mirror of https://github.com/vacp2p/10ksim.git
Revert "WIP: Added versions of plotters"
This reverts commit 54d0a053f5.
This commit is contained in:
Alberto Soutullo
parent
54d0a053f5
commit
c3f15f0039
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@ -1,71 +0,0 @@
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import os
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from matplotlib import ticker
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sns.set_theme()
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language = ["Nim", "Rust", "Go"]
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data_to_plot = ["Rx", "Tx", "Rp", "Tp", "Rpd", "Tpd"]
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y_label = ["KBytes/s", "KBytes/s", "KPackets/s", "KPackets/s", "Packets/s", "Packets/s"]
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scale = [True, True, False, False, False, False]
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folders_grouped = [("../gossipsubdata_2nd/csv/rx/", "../gossipsubdatarust/csv/rx/", "../gossipsubdatago/csv/rx/"),
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("../gossipsubdata_2nd/csv/tx/", "../gossipsubdatarust/csv/tx/", "../gossipsubdatago/csv/tx/"),
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("../gossipsubdata_2nd/csv/rp/", "../gossipsubdatarust/csv/rp/", "../gossipsubdatago/csv/rp/"),
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("../gossipsubdata_2nd/csv/tp/", "../gossipsubdatarust/csv/tp/", "../gossipsubdatago/csv/tp/"),
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("../gossipsubdata_2nd/csv/rpd/", "../gossipsubdatarust/csv/rpd/", "../gossipsubdatago/csv/rpd/"),
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("../gossipsubdata_2nd/csv/tpd/", "../gossipsubdatarust/csv/tpd/", "../gossipsubdatago/csv/tpd/"), ]
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# file_data = ["Rx-500B-1.csv", "Rx-1KB-1.csv", "Rx-2.5KB-1.csv", "Rx-10KB-1.csv", "Rx-20KB-1.csv"]
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# file_data = ["Tx-500B-1.csv", "Tx-1KB-1.csv", "Tx-2.5KB-1.csv", "Tx-10KB-1.csv", "Tx-20KB-1.csv"]
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fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(14, 16), sharex=True, sharey='row')
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for j, group in enumerate(folders_grouped):
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final_df = pd.DataFrame()
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for i, folder_path in enumerate(group):
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if not os.path.exists(folder_path): continue
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files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
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files = sorted(files, key=lambda x: float(x.split("-")[1].split("KB")[0]))
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folder_df = pd.DataFrame()
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for file in files:
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df = pd.read_csv(folder_path + file, parse_dates=['Time'], index_col='Time')
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column_name = file.split("-")[1]
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df_avg = df.mean()
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df_avg_mean = df_avg.median()
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vertical_offset = df_avg.median() * 0.05 # offset from median for display
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folder_df = pd.concat([folder_df, df_avg.rename(column_name)], axis=1)
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folder_df["node"] = language[i]
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final_df = pd.concat([final_df, folder_df])
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final_df = pd.melt(final_df, id_vars=["node"])
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box_plot = sns.boxplot(data=final_df, x="variable", y="value", hue="node", ax=axs[j // 2, j % 2])
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box_plot.set_title(f'{data_to_plot[j]} (N=300)')
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box_plot.set(xlabel='Payload size (KB)', ylabel=f"{y_label[j]}")
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box_plot.tick_params(labelbottom=True)
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# plt.ylabel(f"{y_label[j]}")
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# plt.xlabel('Payload size (KB)')
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# sns.move_legend(box_plot, "upper left", bbox_to_anchor=(1, 1))
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# plt.tight_layout()
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if scale[j]:
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# Create a custom formatter to divide x-axis ticks by 1000
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formatter = ticker.FuncFormatter(lambda x, pos: '{:.0f}'.format(x / 1000))
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# Apply the custom formatter to the x-axis ticks
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box_plot.yaxis.set_major_formatter(formatter)
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plt.tight_layout()
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plt.savefig(f"all.png")
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plt.show()
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# box_plot.figure.savefig(f"{data_to_plot[j]}-melted.png")
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@ -1,37 +0,0 @@
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import os
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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sns.set_theme()
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language = ["Nim", "Rust", "Go"]
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folders_grouped = [("../gossipsubdata_2nd/load/109/", "../gossipsubdatarust/load/109/", "../gossipsubdatago/load/109/"),
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("../gossipsubdata_2nd/load/198/", "../gossipsubdatarust/load/198/", "../gossipsubdatago/load/198/")]
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for j, group in enumerate(folders_grouped):
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for i, folder_path in enumerate(group):
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if not os.path.exists(folder_path): continue
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files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
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files = sorted(files, key=lambda x: float(x.split("-")[1].split("KB")[0]))
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for file in files:
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df = pd.read_csv(folder_path + file)
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# get column as list
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df = df['1m load average'].tolist()
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sns.lineplot(df, label=file.split("-")[1], legend="full")
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plt.xlabel('Time (1min step)')
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plt.ylabel('uptime')
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plt.title(f'Nim Loads ({server[j]})')
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plt.legend(title='Nodes', bbox_to_anchor=(1, 1), loc='upper left')
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plt.tight_layout()
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plt.savefig(f"{language[i]}-{server[j]}.png")
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plt.show()
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@ -1,66 +0,0 @@
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import os
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import re
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import seaborn as sns
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import matplotlib.pyplot as plt
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import pandas as pd
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sns.set_theme()
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#data_folders = ["logs-0.5KB-1/", "logs-1KB-1/", "logs-2.5KB-1/", "logs-5KB-1/", "logs-10KB-1/",
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# "logs-20KB-1/", "logs-40KB-1/"]
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#node_folders = ["../gossipsubdata_2nd/logs/", "../gossipsubdatarust/logs/", "../gossipsubdatago/logs/"]
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#language = ["Nim", "Rust", "Go"]
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data_folders = ["logs-10KB-1/"]
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node_folders = ["../gossipsubdata_2nd/logs/", "../test/logs/"]
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language = ["Before", "After"]
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pattern = r'nds:\s[0-9]+'
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final_df = pd.DataFrame()
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for j, node_folder in enumerate(node_folders):
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folder_df = pd.DataFrame()
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for i, data_folder in enumerate(data_folders):
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file_df = pd.DataFrame()
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column_name = data_folder.split("-")[1][:-1]
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files = os.listdir(node_folder + data_folder)
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for log_file in files:
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values = []
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with open(node_folder + data_folder + log_file, 'r') as file:
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for line in file:
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match = re.search(pattern, line)
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if match:
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value = int(match.group().split(":")[1].strip())
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values.append(value)
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file_df = pd.concat([file_df, pd.Series(values)], ignore_index=True)
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file_df = file_df.rename({file_df.columns[0]: column_name}, axis=1)
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folder_df = pd.concat([folder_df, file_df], axis=1)
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folder_df["node"] = language[j]
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final_df = pd.concat([final_df, folder_df])
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final_df = pd.melt(final_df, id_vars=["node"])
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final_df = final_df.dropna()
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box_plot = sns.boxplot(data=final_df, x="variable", y="value", hue="node")
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plt.ylabel('Arrival Time (ms)')
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plt.xlabel('Payload size (KB)')
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plt.title('Times (N=300)')
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plt.show()
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box_plot.figure.savefig("test.png")
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# remove outliers
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final_df = final_df[final_df["value"] < 1000]
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box_plot = sns.boxplot(data=final_df, x="variable", y="value", hue="node")
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plt.ylabel('Arrival Time (ms)')
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plt.xlabel('Payload size (KB)')
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plt.title('Times (N=300)')
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plt.show()
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box_plot.figure.savefig("test_noo.png")
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@ -1,27 +0,0 @@
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import os
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import re
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pattern = r'[0-9]+\s[A-Za-z]+:\s[0-9]+'
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folder_dir = "../gossipsubdata_2nd/logs/logs-20KB-1/"
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def parse_file(data, folder):
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log_files = os.listdir(folder)
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for log_file in log_files:
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with open(folder + log_file, 'r') as file:
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for line in file:
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match = re.search(pattern, line)
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if match:
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value = int(match.group().split(" ")[0])
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if value not in data:
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data[value] = [[log_file], 1]
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else:
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data[value][0].append(log_file)
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data[value][1] += 1
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all_data = {}
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parse_file(all_data, folder_dir)
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for key, value in sorted(all_data.items()):
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print(f"{key}: {value[1]}")
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