871 lines
36 KiB
Plaintext
871 lines
36 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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"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": 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))"
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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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"with open(\"{}/settings.json\".format(rootdir)) as json_file:\n",
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" settings = json.load(json_file)\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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"## Load all files\n",
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"A group that will be one line in the summarizing graph is a *node-type* + *mode* combination. This group contains the variable *rate*. See the following three groups as example:\n",
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"\n",
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"* InfiniBand (RC): 1KHz, 10KHz, 50KHz, 100KHz\n",
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"* InfiniBand (UD): 1KHz, 10KHz, 50KHz, 100KHz\n",
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"* MQTT (UDP): 1KHz, 10KHz, 50KHz\n",
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"\n",
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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": 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 numpy as np\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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"input_dataset = []\n",
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"output_dataset = []\n",
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"settings_array = []\n",
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"\n",
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"\n",
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"for i, subdir in enumerate(subdirs):\n",
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" input_dataset.append([])\n",
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" output_dataset.append([])\n",
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"\n",
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" # Acquire node type from the directory\n",
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" matchObj = re.match(r'(\\w*)_[A-Z]', subdir, re.M|re.I)\n",
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" \n",
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" # Fill value to array\n",
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" if matchObj:\n",
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" node_type = matchObj[1]\n",
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"\n",
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" # Acquire all tests in that subdirectory\n",
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" for walk_subdir, dirs, files in sorted(os.walk(\"{}/{}\".format(rootdir, subdir))):\n",
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" input_dataset.append([])\n",
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" output_dataset.append([])\n",
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" settings_array.append([])\n",
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" \n",
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" for file in sorted(files):\n",
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" ############################\n",
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" ###### SAVE SETTINGS #######\n",
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" ############################\n",
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" temp_settings = []\n",
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" temp_settings.append(node_type)\n",
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" \n",
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" # Match settings, as described above\n",
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" matchObj = re.match(r'.*?(\\d*)_(\\w*)-(\\d*)-(\\d*)-(\\d*)_output.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 j in range(0,5):\n",
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" temp_settings.append(matchObj.group(j + 1))\n",
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" \n",
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" # Append array to big array\n",
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" settings_array[i].append(temp_settings)\n",
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" \n",
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" ############################\n",
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" ######### LOAD DATA ########\n",
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" ############################\n",
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" \n",
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" # Regex to match input files\n",
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" if re.match(r'.*?_input.csv', file, re.M|re.I):\n",
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" # Load file \n",
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" input_dataset[i].append(np.genfromtxt(\"{}/{}/{}\".format(rootdir, subdir, file), delimiter=','))\n",
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" \n",
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" print(\"Loaded input dataset from: {}\".format(file))\n",
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"\n",
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" # Regex to match output files files\n",
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" elif re.match(r'.*?_output.csv', file, re.M|re.I):\n",
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" output_dataset[i].append(np.genfromtxt(\"{}/{}/{}\".format(rootdir, subdir, file), delimiter=','))\n",
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" \n",
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" print(\"Loaded output dataset from: {}\".format(file))\n",
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"\n",
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" print(\"Settings for this subdirectory: \")\n",
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" print(settings_array[i])\n",
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" print(\"\\n\")\n",
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"\n",
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" # Small sanity check, are arrays of the same size?\n",
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" if len(input_dataset[i]) != len(output_dataset[i]):\n",
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" print(\"Error: There should be as many input files as there are output files!\")\n",
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" exit();"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Number of missing samples at receive side\n",
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"missed_send_arr = []\n",
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"# Percentage of missed samples\n",
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"perc_miss_send_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, subdir in enumerate(subdirs):\n",
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" missed_send_arr.append([])\n",
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" perc_miss_send_arr.append([])\n",
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" \n",
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" for (j, csv_vec) in enumerate(input_dataset[i]):\n",
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" # Get number of missing samples\n",
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" missed_send_arr[i].append(int(settings_array[i][j][5]) - len(csv_vec))\n",
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"\n",
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" # Take percentage\n",
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" perc_miss_send_arr[i].append(round(missed_send_arr[i][j] / int(settings_array[i][j][5]) * 100, 2))\n",
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" \n",
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" print(\"Test {} ({}) is missing {} ({}%) of {} in in-file.\"\n",
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" .format(settings_array[i][j][0], settings_array[i][j][2], missed_send_arr[i][j], \n",
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" perc_miss_send_arr[i][j], settings_array[i][j][5]))"
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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 destination 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Number of missing samples at receive side\n",
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"missed_recv_arr = []\n",
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"# Percentage of missed samples\n",
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"perc_miss_recv_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, subdir in enumerate(subdirs):\n",
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" missed_recv_arr.append([])\n",
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" perc_miss_recv_arr.append([])\n",
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"\n",
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" for (j, csv_vec) in enumerate(output_dataset[i]):\n",
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"\n",
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" # Get number of missing samples\n",
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" missed_recv_arr[i].append(int(settings_array[i][j][5]) - len(csv_vec))\n",
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"\n",
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" # Take percentage\n",
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" perc_miss_recv_arr[i].append(round(missed_recv_arr[i][j] / int(settings_array[i][j][5]) * 100, 2))\n",
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"\n",
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" print(\"Test {} ({}) is missing {} ({}%) of {} in out-file.\"\n",
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" .format(settings_array[i][j][0], settings_array[i][j][2], missed_recv_arr[i][j], \n",
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" perc_miss_recv_arr[i][j], settings_array[i][j][5]))"
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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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"## Check first and second sample from receive & destination 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Check first and second sample\n",
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"\n",
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"first_second_smp_input = []\n",
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"first_second_smp_output = []\n",
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"\n",
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"for i, subdir in enumerate(subdirs):\n",
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" first_second_smp_input.append([])\n",
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" first_second_smp_output.append([])\n",
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" \n",
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" for (j, csv_vec) in enumerate(input_dataset[i+1]):\n",
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" first_second_smp_input[i].append([csv_vec[0][1], csv_vec[1][1]])\n",
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" print(\"First and second sample of test {} ({}): {} and {}, respectively\".format(settings_array[i][j][0],\n",
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" settings_array[i][j][2],\n",
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" int(first_second_smp_input[i][j][0]),\n",
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" int(first_second_smp_input[i][j][1])))\n",
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"\n",
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" for (j, csv_vec) in enumerate(output_dataset[i]):\n",
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" first_second_smp_output[i].append([csv_vec[0][1], csv_vec[1][1]])\n",
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" print(\"First and second sample of test {} ({}): {} and {}, respectively\".format(settings_array[i][j][0],\n",
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" settings_array[i][j][2],\n",
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" int(first_second_smp_output[i][j][0]),\n",
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" int(first_second_smp_output[i][j][1])))\n",
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" \n",
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" print(\"\")\n",
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" "
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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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"## Compare input and output data sets\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"missing_seq = []\n",
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"\n",
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"never_trans_total_arr = []\n",
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"never_trans_after_arr = []\n",
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"\n",
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"perc_never_trans_total_arr = []\n",
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"perc_never_trans_after_arr = []\n",
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"\n",
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"# Loop through input_array, since this is always bigger or equal to output array\n",
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"for i, subdir in enumerate(subdirs):\n",
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" never_trans_total_arr.append([])\n",
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" never_trans_after_arr.append([])\n",
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" \n",
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" perc_never_trans_total_arr.append([])\n",
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" perc_never_trans_after_arr.append([])\n",
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" \n",
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" missing_seq.append([])\n",
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" \n",
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" for (j, csv_vec) in enumerate(input_dataset[i]): \n",
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" l = 0\n",
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" missing_seq[i].append([])\n",
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" for (k, line) in enumerate(csv_vec): \n",
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" try:\n",
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" if line[1] != output_dataset[i][j][l][1]:\n",
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" missing_seq[i][j].append(line[1])\n",
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" else:\n",
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" l += 1\n",
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"\n",
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" except IndexError:\n",
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" pass\n",
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"\n",
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" never_trans_total_arr[i].append(len(missing_seq[i][j]))\n",
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"\n",
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" never_trans_after_arr[i].append(np.sum(missing_seq[i][j] > first_second_smp_output[i][j][0]))\n",
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"\n",
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" # Take percentage\n",
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" perc_never_trans_total_arr[i].append(round(never_trans_total_arr[i][j] / int(settings_array[i][j][5]) * 100, 2))\n",
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" perc_never_trans_after_arr[i].append(round(never_trans_after_arr[i][j] / int(settings_array[i][j][5]) * 100, 2))\n",
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"\n",
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" print(\"Test {} ({}): {} ({}%) samples were never transferred \".format(settings_array[i][j][0],\n",
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" settings_array[i][j][2],\n",
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" never_trans_total_arr[i][j],\n",
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" perc_never_trans_total_arr[i][j]))\n",
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" print(\"{} ({}%) of these after the first sample occured in out-file.\".format(never_trans_after_arr[i][j],\n",
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" perc_never_trans_after_arr[i][j]))\n",
|
||
|
|
"\n",
|
||
|
|
" print(\"\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Calculate medians"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"medians = []\n",
|
||
|
|
"upper_limit = []\n",
|
||
|
|
"lower_limit = []\n",
|
||
|
|
"\n",
|
||
|
|
"for i, subdir in enumerate(subdirs):\n",
|
||
|
|
" medians.append([])\n",
|
||
|
|
" upper_limit.append([])\n",
|
||
|
|
" lower_limit.append([])\n",
|
||
|
|
"\n",
|
||
|
|
" for (j, csv_vec) in enumerate(output_dataset[i]): \n",
|
||
|
|
" medians[i].append(np.median(csv_vec.transpose()[0]) * 1e6)\n",
|
||
|
|
"\n",
|
||
|
|
" if settings['median_plot']['enabled']:\n",
|
||
|
|
" # np.sort(recv[i][j] - enq_send[i][j])[int(np.size(recv[i][j]]) / 2)] would be the approximately the median\n",
|
||
|
|
" # Calculate upper 10% and lower 10%\n",
|
||
|
|
" upper_limit[i].append(abs(medians[i][j] - 1e6 * np.sort(csv_vec.transpose()[0])[int(9 * np.size(csv_vec.transpose()[0]) / 10)]))\n",
|
||
|
|
" lower_limit[i].append(abs(medians[i][j] - 1e6 * np.sort(csv_vec.transpose()[0])[int(1 * np.size(csv_vec.transpose()[0]) / 10)]))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Plot data\n",
|
||
|
|
"### First, define some functions"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Define Fancy Box function we use\n",
|
||
|
|
"def plot_fancy_box(bottom, height, ax):\n",
|
||
|
|
" top = bottom + height\n",
|
||
|
|
" \n",
|
||
|
|
" p = FancyBboxPatch((left, bottom),\n",
|
||
|
|
" width,\n",
|
||
|
|
" height,\n",
|
||
|
|
" boxstyle=\"round, pad=0.005\",\n",
|
||
|
|
" \n",
|
||
|
|
" ec=\"#dbdbdb\", \n",
|
||
|
|
" fc=\"white\", \n",
|
||
|
|
" alpha=0.85,\n",
|
||
|
|
" transform=ax.transAxes\n",
|
||
|
|
" )\n",
|
||
|
|
" ax.add_patch(p)\n",
|
||
|
|
" \n",
|
||
|
|
" \n",
|
||
|
|
"# Define \"find nearest\" function\n",
|
||
|
|
"def find_nearest(array, value):\n",
|
||
|
|
" array = np.asarray(array)\n",
|
||
|
|
" idx = (np.abs(array - value)).argmin()\n",
|
||
|
|
" return array[idx], idx"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Import all necessary libraries to plot"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"from matplotlib.font_manager import FontProperties\n",
|
||
|
|
"from matplotlib.patches import FancyBboxPatch\n",
|
||
|
|
"from matplotlib.ticker import MultipleLocator\n",
|
||
|
|
"import pylab \n",
|
||
|
|
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"from matplotlib import cm\n",
|
||
|
|
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
|
||
|
|
"import matplotlib as mpl\n",
|
||
|
|
"import matplotlib.legend as mlegend"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Start with histograms if they are enabled"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"if settings['histograms']['enabled']:\n",
|
||
|
|
" for i, subdir in enumerate(subdirs):\n",
|
||
|
|
" for (j, csv_vec) in enumerate(output_dataset[i]):\n",
|
||
|
|
" # Create figure\n",
|
||
|
|
" fig = plt.figure(num=None, figsize=(12, 4), dpi=90, facecolor='w', edgecolor='k')\n",
|
||
|
|
"\n",
|
||
|
|
" # Add plot and set title\n",
|
||
|
|
" ax = fig.add_subplot(111)\n",
|
||
|
|
"\n",
|
||
|
|
" # Set grid\n",
|
||
|
|
" ax.set_axisbelow(True)\n",
|
||
|
|
" ax.grid(True, linestyle='--')\n",
|
||
|
|
"\n",
|
||
|
|
" x_limit = 0.00005\n",
|
||
|
|
" bins = np.arange(0, 50, 50 / 100)\n",
|
||
|
|
"\n",
|
||
|
|
" # Data in plot\n",
|
||
|
|
" # http://www.color-hex.com/color-palette/33602\n",
|
||
|
|
" csv_vec_t = csv_vec.transpose()\n",
|
||
|
|
"\n",
|
||
|
|
" ax.hist(csv_vec_t[0] * 1e6, label=settings['histograms']['labels'][0],\n",
|
||
|
|
" edgecolor='black',\n",
|
||
|
|
" bins=bins,\n",
|
||
|
|
" color='#00549f')\n",
|
||
|
|
" ax.axvline(medians[i][j], color='red', linestyle='-', linewidth=1, alpha=0.85)\n",
|
||
|
|
"\n",
|
||
|
|
" # Set axis and calculate values above limit\n",
|
||
|
|
" plt.xlim([0,x_limit])\n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # SET TICKS #######################\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" ticks = np.arange(0, x_limit * 1e6 + 1, 2)\n",
|
||
|
|
"\n",
|
||
|
|
" nearest, nearest_idx = find_nearest(ticks, medians[i][j])\n",
|
||
|
|
" \n",
|
||
|
|
" ticks = np.append(ticks, medians[i][j])\n",
|
||
|
|
"\n",
|
||
|
|
" # Explicitly set labels\n",
|
||
|
|
" labels = []\n",
|
||
|
|
"\n",
|
||
|
|
" for value in ticks:\n",
|
||
|
|
" if value == nearest and np.abs(nearest - medians[i][j]) < 200:\n",
|
||
|
|
" labels.append(\"\")\n",
|
||
|
|
" elif value == (medians[i][j]):\n",
|
||
|
|
" labels.append(\"{:5.3f}\".format(value))\n",
|
||
|
|
" else:\n",
|
||
|
|
" labels.append(str(int(value)))\n",
|
||
|
|
"\n",
|
||
|
|
" plt.yticks(fontsize=10, family='monospace')\n",
|
||
|
|
" plt.xticks(ticks, labels, fontsize=10, family='monospace', rotation=30, horizontalalignment='right', rotation_mode=\"anchor\")\n",
|
||
|
|
"\n",
|
||
|
|
" for value in ax.get_xticklabels():\n",
|
||
|
|
" try:\n",
|
||
|
|
" if int(float(value.get_text())) == int(medians[i][j]):\n",
|
||
|
|
" value.set_color('red')\n",
|
||
|
|
" except ValueError:\n",
|
||
|
|
" # We got some empty values. Ignore them\n",
|
||
|
|
" pass\n",
|
||
|
|
"\n",
|
||
|
|
" minorLocator = MultipleLocator(1)\n",
|
||
|
|
" ax.xaxis.set_minor_locator(minorLocator)\n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # CONFIGURE AXIS ##################\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # Set labels\n",
|
||
|
|
" ax.set_xlabel(settings['histograms']['axis_labels']['x'], fontsize=10, family='monospace', labelpad = 4)\n",
|
||
|
|
" ax.set_ylabel(settings['histograms']['axis_labels']['y'], fontsize=10, family='monospace', labelpad = 6)\n",
|
||
|
|
" \n",
|
||
|
|
" # Set scale\n",
|
||
|
|
" ax.set_yscale('log')\n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # CREATE TEXTBOXES ################\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" off_bigger_50us = round((np.size(csv_vec_t[0][csv_vec_t[0] > x_limit]) / np.size(csv_vec_t[0])) * 100, 2)\n",
|
||
|
|
"\n",
|
||
|
|
" offset_text = '$\\mathtt{{t_{{lat}}>50µs: }}${0: >5.2f}% ($\\mathtt{{\\\\max\\\\,t_{{lat}}}}$: {1:>7.2f}µs)'.format(off_bigger_50us, round(np.max(csv_vec_t[0]) * 1e6, 2))\n",
|
||
|
|
"\n",
|
||
|
|
" # Create text for missed steps\n",
|
||
|
|
" missed_text = ' in: {0:6d} ({1:5.2f}%)\\n'.format(missed_send_arr[i][j], perc_miss_send_arr[i][j])\n",
|
||
|
|
" missed_text += 'out: {0:6d} ({1:5.2f}%)'.format(missed_recv_arr[i][j], perc_miss_recv_arr[i][j])\n",
|
||
|
|
"\n",
|
||
|
|
" # Create text for missed steps\n",
|
||
|
|
" never_transferred_text = 'total: {0:5d} ({1:5.2f}%)\\n'.format(never_trans_total_arr[i][j], perc_never_trans_total_arr[i][j])\n",
|
||
|
|
" never_transferred_text += 'while connected: {0:5d} ({1:5.2f}%)'.format(never_trans_after_arr[i][j], perc_never_trans_after_arr[i][j])\n",
|
||
|
|
"\n",
|
||
|
|
" # Set font properties for headers and text\n",
|
||
|
|
" font_header = FontProperties()\n",
|
||
|
|
" font_header.set_family('monospace')\n",
|
||
|
|
" font_header.set_weight('bold')\n",
|
||
|
|
" font_header.set_size(9.5)\n",
|
||
|
|
"\n",
|
||
|
|
" font_text = FontProperties()\n",
|
||
|
|
" font_text.set_size(9.5)\n",
|
||
|
|
" font_text.set_family('monospace')\n",
|
||
|
|
"\n",
|
||
|
|
" # Set box constraints for wrapper and plot wrapper\n",
|
||
|
|
" left, width = .673, .33\n",
|
||
|
|
" right = left + width\n",
|
||
|
|
"\n",
|
||
|
|
" plot_fancy_box(bottom = 0.46, height = 0.65, ax = ax)\n",
|
||
|
|
"\n",
|
||
|
|
" # Set box constraints for text boxes\n",
|
||
|
|
" left, width = .685, .30\n",
|
||
|
|
" right = left + width\n",
|
||
|
|
"\n",
|
||
|
|
" # Offset boxes\n",
|
||
|
|
" plot_fancy_box(bottom = 0.9085, height = 0.085, ax = ax)\n",
|
||
|
|
"\n",
|
||
|
|
" ax.text(right, 0.975, offset_text,\n",
|
||
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
||
|
|
" transform=ax.transAxes,\n",
|
||
|
|
" color='black', fontproperties = font_text)\n",
|
||
|
|
"\n",
|
||
|
|
" # Missed steps\n",
|
||
|
|
" plot_fancy_box(bottom = 0.695, height = 0.18, ax = ax)\n",
|
||
|
|
"\n",
|
||
|
|
" ax.text(right, 0.868, \"missing samples:\",\n",
|
||
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
||
|
|
" transform=ax.transAxes,\n",
|
||
|
|
" color='black', fontproperties = font_header)\n",
|
||
|
|
" ax.text(right, 0.804, missed_text,\n",
|
||
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
||
|
|
" transform=ax.transAxes,\n",
|
||
|
|
" color='black', fontproperties = font_text)\n",
|
||
|
|
"\n",
|
||
|
|
" # Never transferred\n",
|
||
|
|
" plot_fancy_box(bottom = 0.487, height = 0.175, ax = ax)\n",
|
||
|
|
"\n",
|
||
|
|
" ax.text(right, 0.657, \"samples not transmitted:\",\n",
|
||
|
|
" verticalalignment='top', horizontalalignment='right',\n",
|
||
|
|
" transform=ax.transAxes,\n",
|
||
|
|
" color='black', fontproperties = font_header)\n",
|
||
|
|
" ax.text(right, 0.593, never_transferred_text,\n",
|
||
|
|
" verticalalignment='top', \n",
|
||
|
|
" horizontalalignment='right',\n",
|
||
|
|
" transform=ax.transAxes,\n",
|
||
|
|
" color='black', fontproperties = font_text)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # SAVE PLOT #######################\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" plt.minorticks_on()\n",
|
||
|
|
" plt.tight_layout()\n",
|
||
|
|
"\n",
|
||
|
|
" fig.savefig('{}/{}_{}_{}i_{}j.pdf'.format(rootdir, \n",
|
||
|
|
" settings_array[i][j][0], \n",
|
||
|
|
" settings_array[i][j][2], i, j),\n",
|
||
|
|
" format='pdf') \n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # CREATE HISTOGRAM LEGEND #########\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # create a second figure for the legend\n",
|
||
|
|
" figLegend = pylab.figure(figsize = settings['histograms']['dimensions']['legend'])\n",
|
||
|
|
"\n",
|
||
|
|
" # produce a legend for the objects in the other figure\n",
|
||
|
|
" pylab.figlegend(*ax.get_legend_handles_labels(), loc = 'upper left',\n",
|
||
|
|
" prop={'family':'monospace', 'size':'8'}, \n",
|
||
|
|
" ncol=settings['histograms']['legend_columns'])\n",
|
||
|
|
" \n",
|
||
|
|
" figLegend.savefig(\"{}/histogram_legend.pdf\".format(rootdir), format='pdf')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"if settings['median_plot']['enabled']:\n",
|
||
|
|
" # Create figure and axis\n",
|
||
|
|
" fig_median = plt.figure(num=None, figsize=(12, 4), dpi=90, facecolor='w', edgecolor='k')\n",
|
||
|
|
" ax_median = fig_median.add_subplot(111)\n",
|
||
|
|
"\n",
|
||
|
|
" for i, subdir in enumerate(subdirs):\n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # CREATE MEDIAN PLOT ##############\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" x_data = np.array([])\n",
|
||
|
|
" for k in range(0, len(medians[i])):\n",
|
||
|
|
" x_data = np.append(x_data, k)\n",
|
||
|
|
" \n",
|
||
|
|
" try:\n",
|
||
|
|
" marker = settings['median_plot']['markers'][i]\n",
|
||
|
|
" except KeyError:\n",
|
||
|
|
" marker = 'v'\n",
|
||
|
|
"\n",
|
||
|
|
" ax_median.errorbar(x_data, medians[i], yerr=[lower_limit[i], upper_limit[i]],\n",
|
||
|
|
" capsize = 3.7, elinewidth = 1, markeredgewidth = 1, \n",
|
||
|
|
" marker=marker, zorder = 2 + i, color=settings['median_plot']['colors'][i],\n",
|
||
|
|
" label=settings['median_plot']['labels'][i])\n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # PRINT MISSED STEPS ##############\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" if settings['median_plot']['print_missed_steps']:\n",
|
||
|
|
" for l, median in enumerate(medians[i]):\n",
|
||
|
|
" \n",
|
||
|
|
" p = FancyBboxPatch((x_data[l] + 0.07, median + 0.08), 0.345, 0.29, boxstyle=\"round, pad=0.005\",\n",
|
||
|
|
" ec=\"#dbdbdb\", fc=\"white\", alpha=0.85)\n",
|
||
|
|
" ax_median.add_patch(p)\n",
|
||
|
|
" \n",
|
||
|
|
" ax_median.text(x_data[l] + 0.1, median + 0.15, \"{: >4.2f}%\".format(perc_miss_recv_arr[i][l]))\n",
|
||
|
|
" \n",
|
||
|
|
" # Create bbox patch for legend\n",
|
||
|
|
" #p = FancyBboxPatch((0, 0), 5, 1, boxstyle=\"round, pad=0.5\", ec=\"#dbdbdb\", fc=\"white\", alpha=0.85)\n",
|
||
|
|
" \n",
|
||
|
|
" handles = []\n",
|
||
|
|
" handles.append(p)\n",
|
||
|
|
" text= '% of samples missed by signal generator'\n",
|
||
|
|
" leg2 = mlegend.Legend(ax_median, handles, labels=[text], loc = 'upper left', ncol=1,\n",
|
||
|
|
" prop={'family':'monospace', 'size':'8'})\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
" \n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # SET AXIS OF MEDIAN PLOT #########\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" ax_median.set_xticks(np.arange(0, len(settings['median_plot']['ticks']['x']), 1))\n",
|
||
|
|
" ax_median.set_xticklabels(settings['median_plot']['ticks']['x'])\n",
|
||
|
|
" \n",
|
||
|
|
" if settings['median_plot']['log_scale']:\n",
|
||
|
|
" ax_median.set_yscale('log')\n",
|
||
|
|
" else:\n",
|
||
|
|
" ax_median.set_ylim([settings['median_plot']['ticks']['y'][0], settings['median_plot']['ticks']['y'][-1]])\n",
|
||
|
|
" ax_median.set_yticks(settings['median_plot']['ticks']['y'])\n",
|
||
|
|
" \n",
|
||
|
|
" ax_median.set_xlabel(settings['median_plot']['axis_labels']['x'], fontsize=11, family='monospace', labelpad=6)\n",
|
||
|
|
" ax_median.set_ylabel(settings['median_plot']['axis_labels']['y'], fontsize=11, family='monospace', labelpad=6)\n",
|
||
|
|
" ax_median.set_axisbelow(True)\n",
|
||
|
|
" ax_median.grid(True, linestyle='--')\n",
|
||
|
|
"\n",
|
||
|
|
" ax_median.yaxis.grid(True, linestyle='-', which='major', color='black', alpha=0.8)\n",
|
||
|
|
" ax_median.yaxis.grid(True, linestyle='--', which='minor', color='lightgrey', alpha=0.3)\n",
|
||
|
|
"\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # EXPORT MEDIANS AND CREATE #######\n",
|
||
|
|
" # LEGEND OF MEDIAN TABLE ##########\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" plt.tight_layout()\n",
|
||
|
|
" fig_median.savefig('{}/median_graph.pdf'.format(rootdir), dpi=600, format='pdf', bbox_inches='tight')\n",
|
||
|
|
"\n",
|
||
|
|
" # create a second figure for the legend\n",
|
||
|
|
" figLegend = pylab.figure(figsize = settings['median_plot']['dimensions']['legend'])\n",
|
||
|
|
" \n",
|
||
|
|
"\n",
|
||
|
|
" leg_temp = pylab.figlegend(*ax_median.get_legend_handles_labels(), loc = 'upper left', labelspacing=1.2,\n",
|
||
|
|
" prop={'family':'monospace', 'size':'8'}, ncol=settings['median_plot']['legend_columns'])\n",
|
||
|
|
" \n",
|
||
|
|
" if settings['median_plot']['print_missed_steps']:\n",
|
||
|
|
" leg_temp._legend_box._children.append(leg2._legend_box._children[1])\n",
|
||
|
|
" leg_temp._legend_box.align=\"left\"\n",
|
||
|
|
" \n",
|
||
|
|
" figLegend.savefig(\"{}/median_legend.pdf\".format(rootdir), format='pdf')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Create 3D-Plot if enabled"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"if settings['3d_plot']['enabled']:\n",
|
||
|
|
" for i, subdir in enumerate(subdirs):\n",
|
||
|
|
" fig_3d = plt.figure(num=None, figsize=(16, 7), dpi=90, facecolor='w', edgecolor='k')\n",
|
||
|
|
" ax_3d = fig_3d.gca(projection='3d')\n",
|
||
|
|
" plt.tight_layout()\n",
|
||
|
|
"\n",
|
||
|
|
" # Make data.\n",
|
||
|
|
" X = np.array([])\n",
|
||
|
|
" for k in range(0, len(settings['3d_plot']['ticks']['x'])):\n",
|
||
|
|
" X = np.append(X, k)\n",
|
||
|
|
"\n",
|
||
|
|
" Y = np.array([])\n",
|
||
|
|
" for k in range(0, len(settings['3d_plot']['ticks']['y'])):\n",
|
||
|
|
" Y = np.append(Y, k)\n",
|
||
|
|
"\n",
|
||
|
|
" X, Y = np.meshgrid(X, Y)\n",
|
||
|
|
"\n",
|
||
|
|
" Z = np.array([])\n",
|
||
|
|
" for k in range(0, len(settings['3d_plot']['ticks']['y'])):\n",
|
||
|
|
" for l in range(0, len(settings['3d_plot']['ticks']['x'])):\n",
|
||
|
|
" Z = np.append(Z, medians[i][k * len(settings['3d_plot']['ticks']['x']) + l])\n",
|
||
|
|
" \n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # PRINT MISSED STEPS ##############\n",
|
||
|
|
" # CIRCLE MAXIMUM AND MINIMUM ######\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" \n",
|
||
|
|
" for k in range(0, len(input_dataset[i])):\n",
|
||
|
|
" # Circle maximum and minimum\n",
|
||
|
|
" if Z[k] == np.min(Z):\n",
|
||
|
|
" props = dict(boxstyle='round', pad=0.35, facecolor='white', edgecolor='green', linewidth=4, alpha=1)\n",
|
||
|
|
" elif Z[k] == np.max(Z):\n",
|
||
|
|
" props = dict(boxstyle='round', pad=0.35, facecolor='white', edgecolor='red', linewidth=4, alpha=1)\n",
|
||
|
|
" else:\n",
|
||
|
|
" props = dict(boxstyle='round', pad=0.25, facecolor='white', edgecolor='black', linewidth=1.5, alpha=1)\n",
|
||
|
|
" \n",
|
||
|
|
" # Calculate X, Y, Z coordinates\n",
|
||
|
|
" x = k % (len(settings['3d_plot']['ticks']['x']))\n",
|
||
|
|
" y = np.floor(k / (len(settings['3d_plot']['ticks']['y'])))\n",
|
||
|
|
" z = Z[k]\n",
|
||
|
|
" \n",
|
||
|
|
" # Move box and set color of text\n",
|
||
|
|
" if perc_miss_send_arr[i][k] > 10:\n",
|
||
|
|
" x_delta = 0.14\n",
|
||
|
|
" color = 'red'\n",
|
||
|
|
" else:\n",
|
||
|
|
" x_delta = 0.07\n",
|
||
|
|
" color = 'black'\n",
|
||
|
|
" \n",
|
||
|
|
" y_delta = 0.1\n",
|
||
|
|
" z_delta = 0\n",
|
||
|
|
" ax_3d.text(x - x_delta, y - y_delta, z - z_delta, \"{}%\".format(int(perc_miss_send_arr[i][k])), \n",
|
||
|
|
" fontsize=11, family='monospace', color=color, bbox=props,zorder=1000)\n",
|
||
|
|
"\n",
|
||
|
|
" Z = Z.reshape(len(settings['3d_plot']['ticks']['x']),len(settings['3d_plot']['ticks']['y']))\n",
|
||
|
|
"\n",
|
||
|
|
" # Plot the surface.\n",
|
||
|
|
" surf = ax_3d.plot_surface(X, Y, Z, cmap=cm.Blues, linewidth=135, antialiased=False, shade=True)\n",
|
||
|
|
" ax_3d.plot_wireframe(X, Y, Z, 10, lw=1, colors=\"k\", linestyles=\"solid\")\n",
|
||
|
|
"\n",
|
||
|
|
" # Customize the z axis.\n",
|
||
|
|
" ax_3d.set_zlim(0, settings['3d_plot']['ticks']['z'][-1])\n",
|
||
|
|
" ax_3d.zaxis.set_major_locator(LinearLocator(10))\n",
|
||
|
|
"\n",
|
||
|
|
" ax_3d.set_xlabel(settings['3d_plot']['axis_labels']['x'], fontsize=11, family='monospace', labelpad=14)\n",
|
||
|
|
" ax_3d.set_ylabel(settings['3d_plot']['axis_labels']['y'], fontsize=11, family='monospace', labelpad=8)\n",
|
||
|
|
" ax_3d.set_zlabel(settings['3d_plot']['axis_labels']['z'], fontsize=11, family='monospace', labelpad=8)\n",
|
||
|
|
"\n",
|
||
|
|
" ax_3d.set_xticks(np.arange(0, len(settings['3d_plot']['ticks']['x']), 1))\n",
|
||
|
|
" ax_3d.set_xticklabels(settings['3d_plot']['ticks']['x'])\n",
|
||
|
|
"\n",
|
||
|
|
" ax_3d.set_yticklabels(settings['3d_plot']['ticks']['y'])\n",
|
||
|
|
" ax_3d.set_zticks(np.arange(0, len(settings['3d_plot']['ticks']['z']), 1))\n",
|
||
|
|
"\n",
|
||
|
|
" norm = mpl.colors.Normalize(vmin=np.min(Z), vmax=np.max(Z))\n",
|
||
|
|
" cb = fig_3d.colorbar(surf, shrink=0.8, aspect=10, fraction=0.1, norm=norm)\n",
|
||
|
|
" cb.set_label(settings['3d_plot']['axis_labels']['z'], fontsize=11, family='monospace', labelpad=8)\n",
|
||
|
|
" \n",
|
||
|
|
" fig_3d.savefig('{}/median_3d_graph_{}.pdf'.format(rootdir, settings_array[i][0][2]), format='pdf', dpi=600)\n",
|
||
|
|
" \n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # CREATE LEGEND ###################\n",
|
||
|
|
" ###################################\n",
|
||
|
|
" # create a second figure for the legend\n",
|
||
|
|
" figLegend = pylab.figure(figsize = settings['3d_plot']['dimensions']['legend'])\n",
|
||
|
|
"\n",
|
||
|
|
" # The markers are too big, so lets create smaller markers\n",
|
||
|
|
" ax_custom = figLegend.add_subplot(111)\n",
|
||
|
|
" ax_custom.plot(0,0, marker='s', color = 'green', label=\"$\\\\min\\\\,\\\\tilde{{t}}_{{lat}}$: {:5.3f} µs\".format(np.min(Z)), markersize=8, linestyle = 'None')\n",
|
||
|
|
" ax_custom.plot(0,0, marker='s', color = 'red', label=\"$\\\\max\\\\,\\\\tilde{{t}}_{{lat}}$: {:5.3f} µs\".format(np.max(Z)), markersize=8, linestyle = 'None')\n",
|
||
|
|
" ax_custom.set_visible(False)\n",
|
||
|
|
"\n",
|
||
|
|
" # Create bbox patch for legend\n",
|
||
|
|
" p = FancyBboxPatch((0, 0), 5, 1, boxstyle=\"round, pad=0.5\", ec=\"#dbdbdb\", fc=\"white\", edgecolor='red',alpha=1)\n",
|
||
|
|
" \n",
|
||
|
|
" handles = []\n",
|
||
|
|
" handles.append(p)\n",
|
||
|
|
" text= '% of samples missed by signal generator'\n",
|
||
|
|
" leg2 = mlegend.Legend(ax_custom, handles, labels=[text], loc = 'upper left', ncol=1,\n",
|
||
|
|
" prop={'family':'monospace', 'size':'8'})\n",
|
||
|
|
"\n",
|
||
|
|
" # Extract handles from pseudo plot\n",
|
||
|
|
" handles, labels = ax_custom.get_legend_handles_labels()\n",
|
||
|
|
"\n",
|
||
|
|
" leg_temp = pylab.figlegend(handles, labels, loc = 'upper left', labelspacing=1.2,\n",
|
||
|
|
" prop={'family':'monospace', 'size':'8'}, ncol=settings['3d_plot']['legend_columns'])\n",
|
||
|
|
" \n",
|
||
|
|
" # Concat handles\n",
|
||
|
|
" leg_temp._legend_box._children.append(leg2._legend_box._children[1])\n",
|
||
|
|
" leg_temp._legend_box.align=\"left\"\n",
|
||
|
|
" \n",
|
||
|
|
" # Save figure\n",
|
||
|
|
" figLegend.savefig(\"{}/3d_{}_legend.pdf\".format(rootdir, settings_array[i][0][2]), format='pdf')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"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
|
||
|
|
}
|