Test_Plotting_jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Status of DC/DC converter for MOPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-01-03 12:21:06,050-[Plotting]- INFO    Plotting initialized\n",
      "2020-01-03 12:21:07,054-[tornado.application]- ERROR   Exception in callback functools.partial(<function Kernel.enter_eventloop.<locals>.advance_eventloop at 0x7fa374391f80>)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/dcs/miniconda3/lib/python3.7/site-packages/tornado/ioloop.py\", line 743, in _run_callback\n",
      "    ret = callback()\n",
      "  File \"/home/dcs/miniconda3/lib/python3.7/site-packages/ipykernel/kernelbase.py\", line 310, in advance_eventloop\n",
      "    eventloop(self)\n",
      "TypeError: 'NoneType' object is not callable\n"
     ]
    }
   ],
   "source": [
    "from analysis import analysis\n",
    "from analysis import plotting\n",
    "import numpy as np\n",
    "import logging\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "logging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(name)s - [%(levelname)-8s] (%(threadName)-10s) %(message)s\")\n",
    "logger = logging.getLogger(__name__)\n",
    "import pandas as pd\n",
    "\n",
    "def voltagesupply(pf= 4*5, fl = 1.2, Is=np.arange(0.1,20,0.1),Rc=4):\n",
    "    '''\n",
    "    The function will print the needed voltage supply from the power supply.\n",
    "    Pf is FPGA power\n",
    "    eff is the converter efficiency\n",
    "    Is =array of all the possible currents in a specific range \n",
    "    Rc is the cable resistance as calculated from AWG\n",
    "    '''\n",
    "    # calculate the proposed power voltage\n",
    "    Us =  (pf * fl/Is + Rc * Is)\n",
    "    # calculate the transfered efficiency\n",
    "    Ps= np.multiply(Is,Us)\n",
    "    Trans_eff=[pf*100/Ps[i] for i in range(len(Ps))]\n",
    "    return Us, Trans_eff\n",
    "\n",
    "def cablepowerloss(Is=np.arange(0.1,20,0.1),Rc=4):\n",
    "    '''\n",
    "  Calculate the cable power loss \n",
    "    Is =array of all the possible currents from the power supply in a specific range \n",
    "    Rc is the cable resistance as calculated from AWG\n",
    "    '''\n",
    "    # calculate the cable voltage drop\n",
    "    Uc=[Is[i]*Rc for i in range(len(Is))]\n",
    "    # calculate the cable loss power \n",
    "    Pc = np.multiply(Uc,Is)\n",
    "    return Pc\n",
    "\n",
    "def cableresistanceTemprature(delta_T=np.arange(10,50,1),Rc=4,alpha=.0039):\n",
    "    '''\n",
    "    Calculate the cable power loss \n",
    "    T =array of all the possible Temperatures\n",
    "    Rc is the cable resistance as calculated from AWG\n",
    "    alpha: Temperature coefficient of resistance\n",
    "    '''\n",
    "    return [Rc*(1+alpha*delta_T[i]) for i in range(len(delta_T))]\n",
    "\n",
    "#if __name__ == '__main__': \n",
    "Directory = \"DCConverter/\"\n",
    "PdfPages = PdfPages('output_data/DCConverter' + '.pdf')\n",
    "p =plotting.Plotting()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage I calculations [USA/US15 TO PP3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Is         Us  efficiency       Pc\n",
      "145  1.95  19.455012   52.718602  15.2100\n",
      "146  1.96  19.435547   52.502157  15.3664\n",
      "147  1.97  19.416687   52.286389  15.5236\n",
      "148  1.98  19.398421   52.071302  15.6816\n",
      "149  1.99  19.380740   51.856901  15.8404\n",
      "150  2.00  19.363636   51.643192  16.0000\n",
      "151  2.01  19.347101   51.430180  16.1604\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rc=4 # Cable resistance back and forth\n",
    "pf=4*5 # FPGA power\n",
    "fl=1/0.88 # DC/DC converter factor\n",
    "\n",
    "Is=np.arange(0.5,5,0.01) # all possible currents provided from the source\n",
    "\n",
    "Us, Trans_eff = voltagesupply(pf=pf,fl=fl,Is=Is,Rc=Rc) # gives source potential and Transmited power (PF/PS *100)\n",
    "Pc = cablepowerloss(Is=Is,Rc=Rc) # Power Loss in the cables\n",
    "\n",
    "DataFrame = pd.DataFrame({\"Is\":Is, \"Us\":Us,\"efficiency\":Trans_eff,\"Pc\":Pc})  \n",
    "print(DataFrame[145:152])\n",
    "\n",
    "\n",
    "p.plot_linear(Directory=Directory , PdfPages=PdfPages, x=Is,x_label=r'Delivered current $I_s$ [A]',y=Us,y_label=\"Output Voltage $U_S$ [V]\",\n",
    "               map=True,z=Trans_eff,z_label=\"Transferred Efficiency $P_S/P_F$ [%]\",\n",
    "              text=True,txt =\"Needed power = %0.1f W \\n $R_c$ = %i ohm\"%(pf,Rc), \n",
    "              test= \"Power_supply_voltage\",p = [pf, fl,Rc],\n",
    "              title=\"Power supply voltage needed [For a DC converter of %i$\\%%$ efficiency]\"%(1/fl*100),\n",
    "              line= True, data_line=Pc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Stage II calculations [PP3 To PP0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 source Voltage Us = 3.20 V for 2 Mops to achieve 2.00 V per Mops\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'endStream'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/matplotlib/backends/backend_pdf.py\u001b[0m in \u001b[0;36mprint_pdf\u001b[0;34m(self, filename, dpi, bbox_inches_restore, metadata, **kwargs)\u001b[0m\n\u001b[1;32m   2497\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2498\u001b[0;31m             \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewPage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2499\u001b[0m             renderer = MixedModeRenderer(\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'newPage'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-c960e19f71f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mUm2\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mUs_space\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mUc1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mLimit\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mUm2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mUm1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mUs_space\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUm2\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mz1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mDirectory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDirectory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPdfPages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPdfPages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPdfPages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPdfPages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/git/MyWorkSpace/analysis/plotting.py\u001b[0m in \u001b[0;36mplot_lines\u001b[0;34m(self, x1, y1, z1, Directory, PdfPages)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDirectory\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"output/MopsVoltageDrop.png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0mPdfPages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPdfPages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/matplotlib/backends/backend_pdf.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, figure, **kwargs)\u001b[0m\n\u001b[1;32m   2443\u001b[0m             \u001b[0morig_canvas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2444\u001b[0m             \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFigureCanvasPdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2445\u001b[0;31m             \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pdf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2446\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2447\u001b[0m             \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morig_canvas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2178\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2180\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2182\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2082\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2083\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/matplotlib/backends/backend_pdf.py\u001b[0m in \u001b[0;36mprint_pdf\u001b[0;34m(self, filename, dpi, bbox_inches_restore, metadata, **kwargs)\u001b[0m\n\u001b[1;32m   2507\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2508\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPdfPages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# finish off this page\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2509\u001b[0;31m                 \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendStream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2510\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m            \u001b[0;31m# we opened the file above; now finish it off\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2511\u001b[0m                 \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'endStream'"
     ]
    },
    {
     "data": {
      "image/png": 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dEGEaHojRtFyWXIwxaVMe3URZ9YYdyyM6HUun/EFAiI55/RjVxe5dyVbW55JtunULOgJjAPh84xMUr5sBwMhOxzOu+3TyIu2Y0u8BYk414ZB9/KRCy3rRprTheWpKyyqwIfdNerz8ctARGIPjxChedw8xqohRxeebHqciumXH45ZYsp8ll2xzwglBR2AMECIUiutPCYUIWf9Kq2LJJdssXhx0BKYVqo5Vsaq8hIpoOQChUIj9el5DTqiQSCiPid1/Q16kbcBRmuZkdVNjTJNsr97CbYt+R2l0K5FQDucOuZaeBUX0a7cfJw1+DcAm9GqFrOaSbZ59NugITCszb8MctlZvojJWQVl0O2+s3vkeDIVCllhaKUsu2cZmojTNrCBSSNj7KIkQocCavwyWXLLPk08GHYHJcusr1rCy7Bscxx1wcs/OBzCs/W7khHLp02YgP+55bMARmkxgfS7GmKTNWfMSs1Y9TogwwzvswbT+F5ETzuGMgTaPkPkuSy7Z5g9/CDoCk8VeWf0k1d5cK19s+Zj1lavplt8z4KhMY4lIAXAbcCBQDsxV1TP9KNuSS7YpKAg6ApPF8sOFVMYqAHBwyA/b+62Fuxk3qQxTVUdEdvGrYEsu2ebyy+Hgg4OOwmSBimg5K8qW0iW/Bx1zuwBw+oDf8Oiy26mIVTCl96m0z+0UcJSmHkUiUnvdJlXdMTaMiLQDpgFFquoAqOpqvwKw5GKM+Z7t1Vu4ddGllEfLiDkxfjHoMga3G0n/tkO5YuQ9QYdnEnurjnXXAFfHLQ8G1gNXicgPgW3A71X1bT8CsKvFss2UKUFHYLLAp5vfp7R6GxWxMqqcCl5f/UzQIZnU7AsMrPVze61tcoBBwMequidwKfCMiHTwI4CENRcR6ZJEObH46lZjiMgRwHVACDfpXa2q9o5O1fTpQUdgskD7nE7uzY+Oe+9Kh9yuQYfU6i3e2p3c/MIGt6naWgYsBChR1aUJilwGVAMzAVT1fRFZBwwDPmxqvMnUXFZ4Bypu4OfTpgQhIiHgEeBUVd0DOAV4SESsZpWqQw8NOgLTAq0oW8aXWxdQHasGYNcOezKxy2TaRjowsN0Ijux9asARGr+p6jrgv8BBACIyDOgB+DJAYTJ9LgtVdUxDG4jIxz7EEgM6en93AlaqasyHco0xDZi95kVeWfUk4VCY7vm9OW/odURCORzZ5zSO7HNa0OGZ9DobuF9E/gxU4X7B92VCmGSSyySftqmXdwncccDzIrIdaA8cnkoZCxYsYPVq3y50aJLi4uLAjj24XTu+SsPxgzyndLLzgv9E/klVqBIcWFW6nNc/epmu9EpjdI3T0l6rtWvXBh1CQqr6NXBAOspOmFxUtRxARC4CnlLVkvq2aSwRyQF+Bxylqu+IyN7AEyIyUlW3JVPGqFGjKCoqakoYviguLmbcuHHBBTB7Nn4fPfBzShM7L9ds7c6q8uU4OITCIcaPnEjnvMya0bQlvlYlJd/7qGxVUunT6AC8IiJvici5ft5sA+wB9FbVdwC839uBET4eo3WYOjXoCEwG21Zdytx1n7J0+4od604b8GuK2gymc153ju17VsYlFtMyJX2fi6peA1wjIrsBxwNzRKREVQ/0IY4S3Jt+RFVVREYAPYGvfCi7dWnl35ZM/bZWbWd68Q2URSuIOTEuGHYS+/cYR7f8nlww9PqgwzNZpjFXY60BVuHefNPDjyBUdRVwDvC0iHwCPA6coaob/CjfGAPFG7+gNFpOWbSCilgV/yx5PeiQTBZLuuYiIufg1li6A08Dv1TVz/0KRFUfAx7zq7xWa9asoCMwGapHfme8UfLJCUXoXejLd0OTZUQkmVtL1qrq5IY2SGX4l/7Ahao6P4V9THObMQOuuiroKEwG+Hzz16yv3MTYziNom1PIyI6DOGPgkby4Yg592/Tk3CHHBR2iyUwR4LAGHg8BLyQqJJU+l8uS3dYE6MUXLbkYnln+Oo8um0WIEO1z23DvnpdTGMlnSp/9mNJnv6DDM5ntLFVd1tAGIpJwKJCEfS4i8pEf2xhjms+LK96kIlZJeayCbdVlLNz8ddAhmZZjooj0bWiDZAa3TKbmMiJBG1yInXfWm6D98Y9BR2AyQJ/CHqyv3EzUiRF1ouxSYGODmaT1Ad4VkSW444495Q0Vk5JkksvwJLaJpnpgkyblTbqf1bRAm6u28cH6BXTP78wend05PC4ZPo27vnyC1eUbOL7fQfRpY533JjmqepGIXAzsB5wAXOldxTsTeFZVtyZTTjJ36DfY9mYyzLXXwpFHBh2FaSZbq0qZ/qF770oIOLH/IQykMx3z2vP7XX8RdHjGRyu3diCU27bBbZytub4cy5s8bA7u/Yy/wp0G+UbgL0CbZMqwUYeNacEWbllCRazK61+pZNbKd4IOyWQRERkNXAvcA1QClye7r81EmW2Os8tLW5Pehd2IOW6rdE4owoC2vd0Z0Y1pJBEZitscdiJul8fjwMHeIJdJSzm5iMjrwMd4c7mo6qJUyzBpdOKJQUdg0ujtNV9Ssn0DB/QcTs/CjhS12YXfjjidp5e/Rq+Cbpw15Bj0k4VBh2latldw+1eOV9X/NbaQxtRcXsedGjMMnCIiQ1XVPtEyxdFHw4dNnkTOZKCHFr/DjEVvEHMc7tLXef6A8+hW0J6JXUczsevooMMzWUJVB/lRTsp9Lqr6R9zpiCfiTiRmicWYZvB8yceURauoiFUTdWIUb7BrbYz/ROQlP7ZpTLPYEcBo3JkjfyIiT6qqXYqcKYYMCToCkyYjO/Zm+fYNVMSqiTkOg9p1Dzokk532EZGGhncJASMTFdKYZrF7gHeAF3H7XCyxZJLHHw86AuODteVbmfXtp3TJb8dhfUYTDoW5YvQRtMst4Outazh50CSGdvBzSiVjdjgqiW0qE22QcnJR1f4iUgSMw/pcMs8hh8DLLwcdhWmCbVXlHDtnBluryomEw3y8YRlX7nYkhTl5XDaqofEEjWk6VZ3jRzkJk4uIFNSextib6rgEeN6PIIyP1qU8SoPJMLplFRWxaqqcKFXRKK+t/Jwrd7MbY03LkkzNZaWIrAE+jft5yybyMiY9BrTrRsybeCU3FGHXjn0CjsiY1CW8WkxVOwOHAI96q04BFojITBGxASszzezZQUdgUuA4Dv9Z8Rn36hvo5lUAdM1vx98mnc6BvUZybP89uXncsQFHaVo7EensTXGftKT6XFR1CbAErxlMRELAFcDtwBkpxmnS6frr4YYbgo7CJOmxJXO564vXKY9W8eBX7/D4vmczsH13Rncu4tY9Twg6PNOKichs4EjcPDEfWCsic1T14mT2b9TYYqrqqOr/4d7rYjLJq68GHYFJwasrPqcsWoUDOGD3rphM0lFVtwA/AR5Q1XG4A1gmJZkO/Yvx+lpUdU3c+nygIPV4jTE1xncbyMLNKymPVQHuvSzG1GXr5gIIJRiQeHOUfP8OmSMivYDjcFuqUts5iW164mar0SKSi5tovgLGA/9M9YAmzW69NegITD1WbN/Co/oxHfPyOW34OApycjlHfkj73AIWbl7JUX33YGQnSy4mY1yDO87Y26o6T0QGAV8mu3My87n8tuZvEemEe3e+AM+oqrXBZJqVK4OOwNShrLqKKf96kI3lZeSGw7y3+hsemHwckVCY0wbvHXR4xtRlparu6MRX1a9FJOlvryndRKmqm4C3vB+TiW65BY4/PugoTC3Ltm6kvLqaGA4VsSjvrfom6JCMSeQuYGwS6+pk87kY0wz6tutEXiRCWTXkhCOM7V4UdEjG1ElEJgE/ALp7fe41OgCRZMux5JJtpk0LOoJWz3Ecnv7qfxSv/ZbD+g9nv94DaZubx/OHncb9C+fRMa+AM3edEHSYxtQnD2iHmx/ax63fAvw02UKSTi7evS0nA4NU9VoR6Qf0VNUPki3DNINDDw06glbvYf2IGz+aTVl1Fc9//RmPHHQCe/Yool/7Tly910FBh2dMg7yxxeaIyIOq2uhr41OpuczAHWb/R7hzKm/FvVpsfGMPbtLgxBNtsrCAvbliCWXV7qXFVbEYxWu+Zc8e1gxmWgYRuV1VLwTuFhGn9uOqmtRAd6kklwmqOlZEPvYOsFFE8lLY35hW4cCiIby7ahll1VXkhsNM6Nk36JCMScUj3u8/NaWQVJJLlYhEcG8kRkS649ZkTCYZNSroCFqV5Vs3cc//5pKfk8MFu+1Nl4I2nDB0dzrkFTB/3QoO6juUPbrZvSum5VDVYu93k4beTyW53Ak8C+wiItfjduz8vikHjyciBcBtuDdslgNzVfVMv8pvNR58MOgIWo3KaJSpsx5mY3kp4XCYD1YvZ9aUnxEKhTh8wHAOHzA86BCNSYqIXAVcDYxW1QXeuiNwp7Tvj5srQoCjqh2SKTPp5KKqj4lIMTDZWzVVVRcmH35CN+MmlWGq6oiITbPXGPvvD3N8mevHJLC2bBvbqyqJAbFYjC82rsFxHEKhUNChGZM0ERmLO05k7ZuvbscdV+x/qvq9vpdEUrlarPZImIeKyA9wpzqen+qBa5XdDpgGFNWchKqubkqZrdb27UFH0Grs0qY9PQrbsbJ0C2HCjOne2xKLyRRFIlJ73SbvRvgdvDEi7wFOAv5ba/vlwILGJBZIrVlsT+/nRW/5cGAecLaIPKWqNzcmAM9gYD1wlYj8ENgG/F5V325Cmcb4xnEcHvrkY97+ZhkHDx7CcbuOJicc5vnDTuOJxZ+QF4lw0tA9gg7TmBp1jaJyDW7TV7xrgUdVdUkdyei3wL9FZA5QUbNSVZMaAiaV5NIVGKuq22BHG93TwH5AMW6zVmPlAIOAj1X1EhGZALwoIkO8IZ8TWrBgAatXZ0Zlp7i4OLiD33MPpOH4gZ5TGiV7Xq+uWsFDSxZTEYvxzjdLWfftt0zo2h2A8eQC8Nknn6YtzlRl4+vV0s5p7dq1QR5+X9yp6OPVrrVMwr2V5LJ6yrge94t+Ae6NlSlJJbn0AyrjlquA/qpaJiIV9eyTrGVANTATQFXfF5F1wDAgqZs2Ro0aRVFR8PcSFBcXM27cuOACuOgiuO02X4sM/JzSJJXzevzVNVTE3IsjK2Ixtrdrm7HPSTa+Xi3xnEpKan+2N014Sy54X2TqtWXH4yWqujRBkfsDw4GaWksR8IqInKGq/wG6qOrBjY43hW3/AbwnIld5tZZ3gJki0hb4vLEBAKjqOtz2voMARGQY0ANY3JRyW6W3bEzRdDh86HAKc3LICYcpyMnhRwMHBR2SMU2iqjeqam9VHaCqA3BrOj/2EgvAayLS6OSSytVi14nIv4F9cC9JO1tVa2oVJzc2gDhnA/eLyJ9xa0Wn1u58MqY5LNm0kZveeZMQcOne+zGgU2cOGDCQB6cew0crVzCxqB+779Iz6DCNSbdzgd96LVNVpOtSZM/XuKNiFgBtRGQ/VX0zxTLqpKpfAwf4UVarNmNG0BG0aDHH4finH2dDWRkAH61cyXs/P4tQKMT43kWM7x1806sx6eDVXuKX29ezaVJSuRT5F8AFuO1y83Gvi56LO9aYyRQLF8JeewUdRYu1rbKSTeXlxBz36ssNZaWUVVfTJjdBW7cxWUJEhqvqF979L9+jqh8lU04qfS4X4F5ZsExVfwiMAQK9HMLU4a67go6gRWufl8cePXtRmJNDYU4u43r1tsRiWptfe7//XMdP0uONpdIsVq6q5SKCiOR7me17F0Yb01I4jsOMee/z2teL2bf/AC6Y8APCoRCPTP0pLy76AoApw2wIF9O6qOovvd8/bEo5qSSXEhHpBDwHvCoiG4EVTTm4SYMzbTi2ZL25djV/X7KYsupqdP06uha2YdruY8jPyeGnI20AUNM6ichPGnpcVZ9JppxUrhY72vvzahH5L9ARmJXs/qaZTJoUdAQtxvLS7ZRVVwNQVl3NwnXWymsMMKWBxxzA3+QiIjep6qWwcyhmEbkJuDTZMkwzOOMMmywsSZO69eA/q1fiXWHJ0cNHBh2SMYFT1TP8KCeVZrGD+H4iObSOdcZknMUb1vOH/75ORbSa3+97AGN69WZwu/a8cMIpFK9cwe49eyFduwUdpjFZI2FyEZFzgOnAIBGJHzypPe5d+iaTjLdZp+ty6rNPs8pudCcAAB3ySURBVGb7Nhxg2nP/5MNfngPA4C5dGdyla7DBGZOFkqm5/AO3b+UGvjvA2VZV3ZCWqEzj3Xtv0BFknJjjsLZ0OzXjhlfHomwqLw80JmOyXcLkoqqbgc3AiekPxzTZhAnw/vtBR5FRwqEQU4YN59Wv3aHqRvXYhR5t235vyFhjzE4i0gb3npd+qvpLERkKiKq+lMz+yTSLbQXiJ4sJecspjTNjmkk0GnQEgXIch9vfeJdZny1iTN9eXHPEgRTk5vDngw/lzWVLqYxW88MBg2xSL9Pi5G4KEapu+L53Z5uv7+sHcKdTqbkEtQR4CvAnuTR1fBnTzCKRoCMI1KzPFvHwex9RVlXN6i1b2aV9Oy4+cB/CoRAHDBgYdHjGtCSDVfV4ETkRwJteJensldLAlSKyO+4kNABvqmrmzI5kXK28SWzFpi1URb15V6qjLF2/MeCIjGmxKkWkEK/lSkQGEzcjZSJJjy0mIhcAj+HOs9IDeExEzkstVpN255wTdASBOmTXYRTm5tIuP4+C3BxOmTAm6JCMaamuAl4G+orIY8DruFMfJyWVmsvPgQmquh123EA5F7CREjPJvHlBR9Bsvlyzjt8+8zLbKiq57Mf7M3n4YIo6d2TWeafxSckqhvboSr8unYIO05gWSVVfFZGPcEfADwEXeBM7JiWVUZFDQHxvcdRbZ0wgzvnH8yxctZblGzdz8dP/ZsP2UgC6tWvL5OGDLbEY0wTekPv9gZW440j2E5HBIpJUpSSVmssDwPsi8qy3PBX4eyrBmmbwwANBR9Bs1nvJBCAUgk2l5XRp2ybAiIzJKjOAscCnuBWJUd7fXUXk7LjpkOuUdM1FVW8FzgA2ABuBM1T19sZGbdJk7tygI2g2Z0waR0FuDm3yctmtT08GdO0cdEjGZJOlwBhV3VNVx+HO4bUAOBC4OdHOydzncjfwD1V915uBLKlZyExA7rsv64bddxyHG1+ZwwuffsHQHl2547gj6NymkPN/9AMOHDGEbRWVjO3bm3DYWmmN8dFwVf2sZkFVPxeRMar6dTJTeSXTLPYl8GcR6QU8AcxU1fmNDteYFP1Xv+bJ4gWUVVXx0TcruPGVOdx09CEAjOzVI+DojMlaKiL3Ao97y8cDi0QkH6hKtHMyN1HeAdwhIv2BE4AHRKQAmAk8rqqLGh268d952Xd1+LrtpTjeIBHVsRirt2wLOCJjWoXTcQctvhC3z+Vt4De4iSXhLJWpTBa2DLgJuElExgD3414H3bpvCc80I0YEHYHvDh4xlBlz3mNbuJJoLMY5+00IOiRjsp6qlgF/9n5qS/gNL5XJwnKBQ3BrL5OBOcA1ye5vmsn06S16srAvV6zjor+/yObtZZxz6CRO2n8MndoU8O9fnc4Xq9bQp1NHdunQLugwjcl63kCVNwAjgYKa9ao6KJn9k+nQPwh3ROTDgQ9w29/OrLmZ0hg/XfLgSyxftwmA2198m71HDKB/j860yctlbL8+AUdnTKvyAG7r1G24zWBnkMK9jcnUXC7HndPlNzZ/Swuw776Jt8lgW0p3Dl0UDoXYWpb0UEbGZLW8zRBO0I0eK4VK/w5ZqKqvi0jI6xa5WkTewk04CSXToZ+w48ZkkFtuCTqCJrlgyj5c/+TrhMMhdhvQkxF97WowYwJSLiJh4EsR+RXwLe64kklJaVRk0wJMnNgi+lwcx+GPT73Bc+9/Ru8uHbjn7KMp6tqRoybsyoRh/dhSWs6QXt3s3hVjgnMh0AY4H7gOt2lsWrI7pzK2mDG+eU+/4cV5C6msjvLN2k1c98RrOx7r2bk9w/p0t8RiTLAGqOo2VS1R1TNU9RigX7I7pzLk/rEi0t77+/ci8ow3sJnJJG3bBh1BUrZXVFIzGWTMcdhmfSvGZJrfJbmuTqk0i12pqk+JyD7Aj4E/AfcCdtNBJpkzJ+gI6uU4zo7phfcdOZD+3TuzdI17jchFR+0XZGjGGI+IHAocBvQRkTvjHuoAVCdbTirJpWa4/cOBe1X1eRG5OoX9kyIiVwFXA6NVdYHf5We900+HBx8MOorv0G/XMv0vz7JxWynH7bM7l/7kAPJzc3js1yfyzdpNdG3fhg5tChIXZIxpDiuAYuBI73eNrcBFyRaSSnL5VkT+ijsi5k3e+DK+9tl4zWwTgW/8LLdVWZB5+fgPj73Cui3ubVHPvbeAw8YNZ7cBvYiEwwzcpUvA0Rlj4qnqJ8AnIvKoqiZdU6ktleRyHO4d+n9S1U3eQJaXNPbAtXnJ6h7gJOC/fpVrgldetfP9GQqFvrNsjMksIvI/cAfzq2v0Y1XdLZlyUhlbrBR4Jm55Je4MZX65FnhUVZckM5xzbQsWLGD16tU+htN4xcXFiTdKk/wrrqAiDcdP5Zyi0RjhcGhH/8oxuxVx13834wCDurYjtGk1xcVrfI+xMYJ8rdIpG8+rpZ3T2rVrgw6hQSLSFXgEGAxUAIuBs4Aj/Cg/lbHFCnBHyNwHN6u9jdv3Ut7UIERkEjAeuKyxZYwaNYqioqKmhtJkxcXFjBs3LrgA3nkHjj7a1yKTPSfHcbj+76/yrzc/o12bPO787TGMGNSTcePgmIP2Y2tZBbt0arcj6QQt8NcqTbLxvFriOZWUlAQdQiIOcLOqzgYQkVuAG1X15zUbiMguuJ/NAB+oatLfClPpM3kY2BW4C7gbGIGb9fywPzAcWCIiS4Ei4BUROdin8luPhx8O7NCfLFrBa+8pMcdhy/YKrv/7zllQ2xbk0bNz+4xJLMa0dqq6oSaxeN4D+tcsiMhxuONJHovbLfK+iPw02fJT6XMRVd09bvm/IvJJCvvXS1VvBG7ccSA3wRxhV4u1LNFY7DvL1VEnoEiMafWK6uhe2KSqm+ra2Bvm5RzghbjVVwDja2orItIdeA14OpkAUqm5fCwiE+OCmQC8k8L+pjlc4ts1FglFYzG2l+0cJm+MFLHXqP7k5oQpzM/l0tMnN1ssxpjveAtYUuvnwga2vwt3jpa749aFazWDrSeFnJFKzWUCME1Eai4T7gcsrLmyINkrCJKhqgP8KqvV6dWrWQ6jS9fwqxufYntZJRN3G8AtFx1FJBzm5guPZNPWMtoU5JKXa0PXGeOX/M0QKW+4NSBasWNU5H2B2p0+9dVa/gQMBaaoanzzw8si8grurMPgTnP872TjTeW//5AUtjVBufjiZhm48s+PvMGW7e6QLR8tLOHd+UvYd+xgADq1L0z78Y0xDSpR1aWJNhKR64FxwOGq+p0xmFT1EhH5Ce5FXCHgPlV9NtkAUprmWER2x82IAG95N9sYYx31xrQwIrIr7nxdi4B3vT6aJbhD6/9DVd9V1WeIuwUlFalcinwB8Mu4Az0qIvep6l2NObBJk4MOSkuxm7aWUZCXQ0F+LgC/mTaZc294iu1lFew5sh+Tdh+QluMaY9JDVT+jjpklvc/6P3s3yj8BzFTV+amWn0qz2M+BCTXTG4vITcBc3I4gkymuuMLX4hzH4cGXF/Lx3W8RDof543lHsM8egxjWvzuvzDiH8soq2hTk+XpMY0xwVPUO4A4R6Q+cADzg3ec4E3hcVRclU04qV4uF2Dl4Jd7f1haSaQ44wNfiFi1by/zF66iqjlFRWc1ND+ycdyUcDlliMSZLqeoyVb1JVcfgDst1NLAw2f1Tqbk8gHsTzbO4SeUo4P5UgjUtT04kjPOd5UhgsRhjmo+I5OJeyHUCMBmYA1yT7P6pdOjfKiKzca8cADi9Me1wJs26dWvS7uXlVWzYuJ1denQgEgkzuG839t+tN298/C2FBblcdZZdNGhMNhORg4ATcadX+QB4HDizpkskWQmTi4i8UGtVTVPYgSKCqh6ZygFNmr38cqN3/XLxai787Uyqo1GKenfm7ttOobAgj5/sN5hrzv8pkbjBKI0xWety4B/Ab1R1Q2MLSabmMglYjtuZ8z7Wz5LZTjgBHn+8Ubv+vwfmsL3UvdT92xWbmP2mcujBowG3ecwYk/1U9Yd+lJNMcukJ1FSTTgL+hXtp2md+BGB8tnhxo3ctKMglHA4RizkQgoICu8PeGNM4Cb+OqmpUVV9W1dNwZ4lcDMwWkfPSHp1JG8dxKPl2A+vWb92x7tyzJtOnd2cikRATxw9iv71Tn1fHGGMgyQ59b5bIw3FrLwOAO2nkXZsmzZ5NPDqD4zhcd+MLvDN3MY7jcO5ZkznqiDHs0qMDj/ztl80QpDEm2yWsuYjIQ8C7wFjgGlUdr6rXqeq3aY/OpG7mzISbfLtiI+/MXUxFZTWVVVH+ev/s9MdljGlVkqm5nApsB4YB58fNERDCHQ25Q5piM43x5JPw2982uElBQS6Os/PulcKC3HRHZYxpZRImF1W1y4RauM1byli5chP9+3elsCCPbl3bc+5Zk/nr32dTWJjLNb+fGnSIxpgECtZXkZNb1eA21VVVbG1wi+ZjlwNlmz/84TuLXy5ezfmX/INQyK2h/L97zqBL57YcdcQYjjpiTEBBGmOyndVKsk1BwXcWH3viPcrKKiktrWTLlnJee8OuIDfGpJ8ll2xz+eXfWezUqQ05Oe7LHImE6dDBJvIyxqSfJZcs4jgOZeVVfPX1zmmvf37avozetYi2bfPZb99hHPSjXQOM0BjTWlifS5ZwHIer//g8Q6p78uhFj3D0lLGc/Ysf0r5dAbfddGLQ4RljWhmruWSJDRu38+57i3m6915UVFTz5DPz3GFcjDEmAJZcskRhYR7hUIibix8AoF3bfMJhG2PUGBMMSy4t1Lo1W5j37pdsXL8NgDaFeVx9xVRyciIU9enMjdcdG3CExpjWzPpcWqCvdCW//sX9hMIhcODOh8+k74BuTJowGPYdw6N/PzPoEI0xrZzVXFqgl56eR1lpJaXbKigrreDVlz7e+eBzzwUXmDHGeCy5tEC79OpEXr5b6czLz2GXXp12PjjVhnIxxgTPmsUyXCwW4+MPlgAwZq+BhMNhjjn1B5QsW8cnHy5l/N5DOWTquJ07lJQEFKkxxuxkySXDXX/ZU3w4dzE4MG7SYP5wywnk5ubwm2t+EnRoxhhTL0suGaystIK5s78gGo0BMHeOUlZaQWGb/Pp3mjWrmaIzxjSnvA1l5IYjDW4TjpU1UzSJWZ9LBsvLz6WgTd6O5cLCXPLyE8y9MmNGmqMyxpjEMqLmIiJdgUeAwUAFsBg4S1XXBhpYM1tZsoGFn5YwZEQv+g3sTiQS5qZ7T+POP74IwHm/O4JIJMH3gRdfhKuuaoZojTGmfhmRXAAHuFlVZwOIyC3AjcDPgwyqOX395SouOuNvhEIhYjGHG2ZMY9fd+zF0RG/ueuSsoMMzxpiUZESzmKpuqEksnveA/gGFE4jZr/yP8rIqykorqSiv4pXnP2pcQX/8o7+BGWNMI2RKzWUHEQkD5wAvpLLfggULWL16dXqCSlFxcXHK+0SdbeTmRaiqjJKbGyGSV9GocjouWMDmrl1T3i+RxsTSEth5tRwt7ZzWrm1Vrfrfk3HJBbgL2AbcncpOo0aNoqioKD0RpaC4uJhx48Y1uE00GuPdWZ9QVVHN3oftTn5hHmPHjiU/tyNz5yhj9hrEz88/KHH/Sl3OOgsuvriR0dctmXNqiey8Wo6WeE4lrfyes4xKLiLyJ2AoMEVVY0HHky43nPMAxbMXAvDc32dz+0u/JhwOM+3sHzHt7B8FHJ0xxjRdRvS5AIjI9cA4YKqqVgQdT7o4jsO7sz6lvLSS8tJKli1axfpVm/07wHHH+VeWMcY0UkbUXERkV+ByYBHwrogALFHVowMNLA1CoRC79O3CmpINxGIOuXk5dOzSzr8DnGizThpjkiMiw4CHgK7AemCaqn7pR9kZkVxU9TMgK2e2WrZoJfPfWsTg0UWM2mswADc8/iv+evU/qSir4mdXHEleQYIbI1Nx9NHw4Yf+lWeMyWZ/Ae5R1UdF5BTgr4AvbfMZkVyy1dIvVnDRkbcSrY4RjoT4zR2nss9he9CzX1euut/mXDHGpE2R1wIUb5OqbqpZEJEewFjgIG/VTOBuEenuxw3sGdPnko3mvfE5VZXVVFVWU1FWxetPfZD+gw4Zkv5jGGMy3VvAklo/F9bapi/wrapGAbzfK7z1TWY1lzQaNLIPObk5RKsryS/MRcYOSP9BH388/ccwxmS6fYHa10JvqmvDdLHk4pPqqiivP/0BuvBLhgwUOnZpx7gDRjD9+p8y+9lidt1rEMdOPzD9gRxyCLz8cvqPY4xpVuF1Wwg7VQ1vEyqDNgCUqOrSBEUuB/qISERVoyISAXp765vMkotPbvrVQ8x74zOqq6J8+PJi/vbmleQV5HLwcRM5+LiJzRfIunXNdyxjTIulqmtEZD5wIvCo9/tjvwYMtj4Xn8x74zMqyqqIVsfYtrmMFUtb99APxpgW4WzgPBFZBJznLfvCai4+GTSyD4s+XU60Oko4EqJ7n87BBDJ7djDHNca0OKr6BTAhHWVbzaURFn+8hIeueoI5T76L4zgAXPPgWRx60iRG7zOAW/55IW3bFwYT3PXXB3NcY4yJYzWXFH3zxbdctO+VlJdVUNAmn3UrNnDMhUfQvnNbzr3+OIqLixk4ondwAb76KtxwQ3DHN8YYrOaSsk/nfO7WVhwo317B28+8H3RIxhiTcSy5pEjGD94xUE1+m3z2+NHoYAOq7dZbg47AGGOsWawhVZVVPH/3y2xYtYnDzzyQPkN6MXTsIK597lJefXgOQ8YOZOp5hwYd5netXBl0BMYYY8mlITeddjdzX/iQqvIqZv3tdR5afBcdurRn7IG7MfbA3YIOr2633ALHHx90FMaYVs6axRow//X/UVlWieM4xByHbz5v3TPLGWNMsiy5NGC3A3Z1h8MPQciBfiOCn0Y5oWnTgo7AGGOsWazG5+8t4uX736D/iD5MPe8wIjkRLnv4PJ65/V+sX7mRKef8mA5d2wcdZmKHZlgfkDGmVbLkApR8uZLfHngtFaUV5LfJY+3y9Zx96+nkFeRxwmUtbDLME0+0ycKMMYGzZjHgy+KvCUfc64srSispfvXTgCMyxpiWzWouwIiJQ3Fi7jAu+W3ymXTkngFH1ASjRgUdgTEmDaKr1xCqzmt4m5xKGNhMASXQ6pJLZXkl//jjM6z4ajVTf3UIIycJPQf04I53rueNmW9TNKw3B5+2f9BhNt6DDwYdgTHGtL7kctuZf+XNp+dSWV7F3Bfm8bcFt7FL/+4M2q0/g3brH3R4Tbf//jBnTtBRGGNauVbX57Lg7S+oLHdncwtHwixd8E3AEfls+/agIzDGmNaXXPb+yV4UtMknHAkTCoUYNn5I0CEZY0zWaXXNYmfefCqDdx/Amm/W8aMT96Fzj45Bh+Sv994LOgJjjGl9ySUcDnPQqS24wz6RSy6B224LOgpjTCvX6prFst5bbwUdgTHGWHIxxhjjP0su2WbGjKAjMMYYSy5ZZ+HCoCMwxhhLLlnnrruCjsAYY7LiarEIwKpVq4KOA4C1a9dSUhLgpGKxGPh8/MDPKU3svFqOlnhOcZ9JET/Kq86p8mWb5pINyaUXwMknnxx0HJlj8uSgIzDG7NQL+KoJ+28BNq7p+1XnJLff6O0TqGxILvOAfYGVQDTgWIwxpkYEN7HMa0ohqrpBRIYAHZLcZYuqbmjKMf0Qchwn6BiMMcZkGevQN8YY4ztLLsYYY3xnycUYY4zvLLkYY4zxnSUXY4wxvrPkYowxxneWXIwxxvjOkosxxhjfZcMd+oETka7AI8BgoAJYDJylqmsDDcwnInIVcDUwWlUXBBxOk4lIAXAbcCBQDsxV1TODjarpROQI4DoghPvF8WpVfSbYqFIjIn8CjgEGEPd+E5FhwENAV2A9ME1VvwwqTpOY1Vz84QA3q6qo6m644wjdGHBMvhCRscBE4JugY/HRzbhJZZiqjgauDDieJhOREO4XnFNVdQ/gFOAhEWlp/+PPAfsBy2qt/wtwj6oOA+4B/trcgZnUtLQ3XkZS1Q2qOjtu1XtA/4DC8Y2I5OP+I0/HTaAtnoi0A6YBV6qqA6Cqq4ONyjcxoKP3dydgparGAownZar6tqouj18nIj2AscBMb9VMYKyIdG/u+EzyLLn4zPumeA7wQtCx+OBa4FFVXRJ0ID4ajNuscpWIfCgis0Vkn6CDaiovUR4HPC8iy3BrAKcFG5Vv+gLfqmoUwPu9wltvMpQlF//dBWwD7g46kKYQkUnAeCDb5k3OAQYBH6vqnsClwDMikuyIsxlJRHKA3wFHqWp/YArwhFdTM6bZWXLxkdcZORQ4vqU1R9Rhf2A4sERElgJFwCsicnCQQflgGVCN18Siqu8D64BhQQblgz2A3qr6DoD3ezswItCo/LEc6CMiEQDvd29vvclQllx8IiLXA+OAqapaEXQ8TaWqN6pqb1UdoKoDgBLgx6r6n4BDaxJVXQf8FzgIdlyF1AP3Cr+WrAQoEhEBEJERQE+aNklVRlDVNcB84ERv1Ym4Nc+suBozW9l8Lj4QkV2BBcAioMxbvURVjw4uKn95tZcjsuRS5EHA/biXtVYBV6jqrGCjajoRORm4DLdjH+AqVX0uwJBSJiJ3Aj/BTYzrgPWququIDMe9FLkz7kyL01RVg4vUJGLJxRhjjO+sWcwYY4zvLLkYY4zxnSUXY4wxvrPkYowxxneWXIwxxvjOkosxxhjfWXIxxhjjO5vPxTSaiFwBnAREcW/cO8sbTsXPY2xT1aTGxxKR2cANqvpK3LoLcYfWn97QMXCHtzlJVdM+lpqI3AKsVtU/ecsh3BsDe6tqaQP7DQAWAqqqezR0vsCvgbnASK/cdWk6HWPqZDUX0yjewJZHAGO9OWwOJPixnmYCJ9RadwI7h2pvSCfcqQWawyjg07jlgcDahhJLnK+8+VqggfNV1TJvuxVNjtaYRrCai2msXsC6mnHUar4Ze9+uX1LVUd7yb4B2wIPAy8D7wBjcoXKmqWqpt0+dj9UcTESu8453h7d8Pe63/zvjYnoa+D8RyVfVCq/c3sDb3j4XAz/ztv2bqt4et++NwGARmQ+8qqqXiMhzuMO6FwB3qOp9cfFcCZyMm1DXAcWq+icROQU4H8jzzmd6zVDxcUbz3eSyW63lZDV4vsYEyWouprH+A/QVkUUiMkNE9k9iHwHu82o6W/huTaGhxwD+jjc/iTdnzgnAY/EbqOp64APgEG/VCcATquqIyDjgDGAC7syavxSRMXG7X4ZXK1DVS7x1P1PVccCewPnedNaIyJ64U/GOwR0Ha09v/QjgeGBvr9YQxU1AO09SpDOQp6qr4lY3Krk0dL6plmWM3yy5mEZR1W24o0CfCazFnTvk9AS7La8ZEh54FNgnycdQ1aXAei8hHIw7Ku76Oo4R31QU3yS2D/Csqm73Yn8G2DdBvOeLyCe4M4v2xZ1Ooaas572mp63Ai976ybjPyTyvBjQZd+6YeLVrLRCXXEQkT0TuEpHbReRBEemTIMb6zteYQFmzmGk0r7lnNjBbRP6HW7N4je9+aSmI+7v2N2onycdq/A04HXfE3PvrCes54FYRGQsUqupH3vpQPdvXSUQOwO1HmuQ13c1m57nUV1YIeEhVf9dQ0cQNg+/VwvYGzvZW7YPb/3JtkqHWd77GBMpqLqZRxDU0btUeuBNxrQZ6iEhXEcnH7fSv0c+7EADcOTneTvKxGs/iNgGNB16p4/GaGtVs3OQT/y3+TWCqiLQRkbbA0cBbcY9vBdrHLXcENnqJZThuU1qNt4EpIlLgzfR4uLf+deCn3pzviEgXEelfK8RlwHgRqUlUlwNvxl3NNRfIF5FHvP6bBjVwvsYEypKLaax2wEMi8rmIfIp7yevVqloFXIvbmf0S8EXcPguB07ztuwD3JvkYAKpaiTvR15N1dJLHmwnsDjwet+9HuBcVfODF9jdV/Tju8fXAOyKywLtU+GUgx4vnOtymsZpt5wEvAJ/gNq99CGxW1c+B3wP/8fZ7FffCh/hz+I93Dl+IiOI2tZ0d93iZql6BWws8t4FzbPB8jQmazedimkXtq8iSfazWdmHgI+BYVf0yHXEmS0Taqeo2EWmDWys6048mKRGZgTsNczvcPqIX4x4bQBLPU63ylgJ72n0uprlZn4tpEURkJG5N6NmgE4vnPi+mAtx+Fl/6Ohq62RP36rOOIjI/7l6XOolIIW4TWy47Z6Y0ptlYzcUYY4zvrM/FGGOM7yy5GGOM8Z0lF2OMMb6z5GKMMcZ3llyMMcb4zpKLMcYY31lyMcYY47v/DzEwfFf+/vs7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Um1= 2 # voltage across Mops <= 2V\n",
    "Rc2=24 #ohm Cable resistance back and forth [PP3-PP0]\n",
    "Id=0.025 #A Input current for 1 Mops Id=0.25m after simulation\n",
    "Uc= Rc2*Id  # Voltage drop across the cable for one MOPS\n",
    "Uc2=Rc2*Id*2 # Voltage drop across the cable for 2 Mops\n",
    "Us2 = Uc2 + Um1 # Voltage needed from the power supply\n",
    "print(\"2 source Voltage Us = %0.2f V for 2 Mops to achieve %0.2f V per Mops\"%(Us2,Um1))\n",
    "Us_space = np.linspace(Us2-2,Us2+8)\n",
    "Uc1=Rc2*Id*1 #UC with 1 Mops\n",
    "Um2= Us_space - Uc1\n",
    "Limit =Um2-Um1\n",
    "p.plot_lines(x1= Us_space ,y1 = Um2 ,z1=Limit,Directory=Directory,PdfPages=PdfPages)\n",
    "\n",
    "p.close(PdfPages=PdfPages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}