o7MDITinpLcAepRzH5cd49BeDbfLoLIesQhPbe/HDvf34MM3V5O4Cvq+oPJDzWY8EO1dhvjrcTrd+N1o1fGwhC3Im6tqWNICS3l0YrRR3A53D/fAFf9piLPqkY9H5vD38F6T/QwEhEPjDjeVIPHgHwhzlt608C+J0l1v9u//unwgKPuu+sYlX1UEQUwGXK60OEHxekvccL60KkfQS7aJgaMk8xmom31Uksi7lKbiuJqo5FBLALhLKMaPL8XBdlXrrhH2Ayl59UBIp6e3i07AMgteNvYOIMtwhfghnIjN0m9gAmmodpXc+iyvUBsue0fxMm6Ccld057qcR9E7IwFPX28J9g1cRZ0fofwab7pFlrkoYTtT0FLKIdw1zeRst0KvMuaP8CFk3f81WP2IUNxV8A2PVoOETBbwbwl/3+j3t0HnMvshaRbkbF/bxjX2iaWpsMnaKLrdeQrRECG54nFYM59ZbgRjWvpT0Fy18+D+BURK49yiItQEQ63uSkB+DIm5eMMMkfL5tHvlDVXVU9SmmU0vN9AcDbUtYL+/olAB/1+386ebx+NynWyaHiIPzxXPi0/yWIeuu7Gvp3YQhroDMA8GOw80MSBXCrqs9v8vjIYlDU28W7cb+a+GUA3+HR0A5sjvDAxX04pyqY1Bj/bE9h0fiduKrqWFX3YVH0UqKeFi2LyIGIXMOivxPfbjChiQn7+nXcr14PhNcEob7KWC9ZQHeD6aK5YGnbBTCeM6++0URFjLew9+PQpwd+DObQ93q0usLer7eXcKhkASjqLUJVH6hqB8C7YK1ZRVXfH/op+8l8pKqhr3QXwLWIHNNNq5GcwiLrLEEbYcW52S4UQxfzbdhQ/GHUVQ2IzFx8dCgY19x4/vwKJsSx+CdF/c4YJlyAJlzmzpLrRds6SDzXKqKRk2tftKeqe9H8fajq86r6BgDPwOoaHqKgVxxV5Y23zBssAjqFXaEfw7o5lX5cvK39uR7AepfPXS9j+XDGa7Zh1e1Zr92GRYXqx9GNH0frDXzZrb8mftyJ1juPvp+d+HG0TteXKUzY43226jsdPnv//4cww5/Sj4u3fG7iHzIhM/FIfQA7IVwAONPsPtmk4ngEndkadc5rh7Dvwp6uWFjpBXXJ6WEXqrqXWO8c94fWp3q0+5D6OaZHFcYAdjTK60fHHXOiVkfQaKKGNvHoxFJFkKQecPidLITakOghgC3YsOgwFNUx714vooYqS+eRXRhP/LayF7qqjmDD+0FUTgDsp6y6j4kv/BhWzDd1IaKT3uchf55aea9WN5LcZ7KyvlGENAhsiP0AJuQ7mlLISJoBI3WyMp4HHcAipBOUP5eYLECIWFV1lhnRvG10YUO4O/zMq4d/PiEyv4CJOaertgBG6mRl1Jpn7MCiJBbV1YelKtp9qHwKj45Di1RSEbz47RwTx74dteI3CnpLoKiTtVHVM8+F7vqiaxE5Dz2vSTVZIm2Std4ZKOqVIJo2eApLq+yozTbgKErLoKiT3FDVK8+778BOLDSzqTZzI3Yfxk3NvXtue8zPtxyCWYyI3GJitRvy5RTzlkJRJ7njRXU0s6kuWYYtafSQ0nUtYipa5+dbPJFZzDXswuxIp3vJkxZDUSeFoffNbHqwofkh8+6lEkR6pqi7QHfmCMUIQFdETpMdzPIWeI9MWzsqkDCL6QDY93w5p5aSOyjqZCN4FLELm6K0jUlRHfPuG0Ynrm7dOe//AJPpZPfwofkBgP8I4Gt+/9Y/1yHWmPKWwTWApb8zkaf5vPWCC96piKiI3Prjg0WeLwrPl5/D8uVh/v0+i99IGpzSRkohxczmRCN7SlIs/v6HCuk9jaxiPcIewAyGMueyi8h2yN36a26RYiCT4zEH85i5hjH+/wXr2S6A7qJT+Pyi4RwZ/8u85/Mg0ZY2TBmlWQyZCyN1UgopZjbHNLPZHC7Gu7ALqkufrTD04d0gnDPNaRLFWCF6LrIxSui5frBA+mYME91DzK4JqBQJs5gBTMi3aBZDFoWiTkrF8+6hqG4EO5GxqG4D+IXVPuzCagQTv8MVq6dDJX1hAuqiFtIBM4e8/XtVNzGPi99CpzTmy8lSUNRJJfCTcNLMJuRnWVRXIEEA/bZqNNjzbRUtpHG0XvuLvsgsJnRK29VEpzRCloGiTipHZGYT8pU0s6kwLq5dbGCY20cQLmB55tpWwnua6RL3zWJa29ed5MPDZR8AIVmEKm0RGcFO4KcicgPL93JYsjosnE8Xke4iwuUV5VkjNK/436NktO6pnLzoehSdZNUe86EAMcwKCP0SmCsnuUFRJ5XHo7MjF/cBzMwmTLfiSbF85ubTE9X2W2H2w4wq9itMuq6l8SSAdwB4BMDP+bK8vwdXc6rfFyKqxD+A/U8rtbwlZBEo6qQ2uHgfwQQ+nCQHIsIOceXSBbLz6R6hjmFC3RORnwfwP2GfX6qoz4vmReSfAzgG8AFV/ccrH3mBuPj3MZm2ybnlpHCYUye1JGFmE3eIW6oDGcmFeVayYb54KP76s9H9lQi+87CLhErVWohI34ftQzTPTmlkYzBSJ7UmyrsHM5tLEaGZzYaIBHVWZN3xUZQTT5tsA/iuOdudlVMPfBnAEwCeFZFfBDBW1dGc1xSGH3NsFrPP1BDZNBR10ghcNA5F5Ah2Yg1WpcxfFksQ9ayh9wGmc+MnAIYAvm/Odufl1AHgJQA/DeA7ATwH4DfmHWzeRMVvB7CRgxHMiY9iTkqBok4aRci7e1FdH5ZzH4I2m0WROT/d3/cDmLFQ4AQmgn9h1kYXndrl9RQHAN6vqp+Zs+685jTL8h64NS7MLIYjQ6R06P1OGo8Pi/ZhAhTEnUV1K+KpjmN/2MOkCC6mCx+GTla4Rx7uWNSPfc6xXGPS6GQcPRdy2uFYrsKxpk1983qMIWzYfzv6v65U9cifPwHwLkymtX0BwHnOU+kIWRmKOmkNiWrkM5jgsHhpw0RCDABb60bPLt49WF/xQnLq0WyLbdh3hxeGpJKw+p20BrdBPYQNB49hZjaXbe7RXQYuhj/rDwc5bPLOOjaHbd3hLVsHIhKaq1xg4vxGQSeVhJE6aS10+No8kRHLEzADmW8C+OS6w9cuvNuw3PZahZEJs5gxWGxJagQjddJaQoc4byITrGhDhzg2kSkA7wx3pKpPwSLfN2F+lfsirB2ti0g30Sltn53SSN2gqBOCOzObHdDMZpOEavG1h+AjM5rusmY0Uae0YGMbOqWx3oLUDoo6IRGed9/DZBrWJTvEFYML8Q2A7Zze34V6rQe8U9o12CmNNAjm1AmZAfOrxeLmNEMA4WJqnW11YPPGARPoe8P6KWYxrKMgjYKiTsgCuBgEcQ82oDSzWRN/X69h72mqEC+5vWPYZzQ1Pz6yEe7DovIzXpyRJsLhd0IWwIvqRp53P8J03p1FdSviF0VBXPOc3tb3KWm9qPitAyt+26Ogk6bCSJ2QFfE8cHCro5nNihRoRvMAwOOgiyBpEYzUCVkRL6rbx7SZzTnNbJYjdHDzh3lG62+DXSTQLIa0Boo6IWvic6+DU90VgKGIXHt1dWfOy4kRpret/Z75aMkNTNT7c1YnpFFQ1AnJicjMZgsWLQ5AM5uFcCG+gOW98xDiQqxjCak6FHVCCmCGmQ3FPZtKmNEQUmco6oQUSGRms+uLrmlmk07CjCaPaH0pMxpCmgBFnZANoKpXibz7aci7l3xoVSNPIR7BovU+R0hIW+CUNkJKIGFmA0x6dLfazCZhRrO7rmVrlhkNIU2FkTohJZAwsxnB5lUz727vwzf8fl7ROuBmNDlsj5BKQ1EnpGS8qG4XVlTXwUTcW5N3j5qrHAP4BV98sO4Fjs9PP4O9r3nMgSek0lDUCakICTMbADhvspmN27gOReQWJrgjmP/7DyHf3HrYFuesk8ZDUSekYkRmNltooJmNiGxHfuxdAEequuMjFqGmIG8zmitYVX0jL5AICVDUCakoc8xsaifu3lzlFBOf98zmKgWY0XB6G2kFFHVCakBkZnMIi25v61JU5yMM5wBOYVPMdtyPfV7zm9z84KM58DSjIY2Gok5IjVDVszqY2Xi+fODFb0PY8HcQ84Waq6jqGfI1o7kb0s9hW4RUEs5TJ6TGeKQe5rvfwOZjl9YrPHE8Y9jc+5WPx3PgxwCCM986xxbPgd9h5zbSRBipE1JjvKjuCFYxfwFg4EV1G827pxS/7YfitzU3fQa7OOiJSHedDXkRXm7+8oRUEYo6IQ0gKqpLmtkU2iHOi9/OMSl+2/Xit3n58oVwIc7bOhagGQ1pKBR1QhpGwswm7hCXW949Mos5xXS+fC1b1wzuRJ1mNITMhqJOSEOJOsQlzWxWKjqLzGKuYYJ4AhPzoyLz075tmtEQsgAslCOkJfhw8wBRERuAs3lNZDw6HsBEsJRiPM+nX2IyJW6txjcicgkbxTgss7CQkLxhpE5ISwh5d1jkPtfMxvPlofitAyt+2y1DBH1Yn2Y0hMyBok5Iy3BxzzSzEZG+F7+d+0t2VHU/r+K3NaAZDSFzoKgT0mIiM5vvBfAeWFT+GQCvANhaxiymaGhGQ8h8mFMnpMWk5Nn/PYA/DuD7UQEzmyQ0oyFkNozUCWkhkVnMLSYFYzuq+mOq+o9w38xmUJF53UWY0YQpboTUHkbqhLQIzx8fwIrNTmDV7zNz5R4dD2DCdwKL3kuLakVk6Mdz4i1qCSEORZ2QFuDCfABgGysKs18QDGBudWEbRZjNzDuObUwc7DhsTkgEh98JaSiJTmkD2HD6ymYxKWY2l+uY2axKAWY0hDQGRuqENIy8O6WVvZ+MfedqRkNIU2CkTkhDEJFuQZ3SUkl0iBvBiupuN9EhrgAzGkIaAUWdkJoTdUq79EU7eXZKm0fCzOYI001kCusQhxzNaAhpChR1QmrKjE5ppRWOubjvwTrEASbup0W4tkVmNOOCLx4IqQ3MqRNSI1ZtylIWiWYwV7BjzS0dICKdqv7vhJQBRZ2QGlC0OBZNdDES8t9hShwFmZAcoagTUmESZjFnMCEsu7HKWlTNzIaQJsGcOiEVxPPl57B8eZi2VYVOaWsTFdXtY7qobi3bV0IIRZ2QyuBmMaH4bYiKFL8VRWRms+uLgpkNW6ESsiIcfiekZMo0cakSfB8IWR+KOiElERW/HcCMVE58mlar8aK6IO4h7z5iUR0h8+HwOyEbJjKLCU1Jdt0spvWCDtyZ2YxKMLMhpPZQ1AnZEDPMYjbe6awuJMxsOpiIO/PuhKTA4XdCCoTzs/Ol7vP1CSkaijohBZAo+rqBCTnFJyfq5qxHyKagqBOSIz4s3Mek+G3UhLnlVYZmNoRMYE6dkBwQkb4Xv537oo12SmszkZnNIVhUR1oORZ2QNfDit98C8O9gOd6tpprFVB1VPUuY2fw6zWxI2+DwOyFLwnxuPaCZDWkjFHVCFoRmMfUkYWYDWGMcmtmQRsLhd0Lm4GYxp5iYxezRLKY+JMxsRgB6sLz7kHl30jQYqROSgVdVHwDYBquqG4Xn2QcwgT+BpU9Y1EhqD0WdkAgO1bYLplRI06CoEwIWVbUdFj+SpkBRJ61GRLqYiDnNYlqOi3sf02Y2HKkhtYGFcqSVRGYxl76IZjEkFNUlzWxuaWZD6gJFnbSKqFPaMaY7pbEAjkwRmdns+aJrETmlmQ2pMhx+J42H+VKSB4kOcWzSQyoJRZ00FrbpJEXAdrqkylDUSePw4dED2EmXc5BJYbBDHKkazKmTxuD58nMAp7Bh9pAvp6CTQoiK6vYx3SGuW/KhkZZCUSe1RkQ6IjLw4rchWPxGSkBVL7yobscXXba1Q5z/Jq9FZFj2sbQRijqpJSKy7SeNa9hQ+0hVt1T1iLlNUhaqeqOqhzBxvwJw6gJ3MOelTaIPs1bmaEUJMKdOagXNYkidSNgOt8LMxkfNtgHs03J38zBSJ7XAO6XFZjG7NIshVSfRIe4I03n3xpnZiEiI0m8o6OVAUSeVJjKLOcV0vvyq5EMjZCm8qG4PVlTXwUTcm5R3D2mGUalH0WI4/E4qR4pZDOcBk8bRNB8FT41dYjLzhL/XEmCkTiqDF78dw4rfugCOVHXHhy95giCNIqWobhiK6vzCtm6EKJ0X4CXCSJ2Ujg8/9mEnhTPYSYG5ctI66mpm46MO1/5wZ91j9pktY/ACYWkYqZPSSJjFAHYy2Kegk7aS0iGuLkV1cZS+rqCHtMRg7aNqIRR1slHcmCIUv9EshpAUog5xu77ouqpmNtG0PcBGF9YliDmbLq0Ah9/JRvCr7zBfdwybq1vboiBCNkni91OpDnGeMjgGEFz11tlWBzaM30EOw/hthJE6KZSU4rdDL36rxAmJkDrgRXVHsKK6CwCDYMVagaK6EFnnFaV3YFE6BX0FKOqkECKzmFA8E8xiaEhByIq4mc2R591HAHqwoflhGXl3j9LzNJuJ29mSFaCok1yhWQwhm8GL6nZRboe4IMJrm81EFwhXLJZdHYo6WRsvfhu6mA9gV9k7HlFwCI2QApnRIa4/63Xr4kV7PViNTB5Rep7Fdq2Fok5WJtEprQcrfqNZDCElEJnZbMFGyY4L7hB3N1S+7u/dLxC6sGF8ivoaUNTJ0ni+PBS/hW5Mu/wxElI+Ie8Oi9xHsKK62zyL6qJqfCCfyDpsizU3a0JRJwsjIn0vfjv3RTSLIaSiuLgHM5vQIe42JzObvM1m+vCprmseV+uhqJOZJMxijmHDels0iyGkPkQd4sI88msROV3FzIZmM9WG5jMklZROaSPwR0dII1inQ5yIDGBukDSbqSCM1MkUkVnMLRJmMRR0QppBSoe4YGYzWCDvnneUTrOZHGGkTgDcVZ8OYFXsJ7AfGXPlhLSERTrERZawN56rX3eft76/PZ5v8oGResvxfPkl7pvF8AdGSIuIiupmmdnkbTbTAc1mcoWReguJCl3iaSScW04IucPFPJwnLgD8ZwCfADBW1a0cth+mxB5yOmx+UNRbRJU7PRFCqkl03vhhAN8CCwCO1txmDzY1NpdhfDKBw+8tQES6iU5pNIshhCyE59VPYIIO5DOX/G4aWw7bIhEU9QYTmcVc+qId75TG/BUhZBlis5l1LWG3MfGMp9lMzjxc9gGQ/EmpYt1nvpwQsgoJs5lco3Sel/KHot4QaBZDCCmIUKV+kYMlbN4XCCQBh99rTopZzBHNYgghOVJUlE6zmQKgqNcU75R2Cit+A8y8YY/Fb4SQvPBU3jasSj2PWhz2TC8YDr/XDP+RhR/aGeiXTAgpjiLMZr7AYt3i4Dz1GkCzGELIponmkudtNvMqgC+BPhmFwOH3CuP58iFsiP0AJuQ7qnpEQSeEFEyI0tcWXr9A2IaZXr0T5lC3TBMZsiCM1CtIij3jiMNVhJBN4XPJQ73OVg5z089hc9OPVHUULZ/bRIYsByP1CuHFbzSLIYSUTVFmM1NR/4JNZMgSUNQrgHdKu8b9Tmm8YiWEbJQCzWYyLxBU9UJV92D93QHgUkTOfdieLAGH30uCZjGEkCoiIgMAQ5jZzN6a2+rAPDSAJWbqJJpPjWEpSBbVLQAj9Q0TmcWE5io0iyGEVImiovSFRx5V9cY7we34cQxE5FZEhiyqmw1FfUMkzGI6MD92msUQQipDgWYzK3VjU9VxlHc/wnTefTuH42scFPWC8Xz5OSxfPoYNQe2z+I0QUkGKMJu5yON85+K+Byuq68DE/ZR592mYUy+AhFlMmKpBsxhCSGUp0GzmsIgRSY/UB7ALkStYTVLrRz4ZqedIhlnMFs1iCCE1oBCzmaKE1vPuh7C8+xWAoZvZHLQ5785IPQeiK0aaxRBCasemzGaKhmY2jNTXIjKLCT+GXZrFEEJqyEbMZoomKqo7REuL6ijqKzDDLOaq5EMjhJClKMNspmhU9cyL6nZ90XVbzGw4/L4gkVlMnHfi3HJCSK2pgtlM0bTJzIaiPodEheUN2C6QENIgoir1tVOHXig8gJ0nD/M4vjxpQxtrinoGPkzTh334Z7AvKXPlhJDGEE1ju/Fc9Lrbu4UVqVW+tsiL6oLZTmOK6pYSdRF5HMBPquoHizqgsvEPug8r9Ajzy2v/QRNC6o2I9GHFX8CkN/kVbNh85UjTRyO3c4jSDwAcI4dh/E3iFzYDTM75Z5u8IAm6mlh8pKovrLK9uYVyIvKkiLwoIi8D+CKAJ1bZUZURkU5U/DaE/VC22CmNEFImfm4aegR87IuvYOJzAYuKj11QV8Lne+chYqFAbiVL2LJI6RB37kV1/Vmvy4NI0J9R1Q96wDwG8Msi8txK25wXqftO+6r6rIi8COAJVZVVdlY12lQ8QQipFy4qxzDhHmFG7tdFfayqpQhq3sP4ZZLWQbMoXQiaCuBfq+qPRMsVwKuq+pZltzk3UlfVB6r67LIbrjIpndIOQ6e0kg+NEEKCSJ/CBP1wniuln7v6wdWyhKlbd9PYNrzf3PEmMpvqEBc+0/fmtcFWzVOfYRZTq+EiQkhzEZEuJkPty/imd+BdIDecEy7NbKZIEh3igpnNbZ5mNqr6tKqKqj4dlonIh/3uf1tlm60Qdc+XX4JmMYSQ6jP0v6sM+17BWpRuktLNZoomMrMJBYBFdoj7VwBeBfDMKi9etvq9Njl1msUQQuqGR+mXWDI37ee7a9jo48aKexOe8ZUxmymavP1LROSjAD4E4N0AfhfA06r6YKVtNU3UE8VvNIshjSWafhm4QqKYKvo9hKlQY9iUnamUk0ccoVUwYL+dqemckXFHHJ2c+faS+xzApl2FfdI+rbM1AAALjklEQVTnYQEid7el2pX6667KeI+9oG97k41bqkLewaOIPAng0wDeDLtAe7D0RlR14RuAF+0li79mUzfYieYYgMKqMHtlHxNvvBVxgwnrrX/Xk7dbAAe+3jBjHcWkSLTr97PWO150n7ALgvMZ2zoH0C37/avyLfrMtss+Ft6W/uwO/Ld065/jSp8hgMcBvAbg91d6/ZI7q5yow66QwonkmD8G3pp8cxGORTIIeA9WMxI/p9HvouuiG048QYzD/eDR0IFF2MeRiJ8nttv3ffZTngvbPfbtdHy7YT+XsEKu0t/LKt78c9Syj4O3tT/DWJOWvpAF8Iq//rmlX7vkjioh6omTU7gq4omCt0bfXJiD0B5nrHOQiKBTTygurmG9VKFN7E8BDDK2NZy3T//NXs7aH29379V1uHBaYF0GMRW9RRfHmaPHAH4fwB8CeDyxPIj6i8vut1bV78FdCfalH8DmEO7onDmchNQdz92dw8RxVrOM+HfQSTzOWg8Zv5/kskV/Y/fW8+3vwfL+XdioAknnEDY3OnNOdHQu7ESPByXNUScpqDn1HcLmu18BOBWR64T736MA3gjgsYzN/OoqO17myuNF2NXD48tePeR8xbPQVSxvvDXlBruIVQDnc9YL0XAY7r4X0fvvKTn8nhZFhAj8bpsZ+7xOrDeccXxx9M8oM/t96vn72U8sD6OUd6OTvuw2vJ+wtMgpOBpSqZt/TgNMjzD/LwAfTaz3uP8+VsqpL1T9LiKv+N0ObG77qwC+CeA3tcDmLlFFbh8lGO0TUhWi9pj7mmGWFE2HGgPYhZ08xrDRrLg6/Rj2uwrVyqmtMqOOW7swkbi3f698PoVVyx9iAavQeP9qzl0kBY/UQxOXePRjqptY9LnvqntveBSP5PsrIl2lP0fpRDNXvhvA6wBewCQq/xhMX4uvft/gFc0B7Eu6VhUhb7w14QYvnkJGpBytF0azhv44FM4NonVCVKcwkQ5RuyKK7DDJzV/64zBScJrYZygICgV7l/HjjOOMRwoYTRbznRkiZVTHvyPXsz4f3jb6OcUFrl8F8N8BfHidbVYmpx7lhEK+/AKTfHkrDA0IySDk4DLnLUdzyOP1QkQd5/D6MGG/UMv53UTrDxLrxds6gUWL/WCRmbAHPUusn9nhyvcZOowV3gmrbUQRflptwyGstmHb87tF+JmTBVHrELcPy7v/AoD3Afi763TdK13UQwMC+NUjbEiOxW+ETEgKbBrBXOYsXASrDZPfwE7gwQgmFFHF2woprW60rAfr+nXi24qFO2wjHFdsPpNcJ4tF1yNL4J9zKKhMNYPxi7kjWFrlBtZqdCE/cz9f96PHvCDIAZ0U1W3BiuqGoahu2fe4MFEXkc+JyB+JiIrIayLyRW/jGp7vJjql7Ss7pREyRXSivZlzkRvEMTmqFR5vJ/7e5VV1kiPv+j57yXUyttVJLA/if+HbiS8SkoRtUxRyQET6fj49hl0w7WiUO3dxmIr+dNKwZBf2eRwv4Gc+BtDxi4BbWIOTXJqbkEmHOFXdgneIg/nMT42oiMj3icj/c31V19p/ChQk6iLyRQBPAniDL3oINqzwFf/yncNyb4B9+faUBXCEpHEn6nPWCz/4pBCHx+FE3QXuhsBjbmAn62A+M2tb3cTfeeJ/j0hwGKmviAdGw1AUB+DIz6VpfdcvYCM2l57mnLqYcnHfg43g9EXkPG0IOFwIwJrGhOmVTI8WgGZ3iPuHAP4DgIej1d8A4F+KyHHuou7etX8eQNIfXvwgPoPpTmn8QhCSTVbUnCRL/MPjThQ5p11Ah/W6mPZsjwnHsB2tm3Zs48TzWYRKbUZ6SxLVUAzgboFByOPh8UA05L4Ht/P1C4LtxHoXPgx8CLsIGCa35SzcP12s5TVHZFZEJx3idn3RJ3FfXwN//+GMJ9ZhMOM5gVX5AcCBSGX7wpD5PAJzQiLF8qT/zTKnmELvT1eKo+ZZJ9Yr3I+ap7alqmP/zU4Nv2dEhbPOA4E3+9+Pi8hXF1ifTDMG8OMAngDwSREZAXgA4EspqY+3AvhG9PgCwHcA+BUR+W0Avwbgd9J2kiLs3wLgB31f/bSLiASPAfi3IvJNAC8ljoMsx7w6MylC1OddkZVenEdIjXjgf79Q5kEUxBXsBP/1sg+kxnwDwC8C+GWYuAM2NWoR/qvfHgPwlC97aYHXvwfAm3zdRfgq7DP+QQDfCbuA+NISx0mWoAhR/xSAv4j04QEF8CtKwwlCCkFEtjNSWvOu8NOGyrOGxVfZ1j1U9SOLrEc2Q6JH+CiraNmnHd+o6jNLbDtE+yNMCsA6sHaxLI5eAhH5J5jUqyXR3KNmVf0sJuYW954G8AN575MQckdSiO+q4qNi1DSxDiNssWBPrZdSFZ9V5Z62LVJxomlVu8gYcfXiuW1kTJfLeM2Uh0JU4X3oz1+uMnWrxXwsY7kC+HxRQ+G7AF5OLPsDAH9VV7G9I4TMI6uaPFlAF+at3wmxR2hd2Lz0K0wK6ZLbCq+5SfxNrhdyrJzRUkNcdLNEuw+7QFwmuj5ARqW8b2cPFrmfr3K8bUNVPw4rUIwDZwXweVV9qhBRV9UHqvp+VRUAPwrgXar6qKq+UMT+CCF3Zi79EPG4WAdnsbPketFrD+LnPKIfA+gmovCkWF8klodofht24qfHeIPwz7aHBSreE4TvV5YZTvh+8iJwQXzm2EMAngHwz1T1IVV9CthA0ZqqPsvonJBicQG9gQlqMKoI7U3THN8GPtWoi4SoO+HEfRy1+ezCxDqI/xkm4h/2OUzZFmkGA9jnvbCoR8P1Z8koPWUqI0V9SVT1eY/c71ioSxshpPpE3bpi0rq0DXF/ytlUlzaZ9G9P5sunjKI8eksOm175esyp1xx3qRvBhs8vsWRnPRG5hH2HLvy1oQ5jACuSC4+HLKDOB04vI6QheLS+j0mu+wIp4uonz3godARzCIvXGcMMSIKA38CsnC8S612k7POQgt4YRrCRnHDhtvAIjF/wBUHfB9Bzp7pzAL3Ed4mpmpxgpE4IIWQuUQ/ws0UK5YJ4wy7yTnxZiPiPop4DJEco6oQQQhbGxf1s1mhMlAq6cf/y+LlTbzdKCqAI8xlCCCENZYYpTWwbnFrx7uswQi8QRuqEEELWxgU7NJjpwXwPtso9qvZBUSeEEJIbkbj3Yfl0TlXbIBR1QiqIiHTScpZZywmpImGeOqerbQ6KOiEVJDLtiPtWj7G8RWchhGhs3snaDUbC/xLc7U7g/8e85wv8FwhpJBR1QiqKC+ctgAtV3avA8QQB7sDypl23gl7ktcGkJvV/mfc8IWQxaD5DSHVJdkUrmzFMdGNTGkJIheCUNkKqS7BorYSAei6/EsdCCEmHkToh1aUH3FmxEkLIXCjqhFQQz6cH32xCCFkIDr8TUk1yz6dHFfVLkfN0pK57gifp5LgPQloLRZ2QarJUPj0S7G3YhcBJynz20HN9GfKeE381p/qdELIGFHVCqkkXyM6nxz7b3h/9rnOW98A+B7Abv8ZbsxJCGgxz6oRUkx5mR+lhvni4Hw+rj2DD3P2Cjo0QUlEYqRNSMXwoGpidT++oahhKv8D0sHoYMp/KU6+YUx+r6mj+aoSQKkBRJ6R6BFHPGnofIBLxlN7UwW412eKyCjl1QkiBUNQJqR6Z89M9f34AYCfthW7lOgCwlyyUKyKnzgYzhFQLijohFcDF+NgfdgGMU6Z+dWFD6mmV7aF4bghgv6iiuOiYQnX+uYiMYVXt96a+iUjXjykM+4cpbVeqehQ9H1IFvfj5Iv4HQpoMG7oQ0gCiHtYjr4jfhrW8pHkNIS2C1e+E1ByPdo9hOfiuF9odlHtUhJAyYKROSM0RkVukOLIt2haVENIcKOqEEEJIQ+DwOyGEENIQKOqEEEJIQ6CoE0IIIQ2Bok4IIYQ0BIo6IYQQ0hAo6oQQQkhDoKgTQgghDeH/A3OvIjQ+sK2VAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "image/png": {
       "height": 236,
       "width": 501
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#instatate the all-pass lattice filter to \n",
    "allpasslat_lf=lc_balanced_allpass_lowfreq_lattice_filt()\n",
    "allpasslat_lf.lcapy_self()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{1 - s^{2}}{s^{2} + 1}$$"
      ],
      "text/plain": [
       "     2\n",
       "1 - s \n",
       "──────\n",
       " 2    \n",
       "s  + 1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get this filters abstract transfer function\n",
    "allpasslat_lf.get_tf(with_values=True, ZPK=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title \n",
      "V1 In 0 DC 0V AC 1V 0.0rad SIN(0V 1V 50Hz 0s 0Hz)\n",
      "Rdummy 0 N_1 0\n",
      "Rdummy_1 N_2 0 0\n",
      "L1 In Out 1H\n",
      "L2 N_1 N_2 1H\n",
      "C1 Out N_1 1\n",
      "C2 In N_2 1\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No errors or warnings found during netlist generation.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reset()\n",
    "#create the nets; the last one is needed to deal with singularity issues when dealing with lattice circuits and ground\n",
    "net_in=Net('In'); net_out=Net('Out'); net_outlower=Net('Out2')\n",
    "#create a 1V AC test source and attache to nets\n",
    "vs=SINEV(ac_magnitude=1@u_V); vs['p', 'n']+=net_in, gnd\n",
    "#net_in+=dummy_1[2]\n",
    "\n",
    "#attaceh term_0 to net_in and term_2 to net_out per scikit-rf convention all \n",
    "#other terminals are grounded\n",
    "#but need to add dummy resistors to deal with singular issues and get solvable matric\n",
    "dummy_botin=R(value=0, ref='dummy')\n",
    "dummy_botin[1]+=gnd\n",
    "\n",
    "dummy_botout=R(value=0, ref='dummy')\n",
    "dummy_botout[2]+=gnd\n",
    "allpasslat_lf.SKiDl(net_in, dummy_botin[2], net_out, dummy_botout[1])\n",
    "\n",
    "circ=generate_netlist()\n",
    "print(circ)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Start_freq</th>\n",
       "      <th>Stop_Freq</th>\n",
       "      <th>SamplingInc</th>\n",
       "      <th>StepType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1 Hz</td>\n",
       "      <td>1 GHz</td>\n",
       "      <td>20</td>\n",
       "      <td>decade</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Start_freq Stop_Freq SamplingInc StepType\n",
       "0     0.1 Hz     1 GHz          20   decade"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_responce=qfilter_explorer(circ, 'LC All-Pass Low Frequcy Lattice Filter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_responce.symbolic_tf(allpasslat_lf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz=pz_ease(circ, K=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pz_sim_control</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tf_type</th>\n",
       "      <td>voltage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acqui_mode</th>\n",
       "      <td>pole-zero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_0_terms+-</th>\n",
       "      <td>[In, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_1_terms+-</th>\n",
       "      <td>[Out, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    value\n",
       "pz_sim_control           \n",
       "tf_type           voltage\n",
       "acqui_mode      pole-zero\n",
       "port_0_terms+-    [In, 0]\n",
       "port_1_terms+-   [Out, 0]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: Pole-zero iteration limit reached; giving up after 282 trials\n"
     ]
    },
    {
     "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>Type</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zero(3)</td>\n",
       "      <td>(-0.002000004+0j)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>zero(2)</td>\n",
       "      <td>(-499.999-500j)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>zero(1)</td>\n",
       "      <td>(-499.999+500j)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pole(4)</td>\n",
       "      <td>(-0.000499999-0.9999994j)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pole(3)</td>\n",
       "      <td>(-0.000499999+0.9999994j)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pole(2)</td>\n",
       "      <td>(-0.002000004+0j)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pole(1)</td>\n",
       "      <td>(-999.999+0j)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Type                     Values\n",
       "0  zero(3)          (-0.002000004+0j)\n",
       "1  zero(2)            (-499.999-500j)\n",
       "2  zero(1)            (-499.999+500j)\n",
       "3  pole(4)  (-0.000499999-0.9999994j)\n",
       "4  pole(3)  (-0.000499999+0.9999994j)\n",
       "5  pole(2)          (-0.002000004+0j)\n",
       "6  pole(1)              (-999.999+0j)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.pz_def_ports('In', '0', 'Out', '0')\n",
    "pz.pz_mode_set( display_table=True)\n",
    "\n",
    "pz.do_pz_sim(display_table=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': array([1.00000000e+00, 9.99999986e+02, 5.00000993e+05, 1.00000001e+03]),\n",
       " 'a': array([1.00000000e+00, 1.00000202e+03, 4.00000013e+00, 1.00000208e+03,\n",
       "        2.00000022e+00])}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pz.scipy_pzk(); pz.scipy_tfcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{1.0 s^{2} + 999.997985839844 s + 499998.992920936}{1.0 s^{3} + 1000.00002343545 s^{2} + 1.99999603336759 s + 999.998081344856}$$"
      ],
      "text/plain": [
       "                2                                                   \n",
       "           1.0⋅s  + 999.997985839844⋅s + 499998.992920936           \n",
       "────────────────────────────────────────────────────────────────────\n",
       "     3                     2                                        \n",
       "1.0⋅s  + 1000.00002343545⋅s  + 1.99999603336759⋅s + 999.998081344856"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pz.get_pz_sym_tf()\n",
    "H=pz.sym_tf; H.canonical()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_3d_laplce()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iridium/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:491: UserWarning: Trying to get the real imag data from `self.get_sym_freq_resp`, \n",
      "            you get what you get\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_nyquist_with_pz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where now looking at the Bode and Nichols charts generated from the .pz analysis we see they are almost identical to what was gotten to the .ac analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_bode_from_pz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pz.plot_nichols_from_pz()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add the Razavi T-coil after the previous magnetic section is done¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Citations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1] T. Kuphardt, Lessons In Industrial Instrumentation :, Version 2.30. [online], 2018, Chapter 5 Section \"Transfer Function Analysis\". Available: https://control.com/textbook/ac-electricity/transfer-function-analysis/. [Accessed: 10- Jan- 2021]."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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