134 lines
35 KiB
Plaintext
134 lines
35 KiB
Plaintext
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{
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"metadata": {
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"name": "",
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"signature": "sha256:139f5039e4216784ffa37cf89131cc2bd44141391b1b22b7c9d22ff517637ccc"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"%pylab inline"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"prompt_number": 1
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import pickle \n",
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"\n",
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"# get the file here: https://github.com/poppy-project/pypot/releases/download/2.4.0/data.pickle",
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"with open('data.pickle') as f:\n",
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" data = pickle.load(f)\n",
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" \n",
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"cpu_load = data['cpu_usage']"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"to_plot = (('dell', '2.7.8'),\n",
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" ('dell', 'pypy-2.3.1'),\n",
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"\n",
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" ('odroid', '2.7.8'),\n",
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" ('odroid', 'pypy-2.3.1'),\n",
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" \n",
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" ('pi', '2.7.8'),\n",
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" ('pi', 'pypy-2.3.1'))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 3
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"indices = array([0.5, 1.0, \n",
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" 2.0, 2.5,\n",
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" 3.5, 4.0])\n",
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"\n",
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"width = 0.4\n",
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"\n",
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"with xkcd():\n",
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" fig = plt.figure()\n",
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" ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))\n",
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" \n",
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" x = array([mean(cpu_load[b][p]) for b, p in to_plot])\n",
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" \n",
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" i = arange(0, len(x), 2)\n",
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" ax.bar(indices[i], x[i], width, color='r')\n",
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" \n",
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" i = arange(1, len(x), 2)\n",
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" ax.bar(indices[i], x[i], width, color='g')\n",
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" #for i in range(0, len(x), 2):\n",
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" #ax.bar(indices, x, width)\n",
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" \n",
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" ax.spines['right'].set_color('none')\n",
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" ax.spines['top'].set_color('none')\n",
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" ax.xaxis.set_ticks_position('bottom')\n",
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" ax.yaxis.set_ticks_position('left')\n",
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"\n",
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" ax.set_xticks(indices + width/2)\n",
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" \n",
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" ax.set_xticklabels(['2.7.8\\n PC',\n",
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" 'PyPy',\n",
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" '2.7.8\\n Odroid',\n",
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" 'PyPy',\n",
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" '2.7.8\\n Raspberry pi',\n",
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" 'PyPy'])\n",
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"\n",
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" plt.ylabel('cpu load (%)')\n",
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" plt.title(\"BOARDS COMPARISON\")\n",
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" savefig('cpu_usage.png')"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "display_data",
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"png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEBCAYAAACJy4k1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n14ujjjpKWK1W8bvf/U4IIcSa
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"text": [
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"<matplotlib.figure.Figure at 0x115376dd0>"
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]
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}
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],
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"prompt_number": 5
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [],
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"language": "python",
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"metadata": {},
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"outputs": []
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}
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],
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"metadata": {}
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}
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]
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}
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