325 lines
11 KiB
Plaintext
325 lines
11 KiB
Plaintext
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{
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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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"collapsed": true
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},
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"source": [
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"# Description of Functionality"
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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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"# Algorithm\n",
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"\n",
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"## Load settings from JSON file\n",
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"\n"
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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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"outputs": [],
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"source": [
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"import os\n",
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"from sys import argv\n",
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"rootdir = argv[1]\n",
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"\n",
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"#############################\n",
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"# FOR NOTEBOOK USE #\n",
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"# SET DIRECTORY HERE #\n",
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"# #\n",
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"#rootdir = \"\"\n",
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"# #\n",
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"#############################\n",
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"\n",
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"print(\"Using root directory: {}\".format(rootdir))\n",
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"\n",
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"# Get the subdirs with the different tests\n",
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"subdirs = sorted([ name for name in os.listdir('{}'.format(rootdir)) if os.path.isdir(os.path.join('{}'.format(rootdir), name)) ])\n",
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"print(\"Available subdirs: {}\".format(subdirs))\n",
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"\n",
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"# Get subsubdirs with the several size shifts\n",
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"subsubdirs = []\n",
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"for subdir in subdirs:\n",
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" subsubdirs.append(sorted([ name for name in os.listdir('{}/{}'.format(rootdir, subdir)) if os.path.isdir(os.path.join('{}/{}'.format(rootdir, subdir), name)) ]))\n",
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"\n",
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"print(\"Loaded all subsubdirs!\")"
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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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"outputs": [],
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"source": [
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"import json\n",
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"from sys import exit\n",
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"\n",
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"try:\n",
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" with open(\"{}/settings.json\".format(rootdir)) as json_file:\n",
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" settings = json.load(json_file)\n",
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"except:\n",
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" print(\"Please define a correct JSON file!\")\n",
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" exit()\n",
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"\n",
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"print(\"Succesfully loaded JSON file\")"
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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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"## Import\n",
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"First, import the numpy library, initialize the arrays, and finally load the csv files. \n",
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"\n",
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"Because of the way the C script dumps the variables, the last character of the csv-file will be a comma and thus the last value of the `*_times` arrays will be `NaN`. Hence, the last value has to be eliminated."
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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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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"# Initialize arrays\n",
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"enq_send = []\n",
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"recv = []\n",
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"\n",
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"number_of_datapoints = 0\n",
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"\n",
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"# Load all data and remove the last comma.\n",
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"# This for loop distinguish between tests which measure the enqueue time and tests which\n",
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"# measure the actual send time.\n",
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"for i, subdir in enumerate(subdirs):\n",
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" enq_send.append([None] * len(subsubdirs[i]))\n",
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" recv.append([None] * len(subsubdirs[i]))\n",
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" for j, subsubdir in enumerate(subsubdirs[i]):\n",
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" enq_send[i][j] = np.genfromtxt('{}/{}/{}/enq_send_times.csv'.format(rootdir, subdir, subsubdir), delimiter=',')\n",
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" recv[i][j] = np.genfromtxt('{}/{}/{}/recv_times.csv'.format(rootdir, subdir, subsubdir), delimiter=',')\n",
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" \n",
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" # Remove last comma\n",
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" enq_send[i][j] = np.delete(enq_send[i][j], -1)\n",
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" recv[i][j] = np.delete(recv[i][j], -1)\n",
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"\n",
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" #Print number of datapoints\n",
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" print('Loaded {} + {} datapoints from {}/{}.'.format(np.size(enq_send[i][j]), np.size(recv[i][j]), subdir, subsubdir))\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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"collapsed": true
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},
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"source": [
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"## Process data\n",
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"Now, the data is processed. First, a check for overflows have to be performed. The timestamps are determined with the function `clock_gettime(clockid_t clk_id, const struct timespec *tp)`. Both the `struct tp`, as well as the function are showed below\n",
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"\n",
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"```\n",
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"struct timespec {\n",
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" time_t tv_sec; /* seconds */\n",
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" long tv_nsec; /* nanoseconds */\n",
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"} tp;\n",
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"\n",
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"clock_gettime(CLOCK_MONOTONIC, &tp);\n",
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"```\n",
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"\n",
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"The application only sends the `long tv_nsec` value, which goes from 999999999ns to 0ns. Since transmissions cannot take longer than 1 second, this overflow is resolved by adding 1000000000ns to the receive timestamps and the send confirmation timestamps, if they are smaller than the enqueue- or send timestamps.\n",
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"\n",
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"Subsequentely, the deltas between the enqueue- or send time and the receive time, and the delta between the enqueue- or send time and the send confirmation time are calculated."
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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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"outputs": [],
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"source": [
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"# Initialize arrays\n",
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"enq_send_recv_d = []\n",
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"medians = []\n",
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"upper_limit = []\n",
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"lower_limit = []\n",
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"\n",
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"# Resolve overflow issues and then calculate deltas\n",
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"for i in range(0, len(subdirs)):\n",
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" medians.append([])\n",
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" upper_limit.append([])\n",
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" lower_limit.append([])\n",
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" for j in range(0, len(subsubdirs[i])):\n",
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" recv[i][j][recv[i][j] < enq_send[i][j]] += 1000000000\n",
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"\n",
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" medians[i].append(np.median(recv[i][j] - enq_send[i][j]))\n",
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" \n",
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" # np.sort(recv[i][j] - enq_send[i][j])[int(np.size(recv[i][j]]) / 2)] would be the approximately the median\n",
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" # Calculate upper 10% and lower 10%\n",
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" upper_limit[i].append(abs(medians[i][j] - np.sort(recv[i][j] - enq_send[i][j])[int(9 * np.size(recv[i][j]) / 10)]))\n",
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" lower_limit[i].append(abs(medians[i][j] - np.sort(recv[i][j] - enq_send[i][j])[int(1 * np.size(recv[i][j]) / 10)]))"
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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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"## Plotting\n",
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"\n",
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"The data will now be plotted."
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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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"outputs": [],
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"source": [
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"# Define \"find nearest\" function\n",
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"def find_nearest(array, value):\n",
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" array = np.asarray(array)\n",
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" idx = (np.abs(array - value)).argmin()\n",
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" return array[idx], idx"
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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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"scrolled": 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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"#First, delete all old plots and recreate the directory\n",
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"os.system('rm -rf ./plots')\n",
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"os.system('mkdir ./plots')\n",
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"\n",
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"plots_saved = 0\n",
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"\n",
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"#Start adding data\n",
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"for i in range(0, len(subdirs)):\n",
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" y_data = np.array([])\n",
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" x_data = np.array([])\n",
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"\n",
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" for j in range(0, len(subsubdirs[i])):\n",
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" x_data = np.append(x_data, j)\n",
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" y_data = np.append(y_data, medians[i][j])\n",
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" \n",
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" if i in settings['iter_to_start_new_plot']:\n",
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" width = 1.125 * settings['dimensions']['figure'][0] if i == 0 else settings['dimensions']['figure'][0]\n",
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" fig = plt.figure(num=None, figsize=(width, settings['dimensions']['figure'][1]), dpi=500, 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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" ax.errorbar(x_data, medians[i], yerr = [lower_limit[i], upper_limit[i]],\n",
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" capsize = 3.7, elinewidth = 1, markeredgewidth = 1, zorder = 2+i,\n",
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" color = settings['colors'][(i + settings['skip_label'][i]) % len(settings['colors'])],\n",
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" marker = settings['markers'][(i + settings['skip_label'][i]) % len(settings['markers'])],\n",
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" label = settings['labels'][(i + settings['skip_label'][i]) % len(settings['labels'])])\n",
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"\n",
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" if i in settings['iter_to_end_plot']:\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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" # Generate plot\n",
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" \n",
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" #Labels\n",
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" font_text = FontProperties()\n",
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" font_text.set_size(9.5)\n",
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" font_text.set_family('monospace')\n",
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"\n",
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" # Set axis\n",
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" plt.xlim([0, settings['limits'][0]])\n",
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" plt.ylim([0, settings['limits'][1]])\n",
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"\n",
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" # Set ticks\n",
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" ticks_unmodified = ticks = np.arange(0, settings['limits'][0] + 1, 1.0)\n",
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"\n",
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" # Explicitly set labels\n",
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" labels = []\n",
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" for k in range(0,13):\n",
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" value = 8 * 2 ** k\n",
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" labels.append(str(value))\n",
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"\n",
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" # Set xticks\n",
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" plt.xticks(ticks, labels, fontsize=10.5, family='monospace', rotation=30, horizontalalignment='right', rotation_mode=\"anchor\")\n",
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" \n",
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" if i == settings['iter_to_end_plot'][0]:\n",
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" ax.set_ylabel('$\\\\tilde{t}_{lat}$ [ns]', fontsize=11, family='monospace', labelpad=6)\n",
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" else:\n",
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" ax.get_yaxis().set_ticklabels([])\n",
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"\n",
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" # Set yticks\n",
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" plt.yticks(fontsize=10, family='monospace')\n",
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"\n",
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" ax.set_xlabel('message size [B]', fontsize=10.5, family='monospace', labelpad=4)\n",
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"\n",
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" ax.yaxis.grid(True, linestyle='-', which='major', color='black', alpha=0.8)\n",
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" ax.yaxis.grid(True, linestyle='--', which='minor', color='lightgrey', alpha=0.3)\n",
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"\n",
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" plt.minorticks_on()\n",
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" plt.tight_layout()\n",
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"\n",
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" # Save plot\n",
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" fig.savefig('{}/plot_{}.pdf'.format(rootdir, plots_saved), dpi=600, format='pdf')\n",
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" plots_saved += 1\n",
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"\n",
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" if i == settings['iter_to_end_plot'][0]:\n",
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" # Create and save legend\n",
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" import pylab\n",
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"\n",
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" # create a second figure for the legend\n",
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" figLegend = pylab.figure(figsize = settings['dimensions']['legend'])\n",
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"\n",
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" # produce a legend for the objects in the other figure\n",
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" pylab.figlegend(*ax.get_legend_handles_labels(), loc = 'upper left',\n",
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" prop={'family':'monospace', 'size':'8'}, ncol=2)\n",
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" figLegend.savefig(\"{}/legend.pdf\".format(rootdir), format='pdf')\n",
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" "
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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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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"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": "3.7.0"
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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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