# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # import time import matplotlib.pyplot as plt import csv import struct import argparse import traceback data_types = ['kb', 'kB'] def parse_arguments(): parser = argparse.ArgumentParser( description='Plot throughput from the csv file.', epilog='How to run script: \ python throughput.py -f tests/Wed_Apr_13_08:36:29_2022/tp_receiver.csv -s 0.1') parser.add_argument('-f', '--file', type=str, nargs='*', help='csv file path', default=["tp_receiver"]) parser.add_argument('-s', '--samp_t', type=float, nargs='*', help='specify throughput sample time', default=1.0) try: args = parser.parse_args() except Exception as e: print(traceback.format_exc()) return args def gen_data(num_of_bytes_in_packet: int, last_number_from_previous_data_packet: int): counter = last_number_from_previous_data_packet + 1 rem = num_of_bytes_in_packet % 4 valid_data_len = int((num_of_bytes_in_packet - rem) / 4) total_data_len = valid_data_len + rem data = [0] * total_data_len for i in range(rem, total_data_len): data[i] = counter counter += 1 last_value = data[len(data) - 1] if rem: fmt = "<" + str(rem) + "B" + str(valid_data_len) + "I" else: fmt = "<" + str(valid_data_len) + "I" data_ba = struct.pack(fmt, *data) return data_ba, last_value class Throughput(): def __init__( self, name="tp_chart", mode="rx", total_packets_number=0, bytes_number_in_packet=0, throughput_data_type='kb', flag_plot_packets=True, sample_time=1, test_directory=None): self.name = name self.mode = mode self.total_packets_number = total_packets_number self.bytes_number_in_packet = bytes_number_in_packet self.predef_packet_key = int( (bytes_number_in_packet - (bytes_number_in_packet % 4)) / 4) self.total_bits_number = bytes_number_in_packet * 8 assert throughput_data_type in data_types self.throughput_data_type = throughput_data_type self.flag_plot_packets = flag_plot_packets self.sample_time = sample_time self.test_directory = test_directory if self.test_directory is not None: self.csv_file_name = self.test_directory + "/" + \ time.strftime("%Y_%m_%d_%H_%M_%S_") + self.name + ".csv" else: self.csv_file_name = time.strftime( "%Y_%m_%d_%H_%M_%S_") + self.name + ".csv" self.clean_csv_file() def calc_throughput(self, current_num, last_num, current_time, last_time): if self.throughput_data_type == 'kb': return float( (((current_num - last_num) * self.total_bits_number) / (current_time - last_time)) / 1000) elif self.throughput_data_type == 'kB': return float( (((current_num - last_num) * self.bytes_number_in_packet) / (current_time - last_time)) / 1000) def clean_csv_file(self): file = open(self.csv_file_name, 'w') file.write("Time,Packet\n") def append_to_csv_file( self, timestamp: float = 0.0, packet_number: int = 0): with open(self.csv_file_name, "a") as file: csv_writer = csv.writer(file) csv_writer.writerow([timestamp, packet_number]) def get_average(self, packet_numbers, timestamps): if self.throughput_data_type == 'kb': average_tp = ((packet_numbers * self.total_bits_number) / (timestamps[-1] - timestamps[0])) / 1000 elif self.throughput_data_type == 'kB': average_tp = ((packet_numbers * self.bytes_number_in_packet) / (timestamps[-1] - timestamps[0])) / 1000 return average_tp def save_average(self, tp_csv_filename=None): if self.mode == "rx": timestamps = [] packet_numbers = [] if tp_csv_filename is None: tp_csv_filename = self.csv_file_name else: tp_csv_filename += ".csv" with open(tp_csv_filename, "r") as file: csv_reader = csv.reader(file) next(csv_reader) for row in csv_reader: timestamps.append(float(row[0])) packet_numbers.append(float(row[1])) average_tp = self.get_average(packet_numbers[-1], timestamps) print( f"Average rx throughput: {round(average_tp, 3)} {self.throughput_data_type}ps") with open(self.test_directory + "/average_rx_tp.csv", "a") as file: csv_writer = csv.writer(file) csv_writer.writerow([average_tp]) def plot_tp_from_file(self, filename: str = None, sample_time: float = 1, save_to_file: bool = True): timestamps = [] packet_numbers = [] if filename is None: filename = self.csv_file_name print("Results:", filename) with open(filename, "r") as file: csv_reader = csv.reader(file) next(csv_reader) for row in csv_reader: timestamps.append(float(row[0])) packet_numbers.append(float(row[1])) last_time = 0 last_number = packet_numbers[0] throughput = [] offset = timestamps[0] for i in range(0, len(timestamps)): timestamps[i] -= offset if timestamps[i] - last_time > sample_time: throughput.append((timestamps[i], self.calc_throughput(packet_numbers[i], last_number, timestamps[i], last_time))) last_time = timestamps[i] last_number = packet_numbers[i] average_tp = self.get_average(packet_numbers[-1], timestamps) fig, ax = plt.subplots() if self.flag_plot_packets: ax2 = ax.twinx() ax.plot(*zip(*throughput), 'k-') if self.flag_plot_packets: ax2.plot(timestamps, packet_numbers, 'b-') ax.set_title(self.name) ax.set_ylabel(f"Throughput [{self.throughput_data_type}/s]") ax.set_xlabel("Time [s]") ax.text(0.9, 1.02, f"Average: {round(average_tp, 3)}" f"{self.throughput_data_type}ps", transform=ax.transAxes, color='k') if self.flag_plot_packets: ax2 = ax2.set_ylabel(f"Packets [Max:{len(packet_numbers)}]", color='b') if save_to_file: path = filename.replace(".csv", ".png") plt.savefig(path) plt.show(block=True) if __name__ == "__main__": args = parse_arguments() tp = Throughput(bytes_number_in_packet=247) tp.plot_tp_from_file(*args.file, args.samp_t[0], save_to_file=False)