mirror of
https://git.rwth-aachen.de/acs/public/villas/node/
synced 2025-03-30 00:00:11 +01:00
74592dcb05
This check does not yet make sense. There must be a way to see how many samples got lost at the send side, and how many got lost during communication.
396 lines
12 KiB
Text
396 lines
12 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Benchmark"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 155,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Settings\n",
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"root_dir = '/home/dennis/VILLASnode/tests/benchmarks'\n",
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"benchmark_dir = 'benchmarks_20180801_00-11-11'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load all files\n",
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"### Results\n",
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"...\n",
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"\n",
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"### Source log\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 156,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loaded /home/dennis/VILLASnode/tests/benchmarks/benchmarks_20180801_00-11-11/0_TCP-3-100000-1000000.csv\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import os\n",
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"import re\n",
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"\n",
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"# First, source log\n",
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"\n",
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"# Initialize arrays\n",
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"result_files = []\n",
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"result_paths = []\n",
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"log_paths = []\n",
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"result_array = []\n",
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"\n",
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"# Save complete path of files in an array\n",
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"for subdir, dirs, files in os.walk(root_dir+'/'+benchmark_dir):\n",
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" for file in files:\n",
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" # Regex to match .csv files\n",
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" if re.match(r'.*?csv', file, re.M|re.I):\n",
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" result_paths.append(os.path.join(subdir, file))\n",
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" # Regex to match .log files\n",
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" elif re.match(r'.*?log', file, re.M|re.I):\n",
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" log_paths.append(os.path.join(subdir, file))\n",
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" \n",
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" result_files = files\n",
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"\n",
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"# Loop through array with result files and save the comma separated data into a new array\n",
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"for file in result_paths:\n",
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" print(\"Loaded {}\".format(file))\n",
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" \n",
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" # Load file \n",
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" result_array.append(np.genfromtxt(file, delimiter=','))\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Save characteristics of tests\n",
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"All important settings are contained in the name of the file. We will save them in a separate array. The structure of the name is as follows:\n",
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"\n",
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"```bash\n",
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"root_dir/benchmarks_${DATE}/${ID}_${MODE}-${VALUES IN SMP}-${RATE}-${SENT SMPS}\n",
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"```\n",
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"\n",
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"Thus, we will structure it in the settings_array as follows:\n",
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"\n",
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"* `settings_array[*][0] = ID`\n",
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"* `settings_array[*][1] = MODE`\n",
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"* `settings_array[*][2] = VALUES IN SAMPLE`\n",
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"* `settings_array[*][3] = RATE`\n",
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"* `settings_array[*][4] = TOTAL NUMBER OF SAMPLES`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 157,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Array with settings\n",
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"settings_array = []\n",
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"\n",
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"for file in result_files:\n",
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" settings = []\n",
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" \n",
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" matchObj = re.match(r'(\\d*)_(\\w*)-(\\d*)-(\\d*)-(\\d*).csv', file, re.M|re.I)\n",
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"\n",
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" # Fill values to array\n",
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" if matchObj:\n",
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" for i in range(0,5):\n",
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" settings.append(matchObj.group(i+1))\n",
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" \n",
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" # Append array to big array\n",
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" settings_array.append(settings)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get missed steps from source node\n",
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"..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 161,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"540\n"
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]
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}
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],
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"source": [
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"missing_send_arr = []\n",
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"\n",
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"# This line indicates if we passed the \"node connected\" line. Only after this line\n",
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"# the script will start counting missed steps\n",
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"connected = False\n",
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"\n",
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"for i, file in enumerate(log_paths):\n",
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" F = open(file, \"r\")\n",
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" line = F.readline()\n",
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"\n",
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" missing_send_arr.append(0)\n",
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"\n",
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" # Loop through file\n",
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" while line:\n",
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" #if re.match(r'.*Connection established in node', line):\n",
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" connected = True\n",
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" \n",
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" if connected:\n",
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" #matchObj = re.match(r'.*Missed steps: (\\d*)', line, re.M|re.I)\n",
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" matchObj = re.match(r'.*written=0, expected=(\\d*)', line, re.M|re.I)\n",
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" \n",
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" if matchObj:\n",
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" missing_send_arr[i] += int(matchObj.group(1))\n",
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" \n",
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" line = F.readline()\n",
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" \n",
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" print(missing_send_arr[i])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Process data\n",
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"\n",
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"### Number of samples\n",
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"* First number which appears in `result_array[*][3]` will be the first sample in the benchmark. Ignore all missing samples before that entry.\n",
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"* After that, check how many samples are missing.\n",
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"* Then, calculate the percentage of missing samples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 162,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test 0 missed 68 (0.011%) of 995605 (1000000 before correction) samples\n"
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]
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}
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],
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"source": [
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"# Sequence number of first sample that occurs are receive side\n",
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"start_sequence = []\n",
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"# Real total number of sent messages, after subtraction of first sequence\n",
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"real_total_arr = []\n",
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"# Number of missing samples after first sample\n",
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"missing_recv_arr = []\n",
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"# Percentage of missed samples\n",
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"perc_miss_arr = []\n",
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"\n",
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"# Generate real total and number of missing samples.\n",
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"# Print percentage of missed samples\n",
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"for (i, csv_vec) in enumerate(result_array):\n",
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" start_sequence.append(csv_vec[0][3])\n",
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" \n",
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" # Real total number = total number - start_seqquence\n",
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" # Number missing = real total number - length of data arary\n",
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" real_total = int(settings_array[i][4]) - int(start_sequence[i])\n",
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" missing_recv = real_total - len(csv_vec)\n",
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" perc_miss = round(missing_recv / real_total * 100, 3)\n",
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" \n",
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" print(\"Test {} missed {} ({}%) of {} ({} before correction) samples\"\n",
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" .format(i, number_miss, perc_miss, real_total, settings_array[i][4]))\n",
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" \n",
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" real_total_arr.append(real_total)\n",
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" missing_recv_arr.append(number_miss)\n",
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" perc_miss_arr.append(perc_miss)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"## Plot data\n",
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"We want to plot the offset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 107,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.font_manager import FontProperties\n",
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"import os\n",
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"\n",
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"# Start creating plots\n",
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"for i, val in enumerate(result_array):\n",
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" # Create figure\n",
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" fig = plt.figure(num=None, figsize=(12, 8), dpi=90, facecolor='w', edgecolor='k')\n",
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"\n",
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" # Add plot and set title\n",
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" ax = fig.add_subplot(111)\n",
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" \n",
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" # Set subtitle \n",
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" title = \"Test {} — Mode: {}, Values in Sample: {}, Rate: {}, Total number of Samples: {}\".format(settings_array[i][0],\n",
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" settings_array[i][1], \n",
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" settings_array[i][2], \n",
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" settings_array[i][3], \n",
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" settings_array[i][4])\n",
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" ax.set_title(title, fontsize=12, pad=12, loc='center')\n",
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" \n",
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" # Set grid\n",
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" ax.set_axisbelow(True)\n",
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" ax.grid(True, linestyle='--')\n",
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"\n",
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" bins = 6250\n",
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" x_limit=0.0001\n",
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" \n",
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" # Data in plot\n",
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" # http://www.color-hex.com/color-palette/33602\n",
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" val_t = val.transpose()\n",
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" \n",
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" ax.hist(val_t[2], label='t_sent -> t_recv offset', edgecolor='black', bins=bins, color='#00549f')\n",
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"\n",
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" # Set axis and calculate values above limit\n",
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" plt.xlim([0,x_limit])\n",
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" \n",
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"\n",
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" plt.xticks(np.arange(0, x_limit + 0.000001, 0.00001), fontsize=11)\n",
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" plt.yticks(fontsize=11)\n",
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"\n",
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" # Labels\n",
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" ax.set_xlabel('time [s]', fontsize=13, labelpad=20)\n",
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" ax.set_ylabel('frequency', fontsize=13, labelpad=20)\n",
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" ax.set_yscale('log')\n",
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"\n",
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" # Create text for offset\n",
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" off_smaller_4us = round((np.size(val_t[2][val_t[2] < 0.000004]) / np.size(val_t[2])) * 100, 3)\n",
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" off_smaller_5us = round((np.size(val_t[2][val_t[2] < 0.000005]) / np.size(val_t[2])) * 100, 3)\n",
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" off_smaller_10us = round((np.size(val_t[2][val_t[2] < 0.00001]) / np.size(val_t[2])) * 100, 3)\n",
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" off_bigger_100us = round((np.size(val_t[2][val_t[2] > x_limit]) / np.size(val_t[2])) * 100, 4)\n",
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" \n",
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" offset_text = 'offset < {} for {}% of samples\\n'.format('4µs', off_smaller_4us)\n",
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" offset_text += 'offset < {} for {}% of samples\\n'.format('5µs', off_smaller_5us)\n",
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" offset_text += 'offset < {} for {}% of samples\\n'.format('10µs', off_smaller_10us)\n",
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" offset_text += 'offset > {} for {}% of samples'.format('100µs', off_bigger_100us)\n",
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" \n",
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" # Create text for missed steps\n",
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" #ToDo: Add percentage\n",
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" missed_text = 'missed by villas-pipe: {0: <23}\\n'.format(missing_recv_arr[i])\n",
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" missed_text += 'missed by villas-node: {0: <23}'.format(0)\n",
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" \n",
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" # bbox accepts FancyBboxPatch prop dict\n",
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" font_header = FontProperties()\n",
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" font_header.set_weight('bold')\n",
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" font_header.set_size(10)\n",
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" \n",
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" # Offset boxes\n",
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" ax.text(0.983, 0.88, \"Offset Ranges\",\n",
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" verticalalignment='top', horizontalalignment='right',\n",
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" transform=ax.transAxes,\n",
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" color='black', fontproperties = font_header)\n",
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" ax.text(0.712, 0.84, offset_text,\n",
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" verticalalignment='top', horizontalalignment='left',\n",
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" transform=ax.transAxes,\n",
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" color='black', fontsize=9.25,\n",
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" bbox={'facecolor':'white', 'alpha':0.85, 'pad':0.3, 'boxstyle':'round',\n",
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" 'edgecolor':'#dbdbdb'})\n",
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" \n",
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" # Missed steps\n",
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" ax.text(0.983, 0.70, \"Missed steps\",\n",
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" verticalalignment='top', horizontalalignment='right',\n",
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" transform=ax.transAxes,\n",
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" color='black', fontproperties = font_header)\n",
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" ax.text(0.712, 0.66, missed_text,\n",
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" verticalalignment='top', horizontalalignment='left',\n",
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" transform=ax.transAxes,\n",
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" color='black', fontsize=9.25,\n",
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" bbox={'facecolor':'white', 'alpha':0.85, 'pad':0.3, 'boxstyle':'round',\n",
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" 'edgecolor':'#dbdbdb'})\n",
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"\n",
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" #Create legend\n",
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" ax.legend(loc=1, fontsize=12)\n",
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" \n",
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"\n",
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" #Show plot\n",
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" plt.show()\n",
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" \n",
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" #Save plot\n",
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" fig.savefig('./plots/test{}.png'.format(i))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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