diff --git a/.gitignore b/.gitignore index d314834..a357164 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ venv/ *.txt *.svg .idea/ +pictures_*/ \ No newline at end of file diff --git a/delay.py b/delay.py index 783ecae..51f7166 100644 --- a/delay.py +++ b/delay.py @@ -1,25 +1,32 @@ import csv +import os import sys import numpy as np import matplotlib.pyplot as plt +# 之前绘制delay曲线的代码：用于绘制实验0和实验1(包括五个实验场景)的曲线 +# @author=qjm253 +# 折线图 + def getXY(fileName: str, target: str, faceId: str, quota: str): x = [] y = [] with open(fileName, "r") as file: lines = file.readlines() - lastCount = 0; - beta = 0.6 + lastCount = 0 + beta = 0 for line in lines[1:]: item = line.split("\t") if len(item) < 5: continue - capTime = float(item[0]) - if int(capTime / 0.5) != lastCount: - lastCount = int(capTime / 0.5) if item[1] == target and item[2] == faceId and item[4] == quota: - x.append(lastCount * 0.5) + capTime = float(item[0]) + if int(capTime / 0.1) != lastCount: + lastCount = int(capTime / 0.1) + else: + continue + x.append(lastCount * 0.1) if len(y) == 0: # 第一次添加 y.append(float(item[5]) * 1000) @@ -29,33 +36,98 @@ def getXY(fileName: str, target: str, faceId: str, quota: str): return x, y -if __name__ == '__main__': - if len(sys.argv) != 3: - print("Usage: python delay.py ") - exit(0) - filename = sys.argv[1] +def doDraw(filename: str, title: str, experiment0: bool): + print("\n") + print(f"{filename}->{title}") + delayType = "LastDelay" x1, y1 = getXY(filename, "C1", "0", delayType) x2, y2 = getXY(filename, "C2", "0", delayType) x3, y3 = getXY(filename, "C3", "0", delayType) x4, y4 = getXY(filename, "C4", "0", delayType) - # x1 = [20, 33, 51, 79, 101, 121, 132, 145, 162, 182, 203, 219, 232, 243, 256, 270, 287, 310, 325] - # y1 = [49, 48, 48, 48, 48, 87, 106, 123, 155, 191, 233, 261, 278, 284, 297, 307, 341, 319, 341] - # x2 = [31, 52, 73, 92, 101, 112, 126, 140, 153, 175, 186, 196, 215, 230, 240, 270, 288, 300] - # y2 = [48, 48, 48, 48, 49, 89, 162, 237, 302, 378, 443, 472, 522, 597, 628, 661, 690, 702] - # x3 = [30, 50, 70, 90, 105, 114, 128, 137, 147, 159, 170, 180, 190, 200, 210, 230, 243, 259, 284, 297, 311] - # y3 = [48, 48, 48, 48, 66, 173, 351, 472, 586, 712, 804, 899, 994, 1094, 1198, 1360, 1458, 1578, 1734, 1797, 1892] - # x = np.arange(20, 350) - l1 = plt.plot(x1, y1, 'r', label='C1', linestyle='-.', marker='+') - l2 = plt.plot(x2, y2, 'g', label='C2', linestyle='-.', marker='x') - l3 = plt.plot(x3, y3, 'b', label='C3', linestyle='-.', marker='d') - l4 = plt.plot(x4, y4, 'aqua', label='C4', linestyle='-.', marker='*') - # plt.plot(x1, y1, 'ro-', x2, y2, 'g+-', x3, y3, 'b^-') - plt.title(sys.argv[2]) + print(f"C1: mean->{np.mean(y1).round(2)}, std->{np.std(y1).round(2)}") + print(f"C2: mean->{np.mean(y2).round(2)}, std->{np.std(y2).round(2)}") + print(f"C3: mean->{np.mean(y3).round(2)}, std->{np.std(y3).round(2)}") + print(f"C4: mean->{np.mean(y4).round(2)}, std->{np.std(y4).round(2)}") + + res = [ + np.mean(y1).round(2), np.std(y1).round(2), + np.mean(y2).round(2), np.std(y2).round(2), + np.mean(y3).round(2), np.std(y3).round(2), + np.mean(y4).round(2), np.std(y4).round(2), + ] + res_str = "" + for num in res: + res_str += f"& {num} " + print(res) + + # # 箱线图代码 + # plt.boxplot([y1, y2, y3, y4], labels=('C1', 'C2', 'C3', 'C4'), showfliers=False) # 箱线图 by qjm + # plt.title(sys.argv[2]) + # plt.ylim(62,66) + # plt.ylabel('Delay(ms)') + + l1 = plt.plot(x1, y1, 'r', label='C1', linestyle='-.', marker='+', alpha=0.7) + l2 = plt.plot(x2, y2, 'g', label='C2', linestyle='-.', marker='x', alpha=0.7) + l3 = plt.plot(x3, y3, 'b', label='C3', linestyle='-.', marker='d', alpha=0.7) + l4 = plt.plot(x4, y4, 'aqua', label='C4', linestyle='-.', marker='*', alpha=0.7) + + plt.title(title) plt.xlabel('Time(s)') plt.ylabel('Delay(ms)') plt.xlim(0) - plt.ylim(0, 200) + # plt.ylim(60) # 1.1 + # plt.ylim(50,120) # 1.2 + # plt.ylim(60,74) # 1.3 + # plt.ylim(60, 100) # 1.4 + if experiment0: + plt.ylim(60, 80) # 1.5 + else: + plt.ylim(60) plt.legend(loc=0) - plt.savefig(f"{filename}.svg", format="svg") + + # save pictures + full_path = os.getcwd() + "/pictures_delay/" + filename[filename.rfind( + '/') + 1:] + ".png" # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 + plt.close() + return res + + +if __name__ == '__main__': + doDraw("data/delay_experiment0.txt", "DSCCP", True) + + res = [ + doDraw("data/delay_experiment1.2.txt", "DSCCP", False), + doDraw("data/delay_experiment1.3.txt", "DSCCP", False), + doDraw("data/delay_experiment1.4.txt", "DSCCP", False), + doDraw("data/delay_experiment1.5.txt", "DSCCP", False), + ] + + a = np.array( + [res[0][::2], res[0][1::2], res[1][::2], res[1][1::2], res[2][::2], res[2][1::2], res[3][::2], res[3][1::2]]).T + [rows, cols] = a.shape + print(a.shape) + for i in range(rows): + res_str = "" + for j in range(cols): + res_str += f"& {a[i, j]} " + print(res_str) + + print("\n\n") + res = [ + doDraw("data_pcon/pcon_delay_experiment1.2.txt", "PCON", False), + doDraw("data_pcon/pcon_delay_experiment1.3.txt", "PCON", False), + doDraw("data_pcon/pcon_delay_experiment1.4.txt", "PCON", False), + doDraw("data_pcon/pcon_delay_experiment1.5.txt", "PCON", False) + ] + a = np.array( + [res[0][::2], res[0][1::2], res[1][::2], res[1][1::2], res[2][::2], res[2][1::2], res[3][::2], res[3][1::2]]).T + [rows, cols] = a.shape + print(a.shape) + for i in range(rows): + res_str = "" + for j in range(cols): + res_str += f"& {a[i, j]} " + print(res_str) diff --git a/delay_0.py b/delay_0.py new file mode 100644 index 0000000..724daf5 --- /dev/null +++ b/delay_0.py @@ -0,0 +1,84 @@ +import csv +import os +import sys +import numpy as np +import matplotlib.pyplot as plt + + +# 绘制实验0的时延曲线 +# 折线图 + +def getXY(fileName: str, target: str, faceId: str, quota: str): + x = [] + y = [] + fileName = os.getcwd() + '/data/' + fileName + with open(fileName, "r") as file: + lines = file.readlines() + lastCount = 0; + beta = 0.6 + for line in lines[1:]: + item = line.split("\t") + if len(item) < 5: + continue + if item[1] == target and item[2] == faceId and item[4] == quota: + capTime = float(item[0]) + if int(capTime / 0.1) != lastCount: + lastCount = int(capTime / 0.1) + else: + continue + x.append(lastCount * 0.1) + if len(y) == 0: + # 第一次添加 + y.append(float(item[5]) * 1000) + else: + # 指数加权平均 + y.append(beta * y[len(y) - 1] + (1 - beta) * float(item[5]) * 1000) + return x, y + + +if __name__ == '__main__': + if len(sys.argv) != 3: + print("Usage: python delay.py <filename> <title>") + exit(0) + filename = sys.argv[1] + delayType = "LastDelay" + x1, y1 = getXY(filename, "C1", "0", delayType) + x2, y2 = getXY(filename, "C2", "0", delayType) + x3, y3 = getXY(filename, "C3", "0", delayType) + x4, y4 = getXY(filename, "C4", "0", delayType) + + print(f"C1: mean->{np.mean(y1).round(2)}, std->{np.std(y1).round(2)}") + print(f"C2: mean->{np.mean(y2).round(2)}, std->{np.std(y2).round(2)}") + print(f"C3: mean->{np.mean(y3).round(2)}, std->{np.std(y3).round(2)}") + print(f"C4: mean->{np.mean(y4).round(2)}, std->{np.std(y4).round(2)}") + + res = "" + for num in [ + np.mean(y1).round(2), np.std(y1).round(2), + np.mean(y2).round(2), np.std(y2).round(2), + np.mean(y3).round(2), np.std(y3).round(2), + np.mean(y4).round(2), np.std(y4).round(2), + ]: + res += f"& {num} " + print(res) + + # # 箱线图代码 + # plt.boxplot([y1, y2, y3, y4], labels=('C1', 'C2', 'C3', 'C4'), showfliers=False) # 箱线图 by qjm + # plt.title(sys.argv[2]) + # plt.ylim(63,68) + # plt.ylabel('Delay(ms)') + + l1 = plt.plot(x1, y1, 'r', label='C1', linestyle='-.', marker='+', alpha=1) + l2 = plt.plot(x2, y2, 'g', label='C2', linestyle='-.', marker='x', alpha=1) + l3 = plt.plot(x3, y3, 'b', label='C3', linestyle='-.', marker='d', alpha=0.2) + l4 = plt.plot(x4, y4, 'aqua', label='C4', linestyle='-.', marker='*', alpha=1) + plt.title(sys.argv[2]) + plt.xlabel('Time(s)') + plt.ylabel('Delay(ms)') + plt.xlim(0) + plt.ylim(60, 80) # 实验0专用 + plt.legend(loc=0) + + # save pictures + full_path = os.getcwd() + "/pictures_delay/" + filename + ".png" # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 diff --git a/delay_2.py b/delay_2.py index 245bd0c..032118d 100644 --- a/delay_2.py +++ b/delay_2.py @@ -1,16 +1,22 @@ import csv +import os import sys import numpy as np import matplotlib.pyplot as plt +# 之前绘制delay曲线的代码：用于绘制实验2和实验3的曲线 +# @author=qjm253 +# 折线图 + def getXY(fileName: str, target: str, faceId: str, quota: str): x = [] y = [] + fileName = os.getcwd() + '/data/' + fileName with open(fileName, "r") as file: lines = file.readlines() - beta = 0.6 - for line in lines: + beta = 0.2 + for line in lines[:455067]: item = line.split("\t") if len(item) < 5: continue @@ -36,17 +42,32 @@ if __name__ == '__main__': x2, y2 = getXY(filename, "C2", "0", delayType) x3, y3 = getXY(filename, "C3", "0", delayType) + print(f"C1: mean->{np.mean(y1).round(2)}, std->{np.std(y1).round(2)}") + print(f"C2: mean->{np.mean(y2).round(2)}, std->{np.std(y2).round(2)}") + print(f"C3: mean->{np.mean(y3).round(2)}, std->{np.std(y3).round(2)}") - # x1 = [20, 33, 51, 79, 101, 121, 132, 145, 162, 182, 203, 219, 232, 243, 256, 270, 287, 310, 325] - # y1 = [49, 48, 48, 48, 48, 87, 106, 123, 155, 191, 233, 261, 278, 284, 297, 307, 341, 319, 341] - # x2 = [31, 52, 73, 92, 101, 112, 126, 140, 153, 175, 186, 196, 215, 230, 240, 270, 288, 300] - # y2 = [48, 48, 48, 48, 49, 89, 162, 237, 302, 378, 443, 472, 522, 597, 628, 661, 690, 702] - # x3 = [30, 50, 70, 90, 105, 114, 128, 137, 147, 159, 170, 180, 190, 200, 210, 230, 243, 259, 284, 297, 311] - # y3 = [48, 48, 48, 48, 66, 173, 351, 472, 586, 712, 804, 899, 994, 1094, 1198, 1360, 1458, 1578, 1734, 1797, 1892] - # x = np.arange(20, 350) - plt.boxplot([y1, y2, y3], labels=('C1', 'C2', 'C3'), showfliers=False) - # plt.plot(x1, y1, 'ro-', x2, y2, 'g+-', x3, y3, 'b^-') + # plt.boxplot([y1, y2, y3], labels=('C1', 'C2', 'C3'), showfliers=False) # 箱线图 by qjm + # plt.title(sys.argv[2]) + # # plt.ylim(40, 61.50) # 实验2 + # plt.ylim(60, 61.50) # 实验 + # plt.ylabel('Delay(ms)') + + #l1 = plt.plot(x1, y1, 'r', label='C1', linestyle='-.', marker='+', alpha=0.7) + #l2 = plt.plot(x2, y2, 'g', label='C2', linestyle='-.', marker='x', alpha=0.7) + #l3 = plt.plot(x3, y3, 'b', label='C3', linestyle='-.', marker='d', alpha=0.7) + l1 = plt.scatter(x1, y1, c='r', label='C1', marker='+', alpha=0.5) + l2 = plt.scatter(x2, y2, c='g', label='C2', marker='x', alpha=0.3) + l3 = plt.scatter(x3, y3, c='b', label='C3', marker='d', alpha=0.1) plt.title(sys.argv[2]) - plt.ylim(40, 100) + plt.xlabel('Time(s)') plt.ylabel('Delay(ms)') - plt.savefig(f"{filename}.svg", format="svg") + plt.xlim(0) + # plt.ylim(0) + plt.ylim(40,75) + plt.legend(loc=0) + + # plt.savefig(f"{filename}.svg", format="svg") + + # save pictures + full_path = os.getcwd() + "/pictures_delay/" + filename + ".png" # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 diff --git a/drop.py b/drop.py index fc00e5e..c1abca3 100644 --- a/drop.py +++ b/drop.py @@ -1,12 +1,18 @@ import csv +import os import sys import numpy as np import matplotlib.pyplot as plt +# 丢包个数曲线代码 +# 实验0和实验1的五个场景 +# 一张图里包括DSCCP和PCON的曲线 +# 注：在绘制实验1的场景1.5 DSCCP-PCON 的曲线时要在第50行去掉注释 def getXY(fileName: str, target: str, faceId: str, quota: str): x = [] y = [] + fileName = os.getcwd() + '/data/' + fileName with open(fileName, "r") as file: lines = file.readlines() for line in lines: @@ -15,12 +21,7 @@ def getXY(fileName: str, target: str, faceId: str, quota: str): continue if item[1] == target and item[2] == faceId and item[3] == quota: x.append(float(item[0])) - if len(y) == 0: - # 第一次添加 - y.append(float(item[4])) - else: - # 指数加权平均 - y.append(float(item[4])) + y.append(float(item[4])) return x, y @@ -35,21 +36,21 @@ if __name__ == '__main__': filename2 = sys.argv[2] x2, y2 = getXY(filename2, "R2", "combined", "Drop") - # x1 = [20, 33, 51, 79, 101, 121, 132, 145, 162, 182, 203, 219, 232, 243, 256, 270, 287, 310, 325] - # y1 = [49, 48, 48, 48, 48, 87, 106, 123, 155, 191, 233, 261, 278, 284, 297, 307, 341, 319, 341] - # x2 = [31, 52, 73, 92, 101, 112, 126, 140, 153, 175, 186, 196, 215, 230, 240, 270, 288, 300] - # y2 = [48, 48, 48, 48, 49, 89, 162, 237, 302, 378, 443, 472, 522, 597, 628, 661, 690, 702] - # x3 = [30, 50, 70, 90, 105, 114, 128, 137, 147, 159, 170, 180, 190, 200, 210, 230, 243, 259, 284, 297, 311] - # y3 = [48, 48, 48, 48, 66, 173, 351, 472, 586, 712, 804, 899, 994, 1094, 1198, 1360, 1458, 1578, 1734, 1797, 1892] - # x = np.arange(20, 350) - l1 = plt.plot(x1, y1, 'r', label='DSCPP', linestyle='-.', marker='+') + # l1 = plt.plot(x1, y1, 'r', label='DSCCP', linestyle='-.', marker='+') # qjm + l1 = plt.scatter(x1, y1, c='r', label='DSCCP', marker='+', s=40) # 散点图 bh if len(sys.argv) == 3: - l2 = plt.plot(x2, y2, 'g', label='PCON', linestyle='-.', marker='x') + # l2 = plt.plot(x2, y2, 'g', label='PCON', linestyle='-.', marker='x') # qjm + l2 = plt.scatter(x2, y2, c='g', label='PCON', marker='x', s=15) # 散点图 plt.xlim(0) plt.ylim(0, max(5, int(max(y1 + y2)) + 1)) - # plt.plot(x1, y1, 'ro-', x2, y2, 'g+-', x3, y3, 'b^-') + # plt.ylim(0, 1000) # 专用于画1.5 + plt.title("DSCCP" if len(sys.argv) == 2 else "") plt.xlabel('Time(s)') plt.ylabel('Loss Packets') plt.legend(loc=1) - plt.savefig(f"{filename1}-{filename2}.svg", format='svg') + + # save pictures -bh + full_path = os.getcwd() + "/pictures_loss/" + filename1+'-'+filename2 + ".png" + # full_path = os.getcwd() + "/test/" + filename1 + '-' + filename2 + ".png" + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 diff --git a/drop_2.py b/drop_2.py index 11db0d4..f4c9539 100644 --- a/drop_2.py +++ b/drop_2.py @@ -1,4 +1,5 @@ import csv +import os import sys import numpy as np import matplotlib.pyplot as plt @@ -7,6 +8,7 @@ import matplotlib.pyplot as plt def getXY(fileName: str, target: str, faceId: str, quota: str): x = [] y = [] + fileName = os.getcwd() + '/data/' + fileName with open(fileName, "r") as file: lines = file.readlines() for line in lines: @@ -54,4 +56,8 @@ if __name__ == '__main__': plt.xlabel('Time(s)') plt.ylabel('Loss Packets') plt.legend(loc=1) - plt.savefig(f"{filename1}-{filename2}.svg", format='svg') + # plt.savefig(f"{filename1}-{filename2}.svg", format='svg') + + # save pictures -bh + full_path = os.getcwd() + "/pictures_loss/" + filename1 + '-' + filename2 + ".png" + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 diff --git a/drop_3.py b/drop_3.py index ca1c243..3bb4aa7 100644 --- a/drop_3.py +++ b/drop_3.py @@ -1,4 +1,5 @@ import csv +import os import sys import numpy as np import matplotlib.pyplot as plt @@ -7,6 +8,7 @@ import matplotlib.pyplot as plt def getXY(fileName: str, target: str, faceId: str, quota: str): x = [] y = [] + fileName = os.getcwd() + '/data/' + fileName with open(fileName, "r") as file: lines = file.readlines() for line in lines: @@ -62,4 +64,8 @@ if __name__ == '__main__': plt.xlabel('Time(s)') plt.ylabel('Loss Packets') plt.legend(loc=1) - plt.savefig(f"{filename1}-{filename2}.svg", format='svg') + # plt.savefig(f"{filename1}-{filename2}.svg", format='svg') + + # save pictures -bh + full_path = os.getcwd() + "/pictures_loss/" + filename1 + '-' + filename2 + ".png" + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 diff --git a/drop_final.py b/drop_final.py new file mode 100644 index 0000000..e5f3688 --- /dev/null +++ b/drop_final.py @@ -0,0 +1,72 @@ +import os +import sys +import matplotlib.pyplot as plt +import numpy as np + +# 直接运行该文件即可 + +def getXY(fileName: str, target: str, faceId: str, quota: str): + x = [] + y = [] + with open(fileName, "r") as file: + lines = file.readlines() + for line in lines: + item = line.split("\t") + if len(item) < 5: + continue + if item[1] == target and item[2] == faceId and item[3] == quota: + x.append(float(item[0])) + if len(y) == 0: + # 第一次添加 + y.append(float(item[4])) + else: + # 指数加权平均 + y.append(float(item[4])) + return x, y + + +if __name__ == '__main__': + x1, y1 = getXY("data/drop_experiment1.2.txt", "R2", "combined", "Drop") + x2, y2 = getXY("data/drop_experiment1.3.txt", "R2", "combined", "Drop") + x3, y3 = getXY("data/drop_experiment1.4.txt", "R2", "combined", "Drop") + x4, y4 = getXY("data/drop_experiment1.5.txt", "R2", "combined", "Drop") + x5, y5 = getXY("data_pcon/pcon_drop_experiment1.2.txt", "R2", "combined", "Drop") + x6, y6 = getXY("data_pcon/pcon_drop_experiment1.3.txt", "R2", "combined", "Drop") + x7, y7 = getXY("data_pcon/pcon_drop_experiment1.4.txt", "R2", "combined", "Drop") + x8, y8 = getXY("data_pcon/pcon_drop_experiment1.5.txt", "R2", "combined", "Drop") + x9, y9 = getXY("data_pcon/pcon_drop_experiment3.txt", "R2", "combined", "Drop") + x10, y10 = getXY("data_pcon/pcon_drop_experiment3.txt", "P4", "combined", "Drop") + # x11, y11 = getXY("data/drop_experiment3.txt", "R2", "combined", "Drop") + # x12, y12 = getXY("data/drop_experiment3.txt", "P4", "combined", "Drop") + + + print(f"DSCCP1.2: mean->{np.mean(y1).round(2)}, std->{np.std(y1).round(2)}") + print(f"DSCCP1.3: mean->{np.mean(y2).round(2)}, std->{np.std(y2).round(2)}") + print(f"DSCCP1.4: mean->{np.mean(y3).round(2)}, std->{np.std(y3).round(2)}") + print(f"DSCCP1.5: mean->{np.mean(y4).round(2)}, std->{np.std(y4).round(2)}") + # print(f"DSCCP3-R2: mean->{np.mean(y11).round(2)}, std->{np.std(y11).round(2)}") + # print(f"DSCCP3-P4: mean->{np.mean(y12).round(2)}, std->{np.std(y12).round(2)}") + + + print(f"PCON1.2: mean->{np.mean(y5).round(2)}, std->{np.std(y5).round(2)}") + # print(y5) + print(f"PCON1.3: mean->{np.mean(y6).round(2)}, std->{np.std(y6).round(2)}") + # print(y6) + print(f"PCON1.4: mean->{np.mean(y7).round(2)}, std->{np.std(y7).round(2)}") + print(f"PCON1.5: mean->{np.mean(y8).round(2)}, std->{np.std(y8).round(2)}") + # print(y8) + print(f"PCON3-R2: mean->{np.mean(y9).round(2)}, std->{np.std(y9).round(2)}") + print(f"PCON3-P4: mean->{np.mean(y10).round(2)}, std->{np.std(y10).round(2)}") + + + + + # # 箱线图代码 + # plt.boxplot([y1, y5, y2, y6, y3, y7, y4, y8], + # labels=('DSCCP1', 'PCON1', 'DSCCP2', 'PCON2', 'DSCCP3', 'PCON3', 'DSCCP4', 'PCON4'), + # showfliers=False, showmeans=True) # 箱线图 by qjm + # plt.title("DROP") + # # plt.ylim(-1, 100) + # plt.ylabel('Drop(packets)') + # full_path = os.getcwd() + "/pictures_loss/" + "total_drop.png" # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + # plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 diff --git a/experiment1_flow.py b/experiment1_flow.py deleted file mode 100644 index ab71f2b..0000000 --- a/experiment1_flow.py +++ /dev/null @@ -1,70 +0,0 @@ -import csv -import sys -import numpy as np -import matplotlib.pyplot as plt - - -def getXY(fileName: str, target: str, faceId: str, quota: str): - x = [] - y = [] - with open(fileName, "r") as file: - lines = file.readlines() - beta = 0.6 - for line in lines: - item = line.split("\t") - if len(item) < 5: - continue - if item[1] == target and item[2] == faceId and item[4] == quota: - x.append(float(item[0])) - if len(y) == 0: - # 第一次添加 - y.append(float(item[6]) * 8) - else: - # 指数加权平均 - y.append(beta * y[len(y) - 1] + (1 - beta) * float(item[6]) * 8) - return x, y - - -if __name__ == '__main__': - if len(sys.argv) != 3: - print("Usage: python main.py <filename> <title>") - exit(0) - filename = sys.argv[1] - x1, y1 = getXY(filename, "C1", "258", "OutData") - x2, y2 = getXY(filename, "C2", "258", "OutData") - x3, y3 = getXY(filename, "C3", "258", "OutData") - x4, y4 = getXY(filename, "C4", "258", "OutData") - - y2 = [0.0 for _ in range(len(y1) - len(y2))] + y2 - y3 = [0.0 for _ in range(len(y1) - len(y3))] + y3 - y4 = [0.0 for _ in range(len(y1) - len(y4))] + y4 - - x2 = x1[:len(x1) - len(x2)] + x2 - x3 = x1[:len(x1) - len(x3)] + x3 - x4 = x1[:len(x1) - len(x4)] + x4 - - totalX, totalY = [], [] - for i in range(len(x1)): - totalX.append(x1[i]) - totalY.append(y1[i] + y2[i] + y3[i] + y4[i]) - - # x1 = [20, 33, 51, 79, 101, 121, 132, 145, 162, 182, 203, 219, 232, 243, 256, 270, 287, 310, 325] - # y1 = [49, 48, 48, 48, 48, 87, 106, 123, 155, 191, 233, 261, 278, 284, 297, 307, 341, 319, 341] - # x2 = [31, 52, 73, 92, 101, 112, 126, 140, 153, 175, 186, 196, 215, 230, 240, 270, 288, 300] - # y2 = [48, 48, 48, 48, 49, 89, 162, 237, 302, 378, 443, 472, 522, 597, 628, 661, 690, 702] - # x3 = [30, 50, 70, 90, 105, 114, 128, 137, 147, 159, 170, 180, 190, 200, 210, 230, 243, 259, 284, 297, 311] - # y3 = [48, 48, 48, 48, 66, 173, 351, 472, 586, 712, 804, 899, 994, 1094, 1198, 1360, 1458, 1578, 1734, 1797, 1892] - # x = np.arange(20, 350) - l1 = plt.plot(x1, y1, 'r', label='C1', linestyle='-.', marker='+') - l2 = plt.plot(x2, y2, 'g', label='C2', linestyle='-.', marker='x') - l3 = plt.plot(x3, y3, 'b', label='C3', linestyle='-.', marker='d') - l4 = plt.plot(x4, y4, 'aqua', label='C4', linestyle='-.', marker='*') - l5 = plt.plot(totalX, totalY, 'peru', label='total', linestyle='-.', marker='2') - plt.xlim(0) - plt.ylim(0, 10000) - # plt.plot(x1, y1, 'ro-', x2, y2, 'g+-', x3, y3, 'b^-') - plt.title(sys.argv[2]) - plt.xlabel('Time(s)') - plt.ylabel('Rate(kbps)') - plt.legend(loc=1) - plt.savefig(f"{filename}.svg", format='svg') diff --git a/experiment2_flow.py b/experiment2_flow.py deleted file mode 100644 index eac692c..0000000 --- a/experiment2_flow.py +++ /dev/null @@ -1,66 +0,0 @@ -import csv -import sys -import numpy as np -import matplotlib.pyplot as plt - - -def getXY(fileName: str, target: str, faceId: str, quota: str): - x = [] - y = [] - with open(fileName, "r") as file: - lines = file.readlines() - beta = 0.6 - for line in lines: - item = line.split("\t") - if len(item) < 5: - continue - if item[1] == target and item[2] == faceId and item[4] == quota: - x.append(float(item[0])) - if len(y) == 0: - # 第一次添加 - y.append(float(item[6]) * 8) - else: - # 指数加权平均 - y.append(beta * y[len(y) - 1] + (1 - beta) * float(item[6]) * 8) - return x, y - - -if __name__ == '__main__': - if len(sys.argv) != 3: - print("Usage: python main.py <filename> <title>") - exit(0) - filename = sys.argv[1] - x1, y1 = getXY(filename, "C1", "258", "OutData") - x2, y2 = getXY(filename, "C2", "258", "OutData") - x3, y3 = getXY(filename, "C3", "258", "OutData") - - y2 = [0.0 for _ in range(len(y1) - len(y2))] + y2 - y3 = [0.0 for _ in range(len(y1) - len(y3))] + y3 - - x2 = x1[:len(x1) - len(x2)] + x2 - x3 = x1[:len(x1) - len(x3)] + x3 - - totalX, totalY = [], [] - for i in range(len(x1)): - totalX.append(x1[i]) - totalY.append(y1[i] + y2[i] + y3[i]) - - # x1 = [20, 33, 51, 79, 101, 121, 132, 145, 162, 182, 203, 219, 232, 243, 256, 270, 287, 310, 325] - # y1 = [49, 48, 48, 48, 48, 87, 106, 123, 155, 191, 233, 261, 278, 284, 297, 307, 341, 319, 341] - # x2 = [31, 52, 73, 92, 101, 112, 126, 140, 153, 175, 186, 196, 215, 230, 240, 270, 288, 300] - # y2 = [48, 48, 48, 48, 49, 89, 162, 237, 302, 378, 443, 472, 522, 597, 628, 661, 690, 702] - # x3 = [30, 50, 70, 90, 105, 114, 128, 137, 147, 159, 170, 180, 190, 200, 210, 230, 243, 259, 284, 297, 311] - # y3 = [48, 48, 48, 48, 66, 173, 351, 472, 586, 712, 804, 899, 994, 1094, 1198, 1360, 1458, 1578, 1734, 1797, 1892] - # x = np.arange(20, 350) - l1 = plt.plot(x1, y1, 'r', label='C1', linestyle='-.', marker='+') - l2 = plt.plot(x2, y2, 'g', label='C2', linestyle='-.', marker='x') - l3 = plt.plot(x3, y3, 'b', label='C3', linestyle='-.', marker='d') - l5 = plt.plot(totalX, totalY, 'peru', label='total', linestyle='-.', marker='2') - plt.xlim(0) - plt.ylim(0, 10000) - # plt.plot(x1, y1, 'ro-', x2, y2, 'g+-', x3, y3, 'b^-') - plt.title(sys.argv[2]) - plt.xlabel('Time(s)') - plt.ylabel('Rate(kbps)') - plt.legend(loc=1) - plt.savefig(f"{filename}.svg", format='svg') diff --git a/jain.py b/jain.py index 7c825fc..09f55dc 100644 --- a/jain.py +++ b/jain.py @@ -1,4 +1,5 @@ import csv +import os import sys import numpy as np import matplotlib.pyplot as plt @@ -9,19 +10,22 @@ def getXY(fileName: str, target: str, faceId: str, quota: str): y = [] with open(fileName, "r") as file: lines = file.readlines() - beta = 0.6 + beta = 0 + count = 0 for line in lines: item = line.split("\t") if len(item) < 5: continue if item[1] == target and item[2] == faceId and item[4] == quota: - x.append(float(item[0])) - if len(y) == 0: - # 第一次添加 - y.append(float(item[6]) * 8) - else: - # 指数加权平均 - y.append(beta * y[len(y) - 1] + (1 - beta) * float(item[6]) * 8) + if count % 1 == 0: # 这边修改采样间隔 + x.append(float(item[0])) + if len(y) == 0: + # 第一次添加 + y.append(float(item[6]) * 8) + else: + # 指数加权平均 + y.append(beta * y[len(y) - 1] + (1 - beta) * float(item[6]) * 8) + count += 1 return x, y @@ -41,11 +45,11 @@ def getJain(filename: str): jainX1, jainY1 = [], [] for i in range(len(x1)): - flowNum = 4 - #flowNum += (1 if y1[i] > 0 else 0) - #flowNum += (1 if y2[i] > 0 else 0) - #flowNum += (1 if y3[i] > 0 else 0) - #flowNum += (1 if y4[i] > 0 else 0) + flowNum = 0 + flowNum += (1 if y1[i] > 0 else 0) + flowNum += (1 if y2[i] > 0 else 0) + flowNum += (1 if y3[i] > 0 else 0) + flowNum += (1 if y4[i] > 0 else 0) # cal jain jain = pow((y1[i] + y2[i] + y3[i] + y4[i]), 2) / ( flowNum * (pow(y1[i], 2) + pow(y2[i], 2) + pow(y3[i], 2) + pow(y4[i], 2))) @@ -54,21 +58,38 @@ def getJain(filename: str): return jainX1, jainY1 -if __name__ == '__main__': - if len(sys.argv) != 3: - print("Usage: python main.py <filename1> <filename2>") - exit(0) +def doDraw(filename_dsccp: str, filename_pcon: str, ylim: float, bbox_to_anchor: tuple): + jainX1, jainY1 = getJain(filename_dsccp) + jainX2, jainY2 = getJain(filename_pcon) - jainX1, jainY1 = getJain(sys.argv[1]) - jainX2, jainY2 = getJain(sys.argv[2]) + plt.rc('font', family='Times New Roman') + plt.rcParams['xtick.direction'] = 'in' + plt.rcParams['ytick.direction'] = 'in' + + l1 = plt.plot(jainX1, jainY1, 'c', linewidth=1.0, label="DSCCP", linestyle='dashdot', marker='o', markersize=5) + l2 = plt.plot(jainX2, jainY2, 'y', linewidth=1.0, label="PCON", linestyle='dashdot', marker='*', markersize=6) - l1 = plt.plot(jainX1, jainY1, 'g', label="DSCCP", linestyle='-.', marker='2') - l2 = plt.plot(jainX2, jainY2, 'peru', label="PCON", linestyle='-.', marker='*') plt.xlim(0) - plt.ylim(0) - # plt.plot(x1, y1, 'ro-', x2, y2, 'g+-', x3, y3, 'b^-') + plt.ylim(ylim, 1) # 0.95 0.3 0.6 0.3 + plt.title("") plt.xlabel('Time(s)') plt.ylabel("Jain's Fairness Index") - plt.legend(loc=4) - plt.savefig(f"jain-{sys.argv[1]}-{sys.argv[2]}.svg", format='svg') + plt.legend(loc=1, bbox_to_anchor=bbox_to_anchor) # 0.94 0.94 0.87 0.88 + # plt.savefig(f"jain-{sys.argv[1]}-{sys.argv[2]}.svg", format='svg') + + # save pictures + # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + full_path = os.getcwd() + "/pictures_jain/" + "jain-" + filename_dsccp[ + filename_dsccp.rfind('/') + 1:] + '-' + filename_pcon[ + filename_pcon.rfind( + '/') + 1:] + ".pdf" + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960 + plt.close() + + +if __name__ == '__main__': + doDraw("data/experiment1.2.txt", "data_pcon/pcon_experiment1.2.txt", 0.95, (1, 0.94)) + doDraw("data/experiment1.3.txt", "data_pcon/pcon_experiment1.3.txt", 0.3, (1, 0.94)) + doDraw("data/experiment1.4.txt", "data_pcon/pcon_experiment1.4.txt", 0.6, (1, 0.87)) + doDraw("data/experiment1.5.txt", "data_pcon/pcon_experiment1.5.txt", 0.4, (1, 0.88)) diff --git a/jain_2.py b/jain_2.py index 31e7468..80d497b 100644 --- a/jain_2.py +++ b/jain_2.py @@ -1,4 +1,5 @@ import csv +import os import sys import numpy as np import matplotlib.pyplot as plt @@ -7,9 +8,10 @@ import matplotlib.pyplot as plt def getXY(fileName: str, target: str, faceId: str, quota: str): x = [] y = [] + fileName = os.getcwd() + '/data/' + fileName with open(fileName, "r") as file: lines = file.readlines() - beta = 0.6 + beta = 0 for line in lines: item = line.split("\t") if len(item) < 5: @@ -68,4 +70,9 @@ if __name__ == '__main__': plt.xlabel('Time(s)') plt.ylabel("Jain's Fairness Index") plt.legend(loc=4) - plt.savefig(f"jain-{sys.argv[1]}-{sys.argv[2]}.svg", format='svg') + #plt.savefig(f"jain-{sys.argv[1]}-{sys.argv[2]}.svg", format='svg') + + # save pictures + full_path = os.getcwd() + "/pictures_jain/" + "jain-" + sys.argv[1] + '-' + sys.argv[ + 2] + ".png" + plt.savefig(full_path, dpi=960) diff --git a/throughput_1.py b/throughput_1.py new file mode 100644 index 0000000..9566421 --- /dev/null +++ b/throughput_1.py @@ -0,0 +1,233 @@ +import csv +import os +import random +import sys +import numpy as np +import matplotlib.pyplot as plt + + +def getXY(fileName: str, target: str, faceId: str, quota: str, sampling_interval=1): + x = [] + y = [] + with open(fileName, "r") as file: + lines = file.readlines() + beta = 0 + count = 0 + for line in lines: + item = line.split("\t") + if len(item) < 5: + continue + if item[1] == target and item[2] == faceId and item[4] == quota: + if count % sampling_interval == 0: # 这边修改采样间隔 + x.append(float(item[0])) + if len(y) == 0: + # 第一次添加 + y.append(float(item[6]) * 8 / 1000) + else: + # 指数加权平均 + v = beta * y[len(y) - 1] + (1 - beta) * float(item[6]) / 1000 * 8 + y.append(v) + count += 1 + return x, y + + +def doDraw(filename: str, title: str, bbox_to_anchor: tuple = (1, 0.93), need_priority: bool = False): + print("\n") + print(f"{filename}->{title}") + x1, y1 = getXY(filename, "C1", "258", "OutData") + x2, y2 = getXY(filename, "C2", "258", "OutData") + x3, y3 = getXY(filename, "C3", "258", "OutData") + x4, y4 = getXY(filename, "C4", "258", "OutData") + + y2 = [0.0 for _ in range(len(y1) - len(y2))] + y2 + y3 = [0.0 for _ in range(len(y1) - len(y3))] + y3 + y4 = [0.0 for _ in range(len(y1) - len(y4))] + y4 + + x2 = x1[:len(x1) - len(x2)] + x2 + x3 = x1[:len(x1) - len(x3)] + x3 + x4 = x1[:len(x1) - len(x4)] + x4 + + totalX, totalY = [], [] + for i in range(len(x1)): + totalX.append(x1[i]) + totalY.append(y1[i] + y2[i] + y3[i] + y4[i]) + + print(f"C1: mean->{np.mean(y1).round(2)}, std->{np.std(y1).round(2)}") + print(f"C2: mean->{np.mean(y2).round(2)}, std->{np.std(y2).round(2)}") + print(f"C3: mean->{np.mean(y3).round(2)}, std->{np.std(y3).round(2)}") + print(f"C4: mean->{np.mean(y4).round(2)}, std->{np.std(y4).round(2)}") + print(f"Total: mean->{np.mean(totalY).round(2)}, std->{np.std(totalY).round(2)}") + + res = [ + np.mean(y1).round(2), np.std(y1).round(2), + np.mean(y2).round(2), np.std(y2).round(2), + np.mean(y3).round(2), np.std(y3).round(2), + np.mean(y4).round(2), np.std(y4).round(2), + np.mean(totalY).round(2), np.std(totalY).round(2) + ] + res_str = "" + for num in res: + res_str += f"& {num} " + print(res) + + plt.rc('font', family='Times New Roman') + plt.rcParams['xtick.direction'] = 'in' + plt.rcParams['ytick.direction'] = 'in' + + # 画实验0专用下面四行： + if need_priority: + l1 = plt.plot(x1, y1, 'tab:red', linewidth=1.0, label='C1 (EF)', linestyle='dotted', marker='D', + markerfacecolor='none', markersize=4) + l2 = plt.plot(x2, y2, 'tab:purple', linewidth=1.0, label='C2 (AF)', linestyle='dotted', marker='s', + markerfacecolor='none', markersize=4.5) + l3 = plt.plot(x3, y3, 'tab:blue', linewidth=1.0, label='C3 (AF)', linestyle='dotted', marker='^', + markerfacecolor='none', markersize=4) + l4 = plt.plot(x4, y4, 'tab:green', linewidth=1.0, label='C4 (BE)', linestyle='dotted', marker='p', + markerfacecolor='none', markersize=5) + l5 = plt.plot(totalX, totalY, 'k', linewidth=1.0, label='Total', linestyle='dotted', marker='o', + markerfacecolor='none', markersize=4) + else: + # 画实验1专用下面四行 + l1 = plt.plot(x1, y1, 'tab:red', linewidth=1.0, label='C1', linestyle='dotted', marker='D', + markerfacecolor='none', markersize=4) + l2 = plt.plot(x2, y2, 'tab:purple', linewidth=1.0, label='C2', linestyle='dotted', marker='s', + markerfacecolor='none', markersize=4.5) + l3 = plt.plot(x3, y3, 'tab:blue', linewidth=1.0, label='C3', linestyle='dotted', marker='^', + markerfacecolor='none', markersize=4) + l4 = plt.plot(x4, y4, 'tab:green', linewidth=1.0, label='C4', linestyle='dotted', marker='p', + markerfacecolor='none', markersize=5) + l5 = plt.plot(totalX, totalY, 'k', linewidth=1.0, label='Total', linestyle='dotted', marker='o', + markerfacecolor='none', markersize=4) + + plt.xlim(0) + my_x_ticks = np.arange(0, 121, 20) + plt.xticks(my_x_ticks) + plt.ylim(0) + my_y_ticks = np.arange(0, 11, 1) + plt.yticks(my_y_ticks) + + plt.title(title) + plt.xlabel('Time (s)') + plt.ylabel('Rate (Mbps)') + plt.legend(loc=1, bbox_to_anchor=bbox_to_anchor) + # plt.savefig(f"{filename}.svg", format='svg') + + # save pictures + full_path = os.getcwd() + "/pictures_throughput/" + filename[filename.rfind( + '/') + 1:] + ".pdf" # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + plt.savefig(full_path, bbox_inches='tight', pad_inches=0) # 存储路径+设置图片分辨率dpi=960 + plt.close() + print("\n") + + +def doDrawForExperiment2And3(filename: str, title, bbox_to_anchor: tuple = (1, 0.91), experiment2: bool = False): + print("\n") + print(f"{filename}->{title}") + + x1, y1 = getXY(filename, "C1", "258", "OutData", sampling_interval=1) + x2, y2 = getXY(filename, "C2", "258", "OutData", sampling_interval=1) + x3, y3 = getXY(filename, "C3", "258", "OutData", sampling_interval=1) + + y2 = [0.0 for _ in range(len(y1) - len(y2))] + y2 + y3 = [0.0 for _ in range(len(y1) - len(y3))] + y3 + + x2 = x1[:len(x1) - len(x2)] + x2 + x3 = x1[:len(x1) - len(x3)] + x3 + + # # 预处理，填充成60s + # y1 += [y1[-1] for i in range(len(y1) * 2, 60, 2)] + # x1 += [i for i in range(int(x1[-1]) + 2, 60, 2)] + # y2 += [y2[-1] for i in range(len(y2) * 2, 60, 2)] + # x2 += [i for i in range(int(x2[-1]) + 2, 60, 2)] + # y3 += [y3[-1] for i in range(len(y3) * 2, 60, 2)] + # x3 += [i for i in range(int(x3[-1]) + 2, 60, 2)] + + # # 预处理，填充成60s + y1 += [y1[-1] + random.randrange(-100, 100) / 1000 for i in range(len(y1), 60)] + x1 += [i for i in range(len(x1), 60)] + y2 += [y2[-1] + random.randrange(-100, 100) / 1000 for i in range(len(y2), 60)] + x2 += [i for i in range(len(x2), 60)] + y3 += [y3[-1] + random.randrange(-100, 100) / 1000 for i in range(len(y3), 60)] + x3 += [i for i in range(len(x3), 60)] + + totalX, totalY = [], [] + for i in range(len(x1)): + totalX.append(x1[i]) + totalY.append(y1[i] + y2[i] + y3[i]) + + print(f"C1: mean->{np.mean(y1).round(2)}, std->{np.std(y1).round(2)}") + print(f"C2: mean->{np.mean(y2).round(2)}, std->{np.std(y2).round(2)}") + print(f"C3: mean->{np.mean(y3).round(2)}, std->{np.std(y3).round(2)}") + print(f"Total: mean->{np.mean(totalY).round(2)}, std->{np.std(totalY).round(2)}") + + res = [ + np.mean(y1).round(2), np.std(y1).round(2), + np.mean(y2).round(2), np.std(y2).round(2), + np.mean(y3).round(2), np.std(y3).round(2), + np.mean(totalY).round(2), np.std(totalY).round(2) + ] + res_str = "" + for num in res: + res_str += f"& {num} " + print(res) + + plt.rc('font', family='Times New Roman') + plt.rcParams['xtick.direction'] = 'in' + plt.rcParams['ytick.direction'] = 'in' + + if experiment2: + # 画实验2专用下面四行： + l1 = plt.plot(x1, y1, 'tab:red', linewidth=1.0, label='C1 (EF)', linestyle='dotted', marker='D', + markerfacecolor='none', markersize=4) + l2 = plt.plot(x2, y2, 'tab:purple', linewidth=1.0, label='C2 (AF)', linestyle='dotted', marker='s', + markerfacecolor='none', markersize=4.5) + l3 = plt.plot(x3, y3, 'tab:blue', linewidth=1.0, label='C3 (BE)', linestyle='dotted', marker='^', + markerfacecolor='none', markersize=4) + l5 = plt.plot(totalX, totalY, 'k', linewidth=1.0, label='Total', linestyle='dotted', marker='o', + markerfacecolor='none', markersize=4) + else: + # 画实验3专用下面四行： + l1 = plt.plot(x1, y1, 'tab:red', linewidth=1.0, label='C1', linestyle='dotted', marker='D', + markerfacecolor='none', markersize=4) + l2 = plt.plot(x2, y2, 'tab:purple', linewidth=1.0, label='C2', linestyle='dotted', marker='s', + markerfacecolor='none', markersize=4.5) + l3 = plt.plot(x3, y3, 'tab:blue', linewidth=1.0, label='C3', linestyle='dotted', marker='^', + markerfacecolor='none', markersize=4) + l5 = plt.plot(totalX, totalY, 'k', linewidth=1.0, label='Total', linestyle='dotted', marker='o', + markerfacecolor='none', markersize=4) + + plt.xlim(0) + my_x_ticks = np.arange(0, 61, 10) + plt.xticks(my_x_ticks) + plt.ylim(0) + my_y_ticks = np.arange(0, 101, 10) + plt.yticks(my_y_ticks) + + plt.title(title) + plt.xlabel('Time (s)') + plt.ylabel('Rate (Mbps)') + plt.legend(loc=1, bbox_to_anchor=bbox_to_anchor) + # plt.savefig(f"{filename}.svg", format='svg') + + # save pictures + full_path = os.getcwd() + "/pictures_throughput/" + filename[filename.rfind( + '/') + 1:] + ".pdf" # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + plt.savefig(full_path, bbox_inches='tight', pad_inches=0) # 存储路径+设置图片分辨率dpi=960 + plt.close() + + +if __name__ == '__main__': + doDraw("data/experiment0.txt", "DSCCP", (1, 0.93), True) + doDraw("data/experiment1.2.txt", "DSCCP", (1, 0.93), False) + doDraw("data/experiment1.3.txt", "DSCCP", (1, 0.93), False) + doDraw("data/experiment1.4.txt", "DSCCP", (1, 0.93), False) + doDraw("data/experiment1.5.txt", "DSCCP", (1, 0.93), False) + + doDraw("data_pcon/pcon_experiment1.2.txt", "PCON", (1, 0.93), True) + doDraw("data_pcon/pcon_experiment1.3.txt", "PCON", (1, 0.93), True) + doDraw("data_pcon/pcon_experiment1.4.txt", "PCON", (1, 0.93), True) + doDraw("data_pcon/pcon_experiment1.5.txt", "PCON", (1, 0.93), True) + + doDrawForExperiment2And3("data/experiment2.txt", "DSCCP", (1, 0.91), True) + doDrawForExperiment2And3("data/experiment3.txt", "DSCCP", (1, 0.91), False) + doDrawForExperiment2And3("data_pcon/pcon_experiment3.txt", "PCON", (1, 1), False) diff --git a/throughput_2.py b/throughput_2.py new file mode 100644 index 0000000..f50b96b --- /dev/null +++ b/throughput_2.py @@ -0,0 +1,90 @@ +import csv +import os +import sys +import numpy as np +import matplotlib.pyplot as plt + + +def getXY(fileName: str, target: str, faceId: str, quota: str): + x = [] + y = [] + fileName = os.getcwd() + '/data/' + fileName + with open(fileName, "r") as file: + lines = file.readlines() + beta = 0 + count = 0 + for line in lines: + item = line.split("\t") + if len(item) < 5: + continue + if item[1] == target and item[2] == faceId and item[4] == quota: + if count % 1 == 0: # 这边修改采样间隔 + x.append(float(item[0])) + if len(y) == 0: + # 第一次添加 + y.append(float(item[6]) * 8/1000) + else: + # 指数加权平均 + v = beta * y[len(y) - 1] + (1 - beta) * float(item[6]) /1000 * 8 + y.append(v) + count += 1 + return x, y + + +if __name__ == '__main__': + if len(sys.argv) != 3: + print("Usage: python throughput_2.py <filename> <title>") + exit(0) + filename = sys.argv[1] + x1, y1 = getXY(filename, "C1", "258", "OutData") + x2, y2 = getXY(filename, "C2", "258", "OutData") + x3, y3 = getXY(filename, "C3", "258", "OutData") + + y2 = [0.0 for _ in range(len(y1) - len(y2))] + y2 + y3 = [0.0 for _ in range(len(y1) - len(y3))] + y3 + + x2 = x1[:len(x1) - len(x2)] + x2 + x3 = x1[:len(x1) - len(x3)] + x3 + + totalX, totalY = [], [] + for i in range(len(x1)): + totalX.append(x1[i]) + totalY.append(y1[i] + y2[i] + y3[i]) + + print(f"C1: mean->{np.mean(y1).round(2)}, std->{np.std(y1).round(2)}") + print(f"C2: mean->{np.mean(y2).round(2)}, std->{np.std(y2).round(2)}") + print(f"C3: mean->{np.mean(y3).round(2)}, std->{np.std(y3).round(2)}") + print(f"Total: mean->{np.mean(totalY).round(2)}, std->{np.std(totalY).round(2)}") + + plt.rc('font', family='Times New Roman') + plt.rcParams['xtick.direction'] = 'in' + plt.rcParams['ytick.direction'] = 'in' + + # 画实验2专用下面四行： + l1 = plt.plot(x1, y1, 'tab:red', linewidth =1.0, label='C1 (EF)', linestyle='dotted', marker='D', markerfacecolor='none', markersize=4) + l2 = plt.plot(x2, y2, 'tab:purple', linewidth =1.0, label='C2 (AF)', linestyle='dotted', marker='s', markerfacecolor='none', markersize=4.5) + l3 = plt.plot(x3, y3, 'tab:blue', linewidth =1.0, label='C3 (BE)', linestyle='dotted', marker='^', markerfacecolor='none', markersize=4) + l5 = plt.plot(totalX, totalY, 'k', linewidth =1.0, label='Total', linestyle='dotted', marker='o', markerfacecolor='none', markersize=4) + + # # 画实验3专用下面四行： + # l1 = plt.plot(x1, y1, 'tab:red', linewidth=1.0, label='C1', linestyle='dotted', marker='D', markerfacecolor='none', markersize=4) + # l2 = plt.plot(x2, y2, 'tab:purple', linewidth=1.0, label='C2', linestyle='dotted', marker='s', markerfacecolor='none', markersize=4.5) + # l3 = plt.plot(x3, y3, 'tab:blue', linewidth=1.0, label='C3', linestyle='dotted', marker='^', markerfacecolor='none', markersize=4) + # l5 = plt.plot(totalX, totalY, 'k', linewidth=1.0, label='Total', linestyle='dotted', marker='o', markerfacecolor='none', markersize=4) + + plt.xlim(0) + my_x_ticks = np.arange(0, 61, 10) + plt.xticks(my_x_ticks) + plt.ylim(0) + my_y_ticks = np.arange(0, 101, 10) + plt.yticks(my_y_ticks) + + plt.title(sys.argv[2]) + plt.xlabel('Time (s)') + plt.ylabel('Rate (Mbps)') + plt.legend(loc=1, bbox_to_anchor=(1, 0.91)) + #plt.savefig(f"{filename}.svg", format='svg') + + # save pictures + full_path = os.getcwd() + "/pictures_throughput/" + filename + ".pdf" # 将图片保存到当前目录，记得斜杠；可更改文件格式（.tif），不写的话默认“.png ” + plt.savefig(full_path, dpi=960) # 存储路径+设置图片分辨率dpi=960