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1 | { | |||
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2 | "cells": [ | |||
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3 | { | |||
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4 | "cell_type": "code", | |||
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5 | "execution_count": 19, | |||
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6 | "metadata": { | |||
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7 | "collapsed": true | |||
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8 | }, | |||
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9 | "outputs": [], | |||
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10 | "source": [ | |||
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11 | "import numpy as np\n", | |||
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12 | "import matplotlib.pyplot as plt\n", | |||
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13 | "import pandas as pds\n", | |||
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14 | "import os\n", | |||
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15 | "import glob\n", | |||
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16 | "from multiprocessing import dummy\n", | |||
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17 | "%matplotlib inline" | |||
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18 | ] | |||
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19 | }, | |||
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20 | { | |||
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21 | "cell_type": "code", | |||
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22 | "execution_count": null, | |||
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23 | "metadata": { | |||
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24 | "collapsed": true | |||
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25 | }, | |||
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26 | "outputs": [], | |||
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27 | "source": [ | |||
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28 | "%%sh \n", | |||
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29 | "cd /home/jeandet/Documents/DATA\n", | |||
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30 | "ls *.csv | parallel 'sed -e \"s/,/;/2\" {} > {}.fixed'\n", | |||
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31 | "cd -" | |||
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32 | ] | |||
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33 | }, | |||
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34 | { | |||
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35 | "cell_type": "code", | |||
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36 | "execution_count": 2, | |||
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37 | "metadata": { | |||
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38 | "collapsed": true, | |||
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39 | "scrolled": false | |||
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40 | }, | |||
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41 | "outputs": [], | |||
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42 | "source": [ | |||
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43 | "cal=pds.DataFrame(columns=[\"CH1\",\"CH2\"])\n", | |||
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44 | "zero=pds.DataFrame(columns=[\"CH1\",\"CH2\"])\n", | |||
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45 | "for cur in [\"10mA\",\"50mA\",\"100mA\",\"400mA\"]:\n", | |||
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46 | " df=pds.read_csv(\"/home/jeandet/Documents/DATA/BOOT_CUR_REF_{}.txt.csv.fixed\".format(cur),\n", | |||
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47 | " sep=\";\",\n", | |||
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48 | " skiprows=[0,1,2,3,4],\n", | |||
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49 | " names=[\"CH1\",\"CH2\"],\n", | |||
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50 | " decimal=\",\")\n", | |||
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51 | " cur=float(cur[:-2])/1e3\n", | |||
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52 | " cal.loc[cur]=df[int(1e7):].mean()\n", | |||
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53 | " zero.loc[cur]=df[:int(6e6)].mean()" | |||
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54 | ] | |||
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55 | }, | |||
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56 | { | |||
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57 | "cell_type": "code", | |||
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58 | "execution_count": 3, | |||
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59 | "metadata": { | |||
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60 | "scrolled": false | |||
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61 | }, | |||
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62 | "outputs": [ | |||
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63 | { | |||
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64 | "data": { | |||
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65 | "text/plain": [ | |||
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66 | "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f13b4456320>,\n", | |||
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67 | " <matplotlib.axes._subplots.AxesSubplot object at 0x7f13b419b438>], dtype=object)" | |||
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68 | ] | |||
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69 | }, | |||
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70 | "execution_count": 3, | |||
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71 | "metadata": {}, | |||
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72 | "output_type": "execute_result" | |||
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73 | }, | |||
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74 | { | |||
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75 | "data": { | |||
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76 | "image/png": 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4M9CLrfU4+tWxcC92qWuqvr1xiR6mFwsAiCDQAgCAuBAc6MV6HP1yX4Mu9AaV\nPzVDX7pnnqrpxQIAhkCgBQAAMXW4sVO1Xr/qvAE1doR7sRuW5am6vEC3z51BLxYAcEUEWgAAMOoG\nerG1HkcHGsK92HsXZuv5DcV6gF4sAOA6EWgBAMCo6Ort12v7m1TrdfSroy0KWWlZwVR95+FwL3bm\nZHqxAIAbQ6AFAAAjZqAX+5LHr1f3NepCb1CuaRP05fvmq7rcpQU59GIBADePQAsAAKLuUGOH3B5H\ndb5wL3bK+HHauCxfm8td9GIBAFFDoAUAAFHR3NGtrfVD92LXLJmljDR6sQCA6CLQAgCAm0YvFgAQ\nSwRaAABwQ4Ihq/eOt6nW69cv9zWq6xO92AItyJkc6xEBAGMEgRYAAFyXgV7sFp+jpo4eTRk/Tg8v\ny1d1hUu3zaEXCwAYfQRaAABwRc0d3arzBVTrdXSwoUPjIr3Yb2106YFierEAgNgi0AIAgE/o6u3X\nq/sbVetx9OtjrQpZaXnBVL0Q6cVm0YsFAMQJAi0AAFAwZPWb461yexz9cv/Hvdiv3LdAm8td9GIB\nAHGJQAsAwBh2sKFDbq+juoFebMY4bVqer+pyl26lFwsAiHMEWgAAxpimjm7V+RzVehwdauzUuBSj\n+xZl69sbC7S6OIdeLAAgYRBoAQAYAy70hHuxbu+gXuzsafqbTSXauCyPXiwAICERaAEASFLBkNWv\nj7XK7XX0aqQXWzB9gv5sVbgXOz+bXiwAILENK9AaY74nqUpSSFKzpEestQFjzB9JekaSkdQp6cvW\n2vohzv83SfdKao9sesRa6xvOTAAAjHUHAh1ye/2q8wXU3BnuxVaV5au6vECVt0ynFwsASBrD/Yb2\nx9bab0mSMearkr4t6QlJJyTda609a4xZL+lFSbdf4RpPW2t/Mcw5AAAY0xrbw71Yt3dwLzZHNRUu\n3b+YXiwAIDkNK9BaazsGvZ0kyUa2/2bQ9vclFQzncwAAwO+60NOvX+6L9GKPt8paqWz2NH23qkQb\nl+VrxqT0WI8IAMCIGnaH1hjzA0mfU/i24VVDHPJFSTuucokfGGO+LekNSc9aa3uGOxMAAMkqGLL6\n1bFWuT1+vbq/SRf7gpo9Y4L+PNKLnUcvFgAwhhhr7dUPMGanpNwhdj1vra0bdNxzkjKstd8ZtG2V\npP8h6W5rbdsQ186T1CgpXeHbko9ba797hTkel/S4JBUWFq44derUNf5qAAAkB2utDjR0yO1xVFcf\nUEtnjzJcQzl9AAAgAElEQVQzxmnDsnzVVLhUect0GUMvFgCQPIwxu621ldc87lqB9gY+sFDSdmtt\naeT9MkluSeuttUeu4/z7JD1lrd14rWMrKyvtrl27hjkxAADxrbG9W1t8jtweR4ebOpWWGunFlru0\nil4sACCJXW+gHe5TjoustUcjb6skHYpsL5RUK+lPrhZmjTF51toGE/7Pypsl7RvOPAAAJLrzl3qx\nfv3meJuslcoLp+l7VSXaQC8WAIBPGG6H9ofGmEUKL9tzSuEnHEvhpx1nSfofkVug+gfStTFmu6RH\nrbUBST8zxmQrvLyPb9D5AACMGf3BULgX63X02uBe7P1Fqi53ae7MSbEeEQCAuBS1W45HE7ccAwAS\nnbVW+wMdcnsdbR3Ui924PF815S6toBcLABjDRuWWYwAAcGMa2i9qizcgt9evI03nlZZqtCqyXuyq\nxTkaP45eLAAA14tACwDACBvoxdZ6/Hrvw0G92M2l2rg0T9PpxQIAcFMItAAAjID+YEjvHmvVFq+j\nV/c3qrsvpMIZE+nFAgAQRQRaAACiZHAvts4XUOv5Hk2dkKaaigJ6sQAAjAACLQAAw0QvFgCA2CDQ\nAgBwEzq7+yLrxTqXerEV9GIBABhVBFoAAK7TQC/W7XH02oFwL/aWrIn6aqQXO4deLAAAo4pACwDA\nVQz0Yms94fViB3qxn64oUE2FSxWF9GIBAIgVAi0AAEMInLuoLT5Hbo+jo83hXuz9i3NUXV6gVYuz\n6cUCABAHCLQAAER0dvdpx75GuT2O3j8R7sWuuGW6vr+5VBuX5WnaRHqxAADEEwItAGBM6w+G9O7R\nVtV6Hb22v1E9/eFe7NdWh3uxt2TRiwUAIF4RaAEAY461VvucDtV6/Xq5PqDW872aNjFN/0dlgarL\nC1RROI1eLAAACYBACwAYM5xzF7XF68jtdXSs+bzSU1PCvdgKl1YtylH6uJRYjwgAAG4AgRYAkNQ6\nu/u0Y2+jar1+vf/hGUlS5S3T9YPqUm1YSi8WAIBERqAFACSdvmBI7x5tUa3H0esHmtTTH9KcrIn6\nywcWqrrcpcKsibEeEQAARAGBFgCQFKy12uu0q9bj6OX6gNouhHuxv1c5W9UVLpXPphcLAECyIdAC\nABKa/2yX6nwB1Xr8Ot5yQempKVpdnKPqcpfuoxcLAEBSI9ACABJOR3efduxtUK3H0Qcnwr3YW+dM\n1xfvnqcNS/M0dWJajCcEAACjgUALAEgIfcGQ/utIi2q9jnZGerFzZ07S19cs1OYyerEAAIxFBFoA\nQNyy1mqPv11u78e92OkT0/T7t85WdblLZfRiAQAY0wi0AIC44z/bpS1eR7VeRx9GerEPLMlRdXmB\n7l2YTS8WAABIItACAOJER3eftu9pUK3X0W8jvdjb5szQYyvn6aGleZo6gV4sAAD4JAItACBm+oIh\nvXO4RW6vo9cPNqm3P6R5Myfpr9Ys1OZyl2bPoBcLAACujEALABhV1lrV+9vl9vj18p4GnbnQqxmT\n0vWHt85WdUWBlhdMpRcLAACuC4EWADAqTp/pUp1vUC92XIoeKM5RTXmB7l2UrbRUerEAAODGEGgB\nACOm/WJkvVh6sQAAYAQQaAEAUdXbH9I7R1rk9vq182AzvVgAADBiCLQAgGEb3IvdWh/Q2a4+erEA\nAGDEEWgBADft9JnwerFur6MPW8O92DXFs1Rd7qIXCwAARhyBFgBwQ9ov9mn73ga5PY5+ezLSi507\nQ4/fM0/r6cUCAIBRRKAFAFzTkL3Y7El66sGFqiqjFwsAAGKDQAsAGJK1Vr7T5+T2Onp5UC/2s7cV\nqrrcpWX0YgEAQIwRaAEAn3D6TJfcXkdbBvdil8xSTblL9yykFwsAAOIHgRYAoPauPr2yt0Fur1//\nffKsJOn2uTP0pXvDvdjMDHqxAAAg/gw70BpjviepSlJIUrOkR6y1AWPMfZLqJJ2IHFprrf3uEOfP\nlfRzSVmSdkv6E2tt73DnAgBcXW9/SG8fbpbb6+iNg83qDYY0P3uSnl67SFVl+SqYTi8WAADEt2h8\nQ/tja+23JMkY81VJ35b0RGTfu9bajdc4/0eSfmKt/bkx5l8kfVHST6MwFwDgMtZaeU+fk9vjaNue\ncC82a1K6Pnt7oWoqXFrqohcLAAASx7ADrbW2Y9DbSZLs9Z5rwr813S/ps5FN/1PSCyLQAkBUfdQW\n6cX6HJ1ovaDxA73YCpdWFtGLBQAAiSkqHVpjzA8kfU5Su6RVg3bdaYyplxSQ9JS1dv9lp2ZJOmet\n7Y+890tyRWMmABjr2rv6tG1vQG6Po12nwr3YO+bN0Jfvna91S3PpxQIAgIR3XYHWGLNTUu4Qu563\n1tZZa5+X9Lwx5jlJT0r6jiSPpFusteeNMQ9J2iKp6GYHNcY8LulxSSosLLzZywBAUuvtD+mtw81y\nexy9eSjci12QM1lPr12kzeUuuaZNiPWIAAAAUWOsve47hK99MWMKJW231pYOse+kpEprbeugbUZS\ni6Rca22/MeZOSS9Ya9de7XMqKyvtrl27ojY3ACQya608H52T2+vXtj0NOtfVp5mT0/Xw8nzVlBeo\n1JVJLxYAACQUY8xua23ltY6LxlOOi6y1RyNvqyQdimzPldRkrbXGmNskpUhqG3xuZN9bkj6j8JOO\n/1ThJyMDAK7hVNuFS+vFnmzr0vhxKXqwJFc15S7dXTSTXiwAAEh60ejQ/tAYs0jhZXtO6eMnHH9G\n0peNMf2SLkr6Axv5OtgYs13So9bagKRnJP3cGPN9SV5J/08UZgKApHSuq1fb9jTI7XW0+9RZGSPd\nMTdLX1m1QOtLczWFXiwAABhDonrL8WjhlmMAY0lPf1BvHWqR2+vXW4da1BsMqShnsqorXNpc5lI+\nvVgAAJBkRu2WYwBA9IV7sWdV63G0bU+D2i+Ge7F/fMctqqlwqSSfXiwAAACBFgDiyKm2C6r1hNeL\nPdXWpYy0FD24JFfVFS6tXDBT4+jFAgAAXEKgBYAYO9fVq5f3NMjt8cvz0TkZI905L0tPrlqgdfRi\nAQAArohACwAxEO7FNqvW4+itw83qC1otnDVZz6xbrKqyfHqxAAAA14FACwCjxFqr3afOqtbr6JVL\nvdjx+pM75tCLBQAAuAkEWgAYYSdbL6g2sl7sR2foxQIAAEQLgRYARsDZC73atiegWq8jb6QX+6n5\nWfrq6iKtK83V5PH8+AUAABgufqMCgCi5Wi92c3m+8qbSiwUAAIgmAi0ADMOVerGfu3OOqsvpxQIA\nAIwkAi0A3IQTrRfkvqwXu7YkV9XlLt1NLxYAAGBUEGgB4DrRiwUAAIgv/PYFAFfR0x/UmwebVet1\n9HakF7to1hQ9uz68Xiy9WAAAgNgh0ALAZay12nXqrGo9jl7ZE1BHd7+yp4zXn945R9UVLi3JoxcL\nAAAQDwi0ABBxovWC3B6/3D5Hp89c1IS0VK0tmaXqigLdNT+LXiwAAECcIdACGNPODPRiPY58p8O9\n2Lvmz9RfrF6odaW5mkQvFgAAIG7xmxqAMae7L6g3I+vFvn24Wf0hq8W5U/Tc+sWqKnMpd2pGrEcE\nAADAdSDQAhgTQqFwL9bt9WvbngZ1dvcrZ8p4ff6uOaouL9CS/MxYjwgAAIAbRKAFkNQ+bDkvt9eR\n2+vIfzbci11XGl4v9q4FM5WawsOdAAAAEhWBFkDSOXOhVy/Xh9eLrT99TilGumvBTH19zUKtLaEX\nCwAAkCz4rQ5AUujuC+qNg81ye/16+3DLpV7sNx8K92JnZdKLBQAASDYEWgAJKxSy+u+TZ+T2Onpl\n78e92C/cPVfV5S4V59GLBQAASGYEWgAJ53jLebk94V6sc+6iJqanal1JrqorXPrUfHqxAAAAYwWB\nFkBCaDvfo5frA3J7HdX72y/1Yp9au1APLqEXCwAAMBbxGyCAuNXdF9TOg01yexy9cyTciy3Oy9Tz\nDxVrU1k+vVgAAIAxjkALIK6EQla/PXlGbo+j7Xsb1NnTr1mZ4/XFu+equsKlxbn0YgEAABBGoAUQ\nF441n5fb69cWb+DjXmxprmrKC3Tn/Cx6sQAAAPgdBFoAMdM6qBe7J9KLvbsoW0+vXaQHS2ZpYjo/\nogAAAHBl/LYIYFR19wX1+oEmub3hXmxwUC+2qixfOfRiAQAAcJ0ItABG3EAvttbj1469jZd6sY/S\niwUAAMAwEGgBjJhjzZ2q9Tiq84V7sZPSU7WuNE/V5S56sQAAABg2Ai2AqKIXCwAAgNHCb5YAhm2o\nXuySvEz99YZibVpOLxYAAAAjg0AL4KaEQlbvn2jTFq9zqRebm5mhR1fOVU15gRblTon1iAAAAEhy\nBFoAN+RKvdiaCpfumEcvFgAAAKNnWIHWGPM9SVWSQpKaJT1irQ0YY56W9EeDPqNYUra19sxl5/+b\npHsltUc2PWKt9Q1nJgDR13q+R1t94V7sXifci11ZlK1vrFukB5fkakJ6aqxHBAAAwBhkrLU3f7Ix\nmdbajsjrr0paYq194rJjHpb0l9ba+4c4/98kbbPW/uJGPreystLu2rXrpucGcG3dfUG9dqBJbo9f\n/3W0VcGQVUl+pqrLXdpUlq+cKfRiAQAAMDKMMbuttZXXOm5Y39AOhNmISZKGSsd/KOl/DedzAIyO\ngV6s2+Nox75Gne/pV97UDD22cp5qKlxaOIteLAAAAOLHsDu0xpgfSPqcwrcNr7ps30RJ6yQ9eZVL\n/MAY821Jb0h61lrbM9yZANyYo02dqvU6qvM6CrR3a1J6qtYvzVNNebgXm0IvFgAAAHHomrccG2N2\nSsodYtfz1tq6Qcc9JynDWvudQdt+X9IfW2sfvsK18yQ1SkqX9KKk49ba717h2MclPS5JhYWFK06d\nOnXVuQF80k9eP6K/XLPw0vuWzh5trQ/I7fVrn9Oh1BSjlUUzVV3uohcLAACAmLreW46H1aG97AML\nJW231pYO2uaW9J/W2v+4jvPvk/SUtXbjtY6lQwvcuDnPvqKD312n1w40yu119G6kF1vqylR1eYE2\nLc9X9pTxsR4TAAAAGJ0OrTGmyFp7NPK2StKhQfumKvwE4z++yvl51toGY4yRtFnSvuHMA+B3BUNW\nH3zYJkm69Qc7db6nX/lTM/T4PfNUU+5SEb1YAAAAJKjhdmh/aIxZpPCyPackDX7CcbWk16y1Fwaf\nYIzZLulRa21A0s+MMdmSjCTfZecDuEnWWu112vWduv3ynj53afv5nn5J0mdWFOjrDy6K1XgAAABA\nVETtluPRxC3HwNCOt5zXVl9AW+sDOtF6QempKbpvUbY2leXryf/w6uQPN8R6RAAAAOCaRuWWYwCx\n19jerW17AqrzBbTXaZcx0p3zsvTEvfO0riRPUyemSZKe/A9vjCcFAAAAootACySg9q4+7djXoDpf\nQO+faJO10rKCqfrrDcXauCxfuVMzfuecr60uisGkAAAAwMgh0AIJ4mJvUG8calKdL6C3DzerL2g1\nd+YkffX+IlWV5Wte9uSrnj94yR4AAAAgGRBogTjWFwzp18datdUX0Kv7G3WhN6icKeP1uTvnqKos\nX0tdUxV+SDgAAAAw9hBogThjrZXno7Oq8wX0yp4GtV3o1ZSMcdq4LF9VZfm6fV6WUlMIsQAAAACB\nFogThxs7VedztLU+IP/Zixo/LkUPFM/SprJ83bcoW+PHpcZ6RAAAACCuEGiBGDp9pksv7wloqy+g\nQ42dSk0xunvBTH19zUKtWTJLUzLSYj0iAAAAELcItMAoazvfo+17w08o3nXqrCRpxS3T9d2qEj20\nNE8zJ4+P8YQAAABAYiDQAqPgfE+/Xj/QqDpfQO8ebVUwZFWUM1lPr12kTcvzNXvGxFiPCAAAACQc\nAi0wQnr7Q3rnSIvqfI52HmxSd19IrmkT9NjKeaoqy9fi3Ck8oRgAAAAYBgItEEWhkNUHJ85oa72j\n7Xsb1X6xT9MnpukzKwpUVebSisLpSuEJxQAAAEBUEGiBYbLWan+gQ3U+Ry/XN6ixo1sT01P14JJZ\nqipz6e6imUpLTYn1mAAAAEDSIdACN+lE6wVt9QVUV+/ow5YLSks1undhtr65oVgPFOdoYjr/9wIA\nAABGEr9xAzeguaNbL+9p0Fafo3p/u4yRbpszQ4+tnKf1pbmaNjE91iMCAAAAYwaBFriG9ot9enVf\no+rqHb13vE0hK5XkZ+qbDy3WxmX5yp82IdYjAgAAAGMSgRYYQndfUG8datYWn6O3DrWoNxjSLVkT\n9eSqBdpUlq8FOVNiPSIAAAAw5hFogYj+YEjvfdimOl9Ar+5rVGdPv2ZOHq8/uqNQVWUuLS+YyjI7\nAAAAQBwh0GJMs9bKd/qc6nwBbdvToNbzPZoyfpzWleaqqsylO+dnKZVldgAAAIC4RKDFmHS0qVN1\nvoC21gf00ZkupY9L0erFOaoqy9d9i3KUkZYa6xEBAAAAXAOBFmOGc+6iXq4PqM4X0MGGDqUY6a4F\nM/Xn9y/Q2tJcZWakxXpEAAAAADeAQIukduZCr7bvbdBWX0C/PXlGklQ2e5q+8/ASbViWp5wpGTGe\nEAAAAMDNItAi6XT19uv1A03a6gvonSMt6g9Zzc+epL9as1CbyvJ1S9akWI8IAAAAIAoItEgKfcGQ\n3j3aojpfQK/tb9LFvqDypmboi3fP1aayfC3Jy+QJxQAAAECSIdAiYYVCVrtOnVWdz9H2vQ0629Wn\naRPTVF3hUtXyfN06Z4ZSeEIxAAAAkLQItEgo1lodbOhUXb2jl30BBdq7NSEtVWuWzFJVWb5WFmUr\nfVxKrMcEAAAAMAoItEgIH7V1aWu9ozpfQEebz2tcitE9C7P1zPrFeqB4liaN519lAAAAYKwhBSBu\ntXT26JU9AdXVB+T96Jwk6bY5M/T9zaV6aGmeZkxKj/GEAAAAAGKJQIu40tndp1f3N6nO5+jXx1oV\nslJxXqaeXb9YDy/Pl2vahFiPCAAAACBOEGgRc919Qb19uEVb6x3tPNis3v6QZs+YoK/ct0CbyvK1\ncNaUWI8IAAAAIA4RaBETwZDV+x+2qc7naMe+RnV292vm5HR99rZCbSrLV/nsaSyzAwAAAOCqCLQY\nNdZa7fG3q84X0Mt7Amrp7NHk8eO0tiRXVWX5+tT8LI1L5QnFAAAAAK4PgRYj7njLedX5Atrqc3Sy\nrUvpqSlatThbVWUu3b84RxlpqbEeEQAAAEACItBiRDS0X9S2+gbV1Tva53TIGOlT87P0lfsWaG1p\nrqZOSIv1iAAAAAASHIEWUXOuq1c79jWqzufogxNnZK20vGCqvrVxiTYuy9OszIxYjwgAAAAgiUQt\n0Bpj/krS30vKtta2mvATff5R0kOSuiQ9Yq31DHHeCkn/JmmCpO2SvmattdGaCyPrYm9QOw82qc4X\n0DtHmtUXtJqXPUl/sXqhNpXla+7MSbEeEQAAAECSikqgNcbMlvSgpI8GbV4vqSjyz+2Sfhr583I/\nlfSYpA8UDrTrJO2IxlwYGX3BkH51rFVbfQG9ur9RXb1Bzcocr0c+NUdVZS6V5GfyhGIAAAAAIy5a\n39D+RNI3JNUN2lYl6d8j37a+b4yZZozJs9Y2DBxgjMmTlGmtfT/y/t8lbRaBNq785PUj+trqInk+\nOqs6X0Cv7G3QmQu9yswYp6qyfG1a7tJtc2coNYUQCwAAAGD0DDvQGmOqJDnW2vrLvpVzSTo96L0/\nsq3hsmP8QxyDOHGosUP/+MZR/WK3X865i8pIS9EDxbNUVebSPQtnavw4nlAMAAAAIDauK9AaY3ZK\nyh1i1/OSvqnw7cYjyhjzuKTHJamwsHCkP25Ma+nsUZ3P0UseRwcbOiRJRbMm66m1C7VmSa4mj+dZ\nYgAAAABi77qSibX2gaG2G2OWSporaeDb2QJJHmPMbZIcSbMHHV4Q2TaYE9l+tWMGZnhR0ouSVFlZ\nyUOjoqynP6g3Djbrpd1+vX2kRcHQJ/8nfvtwi94+3KKvre7SX65ZGKMpAQAAAOBjw/qqzVq7V1LO\nwHtjzElJlZGnHG+V9KQx5ucKPwyqfXB/NnJ+gzGmwxhzh8IPhfqcpH8azky4ftZaeU+f00u7/dq2\np0HtF/s0K3O8Hls5T5+ucKlo1hRJ0pxnX9HJH26I8bQAAAAA8Ekjee/odoWX7Dmm8LI9nx/YYYzx\nWWvLIm+/oo+X7dkhHgg14gLnLsrtdfSSx68PWy4oIy1Fa0ty9emKAt21YCYPdwIAAACQEKIaaK21\ncwa9tpL+7ArHlQ16vUtSaTTnwO/q6u3Xjr2NqvX69ZvjbbJWum3uDD1xz3ytX5qrKRlpVzz3a6uL\nRnFSAAAAALg+PN0niYVCVu+faNNLux3t2Negrt6gCmdM1NdWF6mmvECFWROv6zp0ZgEAAADEIwJt\nEjrRekG1Hr9qPY6ccxc1efw4PbwsX59eUaBb50zXZcsrAQAAAEBCItAmifaLfXplT4Ne8vi1+9RZ\npRjp7qJsfWPdIj24JFcT0lkvFgAAAEByIdAmsP5gSO8ebdUvPH69fqBJvf0hFeVM1rPrF6u63KVZ\nmRmxHhEAAAAARgyBNgEdauzQS7v92uILqKWzR9MnpukPb52tT68o0FLXVG4pBgAAADAmEGgTROv5\nHtX5Anppt18HGjo0LsXo/sU5+vSKAq1alKP0cSmxHhEAAAAARhWBNo719Af15sFmveTx6+3/n737\njq6ruvP+/z5qlqvcmwo2phgb29iWLdMJbQgkEDABAhgIBMJMIIVAAsnvN2Ge9ctAZuY3mUx7JqS3\neYBQBgZIH5JJGcsVjAklEEDFlhvuRfU8f+xrrkRcZFvSuefq/VqLJUv7Sv6QC8Sf9d17n1c20NYR\nM6O8jHvfP42LTypn5OCSpCNKkiRJUmIstDkmjmOeq9/CYysaefL5NWzd3cq4YQO46fTJLJxTwXHj\nhiYdUZIkSZJygoU2R6zdupvHVjTy2IoGXt+wkwFFBfzZ9PEsnFvBaceMprDAc7GSJEmS1JmFNkG7\nWtr4yYtNPLq8kd++vpE4hvmTRnLz6Udz4cwJDCstTjqiJEmSJOUsC20f6+iIqX3jbR5d0cCPXljL\nzpZ2KkcO5ONnH8tlc8o5atTgpCNKkiRJUipYaPvImxt38tiKBh5b2UjD5t0MGVDERTMnsHBOBfMm\njaTALcWSJEmSdEgstL1o6+5Wnl61lkdXNLD8rc1EEZx2zGju+rPjOX/aeAaWFCYdUZIkSZJSy0Lb\nw9raO/j1axt5dHkDP/39OlraOjhm7BA+e8FULp1dzviy0qQjSpIkSVJesND2kJebtvHYikYeX9nI\nhu3NDB9UzIfmVXLZnApmVpQRRW4pliRJkqSeZKE9Apt2NPPEc2t4dEUDL67ZRlFBxHumjmXhnArO\nnjqWkqKCpCNKkiRJUt6y0B6i5rZ2nn15PY8sb+SXr6ynrSNmRnkZX3j/NC6eNZFRQwYkHVGSJEmS\n+gULbTfEcczzDVt5dHkD/7lqDVt2tTJ26ABuOm0yl82p4PjxQ5OOKEmSJEn9joX2ANZu3c3jKxt5\ndHkDr2/YyYCiAs6fPp6Fc8o57ZjRFBW6pViSJEmSkmKhfZddLW385MUmHl3eyG9f30gcQ/VRI7jv\nsqO5aOYEhpUWJx1RkiRJkoSFFoCOjpglb77No8sbeOaFtexsaadixEBuP/tYFs4p56hRg5OOKEmS\nJEl6l35daN/cuJPHVjby2IoGGjbvZnBJIRfOmMDCuRXMnzSSggIftSNJkiRJuarfFdpte1p5etVa\nHl3ewLK3NhNFcNoxo7nz/OM5f/o4BpX0u/9JJEmSJCmV+kV7a2vv4DevbeTRFY389MUmmts6mDJm\nMJ+54HgunV3OhLKBSUeUJEmSJB2ivC60rzRt59EVDfzHykbWb29m+KBirpxXyWVzKphVUUYUuaVY\nkiRJktIq7wrtph3NPPn8Gh5d0cDqxm0UFUScdfxYLp9bznumjmVAUWHSESVJkiRJPSAvCm1LWwf/\n9fI6HlneyC9fWU9bR8z0icP4y/dN4+KTJjJ6yICkI0qSJEmSelgqC+26bXuI45hVDVt5dEUDTz6/\nhi27WhkzdAA3njaZy+aUM3X8sKRjSpIkSZJ6USoL7frtzZz35f/mtfU7KCkq4Pxp41g4t4LTjxlN\nUWFB0vEkSZIkSX0glYUWoGxgMX996QwumjmBsoHFSceRJEmSJPWx1Bba5W9tZvlbm1m3bQ+fOu+4\npONIkiRJkvpYagvtm/dflHQESZIkSVKCPHAqSZIkSUqlVBbasUN9DI8kSZIk9Xc9UmijKPp0FEVx\nFEWjM59fE0XRqiiKXoii6HdRFM3az/d9O4qiN6Ioei7z10nd+f3GDSvtidiSJEmSpBQ74jO0URRV\nAucDdZ2+/AZwZhzHm6Moei/wAFCznx9xVxzHjxxpDkmSJElS/9ITE9ovA58B4r1fiOP4d3Ecb858\nuhio6IHfR5IkSZKkdxxRoY2i6BKgMY7j5w/wspuAHx1g/YuZ7clfjqLIw7GSJEmSpG456JbjKIp+\nDozfx9Lngc8Rthvv73vfQyi0p+3nJfcATUAJYVvyZ4H/tZ+fdQtwC0BVVdXBYkuSJEmS8lwUx/HB\nX7Wvb4yiGcAvgF2ZL1UAa4D5cRw3RVE0E3gceG8cx6924+edBdwZx/H7Dvba6urqeNmyZYeVW5Ik\nSZKU26IoWh7HcfXBXnfYl0LFcfwCMLbTb/gmUB3H8cYoiqqAx4BFByqzURRNiON4bRRFEfABYPXh\n5pEkSZIk9S9HfMvxfvwlMAr419BVadvbrqMoegb4SBzHa4AfRFE0BoiA54BbeymPJEmSJCnP9Fih\njeN4UqdffwT4yH5ed2GnX5/dU7+/JEmSJKl/6YnH9kiSJEmS1OcstJIkSZKkVLLQSpIkSZJS6bAf\n25OkKIo2AG8lnaMfqQLqkg6hI+b7mB98H/OH72V+8H3MH76X+cH3MX9Mj+N44MFelMpCq74VRdGG\nOCzlvkgAACAASURBVI7HJJ1DR8b3MT/4PuYP38v84PuYP3wv84PvY/7o7nvplmN1x5akA6hH+D7m\nB9/H/OF7mR98H/OH72V+8H3MH916Ly206o6tSQdQj/B9zA++j/nD9zI/+D7mD9/L/OD7mD+69V5a\naNUdDyQdQD3C9zE/+D7mD9/L/OD7mD98L/OD72P+6NZ76RlaSZIkSVIqOaGVJEmSJKWShVaSJEmS\nlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmS\nJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmS\nJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmS\nJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaS\nJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGV\nJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKllo\nJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoW\nWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWS\nhVaSJEmSlEoWWkmSJElSKlloJUmSJEmpZKGVJEmSJKWShVaSJEmSlEoWWkmSJElSKlloJUmSJEmp\nZKGVJEmSJKVSUdIBDsfo0aPjSZMmJR1DkiRJktQLli9fvjGO4zEHe10qC+2kSZNYtmxZ0jEkSZIk\nSb0giqK3uvM6txxLkiRJklLJQitJkiRJSiULrSRJkiQplVJ5hnZfWltbaWhoYM+ePUlH6RWlpaVU\nVFRQXFycdBRJkiRJygl5U2gbGhoYOnQokyZNIoqipOP0qDiO2bRpEw0NDUyePDnpOJIkSZKUE/Jm\ny/GePXsYNWpU3pVZgCiKGDVqVN5OnyVJkiTpcORNoQXysszulc9/b5IkSZJ0OPKq0CatqamJq666\niilTpjB37lwuvPBCXn31VU488cQur7v33nv5u7/7OwDuuusupk6dysyZM7n00kvZsmVLEtElSZIk\nKXUstM/e1yM/Jo5jLr30Us466yxef/11li9fzn333ce6desO+H3nnXceq1evZtWqVRx33HHcd1/P\n5JEkSZKkfGeh/dX9PfJjnn32WYqLi7n11lvf+dqsWbOorKw84Pedf/75FBWFu7kWLFhAQ0NDj+SR\nJEmSpHyXN7ccd/Gju6Hphe6//lsXHfw142fAe/dfflevXs3cuXP3ufb6669z0kknvfN5U1MTd955\n55+87pvf/CZXXnnlwbNIkiRJkvK00B7Mlrdga33287d+Ez6WVcLwo3r8t5syZQrPPffcO5/fe++9\nf/KaL37xixQVFXHNNdf0+O8vSZIkSWlSMSya2J3X5WehPcAk9U/cWwb3bj3i33L69Ok88sgjh/W9\n3/72t3nqqaf4xS9+4W3GkiRJkvq9cYOjCd15nWdoe8jZZ59Nc3MzDzzwwDtfW7VqFfX19Qf4Lvjx\nj3/M3/zN3/Dkk08yaNCg3o4pSZIkSbknjmHzm/D8Q/DUHd3+tvyc0B6KM+/ukR8TRRGPP/44n/zk\nJ/nSl75EaWkpkyZN4h/+4R8O+H233XYbzc3NnHfeeUC4GOrf/u3feiSTJEmSJOWk9lZoWgV1tVC/\nOHzc0XTIPyaK47gX0vWu6urqeNmyZV2+9tJLL3HCCScklKhv9Ie/R0mSJEl5aPdmqF+aLa+Ny6Ft\nd1grq4KqGqisgaoFMHYa1RUlLFvTftDzmE5oJUmSJEk9J45h8xtdp68bXgprUSFMmAlzb4DK+aHA\nDuvW/U/7ZKGVJEmSJB2+thZY+3ymvC6G+iWwc31YG1AGlfPgxIVhCls+F0oGH/RHrtsZr+3Ob22h\nlSRJkiR13663ob42/FVXC2tWQNuesDZiEkw5O7uFeMwJUHDodxE3bIvXdOd1eVVo4zjO28fepPGs\nsyRJkqSUi2PY9Hqn6WstbHw1rBUUwYRZUH1TtsAOHd+n8fKm0JaWlrJp0yZGjRqVd6U2jmM2bdpE\naWlp0lEkSZIk5bO2ZlizMrt1uL4Wdm0Ma6XDQ2mddRVULoDyOVA8MNG4eVNoKyoqaGhoYMOGDUlH\n6RWlpaVUVFQkHUOSJElSPtm5MbN1ODN9XbMS2lvC2sgpcOz5menrAhh93GFtH+5NeVNoi4uLmTx5\nctIxJEmSJCk3xXHYLvzO9HUxbHotrBWWwISToOajobxW1sCQMcnm7Ya8KbSSJEmSpE5ad3faPpy5\nxGn35rA2cGQorbOvDQV24mwoTt8RRwutJEmSJOWDHeu7ltc1z0FHa1gbdSxMvSiU16oFMOoYyIO7\nhyy0kiRJkpQ2HR2w8ZVsga1bDJvfCGuFA8KFTSf/RXb78OBRyebtJRZaSZIkScp1LbugcXnm8Tm1\n0LAE9mwNa4NGh6lr9Y3h44RZUDQg2bx9xEIrSZIkSblm29rs1uG6xdC0CjrawtqYqTDtA6G8VtbA\nyKPzYvvw4bDQSpIkSVKSOtph/UvZ6Wv9YthSF9aKSqF8Lpzy8VBgK+bBoJHJ5s0hFlpJkiRJ6kvN\nOzLbhzPT14al0LwtrA0ZF6auNbeG86/jZ0BRSbJ5c5iFVpIkSZJ609bGrtPXptUQtwMRjD0BTlyY\n3T48YlK/3T58OCy0kiRJktRTOtph3WqoX5K9gXhrfVgrHhS2D59+R5i+VlTDwOHJ5k05C60kSZIk\nHa7m7WHL8N7pa8MyaNkR1oZOCFPXkz8WPo6fAYXFyebNMxZaSZIkSeqOOA7T1r3ltb4W1r0IcQdE\nBTB2Osy6Kkxfq2qgrNLtw73MQitJkiRJ+9LeButeyBbYulrYviaslQwJW4bPuCtMXyvmQemwZPP2\nQxZaSZIkSQLYsxXql2bK6+JwE3HrrrA2rAKOOjk7fR07HQqtU0nzHZAkSZLU/8QxbHmr6/R1/e+B\nOGwfHj8DZi+CyvnhBuKyiqQTax8stJIkSZLyX3srrF2Vnb7WL4EdTWGtZChUzoNpl4Tpa3k1DBiS\nbF51i4VWkiRJUv7ZvbnT9uHasH24bXdYG14Fk8/ITl/HToOCwmTz6rBYaCVJkiSlWxzD238Mtw7v\nffbrhpfDWlQIE2bC3BvC9LVyAQybkGhc9RwLrSRJkqR0aWuGtc93LbA7N4S1AWVh8jrj8lBey+dA\nyeBk86rXWGglSZIk5bZdb3ctr40roL05rI2YBFPOyU5fx0yFgoJE46rvWGglSZIk5Y44hk2vZctr\nfS1sfDWsFRTDhFkw/+bw7NfKGhg6Ltm8SpSFVpIkSVJyWvfA2ue6Fthdm8Ja6fBQWmddld0+XDww\n2bzKKRZaSZIkSX1n58au5XXNSmhvCWsjp8BxF4QSW7UARh3r9mEdkIVWkiRJUu/o6IBNf8gW2LrF\n8PbrYa2wBCacBDUfDdPXyhoYMibZvEqdPi+0URQVAsuAxjiO3xdF0WTgQWAUsBxYFMdxS1/nkiRJ\nknSEWneHC5vqF0P9klBid28Oa4NGhdI657owfZ1wEhSXJptXqZfEhPYTwEvAsMznXwK+HMfxg1EU\n/RtwE/C/E8glSZIk6VDsWN91+rr2eehoDWujj4Op7wvltbIGRh0DUZRsXuWdPi20URRVABcBXwTu\niKIoAs4Grs685DvAvVhoJUmSpNzS0QEbXg7T17ra8HHzm2GtcEC4sOnkj4UCWzEfBo9KNK76h76e\n0P4D8BlgaObzUcCWOI7bMp83AOV9nEmSJEnSu7XszG4frquFhiWwZ2tYGzwmTF3nfSR8nDALigYk\nm1f9Up8V2iiK3gesj+N4eRRFZx3G998C3AJQVVXVw+kkSZKkfm7b2q7T16YXoCMzdxozFaZ9ILt9\neOTRbh9WTujLCe2pwMVRFF0IlBLO0H4FGB5FUVFmSlsBNO7rm+M4fgB4AKC6ujrum8iSJElSHupo\nh/UvdS2wW+rCWtFAKJ8Lp34i3D5cUQ2DRiabV9qPPiu0cRzfA9wDkJnQ3hnH8TVRFP0QuJxw0/H1\nwBN9lUmSJEnqF5p3QOOybHltWAbN28LakHFh6lpzayiw42dAUUmyeaVuyoXn0H4WeDCKov8PWAl8\nI+E8kiRJUrptbcjcPrwks314NcTtQARjp8GMy0N5raqB4Ue5fViplUihjeP4l8AvM7/+IzA/iRyS\nJElS6rW3wfoXs9PXulrY1hDWigeFLcOn35HdPjxweLJ5pR6UCxNaSZIkSd21Zxs0LA3Pfq2vDduH\nW3aEtaETw9S18vbwcdwMKPSP/Mpf/tMtSZIk5ao4hq31Xaev61+EuAOiAhg3HWZdld0+XFbp9mH1\nKxZaSZIkKVe0t0HTqjB53XsGdvuasFYyJGwZPuMzobyWV0PpsGTzSgmz0EqSJElJ2bMV6pdmpq+L\noXE5tO4Ka2WVcNQp4QbiqhoYO93tw9K7+G+EJEmS1BfiGDa/2XX6uv73QAxRIYw/EWYvypyBXQBl\n5UknlnKehVaSJEnqDW0t0PRCdvpaXws71oW1AcOgYh5M/0CYwJbPhQFDks0rpZCFVpIkSeoJu94O\ntw/vLa+NK6Btd1gbXgWTz8xOX8eeAAWFyeaV8oCFVpIkSTpUcQxv/7HT9uFa2PByWCsogvEzofrD\nYfpaWQPDJiSbV8pTFlpJkiTpYNqaYe3z2fJaXws7N4S10jKomA8zLg/T1/K5UDIo2bxSP2GhlSRJ\nkt5t56Zscd27fbi9OayNmAzHnJu5fXgBjD4eCgqSzSv1UxZaSZIk9W9xDJtey0xfF0NdLWz6Q1gr\nKIaJJ8H8m7Pbh4eOSzavpHdYaCVJktS/tO6BNSu7TmB3bQprA0eE0nrS1WH6OnE2FA9MNq+k/bLQ\nSpIkKb/t2JAprpnp69rnoL0lrI06Bo57L1TODwV21LFuH5ZSxEIrSZKk/NHRARtfzZbX+sXhNmKA\nwpIwca25NZTXyhoYPDrZvJKOiIVWkiRJ6dW6O1zY9E6BrYU9W8LaoFHh1uG5N4TyOuEkKC5NNK6k\nnmWhlSRJUnpsX9d1+rr2eehoC2ujj4MT3p+Zvi6AUVMgipLNK6lXWWglSZKUmzo6YMPLXQvs5jfD\nWlEpTJwDp9yevX140MhE40rqexZaSZIk5YaWndC4PFte65dC89awNnhMKK3zPhKmrxNmQVFJsnkl\nJc5CK0mSpGRsW/uu7cOrIG4Pa2NOgBMvDeW1qgZGTHb7sKQ/YaGVJElS7+toh/W/h7rF4eKmulrY\nWhfWigZC+Vw47ZOhwFbOC8+DlaSDsNBKkiSp5zVvh4Zlmee/1oZfN28La0PGh6nrgj8PH8fPhMLi\nZPNKSiULrSRJko7c1oZO09fFsG41xB1ABOOmw4zLs9uHhx/l9mFJPcJCK0mSpEPT3hYK697pa10t\nbGsIa8WDoWIunH5nKK8V86C0LNm8kvKWhVaSJEkHtmcbNCzNTl8bl0PLjrA2dGIorpW3h4/jZkCh\nf8SU1Df8r40kSZKy4hi21GXLa/0SWP9i2D4cFYTtw7M+BFULwmN0hlcmnVhSP2ahlSRJ6s/aW6Hp\nhU4Ftha2rw1rJUOhohrO/GworxXVMGBosnklqRMLrSRJUn+ye0vYPry3vDYuh9ZdYa2sEo46NTt9\nHTcdCgqTzStJB2ChlSRJyldxDJvfCNuG9xbY9S8BMUSFMH4GzLkulNfKGigrTzqxJB0SC60kSVK+\naGuBplWZ8po5/7pjXVgbMCzcODz90lBey+fCgCHJ5pWkI2ShlSRJSqtdb//p9uG2PWFt+FFw9Fmh\nvFYtgDFT3T4sKe9YaCVJktIgjuHtP2anr3W1sPGVsFZQBBNmQfWN2QI7dHyyeSWpD1hoJUmSclFb\nM6x5Lkxe9/61c0NYKy0LxXXmFaG8TpwDJYOSzStJCbDQSpIk5YKdmzLFNTN9XbMS2pvD2sij4Zjz\noHJ+KLCjj4eCgmTzSlIOsNBKkiT1tTiGjX/IXNxUGwrspj+EtYJimHgSzL85+/icIWOTzStJOcpC\nK0mS1Nta94SJ697pa30t7H47rA0cGUrrSVdntg/PhuKByeaVpJSw0EqSJPW0HRsy5TUzgV3zHHS0\nhrVRx8DxF0JVDVQugNHHQhQlm1eSUspCK0mSdCQ6OmDjq52mr4vDbcQAhSXhwqaT/yJMYStrYPDo\nZPNKUh6x0EqSJB2Kll2wZkV2+lq/BPZsCWuDRofSOveGMH2deBIUDUg0riTlMwutJEnSgWxf13X6\nuvZ56GgLa6OPh2kXh/JatSDcRuz2YUnqMxZaSZKkvTo6YMNL2elr3WLY8lZYKyoN24dPuT0U2Mr5\nMGhksnklqZ+z0EqSpP6rZSc0Ls9OX+uXQvPWsDZ4bLi4af4tYfo6fiYUlSSbV5LUhYVWkiT1H9vW\ndJ2+Nr0AcXtYG3MCnHhpZvtwDYyY7PZhScpxFlpJkpSfOtph3YuZi5tqwxR2a11YKxoIFdVw2qfC\n9LWiGgaOSDavJOmQWWglSVJ+aN4ODcuy09eGZdCyPawNnRBuH17w52H6On4mFBYnm1eSdMQstJIk\nKZ221Heavi6Gdash7gAiGDcdZl4Rpq+VNTC8yu3DkpSHLLSSJCn3tbeFwrq3vNbXwrbGsFY8OGwZ\nPuOucPNwxTwoLUs2rySpT1hoJUlS7tmzFRqWZm4frg3bh1t3hrVh5WHqunf6Ou5EKPSPNJLUH/lf\nf0mSlKw4hi11Xaev614EYogKQmGdfU0or5U1MLwy6cSSpBxhoZUkSX2rvRWaVnV69usS2L42rJUM\nDduHz7o7lNeKahgwNNm8kqScZaGVJEm9a/fmsGV47/S1cTm07gprZVUw6bTs9HXcdCgoTDavJCk1\nLLSSJKnnxDFsfiM7fa2rhQ0vE7YPF8L4GTDnuuwZ2GETk04sSUoxC60kSTp8bS2Z7cOLswV25/qw\nNqAMKufBiQvDs1/L50LJ4GTzSpLyioVWkiR13663w5nXveV1zQpo2xPWRkyCKe/JTl/HnAAFBYnG\nlSTlNwutJEnatziGTa+Hc697C+zGV8JaQRFMmAXVN4Xpa2UNDB2fbF5JUr9joZUkSUFbM6x5Llte\n62th18awVjo8lNaZV4Tp68Q5UDIo2bySpH7PQitJUn+1c2OnZ78ugTUrob05rI08Go49PzN9XQCj\nj3P7sCQp51hoJUnqD+IYNv6h0/R1MWx6LawVFMPE2TD/5jB9rayBIWOTzStJUjdYaCVJykete8KF\nTXuf/VpfG54HCzBwZCits68N09eJs6G4NNm8kiQdBgutJEn5YMf6TtuHa8NZ2I7WsDbqWJh6USiv\nlTUw+liIomTzSpLUAyy0kiSlTUdHuG248/T17T+GtcIBYeJ68l9kC+zgUcnmlSSpl1hoJUnKdS27\n3rV9eAns2RLWBo0O517nfjh8nDALigYkm1eSpD5ioZUkKddsb8qW17rF0LQKOtrC2ujjYdrFYfpa\ntSDcRuz2YUlSP2WhlSQpSR3tsP6l7NbhusWw5a2wVlQK5XPhlI+H8loxDwaNTDavJEk5pM8KbRRF\nlcB3gXFADDwQx/FXoigaCTwETALeBK6I43hzX+WSJKlPteyEhmXZ8tqwFJq3hbUh48KZ1/m3hAI7\nfiYUlSSbV5KkHNaXE9o24NNxHK+IomgosDyKop8BNwC/iOP4/iiK7gbuBj7bh7kkSeo9WxvDM1/r\nl2S2D78AcTsQwdgT4MSF2We/jpjk9mFJkg5BnxXaOI7XAmszv94eRdFLQDlwCXBW5mXfAX6JhVaS\nlEYd7bDuxa6Pz9laH9aKB4Xtw6d9Krt9eODwZPNKkpRyiZyhjaJoEjAbqAXGZcouQBNhS7IkSbmv\neXvYMrx3+tqwDFq2h7WhE8LU9eSPhY/jZ0BhcbJ5JUnKM31eaKMoGgI8CnwyjuNtUaetVXEcx1EU\nxfv5vluAWwCqqqr6IqokSV1tqe80fV0cprFxBxDBuBNh1pWhvFbWwPAqtw9LktTL+rTQRlFUTCiz\nP4jj+LHMl9dFUTQhjuO1URRNANbv63vjOH4AeACgurp6n6VXkqQe094G616AutrsDcTbGsNa8WCo\nqIYz7grltWIelA5LNq8kSf1QX95yHAHfAF6K4/jvOy09CVwP3J/5+ERfZZIk6R17tobtw3W1Yfra\nsBxad4a1YRXZi5sqa8I0ttAn30mSlLS+/H/jU4FFwAtRFD2X+drnCEX24SiKbgLeAq7ow0ySpP4o\njsOzXveW17paWP97IIaoIBTW2deE8lq1AMoqkk4sSZL2oS9vOf4NsL/DROf0VQ5JUj/U3gpNq7oW\n2B1NYa1kKFTOg2mXQFUNlFfDgCHJ5pUkSd3ifilJUv7ZvRnql2bLa+NyaNsd1sqqYPLp2enr2GlQ\nUJhsXkmSdFgstJKkdItj2PxG1+nrhpfCWlQIE2bC3BvC9LWyBoZNTDSuJEnqORZaSVK6tLXA2ucz\n5XVxeAbszswF+QPKwvbhExdmtg/PhZLByeaVJEm9xkIrScptu97OPjanrhbWrIC2PWFtxCSYcnZm\n+roAxkyFgoJE40qSpL5joZUk5Y44hk2vd5q+1sLGV8NaQTFMmAXzPgKV88P24aHjk80rSZISZaGV\nJCWnrRnWrMxuHa6vhV0bw1rp8FBaZ10Vpq/lc6B4YLJ5JUlSTrHQSpL6zs6Nma3DmenrmpXQ3hLW\nRk6B4/4slNjKGhh9nNuHJUnSAVloJUm9I47DduF3pq+LYdNrYa2wBCacBDUfDdPXyhoYMibZvJIk\nKXUstJKkntG6u9P24cwlTrs3h7WBI8MzX2cvCuV14mwoLk02ryRJSj0LrSTp8OxY37W8rnkOOlrD\n2qhjYepFYfpatQBGHQNRlGxeSZKUdyy0kqSD6+iAja9kC2zdYtj8RlgrHBAubDr5Y6G8VsyHwaOS\nzStJkvoFC60k6U+17ILG5ZnH59RCwxLYszWsDRodimv1jeHjhFlQNCDZvJIkqV+y0EqSYNva7Nbh\nusXQtAo62sLamKkw7QOhvFbWwMij3T4sSZJygoVWkvqbjnZY/1J2+lq/GLbUhbWigVA+F075eGb7\n8DwYNDLZvJIkSfthoZWkfNe8I7N9ODN9bVgKzdvC2pBxYepac2u4wGn8DCgqSTavJElSN1loJSnf\nbG3sOn1tWg1xOxDB2Glw4sLs9uERk9w+LEmSUstCK0lp1tEO61ZD/ZLsDcRb68Na8aCwffj0O8L0\ntaIaBg5PNq8kSVIPstBKUpo0bw9bhvdOXxuWQcuOsDZ0IlTVhMfnVNaE7cOFxcnmlSRJ6kUWWknK\nVXEcpq11mduH6xfDuhch7oCoAMZOh1lXhelrVQ2UVbp9WJIk9SsWWknKFe1tsO6F7PS1rha2rwlr\nJUPCluEzPhPKa3k1lA5LNq8kSVLCLLSSlJQ9W6F+aaa8Lg43EbfuCmvDKuCok7PT17HTodD/ZEuS\nJHXmn44kqS/EMWx5q+v0df3vgThsHx4/A2YvCuW1sgbKKpJOLEmSlPMstJLUG9pbYe2q7PS1fgns\naAprJUOhch5MuyS7fXjAkGTzSpIkpZCFVpJ6wu7NnbYP14btw227w9rwKph8Rmb6ugDGngAFhcnm\nlSRJygMWWkk6VHEMb/8x3Dy899mvG14OawVFMH4mzL0hW2CHTUg0riRJUr6y0ErSwbQ1w9rnOxXY\nJbBzfVgrLYOK+TDj8lBey+dAyeBk80qSJPUTFlpJerddb3edvjaugPbmsDZiMhxzDlTODwV2zFQo\nKEg2ryRJUj9loZXUv8UxbHotW17ra2Hjq2GtoBgmzIL5N4ebhytrYOi4ZPNKkiTpHRZaSf1L6x5Y\n+1zXArtrU1gbOCKU1lkfCh/L50DxwGTzSpIkab8stJLy286NXcvrmpXQ3hLWRk6B4y4I5bVqAYw6\n1u3DkiRJKWKhlZQ/Ojpg0x+yBbZuMbz9elgrLIGJs6Hm1uz24SFjks0rSZKkI2KhlZRerbvDhU31\nmZuH62vD82ABBo0KpXXOdWH6OuEkKC5NNq8kSZJ6lIVWUnrsWN91+rr2eehoDWujj4Op7wvltXIB\njJoCUZRsXkmSJPUqC62k3NTRARteDtPXutrwcfObYa1wQLiw6eSPZQpsDQwamWhcSZIk9T0LraTc\n0LIzu324rhYalsCerWFt8JhQWud9JExfJ8yCopJk80qSJClxFlpJydi2tuv0tekF6GgLa2NOgGkf\nyE5fRx7t9mFJkiT9CQutpN7X0Q7rX+paYLfUhbWigVA+F079RJi+Vs4Lz4OVJEmSDsJCK6nnNe+A\nxmXZ8tqwDJq3hbUh46GqJvP4nAUwYSYUFiebV5IkSalkoZV05LY2ZG4e3rt9eDXE7UAEY6fBjMtD\nea2qgeFHuX1YkiRJPcJCK+nQtLfB+hez5bWuFrY1hLXiwVAxF07/dCiv5dUwcHiyeSVJkpS3LLSS\nDmzPNmhYGiaw9bVh+3DLjrA2dGIorpW3h4/jZkCh/1mRJElS3/BPnpKy4hi21nedvq5/EeIOiApg\n3HSY9aHM7cPzoazS7cOSJElKjIVW6i+evQ/ec0/Xr7W3QdOqzPnXxVC/BLavCWslQ6CiGs74THb7\ncOmwvs8tSZIk7YeFVuovfnU/nPwXUL80M31dDI3LoXVXWC+rhKNOyT77dew0tw9LkiQpp/mnVSnf\ndXTA774Sfn3/UUAMUSGMnwGzF2XOwC6AsvJEY0qSJEmHykIr5bOf3Qu//XKnL8Thw6mfgHO/oEXu\nmQAAHL9JREFUkEQiSZIkqcdYaKV8tfE1eOVpKCiCC+6HZ+6Ee7cmnUqSJEnqMQVJB5DUC179KXzt\nbNi1Ca57AubfnHQiSZIkqcdZaKV8Esfw67+Hf78CRhwFt/wSJp0W1s68O8lkkiRJUo9zy7GUL1p2\nwhMfgxcfhxMvh4v/CUoGZdff/cgeSZIkKeUstFI+2PwWPHgNrFsN5/0vOOXjEEVJp5IkSZJ6lYVW\nSrs//gp+eAPE7XDNI3DsuUknkiRJkvqEZ2iltIpjWPy/4XuXwpCxcPOzlllJkiT1K05opTRq3QNP\nfQqe/3c4/iK47KswYGjSqSRJkqQ+ZaGV0mbbGnjoWmhcDmfdA2d8BgrcbCFJkqT+x0IrpUldLTy8\nKNxofOUP4IT3JZ1IkiRJSoyFVkqL5d+Bpz8Nwyvhuidg7AlJJ5IkSZISZaGVcl1bC/zkHlj6dZhy\nDlz+DRg4IulUkiRJUuIstFIu27EBHr4O6n4Hp34CzvkCFBQmnUqSJEnKCRZaKVetWQkPXgu7NsHC\nb8CMy5NOJEmSJOUUC62Ui1Y9DE/eDoPHwE0/gQmzkk4kSZIk5RwLrZRL2tvg51+A//lnOOpU+OB3\nYMiYpFNJkiRJOclCK+WKXW/DIzfCH5+FeTfDBfdBYXHSqSRJkqScZaGVcsG638ODH4Jta+Dif4I5\n1yWdSJIkScp5BUkHAIii6IIoil6Joui1KIruTjqP1Kd+/yR8/Vxo3QM3PG2ZlSRJkrop8UIbRVEh\n8C/Ae4FpwIeiKJqWbCqpD3R0wLN/DQ8vgrEnwC2/hMr5SaeSJEmSUiMXthzPB16L4/iPAFEUPQhc\nAvw+0VRSb9qzDR7/KLzyDJx0LVz0/0NxadKpJEmSpFTJhUJbDtR3+rwBqHn3i6IougW4BaCqqqpv\nkkm9YeNr8ODVsOk1eO/fwvybIYqSTiVJkiSlTuJbjrsrjuMH4jiujuO4eswYH2OilPrDz+BrZ8Ou\njXDdE1Bzi2VWkiRJOky5UGgbgcpOn1dkvibljziGX/89/OCDMKIqnJedfHrSqSRJkqRUy4Utx0uB\nY6MomkwoslcBVycbSepBLTvhidvgxcdg+mVwyb9AyaCkU0mSJEmpl3ihjeO4LYqi24CfAIXAN+M4\nfjHhWFLP2PwWPHgNrFsN594Lp37SLcaSJElSD0m80ALEcfwM8EzSOaQ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GDmz/HAmc3/49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\nJQe0kiRJkqRe+j8t5zRASWSnlgAAAABJRU5ErkJggg==\n", | |||
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77 | "text/plain": [ | |||
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78 | "<matplotlib.figure.Figure at 0x7f13b444b588>" | |||
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79 | ] | |||
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80 | }, | |||
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81 | "metadata": {}, | |||
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82 | "output_type": "display_data" | |||
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83 | } | |||
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84 | ], | |||
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85 | "source": [ | |||
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86 | "cal.plot(marker='+',subplots=True,figsize=(16,16))" | |||
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87 | ] | |||
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88 | }, | |||
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89 | { | |||
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90 | "cell_type": "code", | |||
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91 | "execution_count": 4, | |||
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92 | "metadata": {}, | |||
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93 | "outputs": [ | |||
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94 | { | |||
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95 | "data": { | |||
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|
96 | "text/plain": [ | |||
|
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97 | "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f13b40bf5f8>,\n", | |||
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98 | " <matplotlib.axes._subplots.AxesSubplot object at 0x7f13b40167b8>], dtype=object)" | |||
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99 | ] | |||
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100 | }, | |||
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101 | "execution_count": 4, | |||
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102 | "metadata": {}, | |||
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103 | "output_type": "execute_result" | |||
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104 | }, | |||
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105 | { | |||
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106 | "data": { | |||
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107 | "image/png": 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S/O+DHLo8yRdqrR1Jbkny/5ZSTqi3lPLhUsqaUsqaHTt2nMrH4Sxb1tOVHXv35+9++kTT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\nSpIkSZIKxaArSZIkSSqU/x+VRj1U9ncAeAAAAABJRU5ErkJggg==\n", | |||
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108 | "text/plain": [ | |||
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109 | "<matplotlib.figure.Figure at 0x7f13b41381d0>" | |||
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110 | ] | |||
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111 | }, | |||
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112 | "metadata": {}, | |||
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113 | "output_type": "display_data" | |||
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114 | } | |||
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115 | ], | |||
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116 | "source": [ | |||
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117 | "zero.plot(marker='+',subplots=True,figsize=(16,16))" | |||
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118 | ] | |||
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119 | }, | |||
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120 | { | |||
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121 | "cell_type": "code", | |||
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122 | "execution_count": 5, | |||
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123 | "metadata": {}, | |||
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124 | "outputs": [ | |||
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125 | { | |||
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126 | "data": { | |||
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127 | "text/plain": [ | |||
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128 | "(CH1 47.944705\n", | |||
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129 | " CH2 509.702199\n", | |||
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130 | " dtype: float64, CH1 -42.514737\n", | |||
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131 | " CH2 -44.446032\n", | |||
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132 | " dtype: float64)" | |||
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133 | ] | |||
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134 | }, | |||
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135 | "execution_count": 5, | |||
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136 | "metadata": {}, | |||
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137 | "output_type": "execute_result" | |||
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138 | } | |||
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139 | ], | |||
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140 | "source": [ | |||
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141 | "slopes=(cal.iloc[-2]-cal.iloc[0])/(0.1-0.01)\n", | |||
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142 | "offsets=zero.mean()\n", | |||
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143 | "slopes,offsets" | |||
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144 | ] | |||
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145 | }, | |||
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146 | { | |||
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147 | "cell_type": "code", | |||
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148 | "execution_count": 22, | |||
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149 | "metadata": { | |||
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150 | "collapsed": true | |||
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151 | }, | |||
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152 | "outputs": [], | |||
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153 | "source": [ | |||
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154 | "pool = dummy.Pool(3)" | |||
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155 | ] | |||
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156 | }, | |||
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157 | { | |||
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158 | "cell_type": "code", | |||
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159 | "execution_count": 21, | |||
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160 | "metadata": {}, | |||
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161 | "outputs": [ | |||
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162 | { | |||
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163 | "data": { | |||
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164 | "text/plain": [ | |||
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165 | "<multiprocessing.pool.MapResult at 0x7f12a26da828>" | |||
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166 | ] | |||
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167 | }, | |||
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168 | "execution_count": 21, | |||
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169 | "metadata": {}, | |||
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170 | "output_type": "execute_result" | |||
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171 | } | |||
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172 | ], | |||
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173 | "source": [ | |||
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174 | "files=glob.glob(\"/home/jeandet/Documents/DATA/BOOT_CUR_*C_*V.txt.csv.fixed\")\n", | |||
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175 | "def calibrate(file,slopes):\n", | |||
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176 | " if not os.path.isfile(file+\".calibrated\"):\n", | |||
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177 | " calibrated=pds.read_csv(file,\n", | |||
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178 | " sep=\";\",\n", | |||
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179 | " skiprows=[0,1,2,3,4],\n", | |||
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180 | " names=[\"CH1\",\"CH2\"],\n", | |||
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181 | " decimal=\",\")\n", | |||
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182 | " localOffset=calibrated.iloc[:100000].mean()\n", | |||
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183 | " calibrated= (calibrated - localOffset) / (slopes*1e-3)\n", | |||
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184 | " calibrated.to_csv(file+\".calibrated\",sep=\"\\t\",index=False,float_format='%.3f',chunksize=4*1000*1000)\n", | |||
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185 | " \n", | |||
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186 | "numFiles=len(files)\n", | |||
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187 | "pool.starmap_async(calibrate,zip(files,[slopes]*numFiles))\n", | |||
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188 | "\n" | |||
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189 | ] | |||
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190 | }, | |||
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191 | { | |||
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192 | "cell_type": "code", | |||
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193 | "execution_count": 23, | |||
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194 | "metadata": {}, | |||
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195 | "outputs": [ | |||
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196 | { | |||
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197 | "name": "stdout", | |||
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198 | "output_type": "stream", | |||
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199 | "text": [ | |||
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200 | " adding: BOOT_CUR_40C_2.1V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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201 | " adding: BOOT_CUR_40C_2.0V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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202 | " adding: BOOT_CUR_40C_2.2V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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203 | " adding: BOOT_CUR_30C_2.2V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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204 | " adding: BOOT_CUR_50C_1.9V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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205 | " adding: BOOT_CUR_30C_2.1V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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206 | " adding: BOOT_CUR_40C_2.05V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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207 | " adding: BOOT_CUR_40C_1.9V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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208 | " adding: BOOT_CUR_50C_2.05V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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209 | " adding: BOOT_CUR_50C_2.1V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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210 | " adding: BOOT_CUR_60C_1.9V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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211 | " adding: BOOT_CUR_50C_2.0V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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212 | " adding: BOOT_CUR_60C_2.0V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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213 | " adding: BOOT_CUR_60C_2.1V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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214 | " adding: BOOT_CUR_60C_2.05V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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215 | " adding: BOOT_CUR_50C_2.2V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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216 | " adding: BOOT_CUR_60C_2.2V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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217 | " adding: BOOT_CUR_70C_1.8V.txt.csv.fixed.calibrated (deflated 91%)\n", | |||
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218 | " adding: BOOT_CUR_70C_1.9V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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219 | " adding: BOOT_CUR_70C_2.2V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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220 | " adding: BOOT_CUR_80C_1.9V.txt.csv.fixed.calibrated (deflated 90%)\n", | |||
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221 | " adding: BOOT_CUR_70C_2.05V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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222 | " adding: BOOT_CUR_70C_2.1V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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223 | " adding: BOOT_CUR_70C_2.0V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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224 | " adding: BOOT_CUR_80C_2.05V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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225 | " adding: BOOT_CUR_80C_2.0V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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226 | " adding: BOOT_CUR_80C_2.2V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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227 | " adding: BOOT_CUR_80C_2.1V.txt.csv.fixed.calibrated (deflated 89%)\n", | |||
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228 | "/home/jeandet/ownCloud/TESTS_LFR_BOOT_VS_SUB_1V5/Analysis\n" | |||
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229 | ] | |||
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230 | } | |||
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231 | ], | |||
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232 | "source": [ | |||
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233 | "%%sh \n", | |||
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234 | "cd /home/jeandet/Documents/DATA\n", | |||
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235 | "ls *.calibrated | parallel 'zip {}.zip {}'\n", | |||
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236 | "cd -" | |||
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237 | ] | |||
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238 | } | |||
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239 | ], | |||
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240 | "metadata": { | |||
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241 | "kernelspec": { | |||
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242 | "display_name": "Python 3", | |||
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243 | "language": "python", | |||
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244 | "name": "python3" | |||
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245 | }, | |||
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246 | "language_info": { | |||
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247 | "codemirror_mode": { | |||
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248 | "name": "ipython", | |||
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249 | "version": 3 | |||
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250 | }, | |||
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251 | "file_extension": ".py", | |||
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252 | "mimetype": "text/x-python", | |||
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253 | "name": "python", | |||
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254 | "nbconvert_exporter": "python", | |||
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255 | "pygments_lexer": "ipython3", | |||
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256 | "version": "3.5.3" | |||
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257 | } | |||
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258 | }, | |||
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259 | "nbformat": 4, | |||
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260 | "nbformat_minor": 2 | |||
|
|
261 | } | |||
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