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{
"cells": [
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"from prettytable import PrettyTable\n",
"import matplotlib.pyplot as plt\n",
"from asq.initiators import query\n",
"import json\n",
"\n",
"project_name = 'workspace'\n",
"\n",
"with open(\"qpi.json\") as result:\n",
" final = json.load(result)\n",
"\n",
"def extract_results(node_name, section_name):\n",
" workload_name = []\n",
" workload_score = []\n",
" if section_name == 'system_info':\n",
" for node in final['nodes']:\n",
" if node['name'] == node_name: \n",
" return node['system_info']\n",
" qpi = query(final['nodes']).where(lambda child: child['name'] == node_name) \\\n",
" .select_many(lambda child: child['sections']) \\\n",
" .where(lambda child: child['name'] == section_name) \\\n",
" .select_many(lambda child: child['metrics']).to_list()\n",
"\n",
" for wl in qpi[0]['workloads']:\n",
" workload_name.append(wl['name'])\n",
" workload_score.append(wl['score'])\n",
"\n",
" x_axis = range(len(workload_name))\n",
"\n",
" plt.bar(x_axis, workload_score)\n",
" plt.xticks(x_axis, workload_name, rotation=45)\n",
" plt.xlabel('Workloads')\n",
" plt.ylabel('Score')\n",
" plt.title('Metric Result')\n",
" return plt"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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JYERERCOtBoakSZJukTRD0qH9nD9G0jX161ZJf+s4N6/j3NQ264yIiMG1tuKspFGUvTN2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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccb0451b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"arith = extract_results('node-9', 'arithmetic')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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CmZlVnBTMzKzipGBmZhUnBTMzqzgpmJlZxUnBzMwqTgpmZlZxUjAzs4qTgpmZVZwUzMys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\nOpYc+tdVXG6D43LrXk4KHdQ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"text/plain": [
"<matplotlib.figure.Figure at 0x7fccb0619250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ssl = extract_results('node-9', 'SSL')\n",
"ssl.show()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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kykEqXnyWs97vejGLx6kPRPbREfezjz5unEZDhsImU1UnACdMuo7NTZI1VbVy0nVIG2If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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccb069b310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dpi = extract_results('node-9', 'DPI')\n",
"dpi.show()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fccb04942d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"memory = extract_results('node-9', 'memory')\n",
"memory.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-----------+---------------------+\n",
"| Parameter | Info |\n",
"+-----------+---------------------+\n",
"| product | KVM |\n",
"| disk | 53.7GB (5.9% used) |\n",
"| os | Ubuntu 14.04 trusty |\n",
"| memory | 799.5/3945.4MB |\n",
"+-----------+---------------------+\n"
]
}
],
"source": [
"system_info = extract_results('node-9', 'system_info')\n",
"table = PrettyTable(['Component', 'Value'])\n",
"table.align = 'l'\n",
"for key,value in system_info.iteritems():\n",
" table.add_row([key, value[0]])\n",
"print table"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|
