plot_results.ipynb
156 lines
| 84.7 KiB
| text/plain
|
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pellion
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r657 | { | ||
| "cells": [ | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": 2, | ||||
| "metadata": { | ||||
| "collapsed": false | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [ | ||||
| "import numpy as np\n", | ||||
| "import matplotlib.pyplot as plt\n", | ||||
| "import glob \n", | ||||
| "import pandas as pds" | ||||
| ] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": false | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": 3, | ||||
| "metadata": { | ||||
| "collapsed": false | ||||
| }, | ||||
| "outputs": [ | ||||
| { | ||||
| "data": { | ||||
| "image/png": 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vf/DKXcOZk/oLNmw8WP5OXln0rtZ0EhERkVLH7XCy7uHvyZ59gmx3OMZUwnmX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\nMoILq+Hd1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Nc7j1NBwH2Jmpu974OBBGvtiYvU6L96RW6gT/8VwaDuh4l8fjKLxt1Ow5hD\ndHxuDF88H6FH8UREREQky3G5XNxc4BZuLnDL/7bFnz/OrJ3T2HJwBjn4nebMJ0/ib5zZ/B5fbyzB\n7pTiJPlXoES+OjQu0YYaRWpo7qhs5OT5kyzes4hl++dw4sx68tpdlHPvpYTrAPWAE648bLPliXXf\nTrlCLelS8R7aB+Z1OvYV/aPmkzHmLaD/ZUosUNVau/lqzmetfcPn42pjTBDQF/j4EofA/88NdbnJ\nqswV9l9tDX369CFfvnx/2RYREUFERMSVDhWRiyhVqDBzvrmXMQ8u4M1+KXw7oAdLvl/IC+8k6FE8\nEbmiqKgooqKi/rItPj7eoTQiIiL/XL7A/NxRKRIqRQKeJe9jDy5lya7JnD29kpLmT8rYWPzjx3Es\nHr63hdmdHMwxbsIdUJbS+evQvFQbKuaviDHmCleTjCrFprD1yFYW7pnLxmOxnD//B4XMAcr77aeE\n6yBNgXPuQLbbsuxw1eFw7lo0LNmJ5sGNcbkyXzPyn458eg/4+go1f1xjFoClwBBjTIB30vH9eEYu\n+SoKnLDWJhhjDgPJl6i5MBpqPxBgjMmbavSTb80lDR8+XBNmiqSDHs2b0mMJPDJsLD+815Qn2t/E\n13d+z5fDG1GluJ59F5GLu9gPQD4TZmZqxpgyeEaFh+MZnb0HGItnpd9En7qaeH6oqw8cBD621r6b\n6lx3Aa8BZYHNwABr7a+pal4DHgbyAwuBJ6y1W332F/BepxOQAkwCnrHWnk67uxYREbfLTYNijWlQ\nrPH/tp1NPMuCPTGsO7SA06c3kMu1k+pmDcVSouEo7DziYqktyp6UYE6YmwgIKE/FgvVpWaotxfNm\nvEeusrvjZ4+zcM98Vh5czNHTGwlM2Uew+zDl3AcoZ05SDjgdkItdlGWPXying+pQ+6bWNCnSAH93\ngNPx08Q/aj5Za48AR9IpC0Ad4NiF1e6AxXhWqfPV1rsda22iMSYWaAX8CGA8rd9WwIfe+lggybtt\niremMlD6wnlExDn/fa47L91/mJ59J7Fo7O00jjlO6ydHM+4lrYonItlOFTwjsx8BtgHVgS+AXEA/\nAGNMHjzzX84AHgNqAF8bY45Za7/w1jQCxuEZrf4zcB8w1RhTx1q73lvTH3gKz0rAfwL/BqYbY6r6\nfA8bh+cHvlZAAPAN8DnQI/3+BCIiApDTPydtynaiTdlOf9l+9OwR5u+ZydYjSzh3dhN52EVFs5RC\nyb/BIVh30I/fbDH2pQRzyhQnd86KVC5Qj9DiTSiVt7RGSqWjk+dPsvHIRlYfWs6ekxtITNhFYMp+\nCpgjlHIdIJ85SVMg2c/FXopz2JRiW2BjShQIpU5wGBXyVc7S830Za6/45Nm1ndiYUnjmZLoNeB5o\n7t211Vp72hjTCc/ooyXAeTxNpXeBodba17znKAv8DowEvsLz5ecD4FZrbbS35m7gWzxfwJbhWf2u\nG1Dlwip6xphP8DSxegEn8TSmUqy1zS6TX0sFi9xgE5cs4bX+J1k7rw3lqq7kwZd3MeSeLk7HEpEM\n7kYvFXwjGWNeAB631lb0fn4CeB0oZq1N8m57C7jNWnuL9/N3QC5rbRef8ywGVlprn/R+3gu8a629\nsIhLXjwjwu+31k4wxlQF1uH5m6701rTD08wqaa3df4m8+v4kIuKAPSd3sXDPTLYfW07S+S3kT9lN\nKbObPMYzWDXFGg5RiP0phYi3+UkwhfDzDyZ/rtKUzVeVWkUbUCZPmUz5ONeNcvzccbYc3cKaQyvZ\nfXIzp87txpVyiNzmGIVdxynmOkZBc/x/9SdtbvbaYsQTTEpAWYrmrUXN4CZUK1SXnH45HbwTjxv9\n/Sk9Jxx/Dejp8/nCzbQE5gGJeH5xG47nV76twLMXfrUDsNZuN8Z0BIYB/wJ2Aw9daDx5ayYYYwp7\nrxcMrALaXWg8efXB83jeRCAQ+A3onXa3KiJpoVtoKN3mwvNffM+Et6rx4r1dmPz1z7w1tCTtatZy\nOp6IiBPyA0d9PocC8y40nrymA/2MMfmstfF4VvN9P9V5puP5QRBjTHk8j/X5rgR8whiz1HvsBO91\njl1oPHlF45kvsyHwQxrcm4iIpJESeUpxd5UHgQf/ty0lJYWt8VtYtncW+05uJOH8DgLtPoLNEQqa\nLRRMPo77VAqcgh17YKMN5LAtyDFbgFPk47yrIC53UYICS1AsqBwVC9xChXwVKZirYJaZAD0xOZHj\n545z+Mxhtp/4k32nd3Ls3D5OnNvH+cSDuFOOkosT5HOdpLDrBEU4SgWTQgUgxW047C7EEYpw0pRm\ni18oQTnLUip/DeoWbULx3CXVzPORbs0na20vPCONLrV/Op4vQlc6z1zgshM5WGs/AT65zP7zwNPe\nl4hkcO8/fBev9DhL9yGjmPPfjtwRGkDjHmMZNbQDxfMXdDqeiMgNYYypiOeHuud8Nhfj7/NrHvDZ\nF8+lVwu+sMpvMJ4m0uVqiuGZT+p/rLXJxpijaLVgEZFMweVyUbnAzVQucPNF9yckJ7Dm0CrWH17J\nvlNbOHN+DybpMDk5RgEOUYgtFEo+ivtsCpyFs4dglXVzgjzE29ycsrk4Z3NyhtwkkotkkwtrcuPn\nyoW/X25y+OUll39egvzzkzcwPwVzFKJQziIUzlmYPAF5CHAHEOAOuOpHAa21pNiUv71OJpzk4JmD\nHDpzgMNnD3L83BFOJBzndEI8Z5NOkJh8ipSU0xh7Bj/OkYNz5HadJbc5R5DrPAXMSfJykpzGUt7n\neqddOTniKsgJCnDWlGSnXzBHcpSleL7qVClUh3J5K5HDL0ca/C+VPaTnyCcRkWuWJ0dOfnyvJ7G9\nt/FU31XM+epuak/bS9veP/PNAM0HJSKZx7WsFmyMKQH8Coy31n51pUtw5VV802ol4KtaLVhERDK+\nAHcA9Yo1oF6xBpesSUhOYOPRDWw5upb9p7dx+vx+EhOPQspx/FJOEmDOUIL95DDnCOI0eTiFmxTP\nrMtJwLm/nu+o95VsXSThRyL+JOImGTcpuDBYDBYXFkOK919wFtf/3qd49/3/K8B41uRw4/l15S+r\nkRnADxKsP6fJzRlycZogzhFEginCYVcQh/0KEehflDyBxSicuxQlg8pTIX9l8gbmTbO/taj5JCIZ\nXEi5CiyeWIEvYmYz/EXD2Bcj/6+9O4+vorr7OP75kYQEkBDWALLvyE4AQRSxIIjKUrXsULu41qcu\nrVRf7aNUu1gf61ZttVK1oEQRLMgiKEVBEGRfZN8XgSBbUNmynOePmejlmpAEksxN8n2/XudF5s65\nkzPzC3fO/GbuOXyavJLRv9vL2GEDg26eiEhe5Gu2YDOrDcwDFjrn7girl9NMwKFPMuVUJ3S9+XVS\nwuqsCqlTI3QDZhYFVCYPswXff//9VKpU6ZzXspupUEREIlvZqLK0rd6OttXzNgRGRmYGZzJOc+Js\nKkdOH+bY6cPek0hnjnIy7QSn0lI5k/4NGZlnyHRncJlnyXRpQAa4DBwGVgbDgDKAYVbG/7mM/5RU\nGczKYJQBM6LKxBEbHU/5mEpUjK1MQlxVqpSrTnzZBC6JiSe+bAIVoiuU6q/AJScnk5ycfM5rqamp\nRdqGQhtwvLjTgJkikem+l99mypNt2LfjMtpeM4ff/iGewVd0C7pZIhKgkjTguP/E0zxgGTDKhXXU\nzOxOvJnpEp1zGf5rfwIGhQ04Xs45NzDkfYuANXkYcHy0c+4dM2uBN+B4p5ABx/sAs9CA4yIiIsVe\nUfefSm/qT0SKpWfvGMLmDY0Z9Jvx7F3blpE9O9B9yDus2Lk96KaJiFwUM6sFfAzsAcYANcws0cxC\nn2KaCJwFXjWzy8xsCN6kLKEDjD8H9DOzB8ysuZmNxRs/84WQOs8CvzOz/mbWBhiPN7HLNADn3Ca8\nsTlfMbPOZtYd+BuQnFPiSURERCQnSj6JSLFTPjaW/zwxmuXrjSuGT2Xle/3o2a4yfX/xBimpx3Pf\ngIhIZOoDNAJ+AOwF9gMH/H8Bb1Y6oC/QAFgO/B8w1jn3r5A6i4FhwO14swDfBAx0zm0IqfMkXjLp\nZeAzoBzQzzl3NqQ9w4FNeLPczcCbrTj8a4AiIiIiuVLySUSKrUaJNfn49aFMX7aDFld/wtyXhtK6\n+QmGPDqBM2lpQTdPRCRfnHP/ds5FhZUyzrmosHrrnHNXO+fKO+fqOeeeymZbU5xzLZxz5Zxzbf1Z\nhsPrjHXO1fa309c5ty1s/XHn3EjnXCXnXGXn3G3OuZMFv+ciIiJS0in5JCLFXu/WbVk2fSD/mLOA\nxCbbmPTYKJq23MJdzyeTnp4RdPNERERERERKNSWfRKTEuL33D/h84Q/47YR3KVv+JC/dO4yWnZbz\n6MSpQTdNRERERESk1FLySURKnD+MvIlNKzty+zPJnD4Rz2MjBtHqio95ftYHQTdNRERERESk1FHy\nSURKpOjoKF6+bxjbNjdhyNgJHN7VgPtu7E2H3rN5/aP5QTdPRERERESk1FDySURKtNiYGN56dBTr\nN1fhxgfeYPfqdvysd3c6XT+dyUuWBN08ERERERGREk/JJxEpFapVjOe9p0azZnMM196VzNbFXRl2\nVUe6DJjG9BUrgm6eiIiIiIhIiaXkk4iUKnWrVmP2C6NYtSmDnj97h03zr+Tmbq25fNB/mLFyZdDN\nExERERERKXGUfBKRUqlRYk0+fGkESzee4srRU9gw72pu6tqKLgOnMnXZsqCbJyIiIiIiUmIo+SQi\npVqL2nWYN244SzZ8zVWjp7Dp4x4M7t6WLv2n8e5SJaFEREREREQulpJPIiJAqzr1+O+44SzdeJIe\nP5nM5k+6M6R7O5L6zeClD/4bdPNERERERESKLSWfRERCtKhdh7kvj2D5prNc8/NJ7FyWxF19e9G6\n+0c88sbUoJsnIiIiIiJS7Cj5JCKSjaY1a/PBP0aycXs5fvjQeI7urcvjowbRpO0ybn9mImfS0oJu\nooiIiIiISLGg5JOIyHkkVkrg3T+PZuf2+vzkyTfJTI/mlQeG07jZDn70vxM49vXXQTdRREREREQk\noin5JCKSB7ExMbz64Ai2rG3LmFcnE5+YwuQ/jKJp4+Nc9z9vsPXg/qCbKCIiIiIiEpGUfBIRyYfo\n6Cj+8pNb2LCkB89On0PdtuuY+48hdGwWR49Rb/HxhvVBN1FERERERCSiKPkkInKB7r2xL6s+7Mek\nT1fTus9HLJt8I307NKLLwKlMXLgo6OaJiIiIiIhEBCWfREQu0k1dOrN48s3MX3+AK0ZMZfP8Kxl1\ndVfaXfMBj07UDHkiIiIiIlK6KfkkIlJAujRqykevDmPN1gyuv3ciB7c05bERg2h42WqG/V6Dk4uI\niIiISOmk5JOISAFrUD2R6U+PYs+uOtz29ETKxp3irbGjaNzgK3r+JJm5n68NuokiIiIiIiJFRskn\nEZFCEhsTwz/vH87mld14dvocGndbypK3BtCvQwva95rNb8e/S3p6RtDNFBERERERKVRKPomIFIF7\nb+zLsukDWbThIL3ueJsDm5rzpx/fRMPmmxnw6/Fs2r8v6CaKiIiIiIgUCiWfRESKUFLDxsx+wftK\n3j0vvkV84iFmPjOCjk0q0aX/NJ6YMjPoJoqIiIiIiBQoJZ9ERAIQGxPD3+4eyvpPezJ16Wq6DJ7J\ntsVdePiWG2jQfC0Dx4xn/b49QTdTRERERETkoin5JCISsP5JSXz8+lB270vg5399k/KVjzPjryNI\nalqVpOtm8vC/NTaUiIiIiIgUX0o+iYhEiIpx5XjlgRFsWNKDqUtX023Ye+xd25onbr2Jug130eu2\nN5mxcmXQzRQREREREckXJZ9ERCJQ/6QkPnp1GHt31+b+lydRs8UWFk34IQM6tad50qcM/f0Edn2Z\nEnQzRUREREREcqXkk4hIBIuNieHp2wez6sN+LN38JQMefIOMtBjeHjuKy+qXp+N1M7nv5bc5eeZM\n0E0VERERERHJlpJPIiLFRNv69Zn6l9FsW9uZCfMXcvmQGez/vCXP3TmEOpcepdstU/jjO9M1PpSI\niIiIiESU6KAbICIi+Teyx5WM7AHp6Rn8afI0Zk3KYNO8K1kyJZHna++k+TVLuXloAr+4rjfR0VFB\nN1dEREREREoxPfkkIlKMRUdH8cjQgSx59yb27I/nl/94mzpt17Nyal/u69+X+k220fPWt3h+1gd6\nIkpERERERAKh5JOISAlRMa4cz905hBXv38iuA2W47emJ1Gi6g+WTr+PeG/pQt+Eurhr5tr6aJyIi\nIiIiRUpfuxMRKYGqVYznn/cPh/shJfU4j7yWzPLZ8ax+71oWvlmFZ2rso3G3ZXTrd5ZHR9xA5Usu\nCbrJIhdMyVQRERGRyKbkk4hICZdYKYGX7xsG98FXp0/x6PhJLJoVw/bFnVk6rQ7jfnWcJpfP5rKe\nX/KrkVeQ1LBx0E0WydX+40f54xtzWD63HJsXxAbdHBERERE5DyWfRERKkYpx5Xj69sFwu/e0yFPT\nZjJ7+lfsWNSG5Eeu4+2x6TRqvYz6XbfQv381DVguEWXqsmW89s5Gtn9ah+0runD69DCq1dhLnbaT\nSJ0fdOtEREREJCdKPomIlFLR0VE8dPMNPHSztzx9xQpeeWs92xfVY+H4Qfz3nxV4rNp+GnRaScvu\nx/jl8K50adQ02EZLqbL/+FGeSP6Q5R9FsWd5a77Y2ZkyZTrS4LLVdB81jQGDqnN3n16sXXsNSUlB\nt1ZEREREcqLkk4iIANA/KYn+/hX8/uNH+fPE6az4KJrdy9qwcnZzJj6SSb1ma6nTcRNdesKvB/em\ndkKVYBstJcr6fXt4ceoi1i0pw4E1jdmzuQ1paUOoXPUADTqt5OqfLuOBEVeQ1DAJULZJREREpLhQ\n8klERL6ndkIV/nb3ULjbW566bBnj/7ORHZ/V4PPZPVmUXIMX7jlD3WYrqNlqBy06n+XWGztyVYuW\nwTZcipVPt2xi3LQVbPwsjgPrmrJ3WysyM4eRUDmFum3W0fuuSfS9tob/9c8bgm6uiIiIiFwgJZ9E\nRCRXgzp3ZlDnzgCcSUvjxfff58O5h9m/tiab5nXn00m1efVBqHnpdmq13kS9dke4rtel/OSaHsTG\nxATceokU01esYOKMDWxfHs8X61qwf3cLoAXVEvdQp8162g9azYC+9Rnd40qio3sH3VwRERERKSBK\nPomISL7ExsTwwIB+PDDAW05Pz2Dq8iVMmbOVHasqcmB9U9Z8eB3TnoxiQD+00AAAEepJREFUTPxR\n6ly2jlqt99HpiljuGdCTulWrBbsDUiTS0zN445OFTJuzm92rqrJvXWu+POB9Xa5Wva1c2mYjV966\nlCHXt+CmLp2BekE3WUREREQKiZJPIiJyUaKjo7ila1du6dr129fW79vDy9MXs/Yzx4F19Vj85gDm\njavI09FnqdN0NbVabaNZp9P8+Ib2XNO6dYCtl4Kyaf8+/jV7MWuWnyFlQyJ71rXh+NGrMcukbuMN\nNOq2guu7fMKt/TvQ87JWgAavFxERESktlHySYiE5OZlhw4YF3YxST3GIHJEei1Z16vH8XfXgLm/5\n5Jkz/P39WXw0/wj71tRg64LLWTy5Lv9+CKrV2Eti061Ub5ZCk9aZ9LuyGQM6diQ6OirYncijSI9F\nYfhk00amzF/LhtVpHNpSjUPbGnNwb2Oc+xGxsSe5tMlGWvVZQOuu6dw5qBvt67cGlGQUKWql8fMp\nEikOkUOxiByKRelTprA2bGb1zWycme0ws5NmttXMxppZTFi9tma2wMxOmdluM3swm239yMw2+nXW\nmFm/bOo8Zmb7/d/1oZk1CVtf2czeNLNUMzvmt61Cwe+5FIbk5OSgmyAoDpGkuMWifGwsvx50PTOf\nGcWaeX35MqUu7y1fwYjHJ9Cs5xLSTsWxYkpfxv1qBDdf3pnKVVJp0nYZlw+ayoAHx/PQ61NYumNr\n0LuRreIWi/zYenA/f3xnOrf8bgJXDJ5Myy4LqVLtAD1atuS5O4ew4PUfcuJgDeq0X89ND7/JczM/\n4MvjxvbPk1iYPJiX7h1O+/oNg96NYiMvfSe/TmZYyTCzLmHbUt9JSvTnU3GiOEQOxSJyKBalT2E+\n+dQCMOA2YDveLc9xQHlgDICZVQTmAB8AdwBtgNfM7JhzbpxfpxswEfgNMBMYDkw1sw7OuQ1+nd8A\n9wA/BnYCfwDmmFlL59xZvz0TgUSgF1AWeB14GRhZeIdARERy0j8pif5JSd8up6dnMGPVMmYt3Mq2\n9XBkezX2rW7J8umNmZ4ZzV+A+IQvqVFvF5XqHKBK/RPUb2J0bluLGzu1p3ZCleB2phj76vQpZi5f\nyaK1e9mxNY3DuyuQuieRI3vrcfhQXaA2ANVr7aZ6g520um4B9VucpUfXSxl5VXfKx3YEOga6DyVI\nrn0nn8Prz2wIee1I1g/qO4mIiEikKbTkk3NuDl5iKcsuM3sKuJPvOlAjgRjgZ865dGCjmXUAHsDr\nbAHcC7zvnHvaX37UzPrgdZjuDqnzuHNuOoCZjQZSgEHAJDNrCfQFkpxzq/w6/wPMNLNfO+cOFvDu\ni4hIPkVHR50zq16WlNTjvL1oKUtXpvDF9miO7qrKoS2N+XxeA86crsA4wCyTqtX3klDrAPE1vyS+\n1gmq1UmnQcM4kprVpne7NlSrGB/MjgXs5JkzzN+4kSUbdrF9x1ek7CtD6heXcOJgVVIP1uLwwbqk\np3cHIDr6LNVr76ZKnX0077WYqxt9TId2CQy+KommNesD9YPdmRIuj30n8BJUR51zh3LYlPpOIiIi\nElGKesynBOBoyHJXYIGfeMoyBxhjZpWcc6lAN+CvYduZAwwEMLNGQE3gv1krnXMnzOwz/72T/N9z\nLKvz5JuLd+fwcmBaAeybiIgUgsRKCfzy+j5w/bmvp6dnMHvtSuav3MG2bSc5vDeWE/src3RXHbZ9\nVpsTx6t/W9csk/iEQ1SqeogKVY9Sodpxylc9SXzVNKpUN2rVKkeD2gm0qleb9g0aUDGuXBHvZf6k\np2fw+Rd7WLtrH9u/OMIXB77h8KEMUr+M4eTRcpw8UpFvjlThxJHqHD+SSEZGe6A9AOXKn6BqzX1U\nqnWQBpevpmPdT6nfOJor2tZlYJckKsY1RYOBR5TwvlOW98ysHLAFeDIrieRT30lEREQiSpEln/xx\nBO7Be6opS01gR1jVlJB1qf6/KdnUqen/nIjXETpfnZrAOXcHnXMZZnY0pE64OICNGzfmsFqKUmpq\nKitXrgy6GaWe4hA5FAtPbWBYx0Zh3/rKAPay/9hqlmzZzrY9xzl08CwnjkRz8lg5TqVewrG18Zz8\nqjJfn6hMZmboqfAUsJG4uK+Iq/A1seVOElPuJDFxZ4gpd4bo2DSi4tIpG5dOVNlMomMcm1dt4drb\nHgcgM9Pbist0OIdX8P4lE3BGZtbrzlt25yxDZrqRdiaK9LPRpJ+KIe10LOmn4jh7qhxpp+I4feoS\nTn5TEeeigQp+AcjkkorHKV8xlXKVdhNX6XPqNjjFZVXOUi0xigZ14klqUo+mibX9wdwT/PKdrRuK\n5zkv5FwdF2Q7CloOfaev/eVFeH9Vt+B9pW6gc26GXyeovhOo/xRRdK6IDIpD5FAsIodiEbyi7j/l\nO/lkZn/GG0MgJw5o6ZzbEvKeS4H3gbedc6/m9iv84nKpc771BVGnAcDIkRrWIFIkhYwNI8FRHCKH\nYlF4Tp/2Sl7NHfdI4TUmH77+yivsD7olgWkAfBp0I8IVZN/JOXcEeDbkvSvMrDbwIDCDnBVF3wnU\nf4o4OldEBsUhcigWkUOxiBgNKIL+04U8+fQU8Foudb59msnvEM0DFjrn7girdxDv7luoGpx7Ny6n\nOqHrza+TElZnVUidGqEbMLMooDLfv+uXZQ4wAtgF5OMSRERERIpYHF7HaU4u9YJSkH2n7HwG9A5Z\nDqrvBOo/iYiIFBdF2n/Kd/LJv+N2JNeKfHvXbh6wDPhpNlUWA38wsyjnXIb/Wh9gsz/eU1adXsDz\nIe+71n8d59xOMzvo11nr/954vPEIXgzZRoI/y0tWp6oXXsfrs/Ps58S87KeIiIgELuKeeMpSwH2n\n7HQADoQsB9J38ret/pOIiEjxUWT9J3Mut6erL3DDZrWABXh3vn6MNwgHAM65FL9OPLAJ+BD4C9AG\n+Bdwr3PuX36dbsB84CG86YKH+T93DJkueAze4+y3+r/vcaAV0CprumAzm4V3B+8uvOmCXwWWOudG\nFcoBEBEREcmHPPadRgNn+e4JpZuB3+PNHDzer6O+k4iIiESUwkw+/Rivk3LOy4BzzkWF1GsDvAB0\nBg4Dzzvnngrb1s3AH/HmeN4KPOhPRxxaZyxwO97oqZ8Av3DObQtZn+D/nv54A3ROxktynbzonRUR\nERG5SHnpO/nJp98A9YB0vJt4Tzrn/hO2LfWdREREJGIUWvJJRERERERERESkTNANEBERERERERGR\nkkvJJxERERERERERKTRKPuXAzH5hZjvN7JSZLTGzzkG3qbgys4fNbKmZnTCzFDP7j5k1C6sTa2Yv\nmtlhM/vKzCabWfgUz3XNbKaZfWNmB83sSTMrE1anp5mtMLPTZrbFHz9DcuDHJtPMng55TbEoAmZW\n28wm+Mf5pJmtMbOOYXUeM7P9/voPzaxJ2PrKZvammaWa2TEzG2dmFcLqtDWzBf5n2W4ze7Ao9q+4\nMLMyZva4me3wj/M2M/tdNvUUC5FcqO9UuMzsUf+cHVo2hKwvkPO3fJ+ZXWVm75nZF/5xH5BNHZ0n\nikBusTCz17L5fzIrrI5icZFM13dyAXSyyYaZDQH+CjyKN33xGmCOmVULtGHF11XA3/CmcO4NxAAf\nmFm5kDrPAjfgzdrTA6gNTMla6X8IzQKiga54swDdCjwWUqcBMAP4L9AOeA4YZ2bXFspeFXP+RcFt\neH/foRSLQmbeIL6LgDNAX6Al8CvgWEid3wD3AHcAXYBv8D6HyoZsaqL/3l54MesBvByyjYrAHGAn\n0BF4EBhrZj8vrH0rhh7CO8Z3Ay2AMcAYM7snq4JiIZI79Z2KzOdAIlDTL1eGrLvo87fkqAKwGvgF\n8L0Bc3WeKFLnjYXvfc79fzIsbL1icfF0fSf555xTCSvAEuC5kGUD9gFjgm5bSShANbxZc670l+Px\nLsJ/GFKnuV+ni7/cD0gDqoXUuQPvYj3aX/4LsDbsdyUDs4Le50grwCXAZuAHwEfA04pFkR7/J4D5\nudTZD9wfshwPnAIG+8st/bh0CKnTF2/2q5r+8l14s4hGh9T5M7Ah6GMQKQWYDrwS9tpkYLxioaKS\n96K+U5Ec40eBlTmsK5Dzt0qe4pAJDAh7TeeJyInFa8C753lPC8WiUGKh6zuVXIuefApjZjFAEl52\nFfDmNwbmAt2CalcJk4B3p+Kov5yEl/EOPeabgT18d8y7Auucc4dDtjMHqAS0CqkzN+x3zUFxy86L\nwHTn3Lyw1zuhWBSF/sByM5vkP6q8MvRumpk1xLtTFxqHE8BnnBuHY865VSHbnYv3f+vykDoLnHPp\nIXXmAM3NrFJB71Qx9SnQy8yaAphZO6A73p04xUIkD9R3KlJN/a8bbTezN8ysrv96QfWlJJ90nohI\nPf3+1SYz+7uZVQlZ1w3FojDo+k5ypeTT91UDooCUsNdT8E4schHMzPAewVzonMsap6AmcNY/UYcK\nPeY1yT4m5KFOvJnFXmzbSwozGwq0Bx7OZnUiikVRaIR3V20z0Ad4CXjezEb662vincDP9zlUEzgU\nutI5l4F30s9PrEq7J4C3gU1mdhZYATzrnHvLX69YiOROfaeisQTvKyl9gTuBhsACf6yagupLSf7p\nPBFZ3gdG4z3dPwa4GpjlX4OAYlHgdH0neRUddAOKESPn7xVL3v0duIxzxyjISV6P+fnqWB7qlBpm\nVgfv5HCtcy4tP29FsShIZYClzrn/9ZfXmFkrvITUG+d5X17ikFsdxeFcQ4DhwFBgA15i9jkz2++c\nm3Ce9ykWIrlT36kAOefmhCx+bmZLgd3AYOB0Dm8riPO3XBidJwLgnJsUsrjezNYB24GeeENN5ESx\nuHC6vpM80ZNP33cYyMB7AiRUDb6fdZV8MLMXgOuBns65/SGrDgJlzSw+7C2hx/wg349JYsi6nOrU\nAE44585eTNtLkCSgOrDCzNLMLA3vjtC9/lMfKUCsYlHoDgAbw17bCNTzfz6Id2I93+fQQX/5W2YW\nBVQm9ziAPs+yPAn82Tn3jnNuvXPuTeAZvnsyULEQyZ36TgFwzqUCW4AmXHxfSnG6cDpPRDDn3E68\nz6is2QcViwKk6zvJDyWfwvhPg6zAm/0A+PZRwl54Y4PIBfA/mAYC1zjn9oStXoE3yF/oMW+GdyGe\ndcwXA23CZs3pA6Ty3UX84tBthNRZXBD7UELMBdrgPd3Rzi/L8Z62yfo5DcWisC3CG3QxVHO8O9hZ\nHaWDnBuHeLyxCELjkGBmHUK20QuvA7w0pE4Pv1OVpQ+w2b9oESjP9++cZeKfHxULkdyp7xQMM7sE\naIw32PXF9qU2IBdE54nI5j/1XxXvxh8oFgVG13eSb0GPeB6JBe/x5VN43xdugTf15hGgetBtK44F\n71HMY3hTciaGlLiwOjvxHolNwrs4/yRkfRm8aZvfB9rijXeQAjweUqcB8DXerAjN8aZOPwv0DvoY\nRHIhZLY7xaLIjnknvBlAHsa7cBgOfAUMDakzxv/c6Y+XMJwKbAXKhtSZhZcw7Iw3SPZmYELI+ni8\ni5J/4z0OPcSPy8+CPgaRUvBmxdmDd9euPvBDvLEg/qRYqKjkvaC+U1Ec4//Dm668PnAF8KF//q3q\nr7/o87dKjse+At5NuvZ4Nyju85fr+ut1noiAWPjrnsRL/NXHS1osx0tkxCgWBRoHXd+p5P/vJugG\nRGrx/7B34XWkFgOdgm5TcS3+iSEjmzI6pE4s8De8x2K/At4BaoRtpy4ww/8ASvE/hMqE1bkaL9N+\nyj/pjwp6/yO9APM4N/mkWBTNcb8eWAucBNYDP82mzli/83MSb2aPJmHrE/CeWkv1OwCvAOXD6rQB\n5vvb2AP8Ouh9j6SC11F92u8cfeP/rf6esGnHFQsVldyL+k6FfnyTgX3+8d0DTAQahqwvkPO3SrbH\n/mqy78++GlJH54mAYwHEAbPxnkQ7DewA/kFYElyxKJA46PpOJd/F/ICKiIiIiIiIiIgUOI35JCIi\nIiIiIiIihUbJJxERERERERERKTRKPomIiIiIiIiISKFR8klERERERERERAqNkk8iIiIiIiIiIlJo\nlHwSEREREREREZFCo+STiIiIiIiIiIgUGiWfRERERERERESk0Cj5JCIiIiIiIiIihUbJJxERERER\nERERKTRKPomIiIiIiIiISKH5f9a/u9bCqU0TAAAAAElFTkSuQmCC\n", | ||||
| "text/plain": [ | ||||
| "<matplotlib.figure.Figure at 0x7fef1c4777f0>" | ||||
| ] | ||||
| }, | ||||
| "metadata": {}, | ||||
| "output_type": "display_data" | ||||
| } | ||||
| ], | ||||
| "source": [ | ||||
| "for folder in glob.glob(\"./2016*\"):\n", | ||||
| " inputsig = pds.read_csv(folder+\"/input.txt\",sep=\" \",header=None)\n", | ||||
| " inputsig.columns=[ \"CH{}\".format(i) for i in range(inputsig.shape[1])]\n", | ||||
| " outputsig = pds.read_csv(folder+\"/output_f0.txt\",sep=\" \",header=None)\n", | ||||
| " outputsig.columns=[\"Tstamp\"]+[ \"CH{}\".format(i) for i in range(outputsig.shape[1]-1)]\n", | ||||
| " f, axarr = plt.subplots(1,2,figsize=(14, 6))\n", | ||||
| " inputsig.plot(ax=axarr[0])\n", | ||||
| " axarr[0].legend(loc='upper right')\n", | ||||
| " (outputsig.filter(regex=\"CH*\")[:]/0.8912).plot(ax=axarr[1])\n", | ||||
| " axarr[1].legend(loc='upper right')\n", | ||||
| " plt.show()" | ||||
| ] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": 8, | ||||
| "metadata": { | ||||
| "collapsed": false | ||||
| }, | ||||
| "outputs": [ | ||||
| { | ||||
| "ename": "FileNotFoundError", | ||||
| "evalue": "File b'./simulation//input.txt' does not exist", | ||||
| "output_type": "error", | ||||
| "traceback": [ | ||||
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||||
| "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", | ||||
| "\u001b[0;32m<ipython-input-8-b0157b87c19d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfolder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"./simulation/\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0minputsig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/input.txt\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0minputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m\"CH{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0moutputsig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/output_f2.txt\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0moutputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Tstamp\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m\"CH{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputsig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
| "\u001b[0;32m/home/adminlpp/venv/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 643\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 647\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
| "\u001b[0;32m/home/adminlpp/venv/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
| "\u001b[0;32m/home/adminlpp/venv/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 727\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 731\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
| "\u001b[0;32m/home/adminlpp/venv/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 920\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 921\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 922\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 923\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 924\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
| "\u001b[0;32m/home/adminlpp/venv/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1387\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
| "\u001b[0;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4175)\u001b[0;34m()\u001b[0m\n", | ||||
| "\u001b[0;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:8440)\u001b[0;34m()\u001b[0m\n", | ||||
| "\u001b[0;31mFileNotFoundError\u001b[0m: File b'./simulation//input.txt' does not exist" | ||||
| ] | ||||
| } | ||||
| ], | ||||
| "source": [ | ||||
| "folder=\"./simulation/\"\n", | ||||
| "inputsig = pds.read_csv(folder+\"/input.txt\",sep=\" \",header=None)\n", | ||||
| "inputsig.columns=[ \"CH{}\".format(i) for i in range(inputsig.shape[1])]\n", | ||||
| "outputsig = pds.read_csv(folder+\"/output_f2.txt\",sep=\" \",header=None)\n", | ||||
| "outputsig.columns=[\"Tstamp\"]+[ \"CH{}\".format(i) for i in range(outputsig.shape[1]-1)]\n", | ||||
| "f, axarr = plt.subplots(1,2,figsize=(14, 6))\n", | ||||
| "(outputsig.filter(regex=\"CH*\")- inputsig*0.8912)[150:].plot(ax=axarr[0])\n", | ||||
| "axarr[0].legend(loc='upper right')\n", | ||||
| "(outputsig.filter(regex=\"CH*\")[:100]/0.8912).plot(ax=axarr[1])\n", | ||||
| "axarr[1].legend(loc='upper right')\n", | ||||
| "plt.show()" | ||||
| ] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": true | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": true | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": true | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": true | ||||
| }, | ||||
| "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": "3.5.2" | ||||
| } | ||||
| }, | ||||
| "nbformat": 4, | ||||
| "nbformat_minor": 1 | ||||
| } | ||||