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|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"hide_input": true
},
"source": [
"# OPNFV VSPERF\n",
"# Beyond Performance Metrics: Towards Causation Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### sridhar.rao@spirent.com and acm@research.att.com"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import packages\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from graphviz import Digraph\n",
"import collections\n",
"import glob\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the results to analyze: \n",
"Getting Latest one, if ``directory_to_download`` is empty"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"import paramiko\n",
"import tarfile\n",
"import os\n",
"from stat import S_ISDIR\n",
"RECV_BYTES = 4096\n",
"hostname = '10.10.120.24'\n",
"port = 22\n",
"uname='opnfv'\n",
"pwd='opnfv' \n",
"stdout_data = []\n",
"stderr_data = []\n",
"client = paramiko.Transport((hostname, port))\n",
"client.connect(username=uname, password=pwd)\n",
"session = client.open_channel(kind='session')\n",
"directory_to_download = ''\n",
"\n",
"session.exec_command('ls /tmp | grep results')\n",
"if not directory_to_download:\n",
" while True:\n",
" if session.recv_ready():\n",
" stdout_data.append(session.recv(RECV_BYTES))\n",
" if session.recv_stderr_ready():\n",
" stderr_data.append(session.recv_stderr(RECV_BYTES))\n",
" if session.exit_status_ready():\n",
" break\n",
" if stdout_data:\n",
" line = stdout_data[0]\n",
" filenames = line.decode(\"utf-8\").rstrip('\\n').split('\\n')\n",
" filenames = sorted(filenames)\n",
" latest = filenames[-1]\n",
" directory_to_download = os.path.join('/tmp', latest).replace(\"\\\\\",\"/\")\n",
" print(directory_to_download)\n",
"stdout_data = []\n",
"stderr_data = []\n",
"if directory_to_download:\n",
" # zip the collectd results to make the download faster\n",
" zip_command = 'sudo -S tar -czvf '+ directory_to_download + '/collectd.tar.gz -C ' + directory_to_download + '/csv .'\n",
" session = client.open_channel(kind='session')\n",
" session.get_pty()\n",
" session.exec_command(zip_command)\n",
" while True:\n",
" if session.recv_ready():\n",
" stdout_data.append(session.recv(RECV_BYTES))\n",
" if session.recv_stderr_ready():\n",
" stderr_data.append(session.recv_stderr(RECV_BYTES))\n",
" if session.exit_status_ready():\n",
" break\n",
" if stderr_data:\n",
" print(stderr_data[0])\n",
" if stdout_data:\n",
" print(stdout_data[0])\n",
"\n",
" # Begin the actual downlaod\n",
" sftp = paramiko.SFTPClient.from_transport(client)\n",
" def sftp_walk(remotepath):\n",
" path=remotepath\n",
" files=[]\n",
" folders=[]\n",
" for f in sftp.listdir_attr(remotepath):\n",
" if S_ISDIR(f.st_mode):\n",
" folders.append(f.filename)\n",
" else:\n",
" files.append(f.filename)\n",
" if files:\n",
" yield path, files\n",
" # Filewise download happens here\n",
" for path,files in sftp_walk(directory_to_download):\n",
" for file in files:\n",
" remote = os.path.join(path,file).replace(\"\\\\\",\"/\")\n",
" local = os.path.join('./results', file).replace(\"\\/\",\"/\")\n",
" sftp.get(remote, local)\n",
"# Untar the collectd results if we got it.\n",
"path = os.path.join('./results', 'collectd.tar.gz')\n",
"if os.path.exists(path):\n",
" tar = tarfile.open(path)\n",
" tar.extractall()\n",
" tar.close()\n",
"# Ready to work with downloaded data, close the session and client.\n",
"session.close()\n",
"client.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"strings = ('* OS:', '* Kernel Version:', '* Board:', '* CPU:', '* CPU cores:',\n",
" '* Memory:', '* Virtual Switch Set-up:',\n",
" '* Traffic Generator:','* vSwitch:', '* DPDK Version:', '* VNF:')\n",
"filename = os.path.basename(glob.glob('./results/result*.rst')[0])\n",
"info_dict = {}\n",
"with open(os.path.join('./results', filename), 'r') as file:\n",
" for line in file:\n",
" if any(s in line for s in strings):\n",
" info_dict[line.split(':', 1)[0]] = line.split(':', 1)[1].rstrip()\n",
"df = pd.DataFrame.from_dict(info_dict, orient='index', columns=['Value'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Understand the configuration used for the test."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"filename = os.path.basename(glob.glob('./results/vsperf*.conf')[0])\n",
"file = os.path.join('./results', filename)\n",
"with open(file, 'r') as f:\n",
" for line in f:\n",
" if line.startswith('TRAFFICGEN_DURATION'):\n",
" value = line.split('=')[1]\n",
" value = value.rstrip()\n",
" value = value.lstrip()\n",
" traffic_duration = int(value)\n",
" elif line.startswith('VSWITCH_PMD_CPU_MASK'):\n",
" value = line.split('=')[1]\n",
" value = value.rstrip()\n",
" pmd_cores_mask = value.lstrip()\n",
" elif line.startswith('GUEST_CORE_BINDING'):\n",
" value = line.split('=')[1]\n",
" value = value.rstrip()\n",
" value = value.lstrip()\n",
" guest_cores = value[1:-2]\n",
"\n",
"print(traffic_duration)\n",
"print(pmd_cores_mask)\n",
"print(guest_cores)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OVS-Ports and Cores"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"import collections\n",
"portcores = collections.OrderedDict()\n",
"chunks = []\n",
"current_chunk = []\n",
"file = os.path.join('./results', 'ovs-cores.log')\n",
"with open(file, 'r') as f:\n",
" for line in f:\n",
" if line.startswith('pmd') and current_chunk:\n",
" # if line starts with token and the current chunk is not empty\n",
" chunks.append(current_chunk[:]) # add not empty chunk to chunks\n",
" current_chunk = [] # make current chunk blank\n",
" # just append a line to the current chunk on each iteration\n",
" if \"port:\" in line or 'pmd' in line:\n",
" current_chunk.append(line)\n",
" chunks.append(current_chunk) # append the last chunk outside the loop\n",
"\n",
"core_ids = []\n",
"for ch in chunks:\n",
" port_id = ''\n",
" core_id = ''\n",
" for line in ch:\n",
" if 'pmd' in line:\n",
" core_id = line.split()[-1][:-1]\n",
" if core_id not in core_ids:\n",
" core_ids.append(core_id)\n",
" elif 'port:' in line:\n",
" port_id = line.split()[1]\n",
" if port_id and core_id:\n",
" if port_id not in portcores:\n",
" portcores[port_id] = core_id\n",
"\n",
"# import graphviz\n",
"from graphviz import Digraph\n",
"ps = Digraph(name='ovs-ports-cores', node_attr={'shape': 'box'}, edge_attr={'arrowhead':\"none\"})\n",
"with ps.subgraph(name=\"cluster_0\") as c:\n",
" c.node_attr.update(style='filled', color='green')\n",
" c.node('t0', 'TGen-Port-0')\n",
" c.node('t1', 'TGen-Port-1')\n",
" c.attr(label='TGEN')\n",
" c.attr(color='blue')\n",
"with ps.subgraph(name=\"cluster_1\") as c:\n",
" c.node_attr.update(style='filled', color='yellow')\n",
" c.node('v0', 'VNF-Port-0')\n",
" c.node('v1', 'VNF-Port-1')\n",
" c.attr(label='VNF')\n",
" c.attr(color='blue')\n",
" \n",
"with ps.subgraph(name='cluster_2') as c: \n",
" c.attr(label='OVS-DPDK')\n",
" c.attr(color='blue')\n",
" count = 0\n",
" for port, core in portcores.items():\n",
" id = 'o'+str(count)\n",
" c.node(id, port+'\\nCore-ID:'+ core)\n",
" count += 1\n",
" num = port[-1]\n",
" if 'dpdkvhost' in port:\n",
" ps.edge(id, 'v'+num)\n",
" else:\n",
" ps.edge(id, 't'+num)\n",
"\n",
"ps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dropped Packets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"portcores = collections.OrderedDict()\n",
"chunks = []\n",
"current_chunk = []\n",
"file = os.path.join('./results', 'ovs-cores.log')\n",
"with open(file, 'r') as f:\n",
" for line in f:\n",
" if line.startswith('pmd') and current_chunk:\n",
" # if line starts with token and the current chunk is not empty\n",
" chunks.append(current_chunk[:]) # add not empty chunk to chunks\n",
" current_chunk = [] # make current chunk blank\n",
" # just append a line to the current chunk on each iteration\n",
" if \"port:\" in line or 'pmd' in line:\n",
" current_chunk.append(line)\n",
" chunks.append(current_chunk) # append the last chunk outside the loop\n",
"\n",
"core_ids = []\n",
"for ch in chunks:\n",
" port_id = ''\n",
" core_id = ''\n",
" for line in ch:\n",
" if 'pmd' in line:\n",
" core_id = line.split()[-1][:-1]\n",
" if core_id not in core_ids:\n",
" core_ids.append(core_id)\n",
" elif 'port:' in line:\n",
" port_id = line.split()[1]\n",
" if port_id and core_id:\n",
" if port_id not in portcores:\n",
" portcores[port_id] = core_id\n",
"\n",
"ps = Digraph(name='ovs-dropped', node_attr={'shape': 'box'}, edge_attr={'arrowhead':\"none\"})\n",
"\n",
"def get_dropped(port_id):\n",
" # port_id = 'dpdk0'\n",
" if glob.glob('./pod12-node4/*'+port_id):\n",
" dirname = os.path.basename(glob.glob('./pod12-node4/*'+port_id)[0])\n",
" if dirname:\n",
" if glob.glob('./pod12-node4/'+dirname+ '/*dropped*'):\n",
" filename = os.path.basename(glob.glob('./pod12-node4/'+dirname+ '/*dropped*')[0])\n",
" if filename:\n",
" with open(os.path.join('./pod12-node4', dirname, filename), 'r') as f:\n",
" line = f.readlines()[-1]\n",
" fields = line.split(',')\n",
" return fields[1], fields[2]\n",
" return 'NA','NA'\n",
"\n",
"with ps.subgraph(name=\"cluster_0\") as c:\n",
" c.node_attr.update(style='filled', color='pink')\n",
" c.attr(label='OVS-DPDK')\n",
" c.attr(color='blue')\n",
" count = 0\n",
" for port, core in portcores.items():\n",
" id = 'o'+str(count)\n",
" rx,tx = get_dropped(port)\n",
" c.node(id, port+'\\nRX-Dropped:'+ rx + '\\nTX-Dropped:' + tx)\n",
" count += 1\n",
" num = port[-1]\n",
"ps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting Live Results - T-Rex"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"lines_seen = set() # holds lines already seen\n",
"outfile = open('./counts.dat', \"w\")\n",
"file = os.path.join('./results', 'trex-liveresults-counts.dat')\n",
"for line in open(file, \"r\"):\n",
" if line not in lines_seen: # not a duplicate\n",
" outfile.write(line)\n",
" lines_seen.add(line)\n",
"outfile.close()\n",
"tdf = pd.read_csv('./counts.dat')\n",
"print(tdf.columns)\n",
"ax = tdf.loc[(tdf.rx_port == 1)].plot(y='rx_pkts')\n",
"def highlight(indices,ax):\n",
" i=0\n",
" while i<len(indices):\n",
" ax.axvspan(indices[i][0], indices[i][1], facecolor='RED', edgecolor='BLUE', alpha=.2)\n",
" i+=1\n",
"\n",
"ind = 0\n",
"indv = tdf.ts[0]\n",
"ax.set_xlabel(\"Index\")\n",
"ax.set_ylabel('Count')\n",
"for i in range(len(tdf.ts)):\n",
" if tdf.ts[i] - indv > int(traffic_duration):\n",
" highlight([(ind, i)], ax)\n",
" ind = i\n",
" indv = tdf.ts[i]\n",
"highlight([(ind,i)], ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IRQ Latency Histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"file = os.path.join('./results', 'RUNirq.irq.log')\n",
"tdf = pd.read_csv(file)\n",
"tdf.columns\n",
"exclude = [' <1', ' < 5', ' < 10',' < 50', ' < 100', ' < 500', ' < 1000']\n",
"ax = tdf.loc[:, tdf.columns.difference(exclude)].plot(x=' number', xticks=tdf[' number'], figsize=(20,10))\n",
"ax.set_xlabel('Core #')\n",
"ax.set_ylabel('Count')\n",
"#tdf.plot(x='number')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample Collectd Metric Display - L3 Cache Occupancy in Bytes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"def cpumask2coreids(mask):\n",
" intmask = int(mask, 16)\n",
" i = 1\n",
" coreids = []\n",
" while (i < intmask):\n",
" if (i & intmask):\n",
" coreids.append(str(math.frexp(i)[-1]-1))\n",
" i = i << 1\n",
" return (coreids)\n",
"\n",
"vswitch_cpus = \"['2']\"\n",
"ps = Digraph(name='cpu-map', node_attr={'shape': 'box'}, edge_attr={'arrowhead':\"none\"})\n",
"with ps.subgraph(name=\"cluster_0\") as c:\n",
" c.node_attr.update(style='filled', color='pink')\n",
" c.attr(label='CPU-MAPPINGS')\n",
" c.attr(color='blue')\n",
" c.node('vscpus', 'vSwitch: \\n' + vswitch_cpus)\n",
" # vnf_cpus = cpumask2coreids(guest_cores)\n",
" c.node('vncpus', 'VNF: \\n' + guest_cores)\n",
" pmd_cpus = cpumask2coreids(pmd_cores_mask[1:-1])\n",
" c.node('pmcpus', 'PMDs: \\n' + str(pmd_cpus))\n",
"\n",
"ps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"# Path where collectd results are stored.\n",
"mypath = \"./pod12-node4\"\n",
"file_count = 0\n",
"cpu_names = []\n",
"for level1 in os.listdir(mypath):\n",
" if \"intel_rdt\" in level1:\n",
" l2path = os.path.join(mypath, level1)\n",
" for level2 in os.listdir(l2path):\n",
" if \"bytes\" in level2:\n",
" l3path = os.path.join(l2path, level2)\n",
" if file_count == 0:\n",
" file_count += 1\n",
" df = pd.read_csv(l3path)\n",
" nn = 'cpu-'+ level1[len('intel_rdt-'):]\n",
" # nn = 'cpu-'+ level1.split('-')[1]\n",
" cpu_names.append(nn)\n",
" # print(nn)\n",
" df.rename(columns={'value': nn}, inplace=True)\n",
" else:\n",
" file_count += 1\n",
" tdf = pd.read_csv(l3path)\n",
" nn = 'cpu-'+ level1[len('intel_rdt-'):]\n",
" cpu_names.append(nn)\n",
" tdf.rename(columns={'value': nn}, inplace=True)\n",
" df[nn] = tdf[nn] \n",
"\n",
"ax = df.plot(x='epoch', y=cpu_names)\n",
"ax.set_ylabel(\"MBytes\")\n",
"ax.set_xlabel('Time')\n",
"\n",
"\n",
" \n",
"# df = pd.read_csv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Events "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"filename = os.path.basename(glob.glob('./results/vsperf-overall*.log')[0])\n",
"logfile = os.path.join('./results', filename)\n",
"linecnt = 0\n",
"times = {}\n",
"with open(logfile) as f:\n",
" for line in f:\n",
" line = line.strip('\\n')\n",
" if linecnt == 0:\n",
" times['Start-Test'] = line.split(\" : \")[0]\n",
" linecnt += 1\n",
" if 'Binding NICs' in line:\n",
" times['Binding-NICs'] = line.split(\" : \")[0]\n",
" if 'Starting traffic at' in line:\n",
" sline = line.split(\" : \")[1]\n",
" time = line.split(\" : \")[0]\n",
" speed = sline.split('at',1)[1]\n",
" times[speed] = time \n",
" elif 'Starting vswitchd' in line:\n",
" times['vSwitch-Start'] = line.split(\" : \")[0]\n",
" elif 'Starting ovs-vswitchd' in line:\n",
" times['ovsvswitch-start'] = line.split(\" : \")[0]\n",
" elif 'Adding Ports' in line:\n",
" times['Ports-Added'] = line.split(\" : \")[0]\n",
" elif 'Flows Added' in line:\n",
" times['Flows-Added'] = line.split(\" : \")[0]\n",
" elif 'send_traffic with' in line:\n",
" times['Traffic Start'] = line.split(\" : \")[0]\n",
" elif 'l2 framesize 1280' in line:\n",
" times['Traffic-Start-1280'] = line.split(\" : \")[0]\n",
" elif 'Starting qemu' in line:\n",
" times['VNF-Start'] = line.split(\" : \")[0]\n",
" elif 'l2 framesize 64' in line:\n",
" times['Traffic-Start-64'] = line.split(\" : \")[0]\n",
" elif 'l2 framesize 128' in line:\n",
" times['Traffic-Start-128'] = line.split(\" : \")[0]\n",
" elif 'l2 framesize 256' in line:\n",
" times['Traffic-Start-256'] = line.split(\" : \")[0]\n",
" elif 'l2 framesize 512' in line:\n",
" times['Traffic-Start-512'] = line.split(\" : \")[0]\n",
" elif 'l2 framesize 1024' in line:\n",
" times['Traffic-Start-1024'] = line.split(\" : \")[0]\n",
" elif 'l2 framesize 1518' in line:\n",
" times['Traffic-Start-1518'] = line.split(\" : \")[0]\n",
" elif 'dump flows' in line:\n",
" times['Traffic-End'] = line.split(\" : \")[0]\n",
" elif 'Wait for QEMU' in line:\n",
" times['VNF-Stop'] = line.split(\" : \")[0]\n",
" elif 'delete flow' in line:\n",
" times['flow-removed'] = line.split(\" : \")[0]\n",
" elif 'delete port' in line:\n",
" times['port-removed'] = line.split(\" : \")[0]\n",
" elif 'Killing ovs-vswitchd' in line:\n",
" times['vSwitch-Stop'] = line.split(\" : \")[0]\n",
"\n",
"times['Test-Stop'] = line.split(\" : \")[0]\n",
"#print(times)\n",
"ddf = pd.DataFrame.from_dict(times, orient='index', columns=['timestamp'])\n",
"names = ddf.index.values\n",
"dates = ddf['timestamp'].tolist()\n",
"datefmt=\"%Y-%m-%d %H:%M:%S,%f\"\n",
"dates = [datetime.strptime(ii, datefmt) for ii in dates]\n",
"# print(names)\n",
"# print(dates)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"import matplotlib.dates as mdates\n",
"from matplotlib import ticker\n",
"\n",
"levels = np.array([-5, 5, -3, 3, -1, 1])\n",
"fig, ax = plt.subplots(figsize=(40, 5))\n",
"\n",
"# Create the base line\n",
"start = min(dates)\n",
"stop = max(dates)\n",
"ax.plot((start, stop), (0, 0), 'k', alpha=.5)\n",
"\n",
"pos_list = np.arange(len(dates))\n",
"\n",
"# Iterate through releases annotating each one\n",
"for ii, (iname, idate) in enumerate(zip(names, dates)):\n",
" level = levels[ii % 6]\n",
" vert = 'top' if level < 0 else 'bottom'\n",
" ax.scatter(idate, 0, s=100, facecolor='w', edgecolor='k', zorder=9999)\n",
" # Plot a line up to the text\n",
" ax.plot((idate, idate), (0, level), c='r', alpha=.7)\n",
" # Give the text a faint background and align it properly\n",
" ax.text(idate, level, iname,\n",
" horizontalalignment='right', verticalalignment=vert, fontsize=14,\n",
" backgroundcolor=(1., 1., 1., .3))\n",
"ax.set(title=\"VSPERF Main Events\")\n",
"# Set the xticks formatting\n",
"ax.get_xaxis().set_major_locator(mdates.SecondLocator(interval=30))\n",
"ax.get_xaxis().set_major_formatter(mdates.DateFormatter(\"%M %S\"))\n",
"fig.autofmt_xdate()\n",
"plt.setp((ax.get_yticklabels() + ax.get_yticklines() +\n",
" list(ax.spines.values())), visible=False)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Current and old."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Current Result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"import glob\n",
"filename = os.path.basename(glob.glob('./results/result*.csv')[0])\n",
"filename\n",
"tdf = pd.read_csv(os.path.join('./results', filename))\n",
"pkts = ['tx_frames', 'rx_frames']\n",
"fps = ['tx_rate_fps', 'throughput_rx_fps']\n",
"mbps = ['tx_rate_mbps', 'throughput_rx_mbps']\n",
"pcents = ['tx_rate_percent', 'throughput_rx_percent', 'frame_loss_percent']\n",
"fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 12))\n",
"tdf.plot.bar(y= pkts,ax=axes[0,0])\n",
"tdf.plot.bar(y= fps,ax=axes[0,1])\n",
"tdf.plot.bar(y= mbps,ax=axes[1,0])\n",
"tdf.plot.bar(y= pcents,ax=axes[1,1])\n",
"current_pkt_size = str(tdf['packet_size'].iloc[-1])\n",
"current_rx_fps = str(tdf['throughput_rx_fps'].iloc[-1])\n",
"print(current_rx_fps)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How Current Result compares to Previous ones?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"import urllib\n",
"import json\n",
"import requests\n",
"#json_data = requests.get('http://testresults.opnfv.org/test/api/v1/results?project=vsperf').json()\n",
"json_data = requests.get('http://10.10.120.22:8000/api/v1/results?project=vsperf').json()\n",
"res = json_data['results']\n",
"df1 = pd.DataFrame(res)\n",
"sort_by_date = df1.sort_values('start_date')\n",
"details = df1['details'].apply(pd.Series)\n",
"details[current_pkt_size] = pd.to_numeric(pd.Series(details[current_pkt_size]))\n",
"# details.plot.bar(y = current_pkt_size)\n",
"details_cur_pkt = details[[current_pkt_size]].copy()\n",
"details_cur_pkt.loc[-1]= float(current_rx_fps)\n",
"details_cur_pkt.index = details_cur_pkt.index + 1 # shifting index\n",
"details_cur_pkt.sort_index(inplace=True) \n",
"ax = details_cur_pkt.plot.bar()\n",
"ax.set_ylabel(\"Frames per sec\")\n",
"ax.set_xlabel(\"Run Number\")\n",
"def highlight(indices,ax):\n",
" i=0\n",
" while i<len(indices):\n",
" ax.axvspan(indices[i]-0.5, indices[i]+0.5, facecolor='RED', edgecolor='none', alpha=.2)\n",
" i+=1\n",
"highlight([0], ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Heatmaps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"array_of_dfs = []\n",
"for dirs in glob.glob('./pod12-node4/ovs_stats-vsperf*'):\n",
" dirname = os.path.basename(dirs)\n",
" if dirname:\n",
" port = dirname.split('.')[1]\n",
" if glob.glob('./pod12-node4/'+dirname+ '/*dropped*'):\n",
" full_path = glob.glob('./pod12-node4/'+dirname+ '/*dropped*')[0]\n",
" filename = os.path.basename(full_path)\n",
" if filename:\n",
" df = pd.read_csv(full_path)\n",
" df.rename(index=str, columns={\"rx\": port+\"-rx\" , \"tx\": port+\"-tx\"}, inplace=True)\n",
" df = df.drop(columns=['epoch'])\n",
" array_of_dfs.append(df)\n",
"master_df = pd.concat(array_of_dfs, axis=1, sort=True)\n",
"master_df.columns\n",
"\n",
"# get the correlation coefficient between the different columns\n",
"corr = master_df.iloc[:, 0:].corr()\n",
"arr_corr = corr.values\n",
"# mask out the top triangle\n",
"arr_corr[np.triu_indices_from(arr_corr)] = np.nan\n",
"fig, ax = plt.subplots(figsize=(18, 12))\n",
"sns.set(font_scale=3.0)\n",
"hm = sns.heatmap(arr_corr, cbar=True, vmin=-0.5, vmax=0.5,\n",
" fmt='.2f', annot_kws={'size': 20}, annot=True, \n",
" square=True, cmap=plt.cm.Reds)\n",
"ticks = np.arange(corr.shape[0]) + 0.5\n",
"ax.set_xticks(ticks)\n",
"ax.set_xticklabels(corr.columns, rotation=90, fontsize=20)\n",
"ax.set_yticks(ticks)\n",
"ax.set_yticklabels(corr.index, rotation=360, fontsize=20)\n",
"\n",
"ax.set_title('Heatmap')\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"metadata": {
"author": {
"@type": "Person",
"name": "Sridhar K. N. Rao",
"worksFor": {
"@type": "Organization",
"name": "Spirent Communications"
}
},
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|