| # To run this script run the command 'python3 scripts/generate_plots_flow_flatten_merge.py' in the /benchmarks folder |
| |
| |
| import pandas as pd |
| import sys |
| import locale |
| import matplotlib.pyplot as plt |
| from matplotlib.ticker import FormatStrFormatter |
| |
| input_file = "build/reports/jmh/results.csv" |
| output_file = "out/flow-flatten-merge.svg" |
| # Please change the value of this variable according to the FlowFlattenMergeBenchmarkKt.ELEMENTS |
| elements = 100000 |
| benchmark_name = "benchmarks.flow.FlowFlattenMergeBenchmark.flattenMerge" |
| csv_columns = ["Benchmark", "Score", "Unit", "Param: concurrency", "Param: flowsNumberStrategy"] |
| rename_columns = {"Benchmark": "benchmark", "Score" : "score", "Unit" : "unit", |
| "Param: concurrency" : "concurrency", "Param: flowsNumberStrategy" : "flows"} |
| |
| markers = ['.', 'v', '^', '1', '2', '8', 'p', 'P', 'x', 'D', 'd', 's'] |
| colours = ['red', 'gold', 'sienna', 'olivedrab', 'lightseagreen', 'navy', 'blue', 'm', 'crimson', 'yellow', 'orangered', 'slateblue', 'aqua', 'black', 'silver'] |
| |
| def next_colour(): |
| i = 0 |
| while True: |
| yield colours[i % len(colours)] |
| i += 1 |
| |
| def next_marker(): |
| i = 0 |
| while True: |
| yield markers[i % len(markers)] |
| i += 1 |
| |
| def draw(data, plt): |
| plt.xscale('log', basex=2) |
| plt.gca().xaxis.set_major_formatter(FormatStrFormatter('%0.f')) |
| plt.grid(linewidth='0.5', color='lightgray') |
| if data.unit.unique()[0] != "ops/s": |
| print("Unexpected time unit: " + data.unit.unique()[0]) |
| sys.exit(1) |
| plt.ylabel("elements / ms") |
| plt.xlabel('concurrency') |
| plt.xticks(data.concurrency.unique()) |
| |
| colour_gen = next_colour() |
| marker_gen = next_marker() |
| for flows in data.flows.unique(): |
| gen_colour = next(colour_gen) |
| gen_marker = next(marker_gen) |
| res = data[(data.flows == flows)] |
| # plt.plot(res.concurrency, res.score*elements/1000, label="flows={}".format(flows), color=gen_colour, marker=gen_marker) |
| plt.errorbar(x=res.concurrency, y=res.score*elements/1000, yerr=res.score_error*elements/1000, solid_capstyle='projecting', |
| label="flows={}".format(flows), capsize=4, color=gen_colour, linewidth=2.2) |
| |
| langlocale = locale.getdefaultlocale()[0] |
| locale.setlocale(locale.LC_ALL, langlocale) |
| dp = locale.localeconv()['decimal_point'] |
| if dp == ",": |
| csv_columns.append("Score Error (99,9%)") |
| rename_columns["Score Error (99,9%)"] = "score_error" |
| elif dp == ".": |
| csv_columns.append("Score Error (99.9%)") |
| rename_columns["Score Error (99.9%)"] = "score_error" |
| else: |
| print("Unexpected locale delimeter: " + dp) |
| sys.exit(1) |
| data = pd.read_csv(input_file, sep=",", decimal=dp) |
| data = data[csv_columns].rename(columns=rename_columns) |
| data = data[(data.benchmark == benchmark_name)] |
| plt.rcParams.update({'font.size': 15}) |
| plt.figure(figsize=(12.5, 10)) |
| draw(data, plt) |
| plt.legend(loc='upper center', borderpad=0, bbox_to_anchor=(0.5, 1.3), ncol=2, frameon=False, borderaxespad=2, prop={'size': 15}) |
| plt.tight_layout(pad=12, w_pad=2, h_pad=1) |
| plt.savefig(output_file, bbox_inches='tight') |