272.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import *\n",
    "from matplotlib import *\n",
    "from uncertainties import * \n",
    "from uncertainties import unumpy\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: The measurement of I and V at room temperature is uploaded. By uploading the data, it is assumed that all of the measurement are taken correctly.\n",
    "Step 2: Then, the measurement of I and V at a temperature of -100C is conducted, and the measurement that repeats step 1 in this circumstances is taken and uploaded\n",
    "Step 3: The dataset for -70C, -40C and -10C are uploaded\n",
    "\n",
    "From the measurement, it is known that V2 has got a high sensibility, which uncertainty can take to be +- 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Load Files\"\"\"\n",
    "R = 50000;\n",
    "R = ufloat(50000,0.01*R)\n",
    "N100 = np.load(\"N100.npy\")\n",
    "N70 = np.load(\"N70.npy\")\n",
    "N40 = np.load(\"N40.npy\")\n",
    "N10 = np.load(\"N10.npy\")\n",
    "P25 = np.load(\"P25.npy\")\n",
    "Dataset = np.array([N100,N70,N40,N10,P25])\n",
    "DataTemp = np.array([-100,-70,-40,-10,25])\n",
    "DataTempE = np.array([4,4,4,4,4])\n",
    "DataTemp = unumpy.uarray(DataTemp,DataTempE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: By using the following code, this completes one loop of the data acquicision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
    },
    {
     "data": {
      "image/png": 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UG/vcSMemHWNdWpWmcBCRuBepA9usjC+YsWIq2bs/pm6tulxx/BVc3/v6Kj3ATjxROIhI3AvtwNay0Q/83zHd+Sr3I+rXasDo3qO5rtd11WKAnXiicBCRuJeanMDtF9Tn5vm5eK2NsHkbIzr/grtSx9GiQYtYl1ctKRxEJK59sfEL0jLS+HTDp9RvVI/tm87lxr7XcNc5/WJdWrWmcBCRuOPuLFi/gAkZgQF2Wjdozc+TfsUr77Tg5uYrSP8il7O656ilUQVSOIhI3HB3PljzAWkZaWTmZnJso2O5+5S7aV97ELfPXMZT7QIPzRt41nh1YKtgCgcRibmCwgLmrg4MsLNi6wo6NunIfafdx0XJF1G3dl2e/jA7GARDAUgFdWCrYObusa6h3Pr37+8LFy6MdRkiUkZFA+xMXDKR7/O+J6lZEuNSxjG8y3Dq1NJ314pmZl+6e/9I8/Tpi0ilyy/I51/Z/2LSkkms3bmW7i2788igRxiaWLMH2IknCgcRqTT7Cvbx8sqXmZw5mY27NtK7dW/uGHAHgzoN0gA7cUbhICIV6ukPszmuXT1W57/H1KVTydmTQ3KzPlx47C/547CRemR2nFI4iEiF2bF/B2v9NZ76+AWovYtTvT7X9X2MJ94s5I6r+ykY4pjCQUSiLm9fHulZ6TyX9Rw79u+gb8JAli3uRa8Gefz9Teepq/uplVGcUziISNTk7Mlh+rLpzPxmJrsP7GZI4hDGpoylV+tePLr8Pp7IHcBt5yQqGKoAhYOIlFvoADv7C/czLGkYY/uOpXvL7gDMz84hfVsfbmv9Bemf1WNgcmsFRJxTOIjIUVu7Yy2TMyfzyrevHBxg56a+N5HUPOngMvOLhuNsP0e9m6sQhYOIlNkPeT8wcclE3vjuDWpZrcAAO31vpEOTDmHLZqzNU+/mKkg9pEWk1FZsXcGkjEnMWTWHurXqMqrHKEb3Hq0BdqqouOshbWajgPuAnsAp7r4wZN7dwBigALjN3efEokYROTQCW/N5V5Fmebxne6hfuyEnNr2Ev553qwbYqcZidVkpE7gMmBA60cx6AVcCvYH2wFwz6+HuBZVfoog0braGm9+5C2+4kaZujOh0HXMWdOeXV56hYKjmYhIO7p4FROoAczHwgrvvA743s2+BU4AFlVuhSM3l7gcH2Pls42c0btqMPRvP4SJvwotrTuQp3UiuEeLtYSYdgDUh79cGp4lIBXN3Pl73MaPfHs2Yf48hOy+bX/f/Ne9d/g6jC1vydE4q156qPgo1RYWdOZjZXCDSXap73P3VklaLMC3iHXMzGweMA0hMTDyqGkUECr2Q99e8T1pGGstyl9G2cVt+c+pvuLTbpTSo00B9FGqoCgsHdx9yFKutBTqFvO8IrC9h+2lAGgRaKx3FvkRqtILCAt5Z9Q5pS9JYuXUlHZt05P7U+7mw64XUrV0XUB+Fmize+jn8C5hhZo8SuCHdHfg8tiWJVC/5hfmBAXYyJvLD9h/o0rwLD53xUMQBdtRHoeaKVVPWS4G/A8cAb5jZ1+4+zN2XmtmLwDLgAHCLWiqJRMf+gv28mv0qzyx5hnU719GjZQ/+MugvDEkcUuIAOzcPSg6blpqcoGCoAdQJTqSa23tgLy+tfIkpmVP4cfeP9Gndh1+e8EsGdRykR2bXcHHXCU5EKt7u/N3MXD6TaUunkbs3l35t+vFA6gOc1v40hYIckcJBpJrZvn87z2c9z7NZz5K3L4+eLU7mkd21GbAxB4anMj87h4y1eREvGYkUUTiIVBNb924lPSudGVkz2Jm/k0EdBzGw1eU8+vo+8lu9CI3WH2p9dPVJsS5X4pzCQaSKy9mTw7Sl05i5fCZ7DuxhaOehjO07lp6tewLQtVkO4yfncW2LTNLVDFVKSeEgUkVt3LWRKZlTeGnlS+QX5jO8y3Bu6nMT3Vp2+8lyqckJXNsiU6OwSZkoHESqmDU71vDMkmd4NftVcLgw+ULG9B1D52adIy6vHs5yNBQOIlXEd3nf8cySZw4OsDOy+0hu7HMj7Zu0L3Ed9XCWo6VwEIlzK7auYGLGROb8MIf6tetzdc+rub739bRp1OaI66qHsxwtdYITiVNLc5YyIWMC7695n8ZuXJUyhl/0+gWtGrSKdWlSTagTnEgV8tWmr5iQMYFP1n1CozpN+ZU34xpvSvN+/xnso5CtPgpS4eJtPAeRGsnd+WzDZ9w450aue+s6snKzuDRpHPu/v5sTd/WiObUP3j9I6dg81uVKDaAzB5EYcnc+WvcRaRlpLN68mGMaHsMdA+5gZPeRNKrbiPM65jB+8j71UZBKp3AQiYFCL+T91e8zIWMCWVuyaNe4Hb899bdc0v0S6teuf3A59VGQWFE4iFSigsIC/r3q36RlpPHttm9JbJrIA6kPcEHyBdStVTdsefVRkFhROIhUgvzCfN787k0mLZnED9t/ILl5Mn86808MSxoWNsBOEfVRkFhSOIhUoP0F+3nl21eYnDmZdTvX0bNVTx47+zHOSTyHWnb49iDqoyCxpH4OIhVgz4E9vLzyZSZnTmbT7k2kJKTwyxN+yZkdztRYChI31M9BpJLsyt91cICdLXu30N/r86C3YeD56QoFqVIUDiJRsH3/dmZkzSA9K528fXkkNjiJG/v+ntGL/xpYwEyD7EiVonAQKYete7fy7LJnef6b59mZv5OzO53NuL7j2LG9HeNnfEX3Vu1J1SA7UgUpHESOwubdm5m2dBovrniRvQf2MrTzUMaljOO4VscFFjgmcPNYg+xIVaVwECmDjbs2MjlzMi+teIkCL+D8LudzU9+b6Nqia9iy6sAmVZnCQaQUfjLADnBx8sWM6TOGTs06lbiOOrBJVaZwEDmM7/K+Y1LGJN78/k1qW21G9RjFDb1voF2TdoddTx3YpKpTOIhEsHzLcib+6zr+zW4a1G3ItT2vZXTv0RzT6JhSrX+wA9u8x4HAJSZ1YJOqpMyd4MysMbDX3QsqpqSyUyc4iZbMnEzufu8xftjzOY3duJqm/OLK18laV6BmqFLtlKsTnJnVAq4ErgEGAPuA+ma2GXgTSHP3lVGsV6TSLfpxEWkZaXyyPjDATq1tw/hDnVyGNtrE/HUFaoYqNU5pLiu9D8wF7gYy3b0QwMxaAYOBP5nZbHdPr7gyRaLP3fls42dMWDyBhT8upFWDVvz3yf/NFcddweLVexg/+QOWqBmq1FClCYch7p5ffKK7bwFeAl4ys/BnDYvEqaIBdiZkTCBjcwZtGrbhzgF3MrLHSBrWaQhAanJjNUOVGq004dD0cM+EcfctkcJDJKamjAj894Y3Dk4q9ELeW/0eaRlpZG3Jon3j9tw78F4u6XYJ9WrX+8nqaoYqNV1pwuFLwIFICeFAeO8fkThSUFjA2z+8zcSMiWTnZdO5WWf+cPofGNF1RIkD7KgZqtR0RwwHd+9SGYWIRFs+zusrZ/NM5jOs2r6Kbi268ecz/8ywpGHUrlW7xPXUDFWkdK2Vktz9h8PMN6CDu6+NZmEiR+PpD7Pp2b4hP7KDZ2w76+f/jk6NuzP8mLv40/CrjjjADnCoueq8Q9NSkxMUDFKjlOay0iPB5qyvErjEtBloAHQj0FrpZ8DvAYWDxNSeA3vYZO/w1Lxnoc52Urwel/V6kKffrsdvr+5XqmD4iZD7FSI1TWkuK40ys14E+jncCLQD9gBZwBvAH919b4VWKXIYu/J38cI3LzB92XS27N1Cj9Ynkp3Zl371dzHh7fo8pXsFImVWqsdnuPsy4J4KrkUkIEJLo0jy9uUx45sZpC9LZ/v+7Zze/nTGpYyj37H9eHTlfTyRe4qaoYocpTI9W8nMUoGk0PXcfXpZd2pmo4D7gJ7AKe6+MDh9KPAnoB6wH/hfd3+vrNuX6m3L3i0HB9jZlb+LwZ0GMy5lHH0S+gBqhioSDaUOBzN7FkgGvgaKnqvkQJnDAcgELgMmFJueA1zo7uvNrA8wB+hwFNuXamjz7s1MWTqFWStmsffAXs5NOpexfcceGmAHNUMViZaynDn0B3p5WZ/UF4G7ZwFhA667+1chb5cCDcysvrvvK+8+perasHMDz2Q+w+yVsynwAkZ0HcGYvmPo2jy8i42aoYpER1nCIRNoC2yooFqKGwl8VVIwmNk4YBxAYmJiJZUkFenpD7NJ6dic1OD71dtX89D8p1jw4xxq1bJSDbCjZqgi0VGWcEgAlpnZ5wSezAqAu18UaWEzm0sgTIq7x91fPdyOzKw38Gfg3JKWcfc0IA0Cj+w+YvUS91I6Nmf8jK+4p3VLvmi0kjdnX4h7Lc5ufxG/Sf3VEQfYEZHoKUs43FeWDbv7kLKVEmBmHYHZwHXunn0025AYKmVLo0hatcyh70mvcl/OYup4bWzHWfx5yHiG9zzuyCsXpz4KIuVS6nBw9w8rshAAM2tBoO/E3e7+SUXvT+JDxuYMJmZM5IO1H9CkbhNSdh/P/HWjuHXQCUcXDCJSbqV5fMYOAq2SwmYB7u7NyrpTM7sU+DtwDPCGmX3t7sOA8QR6Xt9rZvcGFz/X3TeVdR8S/xZuXEhaRhoLNiygef3m3HLiLXRrcB53TF/IrS0ySf+spZqhisRIaXpIN432Tt19NoFLR8WnPwg8GO39SfxwdxZsWMCExRNYtGkRrRq04vaTb+fy4y4PDLCjZqgicaFMneBEjpa7M2/tPNIy0sjIyaBNozbcdcpdXNb9soMD7GSs3ahmqCJxQuEgh5TjZnLxZqgQ6JC2eM1WkpO+Jy0jjeVbl9OhSYcSB9hRM1SR+KFwkKgoaob6ZKv2pDZaz0ff/sitr06hTaeP2bBqFUnNknjw9Ac5v+v5EQfYEZH4onCQqCi6BHTL5C2c2vZNPvngCkjIpXH9bjw84GHO7XzuYQfYEZH4onCQqNhXsI/V+XOpn/wYn9TeTes6Xbn3jHsZnDhY4yiIVEEKh+qiHPcLymN3/m5mrZjF1KVT2bxnM7a/I0P2ncgH20fScOAJZQ8GEYkLCgc5Kjv37+SF5S8wfel0tu7bynHNT2LLDz/nqcaLOL3ZWuZf0E/NUEWqMIVDjL5xV1V5+/J4Lus50rPS2bF/B2d0OINxKeP4LKsZt49sTuq8wOeoZqgiVZvCQUold08u05dN54VvXmD3gd2c0+kcxqWMo3dCbwBOahNcUM1QRaoFhUM8iPHZS0l9FDLW5nFp/yZMXTqVWStmsa9gH+clncdNKTfRo2WPmNQqIpVD4VBe1eCyVPE+CvOzc7hl5rucfnIGaS+/RaEXMqLrCG7qexNdmneJdbkiUgkUDnLw/sD4yXlc2HoBs9++E+v4JZ9sMi7tdik39rmRjk07xrpMEalECgcBoE2rbXRKnMxL9ddSx+py5fFXcn3v62nbONJ4TYdRhc+gROQQhUMNl5WbRVpGGnNXz4V6dem7uzvfbL2Gs844i7aNdTNZpKZSONRQizcvJi0jjXlr59GwdmNq5Q3lkdpbOLfRZuYPO0t9FERqOIVDDeLuLPwxMMDOpxs+pUX9Ftx60q3syR3IgNPakzpvNKA+CiKicKjyDtcMtegR2O7O/PXzSctIY9GmRbRu0Jpf9/81o3qMolHdRodWVB8FEQlSOFRxkZqhFl0Scnc+WPMBaRlpZOZmcmyjY7n7lLu5rPtlNKjTINali0gcUzhUcaHNUK9tkUn6jK944qoUdtT+kp+/lsaKrSvo2KQj9512HxclX0Td2hpLQUSOTOFQDaQmJ3Bti0yeyO3HiIE/8HDm3/k+73uSmiXx0BkPMbzLcOrU0q9aREpPfzGqgXkrNzC9cD/tezzEvLxddGjUlUcGPcLQxKEaYEdEjorCoQrbe2Avf13wLC+smA5tt5Ho9RjU80HS5jSgWb+TFQwictQUDlXQ7vzd/HPFP5m6dCo5e3JIbtWH/91Wl1QaYKdczAmtc9QMVUTKReFQhezYv4MXvnmB6cums23fNk5tdyoPn/Uw/Y/tj0294OByR90MVY++EJEghUMVkLcvj/SsdJ7Leo4d+3dwVsezGNt3LCe2OTHWpYlINaVwiGO5e3KZtmwaM7+Zye4DuxmSOISxKWPp1bpXrEsTkWpO4RCHftz148EBdvYX7mdY0jDG9h1L95bdY12aiNQQClIwYmwAAAt8SURBVIc4sm7nOiYvmczsb2fj7lyQfAFj+owhqXlSrEsTkRpG4RBDRc9F6kA+E207b7x8AWAc1+Rn/PXc/6JDkw6xLlFEaiiFQwy1brmFm+fcD402UB/jrHaXMe+L3tx2xWA6NFEzVBGJHYVDDCzLXUZaRhrvrn6X+s0akr/5dC4paMFLa07nKY2hICJxQOFQib7e9DVpGWl8tO4jmtZtys0n3Mw1x1/D5L8/zhO5A7jtnEQFg4jEBYXDUSrNOApwaICdCRkT+GzDZ7So34LbTrqNK4+/kqb1mjI/O4f0bX24rfUXpH9Wj4HJrRUQIhJzCoejdLhxFCB8gJ2EhglhA+wcXKf9HFIbrWfgWeM1PKeIxAWFw1GKNI7Ck1efxMCurXhv9XukZaSxNHcpbRu35Ten/obLul9G/dr1f7KNjLV5gSCY9/hPtnlUz0XSoy9EJIoUDuVwaByFAYwf3IHttRcy6rWJBwfYuT/1fi7semGJA+wcvPyk4TlFJM7EJBzMbBRwH9ATOMXdFxabnwgsA+5z979UfoWlMz87h2e39eL8DrOYtmY1bNxEl+ZdNMCOiFR5sfrrlQlcBkwoYf5jwFuVV07ZfbhyPf/52iRadvsXH9XaR8dGXdmw6jzuOP06zkg+NtbliYiUS0zCwd2zAMwsbJ6ZXQJ8B+yq5LJKZe+Bvby08iWe/HIiBa1zaev1+F1hAmf//BUWfJdLxto8zuimcBCRqi2urnuYWWPgTmAo8OsjLDsOGAeQmJhY4bXtzt/Ni8tfZOrSqeTuzaVfm378MuUhTvv3gxgGZrpfICLVRoWFg5nNBdpGmHWPu79awmr3A4+5+85IZxWh3D0NSAPo37+/l6fWw9mxfwczsmbwbNaz5O3LY2C7gTyS8ggD2g4ILnH4OkVEqqIKCwd3H3IUq50K/NzMHgZaAIVmttfdn4xudUe2de9W0rPSeT7reXbk72BQx0GMTRnLCcecUNmliIhUuri6rOTuZxa9NrP7gJ2VHQw5e3KYtnQaM5fPZM+BPQztPJSxfcfSs3XPyixDRCSmYtWU9VLg78AxwBtm9rW7D6vMGoo//mLjro089Mk/mLfxDZwDnJd0HmP7jqVby26VWZaISFyIVWul2cDsIyxzX0XWUPT4i9+3bsVXjVbyykvDKSgs5PS2w7g79RY6N+tckbsXEYlrcXVZqTKlJidw6/nGb5csphYG2wfy0OBbubB3n1iXJiISc7ViXUAsXXviIE7YfRzbv72L0T1uVzCIiATV6HD44oc8lq27glubLyf9s9XMz86JdUkiInGhxoZD6OOyb0/4IvCE1RlfKSBERKjB4XDwcdmN1gM/fVy2iEhNV2NvSOtx2SIiJauxZw4iIlIyhYOIiISpsZeV4oqG+BSROKMzBxERCaNwEBGRMAoHEREJo3AQEZEwCgcREQmjcBARkTBqylpeaoYqItWQzhxERCSMwkFERMIoHEREJIzCQUREwigcREQkjMJBRETCKBxERCSMwkFERMIoHEREJIzCQUREwigcREQkjMJBRETCKBxERCSMwkFERMIoHEREJIzCQUREwigcREQkjMJBRETCKBxERCSMwkFERMLEJBzMbJSZLTWzQjPrX2xeipktCM5fYmYNYlGjiEhNVidG+80ELgMmhE40szpAOvALd19sZq2B/BjUJyJSo8UkHNw9C8DMis86F8hw98XB5XIruTQRESH+7jn0ANzM5pjZIjO7o6QFzWycmS00s4WbN2+uxBJFRKq/CjtzMLO5QNsIs+5x91cPU88ZwABgN/CumX3p7u8WX9Dd04A0gP79+3t0qhYREajAcHD3IUex2lrgQ3fPATCzN4F+QFg4RM0Nb1TYpkVEqqp4u6w0B0gxs0bBm9ODgGUxrklEpMaJVVPWS81sLXAa8IaZzQFw963Ao8AXwNfAInfXV3sRkUoWq9ZKs4HZJcxLJ9CcVUREYiTeLiuJiEgcUDiIiEgYhYOIiIRROIiISBiFg4iIhFE4iIhIGHOv+k+eMLPNwKpybCIByIlSORVJdUZXVakTqk6tqjO6KrrOzu5+TKQZ1SIcysvMFrp7/yMvGVuqM7qqSp1QdWpVndEVyzp1WUlERMIoHEREJIzCISAt1gWUkuqMrqpSJ1SdWlVndMWsTt1zEBGRMDpzEBGRMAoHEREJU+3CwczOM7PlZvatmd0VYf71ZrbZzL4O/twUMm+0ma0M/owOmX6ymS0JbvMJM7NY1WlmJ5rZAjNbamYZZnZFyDpTzez7kHVOjFWdwXkFIdP/FTK9i5l9FvycZ5pZvfLWWZ5azWxwyLSvzWyvmV0SnFfpn2lwmcvNbFnw9zwjZHrcHKMl1VnZx2h5ag1Or7TjtByfaaUeowC4e7X5AWoD2UBXoB6wGOhVbJnrgScjrNsK+C7435bB1y2D8z4nMDCRAW8Bw2NYZw+ge/B1e2AD0CL4firw83j4PIPzdpYw/UXgyuDrp4FfxbrWYsfBFqBRDD/T7sBXIcdfmzg9Rkuqs9KO0fLWWpnHaXnrrKxjtOinup05nAJ86+7fuft+4AXg4lKuOwx4x923eGBEuneA88ysHdDM3Rd44DcxHbgkVnW6+wp3Xxl8vR7YBETs4RgF5fk8Iwp+oz0HmBWcNI3yf54QvVp/Drzl7rujUFMkpalzLPBU8DjE3TcFp8fbMRqxzko+RstVa0kq6DiNVp0VfYwC1e+yUgdgTcj7tcFpxY0Mnu7OMrNOR1i3Q/D1kbZZWXUeZGanEPgGkh0y+Y/BdR4zs/oxrrOBmS00s0+LToGB1sA2dz9whG1Wdq1FrgSeLzatsj/THkAPM/sk+Nmdd4R1Y3WMllTnQZVwjEaj1so6TqPymVLxxyhQ/cIh0nXW4m11XwOS3D0FmEvgG8Hh1i3NNsuqPHUGNhD4tvgscIO7FwYn3w0cDwwgcOp5Z4zrTPRA1/+rgcfNLLmU24xFrUWfaV9gTsjkWHymdQhcXjgbuAqYZGYtDrNurI7RkuoMbKByjtFo1FpZx2m0PtOKPkaB6hcOa4HQb4MdgfWhC7h7rrvvC76dCJx8hHXXBl+XuM1KrhMzawa8AfzW3T8NWWeDB+wDphA4jY1ZncFLCrj7d8AHwEkEHiLWwsyKxi+PxudZ7lqDLgdmu3t+yDqV/pkGl3nV3fPd/XtgOYE/GHF1jB6mzso8RstdayUep+WqM6gyjtGDG642PwRS9zugC4du+PQutky7kNeXAp/6oZs83xO40dcy+LpVcN4XwEAO3ew7P4Z11gPeBf4rwnbbBf9rwOPAn2JYZ0ugfvB1ArCS4M034J/89Ebff8Tydx8y7VNgcBx8pucB00I+uzUELnPE2zFaUp2VdoxGodZKO07LU2dlHqMHtxuNjcTTD3A+sILANc57gtMeAC4Kvv4/YGnwF/M+cHzIujcC3wZ/bgiZ3h/IDG7zSYI9y2NRJ3AtkA98HfJzYnDee8CSYK3pQJMY1pkarGVx8L9jQrbZlUDrmm+D/wPWj4PffRKwDqhVbJux+EwNeBRYFtz3lXF6jEass7KP0XLWWqnHaTl/95V2jLq7Hp8hIiLhqts9BxERiQKFg4iIhFE4iIhIGIWDiIiEUTiIiEgYhYNIFJjZB2Y2rNi0/zKzf5jZ22a2zcxej1V9ImWlcBCJjucJPPMmVNEzcB4BflHpFYmUg8JBJDpmARcUPfTMzJIIPK76Y3d/F9gRu9JEyk7hIBIF7p5LoDdt0VM0rwRmunqZShWlcBCJntBLS5EeqyxSZSgcRKLnFeBnZtYPaOjui2JdkMjRUjiIRIm77yTwyOfJ6KxBqjiFg0h0PQ+cQGAISADM7CMCT/X8mZmtLd7kVSQe6amsIiISRmcOIiISRuEgIiJhFA4iIhJG4SAiImEUDiIiEkbhICIiYRQOIiIS5v8D1btqoEFTGLEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#One plotting sequence\n",
    "\n",
    "#Calculation of current, including error propagation\n",
    "V2 = unumpy.uarray(N10[:,1],N10[:,1]*0.1);\n",
    "V1n = N10[:,0];\n",
    "V1e = np.repeat(0.001,len(V1n))\n",
    "V1 = unumpy.uarray(V1n,V1e); \n",
    "R = 50000;\n",
    "R = ufloat(50000,0.01*R)\n",
    "Vn10 = V2 - V1\n",
    "In10 = Vn10/R;\n",
    "\n",
    "LnIn10 = unumpy.log(In10)\n",
    "\n",
    "#Plot in normal scale\n",
    "plt.errorbar(N10[:,0], unumpy.nominal_values(In10), unumpy.std_devs(In10), marker = 'x')\n",
    "pyplot.title('I vs V1 diagram for -10C')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()\n",
    "#Plot in natural log scale, together with fitting line of the best fit\n",
    "p,cov = polyfit(N10[:,0],unumpy.nominal_values(LnIn10),1,w=unumpy.std_devs(LnIn10),cov=True)\n",
    "lnItheo = p[0]*N10[:,0] + p[1];\n",
    "plt.plot(N10[:,0], unumpy.nominal_values(LnIn10),'x')\n",
    "plt.errorbar(N10[:,0], unumpy.nominal_values(LnIn10), unumpy.std_devs(LnIn10), fmt='none',marker = 'x')\n",
    "plt.plot(N10[:,0], lnItheo)\n",
    "pyplot.title('In(I) vs V1 diagram for -10C')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('In(I)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final data acquicision is then finished by using the following code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(len(Dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "\"\"\"Dataset = np.array([N100,N70,N40,N10,P25])\n",
    "DataTemp = np.array([-100,-70,-40,-10,25])\n",
    "DataTempE = np.array([4,4,4,4,4])\n",
    "DataTemp = unumpy.uarray(DataTemp,DataTempE)\"\"\"\n",
    "Num = len(Dataset)\n",
    "fig, ax = plt.subplots()\n",
    "i = 0;\n",
    "for Data in Dataset:\n",
    "    Vd = Data[:,1] - Data[:,0];\n",
    "    ErrorV = Data[:,1]*0.1 #The error of V1 can set to be zero \n",
    "    Vd = unumpy.uarray(Vd,ErrorV)\n",
    "    I = Vd/R;\n",
    "    name = \"Temp:\" + str(DataTemp[i]) + \"C\"\n",
    "    plt.errorbar(Data[:,0], unumpy.nominal_values(I), unumpy.std_devs(I), marker = 'x', label = name);\n",
    "    i = i + 1\n",
    "leg = ax.legend();\n",
    "pyplot.title('I vs V1 diagram of various temperatures')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    However, due to the zero voltage of -100C and -70C, the current will start with an array of I = 0 which will make log(I) undefined. Thus, to fix this, the data that has got I = 0 has been removed from the data analysis. Since the approximated form of the equation is only applicable at a V>= 0.1V, therefore the data that has got V < 0.1V is temporarily removed from the analysis. For those data which has got too few datapoints (such as T = -100C), a non-linear fitting method is conducted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g4raO3h3ZsTyCcyFX6fZIE9r3bKC2xLIh7gQc+VltFTplxMZzG9l1aRcvdXiJxh6Ny6QNfRGcToUm/+K2Xs+/R9Lvzpw5FEuXIf4E9m6otrxbJi8M96qJXYsvlBYDK4eBtECLAeBcrZzU6ZQHMRkxvHvwXYJqBzG21dgya0c3B50KT8vUKrRIrcL57S6c3H+FzgMb0fFBP7VllQ2GVFg5HAxpMH6LbgwVDIu0MH3PdKSUzOs+DztRdp0/ereSToWn4fffEdVvGif2XKFjP1+C+vupLem2KTEUtykXVo2F+JMw8nuo0678BeqUKT9G/Mih2EO82flN6lWtV6Zt6eagU6GRUrJ79RmO/XOZwD4NuWuQP0IItWXdFiWG4pYSNr0I5/+GQZ9D457qCdUpEyJTIvkk5BN61O/B0CZDy7w93Rx0KixSSvauO8vRndG079mArkMb26wxwA1Ccf81D47+DPdPh4BRqmnUKRuMFiNTd0/F1cGV2d1ml8v3WB9z0KmQSCnZvyGSsB2XaNujHncPb2LTxgD/heK2IHG0t/svFHfwUvj3Q+jwONz7uroibQCtR1stiiXhSzieeJyPenyEl6tXubSpuTcHIUSAEGK/ECJMCBEshOistiYd2+Pgb+cJ2RZFq3vqcs/IZjZvDPBfKO5GCSbWBjRRQnGf+h02vwZN+8CAj6ECnGdZ4xJ3hhphG20mmN7xhOMsOrKIAf4D6OPXp9za1dwKaSHEH8BCKeVWIUR/YLKU8r6SjtFXSFdO8uIl+f7w/XXbg7ec58Cm87TsVof7x7RA2FXQG+blw7D8IfBqBk9sBmftJfXRGlmhoZwf8wTCYsLO2UnToTEADCYDI38bSYYxg18G/YK7c+lmzLO1FdISyMv+4Q7EqKhFR8MUFTMpZFsUBzadp/ldPtxXkY0hKRJWjoAq3jB6jW4MN4mtRVv9PPRzIlMjmdttbqkbw43Q4pjDK8A2IcSHKObVrahCQoingacBGja0vcVMOndG2NUwpnU4g0lIVv/xFIv7LIajNdi3/hxNg2rR8/GW2FVUY8hMhBXDQJphzDqoWkttRdexbNkyAMaPH6+yksLkRVvFYsJO49FWD8Ue4ocTPzCy+Ui61SvyNlimqGIOQogdQFGxkacBDwCTpJTrhBAjgG+BXgULSikXAYtA6VYqQ7k6GqRgzKQD209j+teLxoHe9BrfquIagzEbfnoUUqPh8U3g1VRtRYVwz3bBM8uVnKg0zeWGzou2qvUMbxm5GUzfPZ0G1RrwasdXVdGgijlIKQvd7PMQQnwPvGz9cw2wpFxE6dgUeTGTTBZJ64TumM550ai9F70ntK64KT4tZlg3AaIPwYjvoGEXtRUVIicqjY6X6iKkIGFJOF4T2hZpEFJaOHfuA3x8hlC1avNy1Wio3RRD7aaaNQaAD4I/IDYrlu8e/A43RzdVNGixWykG6AHsAnoCZ1RVo6NJ8mImnXBphHNGf3zb1qTvhDbYV1RjkBJ+fwtO/gYPvgutBqutqEhyIlMRUmCHQJos5ESmFjIHKS2cPDWdmJhVODhUK3dz0PoU1l2XdvHLmV+Y0HYCAbUCVNOhRXN4CvhUCOEAGLCOK+joFMRvzP84910EDVvV4MGn22DvWEGNAWDf/+DgN9D1BejyrNpqisXZ3x0pJBYJ9g72OPtfP4gqpYWTJ6cRc2U1fr7P4eur3XNRg2RDMrP3zqaZZzOeba/utdGcOUgpdwMd1daho21OH4rlr+8iqN/ck37PtMXB0V5tSWVH+Fr4Yzq0Hgq956qtpkScfatzuEEMnlmudBve87q3huuMwe95/BtNqhDrT0oLKSVz988lNTeVb3p/g5O9k6p6KvCjlk5F5ezhq+xYFkGdJh70f7YdDk62bwwjv9l3LRz3dVzYDRuehYbdYMjXYKf9f9lUVwMXaiaXYAwv6MZQBJvPb2Z71HZeCHiB5jXKt6utKDT35qCjUxKRYfFs//Y4Po2qM+D5djg6274xgBJtNdXVjuDUTGXlM8DVk/DzKPD0g0dXgqOLqhpvloJTWKW0EHHyLa5cWUsjvxdp1Ohl3RgKEJsZy4IDCwjwDuCJ1k+oLQfQ3xx0bIgLRxPYtvgY3r7VeOiF9ji5VIxnmyKjraZdURL2OLjA6LXgVkNtmbeFlGYiIqZYjeEl/P1f0Y2hAFJKZu6ZicliYn73+djbaeOBRzcHHZvg4vFEti4Kx6t+VQa+2B4n14phDFA42uqh+Fj4cThkJcGo1eDpq7bE2+KaMcSuo1Gjl/H3f/nGB1VCVp1axb4r+3g96HUaVtfOgt6K8x+mU2EoGDPp0skktnwdTo06VRj4UgDObo5qyit18kdbdRUWRv07SckBPWo11FVvKuOdIKWZExFvEhu7nkaNXsG/0YtqS9IkUWlRfHz4Y+6uezfDmw1XW8516Oagozki3DMJ90yn99UwvFN82fLFUTxquTLo5QBcqlQsY4D/oq2mugh+FYtxj/obBv0Pmha7VlTTKMYwmdjYDfg3mkSjRi+US7u2ForbZDExbfc0HOwcmNNtjua623Rz0NEU+WMm7Vozn4dOPYu7VxUGvRyIa1V1p/aVJe4GCxPif8ArYzX0mAIdyi5xfFkipZkTJyYTG7cBf/9XaeT3fLm17RJ3BrcrJ8gKFZpe/ZzH8uPLORJ/hPfueY/aVWqrLacQ+piDjqbIi5nklelLrxPjkW65DH4lALfqFdcYAFZ1OsOwjJUQMAbum6K2nNtCMYY3iI3bQGP/18rVGLJCQ2m4eQHewWu5OP5JzedqOJV0ii/CvqCPbx/6Neqntpwi0c1BR1ME1Q6idnpDHop4FoNjBh0n1KKKu7PassqWM9vh11eg8QMw8BObTNhjsZg4fuJ1YuM20tj/Dfz8nivX9m0pFHeuOZe3dr+Fh7MH07tM11x3Uh56t5KOpqhnaMzQE89jsjPQ8/nG3NXENvqPb5uYUFj9ONRurQTTs7e9MRWLxcSJE68Rd/U3GjeejJ/vxHLXYEuhuL8I+4IzyWf44oEv8HTxVFtOsejmoKMZEqIz2PhpKG413Bn66v1U93JVW1LZkhylJOxxq2FN2FNNbUXFUlyOhvzG0KTxZHxVMAawnVDcoVdDWX58OY80fYR769+rtpwS0c1BRxMkxWSy6dNQHBztGTIpsOIbQ1YSrHgEzDnwxG9Qraj0JtpG6UqaxNWrW2jS+E18fdWNkan1UNxZxiym7Z5GnSp1eKPTG2rLuSG6OeioTnJsJhs+CUXYCYZMCsTdW5349eWG0QA/PQYpUTBuI3irH0fnRhRM4GOxGDl+4lXFGJpMwbfhU2pLLPUprGaTiZjTETRo1bZU6vso+COi06NZ2ncpVRyrlEqdZYk+IK2jKilXs9i4MBSkZMikQDxqV3BjsFhg/dNwaT8M/QZ8yz/9462Sl8CncUINEpaEk30hkePHrW8MTd7ShDGUNiajkV8XvsuaudNIiom+4/r2XN7D6tOrGddqHEE+QaWgsOzR3xx0VCM1PpuNC0MxmxVj8PTR/tPUrZIXaXXVxK7Khj+mw4mN0Gc+tHlYRWU3T/4EPhZzLhGRr5PMPzRtMo2GDZ9UW16pY8wxsPHD+UQdDaXn+InUqFv/jupLzUll5p6ZNHZvzIsdbGeluG4OOqqQlqgYgzHXzJBJgdSsV1VtSWXPvi9h/xdw1zPQtfzWANwpeQl8zJiIbfcNGQRXWGPIycpi/XtziDkVQd9nXqbN/b3vuM75B+aTZEji8wc+x9nedqZl6+agU27kxUyq+dkiNi4MJddgYvArgXjV1+4snTslLxT3uUOraLxtKrQcCH0X2NRaBiWBz0XcfdaBRzhNm06nYYPxNz7QxsjOSOeXBTO5eiGS/i+9Totudz6b6PcLv7P1/FaeD3ieVjVblYLK8kM3B51yI8I9k2PVJZ4f7seSKRj0ciDeDSuuMeSF4g5KO0r9ra+TUacjVR9eDBoJyXyzWCy5NOm+h/iEimsMmSnJrJ0/g+Qrlxn02lQad7zrjuuMz4pn3v55tPVqy4S2E0pBZfmiD0jrlAthV8OY2/YKhqxhpKdk03yMG7UbVb/xgTbM3pQM/LOj+O74NKKda/PjfV+Ao21N0bVYcgk/9iLxCdtp1nRGhTSG9MQEVs2eQkrcFYZOnlUqxiClZNbeWRhMBuZ1n4eDne09h+vmoFMuHLoQwoMnn6dqrie/t1zEaecjaksqc+51zOLH8DcxCQeeaP8BHXwaqC3plsgzhoSEHTRrOpMGDZ5QW1KpkxIXy8+z3iQzJZlHpr6Nb7vSCZH+y5lf+Pfyv0zqOAl/d/9SqbO8sT0707E5DBlGHDY3pnpONlubfUOiRzRBtW1jOt9tk5NBwK+PY8hN5lm/j1nY9d7/0n/aABZLjtUY/qRZs9k0qG+bUWJLIjH6EmvnTcNkNDJi5gJq+zcplXqj06N5/9D73OVzF4+1eKxU6lQD3Rx0yhRDppGNn4aSkyhpwFYeSMyk96jFBNSyzSQ2N4XZCGseh9hjuDz2M8ua9VFb0S1hseQQHv4CCYl/0bzZHOrXH6O2pFLn6oVI1s6fgRCCkbPewauhX6nUa7aYmbZ7GnbCjrl3z8VO2G7njOaUCyHaCyH2CSHChRC/CiEqdsd0BSYn28Svn4WRdCWTfs+0pWN6MiMu+FRsY5ASfpsEZ3fAQx+DDRrD0fDnrcbwtiaMYf1HIdcS+ZQGMadPsvrtt3BwdOLROe+VmjEArIhYQcjVEKZ0nkKdqnVKrV410OKbwxLgdSnl30KIJ4E3gBkqa9K5RXINJn77PIyE6AwenNgW3zY1wZr2s0LzzwcQ+gPc+wZ0fEJtNbdEnjEkJu6kefO51K83Sm1Jpc6l40dZ//5cqrh7MHzGfKp71yq1us8mn+XTkE/p2aAngxoPKrV61UJzbw5Ac+Af6+/bgUdU1KJzGxhzzPz2vyPEXUin74Q2NGrnpbak8iF0JeycD+0fg/unqa3mljCbczga/hyJiTtp0XyepozBJe4MNcI23nECn/Ohwfzyzmyqe3kzcs57pWoMRrORqbunUs2pGjO7ztRsjoZbQYvmcAzIs93hQJFTPIQQTwshgoUQwfHx8eUmTqdkjLlmNn9xhNhzqfR+shX+gd5qSyofzu6AX18C//th4Gc2tcjNbM4h/NizJCbuokXz+dSrp51B1NLK8HbmwF42fDCPGvUaMGLWO1T1rFGqOr85+g0RSRHM7DKTmq41S7VutVDFHIQQO4QQx4r4GQw8CTwvhDgMVANyi6pDSrlIShkkpQzy9q4kNyCNYzKa2fLlUS6fSaHX+FY0DdJeXtwy4coRJWGPd0sY8T042E5KU7M5h/DwZ0hM/JsWLRZQr96jaku6jtLI8Hbi3538+sm71G7chOEz5+NW3b1UNYbHh7MkfAmDGg/iAd8HSrVuNVFlzEFK2esGRfoACCGaAQPKXpHOnRA1dhxm7Dje+RWiTyXzwLiWNOtse/kJbouUi0rCHhcPJWGPi+3MnzCbc9j6+0O4uETSquW71K07XG1JhbjTDG9Htm9lx7df0rB1Wwa/MQMnl9JdhJhtymbq7ql4u3kzpbNt5v4uDs0NSAshakkprwoh7IDpwNdqa9IpGQt2hDjfR9zxJO4f24IWXW17lsbNMPKbfVSxpLPUPB1M2fDkNqhuO+dtNhs4Gv4MLi7nsYsaSc0mfdWWVCR3kuEt+Lf1/P3Dt/h36MRDk6bg6FT6Qe8+DfmUC2kXWNxnMdWcKlYoGM2ZA/CYECIvZOUvwDI1xeiUjNls4e+qd5MpG+I/0I1Wd9dVW1K5kOVkYkr8XCw557Ebux5qtVRb0k1jNhs4enQiScl7qH18PO4x95BwPhyvCW1x9tXem0+/90feUnkpJfvX/czeNStp1qU7/V98DXuH0s/Nvf/KflZGrGR0y9F0qdOl1OtXG80NSEspP5VSNrP+TJFSSrU16RSNxWxhzVd7yZSN2eO7jpnJLxJ2NUxtWWVOcEo6EzI/IjDrKC83f4tgT22mpSwKszmbo0efJil5D414E/eYe7BDIE0WciJT1ZZ3x0gp+WflMvauWUnrHg8w4KU3ysQY0nPTmbFnBn7V/Xi5w8ulXr8W0Jw56NgGFo3jX1MAACAASURBVItkx/IIEo/lsq/hesLr/oPRYiQ4LlhtaWWO2DGLwQk7edv/GX7x7snelAy1Jd0UZnM2R44+TVLyXlq1fJ96jUcihcSCRDjY4exfugO15Y20WPhz6dcE//oL7fsMoO8zL2NnXzYRcN89+C7xWfEs6L4AVwfbCqZ4s2ixW0lH40iLZOf3EZw5FIdvL1eWpv2NnQUcHR0rfsyk/V/T8di3fFv3Yb6sNxIXO0E3D+0nKlKM4SmSk/fTquUH1KkzFIDDDWLwzHKl2/CemuxSulksZjN/fPMZx//+k6CBD3Pv6PFlttbgz4t/suncJia2m0hb79LJL61FdHPQuSWkRbJr5UlO7o+l88BGdBrQCIcXmhLumU7vF9+v2KExTmyC36dAi4dYZ3iKRolmPu/bXPMB9czmLI4ceYrklIO0avUhdXyGXNuX6mog1dXA/TZsDGaTkS2ff8Tp/bvpNnw0XR55tMyMITE7kbf3vU3LGi2Z2G5imbShFXRz0LlppJT88/NpTuy5QlB/PzoNaATAg/9bx4MqaytzLh6AX56C+p3gkSVUWxpGtRyTTRhD2JEJpKQconWrD/HxGXzd/vHjbTs/gyk3l18XvkNkyCF6jHmSoIFll5dbSsmcfXPIyM3g2z7f4mhf+mMZWkI3B52bQkrJ7tVnOPbPZTr0bUjngY3UllR+JJyBn0ZC9Xrw2M/g6MqqiV3VVlUiy5YtQ4hc2rb722oMH+HjY/vxfvKTa8hm4wdzuXg8nF4TnqN97/5l2t6mc5vYeWknrwe9ThPP0gnvrWV0c9C5IVJK9q47y9Gd0bR/oAFdhjSuELFjboqMq7DiERD2MGYtVLGN0AhC5OJd62dSUqIrpDEYMjNY/+4crpw5Rb/nJtHq3p5l2t6VjCu8e/BdOtTqwJiW6keqLQ90c9ApESkl+zdEErbjEm3vq8/dw5pUHmPIyYCVwyEzHp74DWrYRkYvkymTOl6rcHC+RPNaCyqcMWSlpbJuwUwSLkbx0KQ3aXbX3WXankVamLFnBhZpYV73edjbWA7w20U3B50SOfjbeUK2RdH6nrrcM7Jp5TEGswnWjofYo/DoT1Cvo9qKbgqTKZPQg4/j4HIJn6MTEUn1yKmWZtMzkfKTkZzE2nnTSY2LZfAb0/APvLVwGrfDTyd/4kDsAWZ1nUWDaraV6vVO0Nc56BRL8JbzBG++QMtudejxWPPKYwxSwuZX4cwfMOAjaG4bw+0mUwZhR54kzXAEn/CJuMd1qTCL2wDS4q+yatabpMVfZeiU2eViDJGpkSw8vJB76t3DI00rV/YA/c1Bp0hCtkVxYNN5mt/lw31jWiDsKokxAPz7IYR8B/e8BkFPqq3mprhmDGlhtKj9LqarXliQ2DvY2/ziNoDkK5dZM3c6udlZDJs+j7rNWpR5myaLiem7p+Pi4MKcbnMqz8ORFf3NQacQYTsusm/9OeqaImny13vYVRJjGPnNPr5Y+Db8NQ/aPQo9bSMBocmUTtiR8aSlhdG69afUa/0IhxvEcM4rSbPxkm6FhIsX+HnWm5hycxg+c0G5GAPAt+HfEp4QzvQu0/F2q3xpAfQ3B53rOLozmj1rz9K4gzfyyJesqZNG76thFXtxm5W2OSFMTP0EGvWAQZ/bRMIekymdsLDxpKWH06b1Z9SqpXSBPfycdhL2FEdeXuihr3Uotkxc5FnWLpiJvYMDI2a+Q8365dPnfyLxBF8f+Zp+jfrxoJ9tdCuWNvqbg841jv1zmX9XnaZRey9qDTQxveNpVvhf4ak/nqr4AfViw5mUPI8Lzg0J7feNTSTsMZnSCS3CGCoKl0+eYPXbU3FyceHR2e+VmzHkmHOY+u9UarjUYNpdtpXutTTRzUEHgBN7Yvj7x1P4tq1J3wltCEk4jElILHZU/IB6KZfI/WEYqY5VGB7wHkMjrhKcmqm2qhJRjOEJ0tPDadPmM2rV0mY+hpIoKTd0VHgYaxfMoIqHJyNnv4eHT/nlyvhf6P84l3qOOXfPwd3Z9sdrbhfdHHQ4uf8KO1ecpGGrGjz4dBvsHe0Iqh2EgxRKQD27ChxQLzsFVg5HGjMZ1fY9rrjUxmiRmo60+p8xHKNtm8+p5W17xlBSbuhzhw+w/r05eNTyYeTsd6nuVX79/cGxwXx3/DtGNBtB93rdy61dLaKbQyXn9MFY/vougvrNPen3TFscHJUFPgG1Apgf0pQxkXVY3GdxxRxzMOXAqjGQeJYLg5Zy2s0fpMRRw5FWjcY0QsMeJz39OG3b/A9v7z5qS7otissNfWrfv2z6aAFeDfwYMesdqnh4lpumTGMm0/dMp361+rwW9Fq5tatV9AHpSszZw1fZsTyCOk086P9cOxycrl/52TK1Ci1Tq+BbEY3BYoENz8KFf+HhJTRv04d2hw+Q6mrH531baTKgntGYRljY46RnRFiN4Uap2LVLUbmhj+3czh/ffE7d5i0Y+uZsnN3cylXTB4c+ICYjhu/6fYebY/m2rUV0c6ikRIbFs/3b4/g0qs6A59vh6FQ4JIDvD9+roKyc+HM2HFsHvWZDu+EAuBssuBssGjWGVELDHicj46TNGwMUzg196upl/lr6Nb7tAhn82jQcXVzKVc8/0f+w7sw6nmzzJIG1bCezX1mim0Ml5MLRBLYtPoa3bzUeeqE9Ti6V7GtwcDHs+RSC/g/ufuXaZq1GWlWMYRwZGadp1/ZLvLzKNshceWGo3RRD7aYcu3iOf39cTuOgu3jo5TdxcCrfmWIphhRm7Z1FU8+mPB/w/I0PqCToYw6VjKjjiWxdFI5X/aoMfLE9Tq6VzBgifoMtb0Dz/tD/A82vZTAaUwgNG1vhjAFgyKuB1Gpwgn9/XE7zbvcycNJb5W4MUkrm7p9LSk4K73R/Byd77U9hLi8q2Z2hcnMpIomtX4VTo04VBr4UgLNbxU5WUohLh2Dd/ylB9B75FjQeXdNoTCE0dBwZmWesxnC/2pJKDSklf/+whMObN9Lm/t70fvoF7FT4PLae38ofUX/wcoeXaV6jebm3r2V0c6gkXD6VzJYvj+KWm0CHk9twqbJYbUnlxshv9uFjusynmZOhWh0YtQqctD3gmGcMmVlnaNfuK7xq3gcoSXzAtjO4WSxm/lzyFUf//J3AfgO5f9xTCLvy78SIy4xj3oF5tPduzxOtnyj39rWObg6VgJizKfz25VGqebkSdPYPnMhRW1K5Ut2cwltJ08EZGLMOqnipLalEjMZkQkLHkZV1lnZtv6ZmzR5qSyo1LGYzv3+5kIjdu+g8ZDjdHx2nSkA7KSWz9s7CZDExv/t8HOz0W2FBVBlzEEIMF0IcF0JYhBBBBfa9JYQ4K4Q4JYSwvdU9GiM2MpXfPj9CVQ9nBr8SQKR7Iqv9Yit+OIw8cjN5PWUWHpYkIgYth5qN1VZUIrm5SYSEjrUawzeFjME92wW/RE9yotJUUnj7mIxGfl34LhG7d9H90XHc89jjqkU6XXN6DXti9vBqx1fxre6rigato9aA9DHgYeCf/BuFEK2AR4HWwIPAl0IIbXcMa5i4C2n8+lkYrtWdGPxKIGdyIpjW4UzliZdkNpHy8+M0yT3DxJYz6BfnrumwGLm5SYSGjSUr65zVGO69bn9OVBodL9WlcUINEpaE25RBGHMMbPxwHmcP7eP+x5/irqEjVNNyMe0iHwZ/SNc6XRnZfKRqOrSOKuYgpYyQUp4qYtdg4GcpZY6U8jxwFuhcvuoqBvEX0/n1szBcqjoyZFIgVT2dCY4LrjzxkqSErZPxiNzO9CYvss37Hk2HxfjPGM7Tru2iQsYAkBOZipACO4RNJfHJzc7il3dnc+FICH0mvkSH/oNV02K2mJm2exoOwoG373670uVouBW0NpW1HnAp39/R1m2FEEI8LYQIFkIEx8fHl4s4WyEhOoONn4bi6GLP4FcCqVZDWVBUaeIlAexeCMHfciXoeb6rM1TTYTFycxMJDR1DVtZ52rdbTM2a9xRZztnfHSkkFiTCwc4mkvgYMjJYO28Gl0+eoP+Lr9O2p7rhPpYfX05YfBhTu0zFp4qPqlq0TpmNwgghdgBFXf1pUsqNxR1WxDZZVEEp5SJgEUBQUFCRZSojiTEZbPwkFAdHe4ZMCqS6l+u1fXnxksI90+n94vsVM14SwNHV8OccaDOMOv3n0e6HQ5oNi5Gbm0BI6Fiys6No324xNWrcXWxZZ9/qHG4Qg2eWK92G99RsEp+8PA19J/izdv4Mki5fYtCrU2nSqYuquk4lneKLsC/o7dubAY0GqKrFFigzc5BS3s76/mggf9D2+kBM6Siq+CTHZrLxkzDs7AVDJgXi7l14umaFjpcEEPk3bHgO/O6BIV+CnZ1mw2IoxjCG7OxLNzSGPFJdDaS6Grhfo8aQhyk3jVWzp5CWEM+QyTPxa198Qp/yINecy9TdU6nuVJ0ZXWbo3Uk3gdbmb20CfhRCfAzUBZoCB9WVZBukXM1iw8JQkJIhr3bAo3bR8/grdLykuONKlNWaTWDkCnBwBrQZFqOwMXS7qeNsYX2DXXQIsTFbkE6CR6bOoX7LNmpL4qsjX3E6+TSf9/wcT5fyi/Rqy6g1lXWoECIa6ApsFkJsA5BSHgdWAyeA34HnpZRmNTTaEqnx2ayb/iemlDQGvxKIp4+2npDLhdTLsGIYOFWBMWvB1UNtRcWSk98Y2i+5aWOwBS7v+INLUWuRpmw6n7lEDYNRbUmEXQ1j6bGlDG0ylPsa3Ke2HJtBrdlK66WU9aWUzlLK2lLKvvn2zZdSNpZSNpdSblVDny2RlpjNxoWhmIU9XQzbqFlPewOuZY4hFVYOh5x0GL0G3OurrahYcnLiCQkZTXZ2NAHtv6WGp/JWs2zZsmurn22V+KjzbFj+NRLBXedicE/PupanQS2yjFlM2z0NHzcfJnearGxcNkD50SkRrc1W0rkFMpINbFwYSq7BRG2HjfzeMKLir10oiClX6UpKOAUjfwCftmorKpacnHhCQkdjMFwmoP23eHqqO0Bbmlw5e4rVc97C3sWFzuevUi3HhLDmaVCTjw9/zKX0S8zrPo+qTpXwwekO0M3BRslMyWHDwlAMGUaaj3Xj7Y77Ks/itjykhE0vwPl/YND/oLF2A9Pl5FwlJHQ0OTlXCGi/FE/Pu9SWVGpEnzjGmrnTca5alccWLCSp92Tig4bRcNlS3ALVy42w9/JeVp1axZhWY+jko65J2SK6OdggWWm5bPwklKzUXAa+FMAph7DKs7gtP3/NhaOroOd0CHhMbTXFkt8Y2rdfiqdn4XWdthoW40LYYda9M4tqNWoycva7uNeqjaF2U5ICBqtqDKk5qczYOwN/d39eCnzpun2Hs2vzRWIHDkclq6TONtDNwcbITleMIT3JwEMvtMfH371yLW7L49C38O9H7HDtB/e8rraaYsnJiSMkdBQ5OXEEtF+Gp0fhJ1hbDYtx5tA+NnwwF8+69Rg55z2q1dBOQMN3Dr5DUnYSC+5ZgIvDf1nlDkclM/rSID5K6MzoJft1gygBrU1l1SkBQ4aRjZ+EkRafzYAX2lO3qTIjp9Isbsvj1FbY8jqHnTvzrfsL9NLonHXFGEaTk3OVgPZL8fAo2rSLCouh1QVueUTs3sXWLz7Gp3FTHp4yB5eq//XnD31N3TUNf1z4g82Rm3mu/XO0rtn6un37IxPJlfZYsMNosrA/MpGOvvrU1qLQzcFGMGQa2fhpKClxWQx4vh31m1//ha7wi9vyiD4Ma5+EOu2Z6ziNuCrOBKdmam6BmyEnlpCQ0eTmxpdoDJAvLIYEewd7zYfFOPrn72xf/AUNWrZhyOQZOLlqJzdGQnYCc/fPpXXN1kxoN6HQ/i7+NXESZowSHB0c6OJfUwWVtoFuDjZATraJXz8LI+lKJv2fbUeDljUKlanQi9vySIqEH0dAFW/CHlrOwYhkLAKGhZ1lbUATzRiEwXCFkNDR5OYmEhCwDA/3jiWWt5WwGACHN29k1/eLaRTQkYGvTcXRyVltSdeQUjJ772yyTdks6L4AR7vCmQ47+nqyssEm9mfVo8vwV/W3hhLQzUHj5BpM/PZ5GAnRGTw4sS2+rSvpk05mAqx4BKQFxvzCP5muWEQyCHEt2qoWzEExhlHk5iYRGLAMd/eb62LRelgMKSUH1q9mz6ofaHpXNwa89Ab2DtpKM7vh7Ab+jv6byZ0m4+/hX2y5jq5xdHSNA90YSqREcxBCpFN04DsBSCmlNr/JFQRjjpnf/neEuAvpPPhUGxq1086AX7mSmwU/PQppMTBuE3g1oZtjJnYSLEgc7e00EW3VYIixvjEkERiwHHf3m5+to+WwGFJKdv/0HQc3rqXVPffT99lXsLPXVpqV6PRo3j34Lp18OjG65eiSC4/fXD6ibJwSzUFKWa28hOhcjzHXzOYvjxB7LpXe/9ca/0BvtSWpg8UM6yZAdLCyyK2hsj4gyL0K7aJzNRNt1WCIUcYYjEkEBnyHu7sy9mPrOZ+lxcLO7xYT+vuvtOv1IL3+7zlV8j2XhEVamLFHCaY37+552Alt6bNV9KuoQUxGM1u/Osrl0ykEZP+N06dvqC1JHaSErW/Cqc3Q7z1oOfC63e4GCw2TTZowhsMhowoZg61jsZjZ9s1nhP7+Kx0HDKHXhOc1ZwwAK06sIDgumDc7vUndqnXVllNh0N4nXckxGy1s/foYl04m88C4ltQzn1dbknrs/QwOLYZuL8JdE9VWUyTZ2Zc5HDIKkymFwMDvCxmDrS5uM5tMbPnsQ47v2kHXYY+RdDWQDR+Hqi2rEOdSzvFpyKfc1+A+hjQZoracCoVuDhrCbLLw++JjXDyeyP2jW9Ciax0i3DNZ7RdbeUJi5BG+FrbPhNYPQ6+3iyyyamJXVcNxZ2dHExJqNYaA73Gv3v66/ba6uM2Um8umjxdwat+/3Dt6PN2Gj9Zk/gOjxchb/75FFccqzOo6S5MabRndHDSC2WzhjyXHuXA0gR6PNaNV97qEXQ1jWoczlS5m0pzPvsa0biL43g1DvwYNdmX8ZwxpBAZ8T/Xq7QqVscWcz0aDgQ0fzCXy8EEeePJZOg16RG1JxbL46GIikiKY2XUmXq6VdLJGGaK9/7pKiMVsYcfSE0SGxdN9RFPa9FBCTgfHBVe+mElXI3g9+W1iHerCoyuvJezREv8ZQ3qxxgC2l/M5JyuTde/M5GL4ER58bhIBff8La+0Sd4YaYRvJCtVG19KxhGMsOrqIgf4D6eV7O0kndW6Ebg4qY7FIdiyP4Ozhq3R7pAnte/6XJbXSxUxKi4EVw8gRzrzjORdctTcPPTv7EiEhj2EyZViNofgQ4XmL2855JeE1oa2mF7dlp6exZu50rpw5xYCXJ9O6xwPX9mWFhtJw8wK8g9dycfyTqhuEwWRg6u6peLl6MeWuKapqqcjo5qAi0iLZ+X0EZw7F0WWIP4G9G163Py9m0pjIOizus7hix0wypCkJewwpTPeZT4h3PYJTM9VWdR3Z2RcJCRmFyZxJYGDJxpBHqquBCzWTNW0MmSnJrJ7zFgmXLjD49ek079r9uv1ZBw8hLCaEtCCNRtUT+Hwa8innU88z9+65VHfS7nW1dfQV0iohLZJdK09ycn8snQc2ouODfkWWqxQxk0y5sHosxJ/k9JDv2RBfV5WwGCWtScjKiiIkdDRmczYdAn+gWrXWhcrYImkJ8aydN52MpEQenjKbhm3aFyrj1rkT0s4BLCbsVE7gc/DKQVZErOCxFo/Rta72coNXJHRzUAEpJZuf+oooxxYE9fej04BGaktSDynh15cgchcM/pLf3YOwJMRoKiyGYgyjMJsNVmNoddOL27S8+C0l9gpr5k3DkJHBI9PmUq95yyLLuQUGcnHAVNyunKDTq0NUy9OQnpvO9D3T8avux6SOk1TRUJnQzaGckVKye/UZohxb0Dg3nM4DS85eVuED6u2cD0d+gvumQuBouqVqKyxGVtYF6xvDf8ZQEUiMvsiaedMxm0yMmLmA2v5NSixvqN0UQ+2mqibwef/Q+8RlxfF9v+9xdXBVTUdlQR9zKEeklOxdd5ajO6NpZDxOC+Phyj03+/By+OcDCBwLPZTk73lhMRolmFSPtJpnDBZLDh0CV1QYY4g7f45Vs6eAlIyc9c4NjUEL/HXxLzac3cD/tfk/2nsX7vrSKX30N4dyQkrJ/g2RhO24RNv76pP57xesqZtO76thFXuguThOb4PfXoUmveGhhZDPJN0NFtwNlnI3BvdsFzyzXMmJSsPsnUhI6BgsllwCA1dQrWqLYstqebC5IDGnI/jlndk4ubkxfMZ8PH1uLtyEmgl8kgxJzNk3hxY1WvBs+2dV01HZUOXNQQgxXAhxXAhhEUIE5dteUwixUwiRIYT4nxrayoqDv50nZFsUre+pS7X7M5nesfItbrvG5RBY8wT4tIHhy8Fe/dDP+Vczx6zczuFDj2Gx5CpvDAWMwVZXPl88dpS182bgWr06j855D0+fuqz/KIT1H4WoLa1YpJS8ve9t0nPTlRwNGviuVBbU6lY6BjwM/FNguwGYAWg3KfBtELzlPMGbL9CyWx16PNacw1cPV77FbXkknbcm7PGCUWvAufCYghphMfJWM5vc4ogKXIDFrBhD1arNiy1rSyufI0MPsf7d2VT3rsXI2e9R3auW2pJuit8if+PPi3/yYuCLNPVsqracSoUq3UpSygigUH+7lDIT2C2E0H4n6E0Ssi2KA5vO0/wuH+4b0wJhJ64tbjNZJI6OlWBxWx5ZSbByGJiN8MQWqFZbbUXXcPZ3J6fKFS53fB+Embb1lxZpDHllbSmt5+kDe9j86Qd4NfTlkalv41Zd23rziM2MZcGBBXSo1YFxrcapLafSYbNjDkKIp4GnARo2bHiD0uoQtuMi+9afo2lQLXo+3hI7O8UM8xa3hXum0/vF9yvHmIMxW0nYk3IJxm0E72ZqK7oOk1c8Fzq/gx2SQP/l1Gha/KwcW0rrefzvP9n21afUadqch9+ajbOb+tnybgaLtDB9z3TM0sy87vOwt9NWcqHKQJmZgxBiB+BTxK5pUsqNd1q/lHIRsAggKCioqGx1qhA1VnnCSX1yAXvWnqVxoDe9xre6Zgx5VIrFbXlYzPDLU1guHeRTj7eY5KutxUuZmecICR2NtLMQEzeGB/reeLqm1tN6AhzZvoUdS76kYZv2DHljBo4uLoXKuMSdwe3KCbJCharTVAvy88mfOXDlADO7zqRBtQY3PkCn1Ckzc5BSVtpoWFEOzQhfdZpG7b3oPaE1dvaVeMawlLBtKkT8yg/VJ7Lf9V61FV1HZuZZQkKVtJJxcWMwGStGxr3gX3/h7xVL8e/QiYGT3sLByalQmbyYScJi4uL4TTRctlQTBnEh9QILDy+ke73uDGs6TG05lRab7VbSKhcdmhLu3A3ftjXpO6EN9sUYQ4Vf3JbHvi/gwNfQ5Tm2RA1SW811ZGSeITR0DAAdAldyT3fbH+qSUrJv7U/sW/sjzbp0p/+Lr2HvUPQMn6JiJqltDiaLiWm7p+Fk78ScbnMq9zoglVHFHIQQQ4HPAW9gsxAiTErZ17rvAlAdcBJCDAH6SClPqKHzVjm5/wpHnbthdoyk7hBP7B0r8RsDwLFf4I9p0Gow9JlP6g+HSHW1Izg1s1zXMBQV6iIj4zQhoWMQwo4OgSupUqXxLdWpxbAYUkr+WbmM4F9/oapXIEZzr2KNAbQVMymPpceWcjThKB/c+wG13GxjRlVFRa3ZSuuB9cXs8ytfNaXD6YOx/PldBDHVz7C1+SJ+2Glf8SOplsSFPbB+IjTsCkMXEZyezdH6TqoE1CuIYgyjEcL+toxBi0iLhT+Xfs2R7Vto32cAacmdEKLkhxOtxEzKIyIxgq/CvqKfXz8ebPSgqlp09PAZpcLZw1fZsTwCUSeb35stxuhgrHzrF/ITfwp+fgw8/eDRH8HRhb0pGVgE1wXUU4OMjFNWY3CgQ+CPFcIYLGYzv3/1CUe2b6HToEd44MlnbmgMeRhqNyUpYLDqxpBjzmHq7ql4ungyrcs0VbXoKOhjDndIZFg82789jk+j6vg+6sG3241Kcp7KtH4hP+mxsGIY2DvD6LXgVgOAbh5VVQ+opxjDGOyEIx06rMTNzfaj4ZpNRrZ89iGnD+yh24jRdHn40TvupzcajURHR2MwGEpJ5Y1Jy0njhbovUMOlBjGRMcQQU25tVwZcXFyoX78+jo43v8JcN4c74MLRBLYtPoa3bzUeeqE9Tq4OlW/9Qn5y0pWEPVmJMH4zePpe25UXUC/V1Y7P+7Yq1y4l92wXPGQyh4NHYe/gXGGMwZibw68fv8P50GB6jP0/gh4aWir1RkdHU61aNfz8/MplQDjTmMmF1As0cGlA3ao3F+tJ5+aRUpKYmEh0dDSNGt389143h9sk6ngiWxeF41W/KgNfVIwBKtn6hfyYjbD6cYg7DqNWQd3C3RRqBNTLiUqjTbKZyx2/gSxH2jT9pkIYQ64hmw3vz+XSiXB6P/UC7Xpd30d/s+sXigqoZzAYys0YzBYzlzMu42jvSG037ayYr0gIIahZsybx8fG3dFylH3OIGjvu2sK1my13KSKJrV+FU6NOFQa+FICzW+UOBjby67389eEoOPcnDPwEmvZWW9I1ks8d5nLHD7CzONIgeAp20R5qS7pjDJkZrJ0/g+gTx+j3/KuFjKE0cj7fsjEsG6D83CJxWXEYzUbqVa2nr4IuQ27H6Cu9Odwql08ls+XLo3jUdmXQywG4VKncxgAwLGMlPbP/gB5vQgftxMBJTz/BKbvXsLM4Uu/QFJyNdTQfB+lGZKWlsubtacSdO8vASVNodU/hZFFay/lcHOm56SQbkqnpWpMqjrYR1qMyoZvDLZBkV4vfvjxKNS9XBr0ciGvVwqtOfX/4vvIscAMI+YHhGSvY5dob7nurxKLlGW01Pf04IaFjsXd0I/nyE1yq4oDXXA9OLAAAIABJREFUhLaajoN0IzKSElk95y2SLl9iyBvTaXpXtyLL5a1fkMIOoZH1CwUxWUzEZMTg7OBcaD1DYmIiAQEBBAQE4OPjQ7169a79nZubW646LRYLffv2xcPDgyFDhly379y5c3Tu3JkmTZowatQojEYjoHTLDRs2jCZNmtC1a1cuXrx4S23OnTuXVatWFbu/f//+BAT8120tpeT999+nefPmtGnThoCAAFauXHlLbRaFbg43SbKdNwdcelPVw5nBrwTgVr2wMVQ6zu6AX1/miFMHFrm/fF3CHjVJSz9GSOhYHOzd6NjhR5IdqnChZrJNG0Na/FVWzZ5CWkI8D781m0aBxc+Ey1u/EB80TDMhMQpyJfMKZmmmXtV62BWYdluzZk3CwsIICwvjmWeeYdKkSdf+dioiDEhZIoRg8uTJLF++vNC+N954g8mTJ3P27Fnc3NyulVm0aBE+Pj6cPXuW559/nrfeKvzQNH36dFasWFFkm9u3b6d376K7ZlevXo2Hx/Vdo1988QU7d+4kODiYY8eOsWvXLsxm862daBHo5nATXI1KY59rL4z2GTQZ60QVd2e1JalPTJgyAF27FQs9p2EW5T+3YdmyZddWP+eRln6M0NBxONhXoUOHH3F11WbE3lshKeYyP896k+yMNLz9xxD8u+mGx2hl/UJRpOakkpaThrer9y3ngv7uu+/o3LkzAQEBPPfcc1gsFkwmEx4eHrzxxht06NCBvn37cuDAAXr06IG/vz9btmwBYMmSJQwdOpS+ffvSvHlz5s2bd8P2hBA88MADVK16/dRrs9nMP//8w9Chygyxxx9/nA0bNgCwceNGHn/8cQBGjBjBtm3bbvr8UlJSAKhRo0ahfWlpaXz22WeFzGbBggV8/fXXVKtWDQAPDw/Gjbvz7t1Kbw4R7pms9ostNhtb/MV0fll4mFSnDH5q9wkv7J9Y+TK3FSQ5SknY4+oJo9YQ61aNi54OBKdmqiorLS2c0NCxODhUtRqD7UfzTLh4gVWz38RkzGXEzHdwqWrb52Q0G7mScQVXR1e8XL1u6dhjx46xfv169u7dS1hYGKb/b+/ew6Kq9gaOfxcMMAgKIioqKeqUN1DAS8e0TiZpWaYZeXvL1KOlvXXK3rKLN7KraeVrnvfUiZOamqiVqZk3NCvzeAdRVBIVBQ0Q5CIoIsx+/5iL4AzIcJk9A+vzPDwOe8+evdYsmd/stfb6rZISYmJiAMjLy2PQoEEcPnwYd3d3oqKi2LFjB2vXrmX27Nnm19i/fz8xMTEcPnyYb775hvh4w9/y4MGDyczMrHJZLl26hL+/P66uhkH0wMBALly4AMCFCxe44w5DO7m7u+Pl5WX+0L+dbdu2ERFhPWfpjBkzeP311/H0vBlQc3JyuHHjBu3atbN6TE006FtZ4zPjmRF+ihKhsGbbZIt0F1lpBaz/3zhKNcVs1C3mimcurnpXDmYcbHhzGEyuXjbMZSgpgnEbOKg0cYi0GPn5CcTFP4NG05jwsG/w9Ay0exlqW/rpU3z3/mw0bm5EznyPZoF3AI67pOftKIrCxcKL6NHTxquNzXfQxMbGcuDAAXr1MnSpXbt2zfwh7Onpae6KCQkJwcfHB41GQ0hICCkpKebXGDx4ME2bNgVg+PDh7N69m9DQUJu+3ZvqcitTfSraFx8fz/jx4wFIT0/Hw8ODBQsWALBr1y58fX3ZsmULU6darpN96NAh0tLSGDp0KMnJyZWWo7Y06OBwMOOgxXKdpg/9yxcL2fC/cWjcXOk23p+v9uQ07JnPADeKIOa/IOcsPL0OWnRmz7kMi7QY9g4ONwNDE2NgaFNuvyMmybudtJOJrPswCq13E56c9R6+La0tjeKgsk4Z/vUvv6xnzvUcCooLCPAKwENje9esoihMnDiRd955p9z2kpKScmMRLi4ueHh4mB+XlNzshrs1IN36+549e3j++ecBQ3fNkCFDrJalRYsWZGVlUVpaiqurK2lpabRubZjAFxgYSGpqKgEBARQXF1NYWIiPjw+hoaHmK5WZM2fSuXNnnnrqqXKve+jQIXr27ElxcTF9+vQBYMSIEfj5+bFv3z6CgoIoKSkhMzOTgQMHsmPHDtzc3Dh//nytL3rWoLuVTMt1uujBzeXmh35OeiE/LIxDuAiGTwujb+eevHf4Tp4606pBJdMb9cV/GPXFfwy/6PXwwxQ4vwce/xyC+gM302KgKLi5CLunxXB3v0hc/Dg0Gh+rgcEZnUuI57v3Z+Pl68fot+dVKzA8/j/hVie4qaW4tJiMwgy83Lzw01r2p1dFREQEa9asISsrCzDc1WTrnUDbtm0jNzeXq1evsn79evr161du/z333GMe/K4oMAC4urpy7733sm6dIX/osmXLGDZsGACPPfYYy5YtAwwDyIMGDapS2Y4cOUJISAguLi64u7ubyzF79mxeeOEFLl68SEpKCrt27aJr167s2LEDgDfeeIPnn3+eK1euAIZxiy+//NKm98WaBh0cTMt1lv3Qz828yvpP40BRGD4tDN+WjQDDzOeRKQENJjBY2D4LEtfBg+9A8BPmzaa0GO2zSuzepeSnZBHQ/BtcaULP8PoRGE4f2se6eVE0bdmKUVEf0riZbf3yjkhRFC4UGPrj23jb3p1kEhISwpw5c4iIiKB79+4MGjSIjIwMm16jf//+jB07lrCwMMaMGWO+JbSyMYe+ffsyZswYtm7dSmBgoPlDef78+cybNw+dTkdBQYG5y+jZZ5/lzz//RKfTsXjxYt5///0qlW3z5s089JDt2WhffPFF+vXrR8+ePQkODmbAgAEWA+jVIeqyz8peevXqpRw8WL0MqKZZz+2Wf01+1jXWfXyYkht6hk8Lo1kbb6vPayhMVw2re8TDljegz3Pw8DyLW1bNz6ulOQzW1l+4VVbS7xxNeQ7XG960jX+TVk8PcOpbVQFO7vmVzYs/pkVQB0a8NRdP78YWz9k8fXWdp9g+ceIEXbp0qf4L3NKtlHUti4zCDNp4t8FXq94M9ejoaI4dO8bChQtVK0NlHnjgAVavXk3z5nWzGqG1dhVCHFIUxWo/eYO+cigrP/saP3wSx43iUoa9HFouMDRkfa7thi1vQudH4aEPHGIuQ15eHMcuTMX1RmPuOPAGmkI/rp/JU7tYVq37+DDrPr79IPKxn7ezadF8Wt/VhciZ71kNDLWRFsPeikqKyLyaSRP3Jvh4OPfs9Lq2c+fOOgsM1dGgB6RNrolG/PppHMVFJQx7OQz/QMs/zIZ0xWDSqTiRF3PnQWBveCIaHCD3TV7eYeLiJ+Cm8SPg4Eu4XvdDuLk4dVqMw5s38vPSL2jXPYxhr87AzUNr9XmOuKxnZfSKngsFF3ARLrTybqX6kp+TJk1S9fzOpsFfOfh/9iUHg56hqOAGQ/8eSvO2loGhQco6xWuXo8hybQFjYsDNtslKdSE37xBx8RNwd29Gzz6rONKiiNP+l506Lca+H9by89Iv0PX+C8Onz64wMIBzpMUo69LVSxSVFNHaqzUaF/k91Nk06Ba7ml/M+oVxXM0r5rGXQmkZ5JwfMLXuSgasGEGpcOHv7T/gjRItld28a498Sbl5h4iPn4C7uz/h4d+g9Qggz7OIPM8iBjhhYFAUhd9Xr2DfutV07vdXHnp+Gq6ayv8cHW1Zz4oU6t3IVQS517Lw1frSxMP52keSwYEbxaU8+kIPApy4W6JWXS+Ab0ZSWnCJMd0/Ja5JW9XXfM7NPUj8kYm4uzcnPHwlWg/17/k3jSNU53ZRRVHYtexLDm/eQMgDg4iY/N+4VLHLrqjlnRS1vNPhAoPp/Rj0QnfOFjcBj0sIxZUmGsfpQ5ds06CDg3+gN0/N7YurpuH0rlV6Z1FpCXw7AdIT2DTwn8QVd1J1chuYAsMEPDxaEh62Eg8P514QRq8vJfbLf3B05zbCH36M+5+ZrHpffG0qvF4CbvkIUYK+2J9rxXoaV9xTJjmwhvOpWIGGFBgqpSiw6RU4tQ0e+ZjWIUNVm9zmc01LUHZTMk/uqleBobSkhM2LP+Hozm3c/fiocoGhqnc1OTqNpgThWohS4o1QtHh52P7905FSdsfGxprPHRoaioeHBz/++CNQccruqnrwwQdJT0+3ui8vL49WrVrx8ssvm7fl5+czefJkOnbsSLdu3bj//vs5cKDu1uqQn4ySwa8L4PAyuPdV6DVRtclt18/l0zO1Na1LL3Hs/PO4u7QgPOwbpw8MJTdu8OPCDzn5+y/0H/MM/Uc/Xa+uGEyaenrhr7jTHFfa+3tVKzg4UsruiIgI87m3b9+Ot7e3OTFeRSm7y+rfvz9paWkW2wsLC7ly5QoBAda7SN966y0GDCi/kNPEiRPNqcATExOJjo42zxavC6oEByHEk0KIRCGEXgjRq8z2B4UQh4QQR43/PqBG+RqcuJXw87vQYww8MNO82adIT9ucEvuu+XwmjyLfP7gQ9iluRX7cdeNjPDxaWH3uhAkTHD5vkjbjFD5x6/h+1mskH9jLgPHPcffwJ9UuVp0RQtBSQEvNtWoFhtuxd8rustauXcujjz6KVqutNGV3VezcuZMHHrD+8bZ//35yc3PL7U9KSiI+Pp63337b/KVCp9Px8MMP21QHW6h15XAMGAH8esv2LGCooighwDPAcnsXrMFJ3gEb/w4d7oehi+p0kpu19Rduda3VKS6Ef4KmyI+2R96kcccOdVaeunY1Lo7Wmz8gJec/pJ45xf0PDyf84aE1ek1Hy5lkT2qn7I6JiWHMmDFA5Sm7q6KiVBmlpaW89tprzJ8/v9z2xMREwsLCcHGx30e2KgPSiqKcAMuMiIqilJ3ymQhohRAeiqJct2PxGo4/E2DNOGjeGUYuB426q9vl5OzlePrfKSnxgTPPETDuXqedvwCQ8/vvHAjyJ9/Tg9DUS7S7rle7SE5NzZTdaWlpJCUlmbuUKkvZHR0dzeLFiwFITk5m8ODBuLm5odPp+PbbbwHYu3cvixYtsniNzz77jGHDhpkzvKrJke9WegKIqygwCCGeBZ4Faj1VbYOQm2pYl0HrA/+1FrTqfghfzvkPR45MwtPzDs6fewS9TwkPOHFgKMzNYfvJOPK1HoSdy6TV9RKHn7Tm6NRM2b169WqeeOIJNMa5KJWl7J40aZJ5Nnb//v2JiYkhMPDm+iJJSUm0b98ejUZjcb69e/eyZ88eFi1aREFBAcXFxXh5eTFu3Dji4+PR6/V2u3qos+AghIgFrI22zFAUZf1tju0GzAMqzHWrKMq/gH+BIfFeDYra4Hjpr8DKSLhxDSZugSY1+5ZSlUR5lbl8eQ9HEibj6XkHYWErOHF8Y43Ko7Yr2VmsfXcmV/JzuSMgAhdtLm0deNKas4iIiCAyMpKXXnoJf39/srOzKSwstOlbtillt7u7O+vXr2flypXl9ptSdt9q1apVfPrpp+bfy6bsjoyMLJey+3a2bNliHiu49Xxl04SbEgW+9957gOGKaO7cucyZMwchBElJSfzxxx8MHVqzrsqK1FkIUhQlQlGUYCs/twsMgcA6YJyiKKfrqnz1Tbm1Fypx1aOEF6+8g/7yGRi9Elp2rfC5q5/rW+ezny9f/t0cGMLDVuDh7hwpqrUZp/CLX2+R/C43I52YOa9TmJPNE2/NRRP0V4ddy9nZqJWyOzk5mczMTPr3719ue0Upu29ny5Yt1UrNvWTJElJTU9HpdAQHBzNlypQ67X5yqG4lIYQvsAl4U1GU39UuT31zMPcKkwo+JuxqAn/vOptxfuGVpsWoazcDQzvCw1bg7t5MxdJUfdazKTuq0JdwfsIG2i75ikZhYWRfSOXbd2ZQcuMGT856n4COd3Jgk/PPXaiKcu/ZLSvA1URUVFS538eOHcvYsWMtnld2jeaydyFpNJpy+1q2bMmqVassjq9szEGn01ldVEin0912nsHu3bvL/V5UVER2dna5bqaK3Joo0MfHh3//+9+3Pa62qHUr6+NCiDSgL7BJCGFqmRcAHTBLCBFv/LF+H6NkMxE7h2FZPzO3wxS+az6QPbkFqpUl+/JujiRMppFnkEMEBltYy46amXKG1VFvoNfrGTnnAwI61t4HpFR/aLVa9u/fr3YxqkStu5XWYeg6unX7u4BtNx9LVbPvC3oe+zdLWg3n/9qMQqvCkp4m2dm/kXD0ORo1ak9Y6NdOFRjgZnZU9CW4uLlxJTCATXPfxE3ryZMz38Ovte0r0mkzTtHoz+NcjROyC6qOyJTdtnGobiWpjpzYCJtfh06PsPb6c7TPLuWzwZ1UyZWk1Z4m4ehHNGrU0RgYLNcTdvSJbWWzo7aJ7MHGNcvx8vElcua7+LSwfSZ3Rd1UkqQmmT6jvju/D76bBIG94IloGl8Xdp/1bNLMJZUWzdfiqQkiPGy51cDgLIpa3klaUFc2rV1B42b+jIr6sFqBAax3U0mS2uSVQ32WlQyrRhtuVR0TA+6NVCtK+onNeLdeiXtBa1r//hL6O1yhnX3OXZP02hUpvHycjNNradEuiCdmzKVRE+sp36tyzlu7qZxxPkRNb2eWHI8MDvVVQSasGAHCBZ76DrzUu0U0K3sXx/+chntha+44OB2XUi+un8lz2tnPJ377mYzkNXh4t+HJ2e+h9arZ2I2zLOIjNSyyW8nBVXX+QjnFhfDNSEOAGLsG/NTLT5SV9TMJCVNp5K6j9aFXESVeCI3zrvmcELuFn/7xCdrG7WjVaVyNA4NJUcs75XwII0dK2W1iLYX2gQMHCA4ORqfTMW3aNJtfMzw8vMI032fPnsXLy4uFCxeat128eJGRI0ei0+no2rUrjzzyCMnJybZXpopkcKhvSktg7QT48wg8uQQCe6pWlKysn0k4+jze3nfS8+4VxLfKd+o1nw9t+oHtXy6mfWhPAjo9hYurh9pFqpccKWW3ibUU2lOmTGHJkiWcOnWKxMREtm/fbnFcUFCQ1dc7deoU7dq1w83Nzer+V155pVzGVUVRGD58OIMGDSI5OZnjx4/zzjvv2DwJ0BYyONQTeVoXzvu6cumHl+DUVhiyADrVLJ1vVbKows3Fea6fyzdvy8raScLRqXh730VY6HLc3HzJ8ywipVmO0wUGRVHY+10Mu76O5s6772HYqzNwcbH+Ry3VLTVSdltLoZ2amkpRURG9e/dGCMHTTz9tU8rusik0bvXtt9/SuXNnOnfubN5mWkui7O244eHh9OvXr8rntJUMDvXAwbxCEgLdGXptNc2PruDP3i9C77/Z5dymxXk6ZvmRFX2U6+fyuZS1w3jF0NkYGJyzCwkMgeG3Vcv4fc0Kut73AI++9DquGhkY1KBGyu6KUmhfuHDBnBEWbE/ZXVEKjStXrvDxxx8zc+bMctuPHTtGz5727QWQA9L1wJ7cAkZkbuOtlGi+a/EgF7q9wN8reG5t50q6fiYPoQhcECgletJP/8RpEUVj7y6Ehi7Dzc25rhLKUvR6di79F/Fbf6THgw8zcOJUhB3z6UvlqZGyu6IU2pWl7J47dy7ff/89YBgnMOVvuu+++1i0aBFFRUVkZmZazSY9a9YsXnvtNby87H+r+a1kcKgHBucdZuof89jtE8YbXd5gVdPGdju3RwcfFKGgV+BqQDwX+QeNG3cjtMdSpwsMZWcpa3t0Z9vnn5H4Syw9H32cvz41sVx654a64I6a1EjZXVEK7SlTppCammo+rmzK7tmzZ5uvVoKCgiyyvP7yyy/cd999gKELydS9tXTpUvbv388PP/zAK6+8Qm5urrku3bp1M69dbS8yODi79KN0+nEyZzVtiQqYw6rwLnad4ObRrgmH7riIr1sydFxB4ybBhIUuRaOxX4CqDWVnKadM2MAfwwZz+uQx+kaOpW/kmHq53rOzUSNld2UptD08PMxXMsuXL+fVV1+tUhm2bNnCI488AkBkZCSRkZHmfXv27DE/njlzJv7+/kydOhVFUZgxYwZfffUVEydOBGDfvn0UFxdz7733Vrn+tpDXyM4sL82wYI9HYz5qOpdm+VpVZj4X+8Wj7/A1jZuEOGVggJuzlPUoHGrVlNMnj3HfUxO558mxMjA4CLVSdlfkn//8J+PHj0en09GlSxdzt9bt/PrrrzZ/oAshWL9+PT/99BMdO3YkODiYd999t+Gk7JZscC0XVkQa5jRM3MLlH/Jvf0wdyMzcin/z7ykubkVY6BKnDAxgmKV8w9WNuLbNyG7syb2DhtJ76Ai7nd/Zu6nqama0I6TsNrk1cd/dd99NYmJipceUHe8AOHfuHK1btzZ3e1Xm1rup2rRpY15m1B7klYMzKrkOq5+C7GQYtQJadlOlGJmZWziW+CLF11uTmTHWaQMDgGunu/g9PIzsxp488MgI+vztObWLJNVD7dq1Y+NG51jpUF45OBu9Htb/N6T8Bo//Czr8VZViZGRuJjHxJZo06cH5cwNRlNt/E3LUvDtX8/P4/oM5XCvOpoVuFGHjnla7SFIdkCm7bSOvHJzNzrlwdC0MnA09RqlShIzMn4yBIZTQHkuqFBgcVWFuDmvefpOs1HME3DkGbz91rsIkydHI4OBM9n8Juz+FXhOh/yuqFCEj40cSE1+mSZMwQnt8hUajzoJBtSE/K5OYOdPJv5TJiDeiaOTbSe0iSZLDkMHBweVpXTjfVMOpw9/D5ulw10Pw8HxQ4Q6ajIwfSTz+Cj5Nwp0qMGgzTuEXv56rcXHmbTnpF4mZ8zrX8vOJnPkObYN7qFhC51etBJGSQ5NjDg7MlBajx5XjBP44jcIW3fGK/Apc7d9s6ekbSDz+P/j69qJH92g0GvVncFaFtVXWrvr78e27M9GXlvLk7Pdp2b6j2sWUJIcjrxwc2J7cAtoWpbEi8U0y3JvxzYB/grv1D+XVz/Wt9dQYJjcDQ2+nCgxgucpa6o7trH77TRCCUVEfysDggBwpZbder2fw4MH4+voyfPjwcvtOnz5Nnz590Ol0jB07tsL02xV58MEHSU9Pt7rPWorw/Px8Jk+eTMeOHenWrRv3338/Bw7U3aqBMjg4sHt8vZlz5nNQFMb3mE9YKzstnVZGI6+j5sAQ2sN6YJgwYYLD3olkWmVNES7kNPZiW8I+3LVaRkfNo1mgZW4bSX2OlLJbCMH06dNZunSpxb7XXnuN6dOnk5ycTKNGjaw+p3///qSlpVlsLyws5MqVKwQEBFg9r7UU4RMnTiQgIIDk5GQSExOJjo4mKyurWvWqChkcHFgvHy+ivV5hWtuPWHDPALvPfm7kdZRmzTbQ1LcPoT2icXVVb5nR6jKtsnY6fBAHdK3x8mvGqKh5+Aa0UrtoUjXYO2W3EIKBAwfi7V1+fK20tJRff/2Vxx9/HIBnnnnGppTdO3fuLJcCvCxrKcKTkpKIj4/n7bffNs/Y1+l0Fab9rg2qBAchxJNCiEQhhF4I0avM9j5CiHjjzxEhxONqlM+RaG404vq1ILsHhpyc/TRrtgFxpSOdm37ilIHBJNtdz6nSs/gGtGZU1Dya+DdXu0hSNaiRsrsily5dwt/fH1dXV8D2lN2bN2+2mrK7ohThiYmJhIWF4WLHrMBqDUgfA0YAX1jZ3ktRlBIhRCvgiBBio6IoJRavINUpzzwd/slP4HsugpxDybhO8nS6RXpu0uPeKICRcz7As7Gz1kFSI2V3RSpL2R0dHc3ixYsBSE5OZvDgwbi5uaHT6czpL/bu3cuiRYssXqOiFOFqUCU4KIpyAizT5SqKcrXMr1rAsgUkuyg+W4jf2UcM6zQoeq6fyXPa4ODVtAuNfDvJwODk1EjZXTYra1ktWrQgKyuL0tJSXF1dy6XsnjRpknk2dv/+/YmJiSEwMNB8bFJSEu3bt0ej0VQ5Rfi4ceOIj49Hr9fb7erB4W5lFULcDXwFtAOellcN6ii7ToOrxhWPDs67mhuAELf/g3L25Hf1nRopuyvi6urKvffey7p164iMjGTZsmUMGzasSmUou0SoLSnCQ0JCmDt3LnPmzEEIQVJSEn/88QdDhw6t0nltVWchSAgRK4Q4ZuWn0ndQUZR9iqJ0A3oDbwohtBW8/rNCiINCiIOXLl2qiyo0aKZ1Gk77X8Z/UojTXjVI9YdaKbv79u3LmDFj2Lp1K4GBgezYsQOA+fPnM2/ePHQ6HQUFBYwfP75KZahoidDbWbJkCampqeh0OoKDg5kyZUqddj8Ja31n9iKE2AW8qijKwQr2/wy8VtF+k169eikHD1b6FKdlmnVaV3MYKrNkyRLAcRPmVdW6jw8D8sqgqk6cOEGXLl3ULkatM30TX7hwoWplKCoq4r777mP//v12P7e1dhVCHFIUpZe15zvUraxCiPZCCI3xcTugE5CiaqEkSZJqiVarVSUwVIcqYw7GW1Q/A5oDm4QQ8YqiDAb6A28IIW4AeuB5RVHqbpaHJEkNhkzZbRu17lZaB6yzsn05sNz+JZIkSZLKcqhuJUmSJMkxyOAgSVKNnXt6HOeeHqd2MaRaJIODJEmSZEEGBwdXl6m4JckRqZ2ye/To0XTq1Ing4GAmTZpknmEdGxuLj4+PuSymyWlV9dtvvzF16tQK93/44YcIIcjNzTVv27RpEz179qRr16507tyZ119/vXqVqgYZHCRJcihqp+weN24cJ0+eJCEhgby8PPN8H4ABAwaYyzJjxgyLY2NjYyu8K6qyyW8pKSn88ssvtGnTxrztyJEjvPzyy6xatYrjx49z7NgxgoKCalY5G8jgINV7j/9PuJwAV0/YI2X3kCFDEELg4uJCnz59rK7HUB07d+5k4MCBVvdNmzbNIhPrvHnzmDVrFnfddRcAGo2m0iuP2iaDgyRJTsHeKbuLi4tZuXJluW/7u3fvpkePHgwZMoTjx49XuewZGRl4eXlZrAsB8N1339GhQweCg4Mt6tuzZ88qn6O2OVziPUmSJGvsnbJ7ypQpRERE0LevYcyvd+/epKSk4O3tzcaNGxkxYgQnT54EoFevXpSUlFBQUECKmjzZAAAHBklEQVROTg6mdD4LFiwgIiKCrVu3MnjwYItzFBQU8NFHHxEbG1tL71LtkcFBkiSnYI+U3SazZs0iLy+P6Oho8zYfn5uZiYcOHcrUqVPJzc3F19fXHAxiY2OJiYkpdxwYFvd56623AMOYRkJCAm3btmXu3LmcPXuWkJAQANLT0+nevTuHDh2iW7du5n/VILuVJElyChEREaxZs8a8bnJ2djbnz5+36TVMKbuvXr3K+vXr6devn8VzPv/8c3bt2sXKlSvLrZ2Qnp5ufrx37140Gg2+vr63Pader+fEiRPmAPD1118THx/Phg0bCA0NJTMzk5SUFFJSUggICCAhIYHmzZszffp03n33XZKTkwHDKnGffPKJTfWtCRkcJElyCvZI2V1aWsoLL7zAn3/+yV/+8pdyt6zGxMTQrVs3QkNDmTZtGqtXr67SOffv32/uCrNFWFgYCxYsYOTIkXTp0oWQkBDsuTyBqim7a0t9TtktSfYmU3bXrqioKIKDg4mMjLTreW9la8puOeYgSZJUh6KiotQuQrXI4CBJUoMgU3bbRo45SJJkoT50N0s3Vac9ZXCQJKkcrVZLdna2DBD1hKIoZGdno9VqbTpOditJklROYGAgaWlpdr0zRqpbWq2WwMBAm46RwUGSpHLc3Nxo37692sWQVCa7lSRJkiQLMjhIkiRJFmRwkCRJkizUixnSQohLwLkavIQ/kFVLxVFTfakHyLo4ovpSD5B1MWmnKEpzazvqRXCoKSHEwYqmkDuT+lIPkHVxRPWlHiDrUhWyW0mSJEmyIIODJEmSZEEGB4N/qV2AWlJf6gGyLo6ovtQDZF1uS445SJIkSRbklYMkSZJkQQYHSZIkyUK9Dg5CiIeEEElCiGQhxBtW9o8XQlwSQsQbfyaV2feMEOKU8ecZ+5bcUg3rUlpm+wb7ltzS7epifM5IIcRxIUSiEOKbMtsdpl1qWA+nahMhxKdlyvuHECK3zD6HaRNjeWpSF2drl7ZCiJ+FEHFCiAQhxJAy+940HpckhBhs88kVRamXP4ArcBroALgDR4CutzxnPLDYyrF+wBnjv02Nj5s6Y12M+wrUbg8b63InEGd6z4EWjtYuNamHM7bJLc9/EfjK0dqkpnVxxnbBMBg91fi4K5BS5vERwANob3wdV1vOX5+vHPoAyYqinFEUpRiIAYZV8djBwHZFUS4ripIDbAceqqNyVkVN6uJoqlKXycA/jO89iqJkGrc7UrvUpB6Oxtb/X2OAVcbHjtQmULO6OJqq1EUBmhgf+wAXjY+HATGKolxXFOUskGx8vSqrz8GhDZBa5vc047ZbPWG8HPtWCHGHjcfaS03qAqAVQhwUQuwVQgyv05LeXlXqchdwlxDid2OZH7LhWHupST3A+doEACFEOwzfRHfaeqyd1KQu4HztEgU8JYRIA37CcCVU1WMrVZ+Dg7Cy7db7djcCQYqidAdigWU2HGtPNakLQFvFML1+LLBQCNGxbopZJVWpiwZDl8z9GL7ZRQshfKt4rL3UpB7gfG1iMhr4VlGU0mocaw81qQs4X7uMAZYqihIIDAGWCyFcqnhspepzcEgDyn57DuTmJRcAiqJkK4py3fjrl0DPqh5rZzWpC4qiXDT+ewbYBYTVZWFvoyrvbRqwXlGUG8ZL4iQMH7KO1C41qYcztonJaMp3wzhSm0DN6uKM7fI3YA2Aoij/AbQYEvHVvF3UHnSpw8EcDYbBsfbcHMzpdstzWpV5/Diw1/jYDziLYYCtqfGxn5PWpSngYXzsD5yikgE6B6nLQ8CyMmVOBZo5UrvUsB5O1ybG53UCUjBOnjVuc5g2qYW6OF27AJuB8cbHXTAEAAF0o/yA9BlsHJBWpdJ2fHOHAH9gGKmfYdw2F3jM+PgDINH4Jv4MdC5z7EQMgzjJwARnrQtwD3DUuP0o8DcnqIsAPgGOG8s82hHbpbr1cMY2Mf4eBXxo5ViHaZOa1MUZ2wXDXUm/G8scDwwqc+wM43FJwMO2nlumz5AkSZIs1OcxB0mSJKmaZHCQJEmSLMjgIEmSJFmQwUGSJEmyIIODJEmSZEEGB0mqBUKIXbdmvhRCvCyE+D8hxBYhRK4Q4ke1yidJtpLBQZJqxyoMM27LMs3AnQ88bfcSSVINyOAgSbXjW+BRIYQHgBAiCGgN7FYUZQdwRb2iSZLtZHCQpFqgKEo2sJ+b6apHA6sVOctUclIyOEhS7SnbtWSR1E2SnIkMDpJUe34ABgohwgFPRVEOq10gSaouGRwkqZYoilKAIc3zV8irBsnJyeAgSbVrFdADw5KOAAghfgPWYriqSKvWYu+SZGcyK6skSZJkQV45SJIkSRZkcJAkSZIsyOAgSZIkWZDBQZIkSbIgg4MkSZJkQQYHSZIkyYIMDpIkSZKF/wfygET1EWIoBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[46.80851556541192+/-5.893199471589424\n",
      "  -45.09220557218853+/-4.277965873294411]\n",
      " [30.632756042658034+/-2.5337383039757686\n",
      "  -31.909114902411762+/-1.6473261520276363]\n",
      " [31.960603307852004+/-1.0162202357215226\n",
      "  -31.12293020404673+/-0.6094216142373619]\n",
      " [36.429479411950275+/-0.7404157048644742\n",
      "  -32.506966837419434+/-0.42144574474551455]\n",
      " [32.81637986068146+/-1.0567998763290194\n",
      "  -28.187718564578294+/-0.5781977085653761]]\n"
     ]
    }
   ],
   "source": [
    "#LOOP version\n",
    "Dataset = np.array([N100,N70,N40,N10,P25])\n",
    "DataTemp = np.array([-100,-70,-40,-10,25])\n",
    "DataTempE = np.array([4,4,4,4,4])\n",
    "DataTemp = unumpy.uarray(DataTemp,DataTempE)\n",
    "Num = len(Dataset)\n",
    "fig, ax = plt.subplots()\n",
    "i = 0;\n",
    "plist = []\n",
    "for Data in Dataset:\n",
    "    Vd = Data[:,1] - Data[:,0];\n",
    "    ErrorV = Data[:,1]*0.1 #The error of V1 can set to be zero \n",
    "    Vd = unumpy.uarray(Vd,ErrorV)\n",
    "    ApproxGood = np.where(Vd > 0.1)\n",
    "    Vd = Vd[ApproxGood]\n",
    "    I = Vd/R;\n",
    "    LnI = unumpy.log(I)\n",
    "    name = \"Temp:\" + str(DataTemp[i]) + \"C\"\n",
    "    plt.plot(Data[:,0][ApproxGood], unumpy.nominal_values(LnI), '.')\n",
    "    plt.errorbar(Data[:,0][ApproxGood], unumpy.nominal_values(LnI), unumpy.std_devs(LnI), fmt = 'none', label = name);\n",
    "    p,cov = polyfit(Data[:,0][ApproxGood],unumpy.nominal_values(LnI),1,w=unumpy.std_devs(LnI),cov=True)\n",
    "    lnItheo = p[0]*Data[:,0][ApproxGood] + p[1];\n",
    "    plt.plot(Data[:,0][ApproxGood], lnItheo)\n",
    "    error = sqrt(np.diagonal(cov));\n",
    "    p = unumpy.uarray(p,error)\n",
    "    plist.append(p)\n",
    "    i = i + 1\n",
    "leg = ax.legend();\n",
    "pyplot.title('I vs V1 diagram of various temperatures')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()\n",
    "\n",
    "plist = np.asarray(plist)\n",
    "print(plist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, it is obvious that some of the linear fit is not fitting well, which it is decided then to fit them separately by non-linear fitting method to get an appropriate result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.110520868952938+/-0.10830855892631906\n",
      " -3.3097513836585613+/-4.615321162888029]\n",
      "The bandcap energy is\n",
      "-1.11+/-0.11\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": [
    "\"\"\"unumpy.nominal_values(LnI)\n",
    "unumpy.std_devs(LnI)\"\"\"\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(unumpy.nominal_values(plist[:,1]),unumpy.nominal_values(plist[:,0]),'x');\n",
    "plt.errorbar(unumpy.nominal_values(plist[:,1]), unumpy.nominal_values(plist[:,0]), xerr = unumpy.std_devs(plist[:,1]),yerr = unumpy.std_devs(plist[:,0]), fmt = 'none');\n",
    "p,cov = polyfit(unumpy.nominal_values(plist[:,1]),unumpy.nominal_values(plist[:,0]),1,w=unumpy.std_devs(plist[:,0]),cov=True);\n",
    "theo = p[0]*unumpy.nominal_values(plist[:,1]) + p[1];\n",
    "plt.plot(unumpy.nominal_values(plist[:,1]), theo)\n",
    "error = sqrt(np.diagonal(cov));\n",
    "p = unumpy.uarray(p,error);\n",
    "print(p)\n",
    "print(\"The bandcap energy is\")\n",
    "print(p[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1314738856782224e+20\n",
      "2.6103812610378302e-20\n",
      "2.1314738856782224e+20\n",
      "2.6103812610378302e-20\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": [
    "\"\"\"Dataset = np.array([N100,N70,N40,N10,P25])\n",
    "DataTemp = np.array([-100,-70,-40,-10,25])\n",
    "DataTempE = np.array([4,4,4,4,4])\n",
    "DataTemp = unumpy.uarray(DataTemp,DataTempE)\"\"\"\n",
    "Num = len(Dataset)\n",
    "fig, ax = plt.subplots()\n",
    "i = 0;\n",
    "for Data in Dataset:\n",
    "    Vd = Data[:,1] - Data[:,0];\n",
    "    ErrorV = Data[:,1]*0.1 #The error of V1 can set to be zero \n",
    "    Vd = unumpy.uarray(Vd,ErrorV)\n",
    "    I = Vd/R;\n",
    "    name = \"Temp:\" + str(DataTemp[i]) + \"C\"\n",
    "    plt.errorbar(Data[:,0], unumpy.nominal_values(I), unumpy.std_devs(I), marker = 'x', label = name, fmt = 'none');\n",
    "    i = i + 1\n",
    "\n",
    "x = np.arange(0.5,0.8,0.001)\n",
    "I = exp(unumpy.nominal_values(plist[0][0])*x+ unumpy.nominal_values(plist[0][1]));\n",
    "print(exp(unumpy.nominal_values(plist[0][0])))\n",
    "print(exp(unumpy.nominal_values(plist[0][1])))\n",
    "plt.plot(x, I)\n",
    "\n",
    "x = np.arange(0.5,0.77,0.001)\n",
    "I = exp(unumpy.nominal_values(plist[1][0])*x+ unumpy.nominal_values(plist[1][1]));\n",
    "print(exp(unumpy.nominal_values(plist[0][0])))\n",
    "print(exp(unumpy.nominal_values(plist[0][1])))\n",
    "plt.plot(x, I)\n",
    "\n",
    "x = np.arange(0.5,0.74,0.001)\n",
    "I = exp(unumpy.nominal_values(plist[2][0])*x+ unumpy.nominal_values(plist[2][1]));\n",
    "plt.plot(x, I)\n",
    "\n",
    "x = np.arange(0.5,0.69,0.001)\n",
    "I = exp(unumpy.nominal_values(plist[3][0])*x+ unumpy.nominal_values(plist[3][1]));\n",
    "plt.plot(x, I)\n",
    "\n",
    "x = np.arange(0.5,0.64,0.001)\n",
    "I = exp(unumpy.nominal_values(plist[4][0])*x+ unumpy.nominal_values(plist[4][1]));\n",
    "plt.plot(x, I)\n",
    "\n",
    "leg = ax.legend();\n",
    "pyplot.title('I vs V1 diagram of various temperatures')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1314738856782224e+20\n",
      "2.6103812610378302e-20\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1646379d548>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0.5,0.8,0.001)\n",
    "I = exp(unumpy.nominal_values(plist[0][0])*x+ unumpy.nominal_values(plist[0][1]));\n",
    "print(exp(unumpy.nominal_values(plist[0][0])))\n",
    "print(exp(unumpy.nominal_values(plist[0][1])))\n",
    "plt.plot(x, I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refit -100,-70,-40C by using non-linear curve fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.49868121e-23 3.31056463e+24]\n",
      "[1.74723881e-23 4.94864814e+24]\n",
      "[-52.55487161177517+/-1.165850880293466]\n",
      "[56.45916099010476+/-1.4948048724302259]\n"
     ]
    }
   ],
   "source": [
    "#Non-lienar bulk fit\n",
    "def myfitfn(V,Is,A):\n",
    "    return Is*(A**(V)-1);\n",
    "\n",
    "lnIs = [];\n",
    "qnkT = [];\n",
    "\n",
    "#Fit -100\n",
    "Data = N100;\n",
    "\n",
    "#Current calculation and its error propagation\n",
    "R = 50000;\n",
    "R = ufloat(50000,0.01*R)\n",
    "V2 = unumpy.uarray(Data[:,1],Data[:,1]*0.1);\n",
    "V1n = Data[:,0];\n",
    "V1e = np.repeat(0.001,len(V1n))\n",
    "V1 = unumpy.uarray(V1n,V1e); \n",
    "Vd = V2 - V1\n",
    "I = Vd/R;\n",
    "\n",
    "#Plotting experimental data\n",
    "plt.errorbar(Data[:,0], unumpy.nominal_values(I), unumpy.std_devs(I), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(Data[:,0],unumpy.nominal_values(I),'x')\n",
    "\n",
    "#Plotting initial guess for non-linear fitting\n",
    "A = 2.1314738856782224e+20;\n",
    "Is = 2.6103812610378302e-20;\n",
    "Vtheo = np.arange(0.5,0.801,0.001)\n",
    "Itheog = Is*(A**(Vtheo)-1);\n",
    "#plt.plot(Vtheo, Itheog)\n",
    "pguess = [Is,A];\n",
    "#print(pguess)\n",
    "\n",
    "#Non-linear fitting\n",
    "p,cov = curve_fit(myfitfn,Data[:,0],unumpy.nominal_values(I),sigma = unumpy.std_devs(I),p0=[Is,A]);\n",
    "\n",
    "#Plotting fitted curve\n",
    "Itheo = p[0]*(p[1]**(Vtheo)-1);\n",
    "plt.plot(Vtheo, Itheo)\n",
    "pyplot.title('I vs V1 diagram of -100C')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()\n",
    "\n",
    "#Step 5: Getting error of each parameter\n",
    "error = sqrt(np.diagonal(cov))\n",
    "print(p)\n",
    "print(error)\n",
    "\n",
    "Is= ufloat(p[0],error[0]);\n",
    "expqnkT = ufloat(p[1],error[1]);\n",
    "\n",
    "#Step 5: Record the getted ln(Is) and q/nkT to the array for plotting and Eg0 calculation\n",
    "lnIselement = unumpy.log(Is);\n",
    "qnkTelement = unumpy.log(expqnkT);\n",
    "\n",
    "lnIs.append(ufloat(unumpy.nominal_values(lnIselement),unumpy.std_devs(lnIselement)))\n",
    "qnkT.append(ufloat(unumpy.nominal_values(qnkTelement),unumpy.std_devs(qnkTelement)))\n",
    "\n",
    "print(lnIs)\n",
    "print(qnkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.21662499e-20 2.10548178e+21]\n",
      "[3.91192254e-20 3.52208813e+21]\n",
      "[-52.55487161177517+/-1.165850880293466, -44.88336918909627+/-1.2161574780576732]\n",
      "[56.45916099010476+/-1.4948048724302259, 49.09883126804802+/-1.6728181461541567]\n"
     ]
    }
   ],
   "source": [
    "#Fit -70\n",
    "Data = N70;\n",
    "V2 = unumpy.uarray(Data[:,1],Data[:,1]*0.1);\n",
    "V1n = Data[:,0];\n",
    "V1e = np.repeat(0.001,len(V1n))\n",
    "V1 = unumpy.uarray(V1n,V1e); \n",
    "Vd = V2 - V1\n",
    "I = Vd/R;\n",
    "plt.errorbar(Data[:,0], unumpy.nominal_values(I), unumpy.std_devs(I), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(Data[:,0],unumpy.nominal_values(I),'x')\n",
    "\n",
    "A = 2.1314738856782224e+20;\n",
    "Is = 2.6103812610378302e-20;\n",
    "Vtheo = np.arange(0.5,0.77,0.001)\n",
    "Itheog = Is*(A**(Vtheo)-1);\n",
    "#plt.plot(Vtheo, Itheog)\n",
    "pguess = [Is,A];\n",
    "\n",
    "p,cov = curve_fit(myfitfn,Data[:,0],unumpy.nominal_values(I),sigma = unumpy.std_devs(I),p0=[Is,A]);\n",
    "Itheo = p[0]*(p[1]**(Vtheo)-1);\n",
    "plt.plot(Vtheo, Itheo)\n",
    "pyplot.title('I vs V1 diagram of -70C')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()\n",
    "error = sqrt(np.diagonal(cov))\n",
    "print(p)\n",
    "print(error)\n",
    "\n",
    "Is= ufloat(p[0],error[0]);\n",
    "expqnkT = ufloat(p[1],error[1]);\n",
    "\n",
    "lnIselement = unumpy.log(Is);\n",
    "qnkTelement = unumpy.log(expqnkT);\n",
    "\n",
    "lnIs.append(ufloat(unumpy.nominal_values(lnIselement),unumpy.std_devs(lnIselement)))\n",
    "qnkT.append(ufloat(unumpy.nominal_values(qnkTelement),unumpy.std_devs(qnkTelement)))\n",
    "\n",
    "print(lnIs)\n",
    "print(qnkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.364852531871284\n"
     ]
    }
   ],
   "source": [
    "T = -100+273.15;\n",
    "q = 1.602e-19;\n",
    "k = 1.38064852e-23;\n",
    "print((q/(k*T))/log(p[1]));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.54253607e-16 2.18559157e+16]\n",
      "[8.21246514e-16 4.05112668e+16]\n",
      "[-52.55487161177517+/-1.165850880293466, -44.88336918909627+/-1.2161574780576732]\n",
      "[56.45916099010476+/-1.4948048724302259, 49.09883126804802+/-1.6728181461541567]\n"
     ]
    }
   ],
   "source": [
    "#Fit -40\n",
    "Data = N40;\n",
    "V2 = unumpy.uarray(Data[:,1],Data[:,1]*0.1);\n",
    "V1n = Data[:,0];\n",
    "V1e = np.repeat(0.001,len(V1n))\n",
    "V1 = unumpy.uarray(V1n,V1e); \n",
    "Vd = V2 - V1\n",
    "I = Vd/R;\n",
    "plt.errorbar(Data[:,0], unumpy.nominal_values(I), unumpy.std_devs(I), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(Data[:,0],unumpy.nominal_values(I),'x')\n",
    "\n",
    "A =  8.58526237e+16;\n",
    "Is = 3.54321776e-16;\n",
    "Vtheo = np.arange(0.5,0.725,0.001)\n",
    "Itheog = Is*(A**(Vtheo)-1);\n",
    "plt.plot(Vtheo, Itheog)\n",
    "pguess = [Is,A];\n",
    "\n",
    "p,cov = curve_fit(myfitfn,Data[:,0],unumpy.nominal_values(I),sigma = unumpy.std_devs(I),p0=[Is,A]);\n",
    "Itheo = p[0]*(p[1]**(Vtheo)-1);\n",
    "plt.plot(Vtheo, Itheo)\n",
    "pyplot.title('I vs V1 diagram of -40C')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()\n",
    "error = sqrt(np.diagonal(cov))\n",
    "print(p)\n",
    "print(error)\n",
    "\n",
    "Is= ufloat(p[0],error[0]);\n",
    "expqnkT = ufloat(p[1],error[1]);\n",
    "\n",
    "#Is= ufloat(Is,error[0]);\n",
    "#expqnkT = ufloat(A,error[1]);\n",
    "\n",
    "lnIselement = unumpy.log(Is);\n",
    "qnkTelement = unumpy.log(expqnkT);\n",
    "\n",
    "#lnIs.append(ufloat(unumpy.nominal_values(lnIselement),unumpy.std_devs(lnIselement)))\n",
    "#qnkT.append(ufloat(unumpy.nominal_values(qnkTelement),unumpy.std_devs(qnkTelement)))\n",
    "\n",
    "print(lnIs)\n",
    "print(qnkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0343994496524562\n"
     ]
    }
   ],
   "source": [
    "T = 25+273.15;\n",
    "q = 1.602e-19;\n",
    "k = 1.38064852e-23;\n",
    "print((q/(k*T))/log(p[1]));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.52753806e-16, 6.87989193e+17]\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.52683746e-16 6.06134762e+17]\n",
      "[2.10965839e-16 4.42761174e+17]\n",
      "[-52.55487161177517+/-1.165850880293466, -44.88336918909627+/-1.2161574780576732, -35.33133792557879+/-0.4660336071547089]\n",
      "[56.45916099010476+/-1.4948048724302259, 49.09883126804802+/-1.6728181461541567, 40.94587873650836+/-0.7304665589104422]\n"
     ]
    }
   ],
   "source": [
    "#Fit -10\n",
    "Data = N10;\n",
    "V2 = unumpy.uarray(Data[:,1],Data[:,1]*0.1);\n",
    "V1n = Data[:,0];\n",
    "V1e = np.repeat(0.001,len(V1n))\n",
    "V1 = unumpy.uarray(V1n,V1e); \n",
    "Vd = V2 - V1\n",
    "I = Vd/R;\n",
    "plt.errorbar(Data[:,0], unumpy.nominal_values(I), unumpy.std_devs(I), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(Data[:,0],unumpy.nominal_values(I),'x')\n",
    "\n",
    "A = 6.87989193e+17;\n",
    "Is = 4.52753806e-16;\n",
    "Vtheo = np.arange(0.5,0.685,0.001)\n",
    "Itheog = Is*(A**(Vtheo)-1);\n",
    "#plt.plot(Vtheo, Itheog)\n",
    "pguess = [Is,A];\n",
    "print(pguess)\n",
    "\n",
    "p,cov = curve_fit(myfitfn,Data[:,0],unumpy.nominal_values(I),sigma = unumpy.std_devs(I),p0=[Is,A]);\n",
    "Itheo = p[0]*(p[1]**(Vtheo)-1);\n",
    "plt.plot(Vtheo, Itheo)\n",
    "pyplot.title('I vs V1 diagram of -10C')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()\n",
    "error = sqrt(np.diagonal(cov))\n",
    "print(p)\n",
    "print(error)\n",
    "\n",
    "Is= ufloat(p[0],error[0]);\n",
    "expqnkT = ufloat(p[1],error[1]);\n",
    "\n",
    "lnIselement = unumpy.log(Is);\n",
    "qnkTelement = unumpy.log(expqnkT);\n",
    "\n",
    "lnIs.append(ufloat(unumpy.nominal_values(lnIselement),unumpy.std_devs(lnIselement)))\n",
    "qnkT.append(ufloat(unumpy.nominal_values(qnkTelement),unumpy.std_devs(qnkTelement)))\n",
    "\n",
    "print(lnIs)\n",
    "print(qnkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9504611514044241\n"
     ]
    }
   ],
   "source": [
    "T = 25+273.15;\n",
    "q = 1.602e-19;\n",
    "k = 1.38064852e-23;\n",
    "print((q/(k*T))/log(p[1]));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.52753806e-16, 6.87989193e+17]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in power\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.20512538e-13 2.63207042e+15]\n",
      "[5.28863478e-14 1.98489307e+15]\n",
      "[-52.55487161177517+/-1.165850880293466, -44.88336918909627+/-1.2161574780576732, -35.33133792557879+/-0.4660336071547089, -29.747022596395855+/-0.4388451906112011]\n",
      "[56.45916099010476+/-1.4948048724302259, 49.09883126804802+/-1.6728181461541567, 40.94587873650836+/-0.7304665589104422, 35.50654716466422+/-0.754118527425591]\n"
     ]
    }
   ],
   "source": [
    "#Fit 25\n",
    "Data = P25;\n",
    "V2 = unumpy.uarray(Data[:,1],Data[:,1]*0.1);\n",
    "V1n = Data[:,0];\n",
    "V1e = np.repeat(0.001,len(V1n))\n",
    "V1 = unumpy.uarray(V1n,V1e); \n",
    "Vd = V2 - V1\n",
    "I = Vd/R;\n",
    "plt.errorbar(Data[:,0], unumpy.nominal_values(I), unumpy.std_devs(I), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(Data[:,0],unumpy.nominal_values(I),'x')\n",
    "\n",
    "A = 6.87989193e+17;\n",
    "Is = 4.52753806e-16;\n",
    "Vtheo = np.arange(0.5,0.64,0.001)\n",
    "Itheog = Is*(A**(Vtheo)-1);\n",
    "#plt.plot(Vtheo, Itheog)\n",
    "pguess = [Is,A];\n",
    "print(pguess)\n",
    "\n",
    "p,cov = curve_fit(myfitfn,Data[:,0],unumpy.nominal_values(I),sigma = unumpy.std_devs(I),p0=[Is,A]);\n",
    "Itheo = p[0]*(p[1]**(Vtheo)-1);\n",
    "plt.plot(Vtheo, Itheo)\n",
    "pyplot.title('I vs V1 diagram of 25C')\n",
    "pyplot.xlabel('V1')\n",
    "pyplot.ylabel('I')\n",
    "plt.show()\n",
    "error = sqrt(np.diagonal(cov))\n",
    "print(p)\n",
    "print(error)\n",
    "\n",
    "Is= ufloat(p[0],error[0]);\n",
    "expqnkT = ufloat(p[1],error[1]);\n",
    "\n",
    "lnIselement = unumpy.log(Is);\n",
    "qnkTelement = unumpy.log(expqnkT);\n",
    "\n",
    "lnIs.append(ufloat(unumpy.nominal_values(lnIselement),unumpy.std_devs(lnIselement)))\n",
    "qnkT.append(ufloat(unumpy.nominal_values(qnkTelement),unumpy.std_devs(qnkTelement)))\n",
    "\n",
    "print(lnIs)\n",
    "print(qnkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0960645333573285\n"
     ]
    }
   ],
   "source": [
    "T = 25+273.15;\n",
    "q = 1.602e-19;\n",
    "k = 1.38064852e-23;\n",
    "print((q/(k*T))/log(p[1]));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resultant plot is better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.08724233  8.7046885 ]\n",
      "[0.0246856  1.26320576]\n"
     ]
    }
   ],
   "source": [
    "#Convert array into numpy array\n",
    "T = np.array([-100,-70,-40,-10,25])\n",
    "qnkT = np.asarray(qnkT)\n",
    "lnIs = np.asarray(lnIs)\n",
    "\n",
    "#Plot the experimental data\n",
    "plt.errorbar(unumpy.nominal_values(qnkT), unumpy.nominal_values(lnIs), xerr=unumpy.std_devs(qnkT), yerr=unumpy.std_devs(lnIs), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(unumpy.nominal_values(qnkT),unumpy.nominal_values(lnIs),'.')\n",
    "\n",
    "#Line fitting\n",
    "p,cov = polyfit(unumpy.nominal_values(qnkT),unumpy.nominal_values(lnIs),1,w=unumpy.std_devs(lnIs),cov=True);\n",
    "\n",
    "#Plotting theoretical line\n",
    "x = np.arange(35,60,1)\n",
    "y = p[0]*x + p[1];\n",
    "\n",
    "\n",
    "#Getting Eg0\n",
    "plt.plot(x,y)\n",
    "pyplot.title('ln(Is) vs q/nkT')\n",
    "pyplot.xlabel('q/nkT')\n",
    "pyplot.ylabel('ln(Is)')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#Error extracting\n",
    "print(p)\n",
    "error = sqrt(np.diagonal(cov));\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EvC8i9gUOAv5G0n7A8cCVEbE3cGW+b2ZmPVJb8EfEioi4MQ8/BSwHdgGOBs7Mk50JvLauGszMbKye9PFL2gPYH7gO2CkiVkB6cwB27DDPUknLJC1bvXp1L8o0MytC7cEvaUvgQuC4iHiy2/ki4rSIWBwRi+fPn19fgWZmhak1+CUNkEL/nIi4KDevlLQgj18ArKqzBjMz21CdV/UIOB1YHhGfroy6FFiSh5cA36irBjMzG6u/xmW/GHgz8DNJN+W2DwInARdIOha4H3h9jTWYmVmL2oI/In4IqMPow+par5mZjc/f3DUzK4yD38ysMA5+M7PCOPjNzAqzUcEvaa6kvrqKMTOz+o0b/JIakv5c0rclrQJ+AazIv7b5KUl796ZMMzObLBMd8V8F7AWcADwrInaLiB2BlwDXAidJ+suaazQzs0k00XX8h0fEutbGiHiM9FMMF+afZTAzsxlioiP+6yZaQLs3BjMzm74mCv5O37w1M7MZaqKunvmS/rbTyJYfXzMzsxlgouDvA7bER/5mZrPGRMG/IiL+qSeVmJlZT7iP38ysMBMF/1ETLSD/a0UzM5shJgr+MySdIumlkuY2GyXtKelYSd8Fjqi3RDMzm0zj9vFHxGGSjgTeDrxY0jxgHXA78G1gSUT8qv4yzcxsskz4H7gi4jLgsh7UYmZmPdD1v16UtAuwsDpPRFxTR1FmZlafroJf0ieANwI/B4ZzcwAOfjOzGabbI/7XAvtExJo6izEzs/p1+49Y7gb8K5xmZrNAt0f8vwVuknQlsP6oPyLeU0tVZmZWm26D/9J8MzOzGa6r4I+IM+suxMzMemPc4Jf0M9LVO2NGARERf1xLVWZmVpuJjvj/NP/dEni6Zdyhk1+OmZnVbdyreiLivoi4DzifdB3//cAq4O+ApfWXZ2Zmk63byzkPBHYH/gv4CfAw8OK6ijIzs/p0G/zrgN8BmwObAfdExEhtVZmZWW26Df6fkIL/hcDBwDGS/qO2qszMrDbdXsd/bEQsy8O/Ao6W9OaaajIzsxp1dcRfCf1q29cmvxwzM6tbt109ZmY2Szj4zcwKU1vwS/qqpFWSbq20nSjpIUk35duRda3fzMzaq/OI/wza/yP2z0TEonzzv3Q0M+ux2oI//1vGx+pavpmZPTNT0cf/Lkm35K6g7TpNJGmppGWSlq1evbqX9ZmZzWq9Dv4vAnsBi4AVwCmdJoyI0yJicUQsnj9/fq/qMzOb9Xoa/BGxMiKG8889fBk4oJfrNzOzHge/pAWVu68Dbu00rZmZ1aPbn2zYaJLOBQ4BdpD0IPBh4BBJi0j/3OVe4O11rd/MzNqrLfgj4pg2zafXtT4zM+uOv7lrZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWEc/GZmhXHwm5kVxsFvZlYYB7+ZWWFqC35JX5W0StKtlbZ5kq6QdEf+u11d6zczs/bqPOI/Aziipe144MqI2Bu4Mt83M7Meqi34I+Ia4LGW5qOBM/PwmcBr61q/mZm11+s+/p0iYgVA/rtjpwklLZW0TNKy1atX96xAM7PZbtqe3I2I0yJicUQsnj9//lSXY2Y2a/Q6+FdKWgCQ/67q8frNzIrX6+C/FFiSh5cA3+jx+s3Milfn5ZznAj8G9pH0oKRjgZOAV0i6A3hFvm9mZj3UX9eCI+KYDqMOq2udZmY2sWl7ctfMzOrh4DczK4yD38ysMA5+M7PCOPjNzArj4DczK0xtl3OamU1XEcFIwLrhEYZGgqHhEdYNB0MjIwwNx/r2dcMjDI9EGldpG8rTVudpXU6apzqc17V+He3XNzQcaZ153IlHPY8XLJzcX7B38JtZVyJifTitGx4NqXS/0lYJwGqADQ2PsC6PGxoebW+GazX42s4z0j5IR+dJbcMj1TrGhmpz/l6RYKDRoL9P9DdEf1+D/oYY6BttGx1uMNAn+hpiy4H+PE6TXpOD36zHRkY2DL1qiK4dHknBNJSHm+NHRlg3NNImeEdY2wzISnvbcB6JvIz284xOl9ZfXU4vw7Kv0RqGKRD7+3JbDs+ByrjB/gZb9DUYaCjNs354NEiboVod19+nCUJ5dJ7+hlrWveG4gb4GfTmoW9umGwe/zQrNMF07NBpka4dGWJvDK7WPsHYoNrw/vOE864ZHWDM0sn64OW7t8GjwbhDIebrWEG89Al43lAM1HwXXqRpKg/2NNNyfAq4ZaAM5vAb6Gmw+2GCwGarN9jxPM1SbITawft7R4f7qcCU0+xpj21rDt68lSPsbojENg3K2cfDbhJof8athuW64cr8ZsOtDcnh9wG4YsqPzbBi6o0e4zeU0A3t9e0sYrx3eMMCHagjTatgN9jfWB15/nxisBmijwZyBBnPn9I8Tji3zVI8a+1PwNocHGq3ztF9muxDvbwjJwWnjc/BPQxHBmhx+a9aNBuTaoRHWDA1Xhkcq0w23TDcayM1xa6rt1eUNj3603zBkY/0yJpsEg32NdGuGan8Kr2rbYF+DrQcHGGwJ34G+BnP6RwOvOv3odGmeweY8/c3xo8FZHT9YXV5fw0eeNms5+LOR/BG+GpjtgnY0cIcrwdohnPO4NcPV+dvPt0EgT1LQNgSD/Q3m9PetD7g5A/lvf+4XHexnm2ZA9velsNwgPBtjQnd9WFaCejRUR0N3TktQj4Zs+nhvZlNjVgf/1358L1csX8XaoeExwbph6A5P2omrhhgN2v7RgE2h28ecvhS2244Z12Cwr2+DeVr/Dvb1jQ63jK+Ge7PN4Wpm7czq4P/N2mF+/bt1zOlrMHewn3lbjA3Nwb6+9UfBoyFana5vg3HV9rHB7bA1s+lvVgf/O162F+942V5TXYaZ2bTiw1Mzs8I4+M3MCuPgNzMrjIPfzKwwDn4zs8I4+M3MCuPgNzMrjIPfzKwwDn4zs8I4+M3MCuPgNzMrjIPfzKwwDn4zs8I4+M3MCuPgNzMrjIPfzKwwDn4zs8I4+M3MCuPgNzMrzJT8z11J9wJPAcPAUEQsnoo6zMxKNJX/bP3lEfHIFK7fzKxI7uoxMyvMVB3xB3C5pAC+FBGntU4gaSmwNN99WtLtz3BdOwAz/ZOFt2HqzfT6wdswHfS6/oXtGhURPawhr1TaOSIelrQjcAXw7oi4pqZ1LZvp5xC8DVNvptcP3obpYLrUPyVdPRHxcP67CrgYOGAq6jAzK1HPg1/SXElbNYeBVwK39roOM7NSTUUf/07AxZKa6//3iPhOjesbc/5gBvI2TL2ZXj94G6aDaVH/lPTxm5nZ1PHlnGZmhXHwm5kVZlYFv6TXS7pN0oikxS3jTpB0p6TbJb2q0n5EbrtT0vG9r7ozSYskXSvpJknLJB2Q2yXpc7nmWyQ9f6pr7UTSu/P+vU3SJyvtbR+P6UrS30kKSTvk+zPpMfiUpF/kOi+WtG1l3Ix4HKbz67QTSbtJukrS8vz8f29unyfpCkl35L/b9by4iJg1N2BfYB/gamBxpX0/4GZgDvBs4C6gL9/uAvYEBvM0+031dlTqvhx4dR4+Eri6MvyfgICDgOumutYO9b8c+B4wJ9/fcbzHY6rrHWc7dgO+C9wH7DCTHoNc6yuB/jz8CeATM+lxmO6v03HqXgA8Pw9vBfwy7/NPAsfn9uObj0cvb7PqiD8ilkdEu2/4Hg2cFxFrIuIe4E7SdwcOAO6MiLsjYi1wXp52ughg6zy8DfBwHj4aOCuSa4FtJS2YigIn8E7gpIhYA+u/twGdH4/p6jPA+0mPR9NMeQyIiMsjYijfvRbYNQ/PlMdhur9O24qIFRFxYx5+ClgO7EKq/cw82ZnAa3td26wK/nHsAjxQuf9gbuvUPl0cB3xK0gPAycAJuX261930HOAlkq6T9ANJL8ztM6V+JB0FPBQRN7eMmjHb0OJ/kj6pwMzZhplSZ0eS9gD2B64DdoqIFZDeHIAde13PVP465zMi6XvAs9qM+lBEfKPTbG3agvZvfD29vnW87QEOA/5XRFwo6Q3A6cDhdN6enpug/n5gO1JXyAuBCyTtyTSqHybchg+SukrGzNambVpuQ/N1IelDwBBwTnO2NtNPx+u7Z0qdbUnaErgQOC4inszfYZpSMy74I+LwZzDbg6R+2qZdGe026dTeE+Ntj6SzgPfmu18HvpKHx9uenpqg/ncCF0XqzLxe0gjpR6qmTf3QeRsk/RGp7/vm/GLdFbgxn2SfEdvQJGkJ8KfAYfnxgGm2DeOYKXWOIWmAFPrnRMRFuXmlpAURsSJ3D67qvIR6lNLVcynwJklzJD0b2Bu4HvgJsLekZ0saBN6Up50uHgZelocPBe7Iw5cCb8lXlhwE/Lr50XGauYRUN5KeQzox9widH49pJSJ+FhE7RsQeEbEHKYCeHxG/YuY8Bkg6AvgAcFRE/LYyakY8Dkz/12lbSkcLpwPLI+LTlVGXAkvy8BKgU09Ffab6zPdk3oDXkV6ca4CVwHcr4z5EujLgdvKVMrn9SNLZ9rtIH4unfDsqtR0M3EC6iuE64AW5XcAXcs0/o3IF03S6kYL+bNJvMd0IHDrR4zGdb8C9jF7VMyMeg1zrnaQ+8pvy7dSZ9jhM59fpODUfTOqSuqWy748EtgeuJB3IXQnM63Vt/skGM7PClNLVY2ZmmYPfzKwwDn4zs8I4+M3MCuPgNzMrzIz7ApfZVJPUvBwP0rdlh4HV+f4BkX5Pxmza8uWcZptA0onA0xFx8lTXYtYtd/WYmRXGwW9mVhgHv5lZYRz8ZmaFcfCbmRXGwW9mVhhfzmlmVhgf8ZuZFcbBb2ZWGAe/mVlhHPxmZoVx8JuZFcbBb2ZWGAe/mVlh/j+WNIWy3QJ8eQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = 1.602e-19;\n",
    "k = 1.381e-19;\n",
    "\n",
    "T = np.array([-100,-70,-40,-10,25])\n",
    "x = 1.5;\n",
    "TK = T + 273.15;\n",
    "lnI = x*log(TK);\n",
    "plt.plot(T,lnI)\n",
    "pyplot.title('The expected magnitude of the fix term xln(T), vs T')\n",
    "pyplot.xlabel('T')\n",
    "pyplot.ylabel('xln(T)')\n",
    "ylim(top=30)  # adjust the top leaving bottom unchanged\n",
    "ylim(bottom=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4986812085465664e-23+/-1.7472388062632898e-23\n",
      " 3.2166249921309417e-20+/-3.911922538287249e-20\n",
      " 4.526837455877283e-16+/-2.1096583885655355e-16\n",
      " 1.2051253814514457e-13+/-5.288634777334561e-14]\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-05d4b48d314c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd_devs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd_devs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'none'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcov\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpolyfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd_devs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcov\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36merrorbar\u001b[1;34m(x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, data, **kwargs)\u001b[0m\n\u001b[0;32m   2559\u001b[0m         \u001b[0mlolims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlolims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muplims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muplims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlolims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlolims\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2560\u001b[0m         \u001b[0mxuplims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxuplims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrorevery\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrorevery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcapthick\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcapthick\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2561\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2562\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2563\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1597\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1598\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1599\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1600\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36merrorbar\u001b[1;34m(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)\u001b[0m\n\u001b[0;32m   3377\u001b[0m             \u001b[0mnoxlims\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m~\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxlolims\u001b[0m \u001b[1;33m|\u001b[0m \u001b[0mxuplims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3378\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mnoxlims\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnoxlims\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3379\u001b[1;33m                 \u001b[0myo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxywhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnoxlims\u001b[0m \u001b[1;33m&\u001b[0m \u001b[0meverymask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3380\u001b[0m                 \u001b[0mlo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mro\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxywhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnoxlims\u001b[0m \u001b[1;33m&\u001b[0m \u001b[0meverymask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3381\u001b[0m                 \u001b[0mbarcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mro\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meb_lines_style\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mxywhere\u001b[1;34m(xs, ys, mask)\u001b[0m\n\u001b[0;32m   3329\u001b[0m             \u001b[0mys\u001b[0m \u001b[0mare\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3330\u001b[0m             \"\"\"\n\u001b[1;32m-> 3331\u001b[1;33m             \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3332\u001b[0m             \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3333\u001b[0m             \u001b[0mxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mthisx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mthisx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    },
    {
     "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": [
    "T = np.array([-100,-70,-40,-10,25])\n",
    "Te = np.array([4,4,4,4,4])\n",
    "T = T + 273.15;\n",
    "T = unumpy.uarray(T,Te)\n",
    "x = unumpy.log(T);\n",
    "Is = unumpy.exp(lnIs)\n",
    "print(Is)\n",
    "\n",
    "plt.errorbar(unumpy.nominal_values(x), unumpy.nominal_values(lnIs), xerr=unumpy.std_devs(x), yerr=unumpy.std_devs(lnIs), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(unumpy.nominal_values(x),unumpy.nominal_values(lnIs),'.')\n",
    "p,cov = polyfit(unumpy.nominal_values(x),unumpy.nominal_values(lnIs),1,w=unumpy.std_devs(lnIs),cov=True);\n",
    "x = np.arange(5,6,0.1)\n",
    "print(x)\n",
    "y = p[0]*x + p[1];\n",
    "print(p)\n",
    "plt.plot(x,y)\n",
    "error = sqrt(np.diagonal(cov));\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4986812085465664e-23+/-1.7472388062632898e-23\n",
      " 3.2166249921309417e-20+/-3.911922538287249e-20\n",
      " 4.526837455877283e-16+/-2.1096583885655355e-16\n",
      " 1.2051253814514457e-13+/-5.288634777334561e-14]\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-05d4b48d314c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd_devs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd_devs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'none'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcov\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpolyfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnominal_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd_devs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlnIs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcov\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36merrorbar\u001b[1;34m(x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, data, **kwargs)\u001b[0m\n\u001b[0;32m   2559\u001b[0m         \u001b[0mlolims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlolims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muplims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muplims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlolims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlolims\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2560\u001b[0m         \u001b[0mxuplims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxuplims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrorevery\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrorevery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcapthick\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcapthick\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2561\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2562\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2563\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1597\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1598\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1599\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1600\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36merrorbar\u001b[1;34m(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)\u001b[0m\n\u001b[0;32m   3377\u001b[0m             \u001b[0mnoxlims\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m~\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxlolims\u001b[0m \u001b[1;33m|\u001b[0m \u001b[0mxuplims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3378\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mnoxlims\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnoxlims\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3379\u001b[1;33m                 \u001b[0myo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxywhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnoxlims\u001b[0m \u001b[1;33m&\u001b[0m \u001b[0meverymask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3380\u001b[0m                 \u001b[0mlo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mro\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxywhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnoxlims\u001b[0m \u001b[1;33m&\u001b[0m \u001b[0meverymask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3381\u001b[0m                 \u001b[0mbarcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mro\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meb_lines_style\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mxywhere\u001b[1;34m(xs, ys, mask)\u001b[0m\n\u001b[0;32m   3329\u001b[0m             \u001b[0mys\u001b[0m \u001b[0mare\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3330\u001b[0m             \"\"\"\n\u001b[1;32m-> 3331\u001b[1;33m             \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3332\u001b[0m             \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3333\u001b[0m             \u001b[0mxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mthisx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mthisx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    },
    {
     "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": [
    "T = np.array([-100,-70,-40,-10,25])\n",
    "Te = np.array([4,4,4,4,4])\n",
    "T = T + 273.15;\n",
    "T = unumpy.uarray(T,Te)\n",
    "x = unumpy.log(T);\n",
    "Is = unumpy.exp(lnIs)\n",
    "print(Is)\n",
    "\n",
    "plt.errorbar(unumpy.nominal_values(x), unumpy.nominal_values(lnIs), xerr=unumpy.std_devs(x), yerr=unumpy.std_devs(lnIs), marker = 'x', fmt = 'none', label = name);\n",
    "plt.plot(unumpy.nominal_values(x),unumpy.nominal_values(lnIs),'.')\n",
    "p,cov = polyfit(unumpy.nominal_values(x),unumpy.nominal_values(lnIs),1,w=unumpy.std_devs(lnIs),cov=True);\n",
    "x = np.arange(5,6,0.1)\n",
    "print(x)\n",
    "y = p[0]*x + p[1];\n",
    "print(p)\n",
    "plt.plot(x,y)\n",
    "error = sqrt(np.diagonal(cov));\n",
    "print(error)"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}