RECORD/graph_simulation_vs_realWorld.py at main · ErJedermann/RECORD · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
import numpy as np
import os
import csv

import plot_measurement_row

def read_output_csv(filename: str, old_output_setting: bool = False):
    filename = filename
    if os.path.exists(filename):
        all_rows = []
        with open(filename, 'r') as csvinput:
            reader = csv.reader(csvinput)
            for row in reader:
                if row[0][0] == '[':
                    row[0] = row[0][1:]
                if row[-1][-1] == ']':
                    row[-1] = row[-1][:-1]
                all_rows.append(row)
        csvinput.close()
        if old_output_setting:
            print(f"WARNING: not switching column->row, due to old-output-setting activated!")
            all_colums = np.array(all_rows, dtype=float)
        else:
            all_colums = np.array(all_rows, dtype=float).T
        return all_colums  # [[l1_atk1, l2_atk1], [l1_atk2, l2_atk2], [l1_atk3, l2_atk3]]
    else:
        print(f"ERROR: read_output_csv: file ({filename}) does not exist!")


def combine_rec_outputs_to_plot(x_axis: [float], x_label: str, figure_name: str, output_folder: str,
                                output_mapping: [(int, int, str)], atk_types: [str], real_world_data: dict = None):
    if not os.path.exists(output_folder) or not os.path.isdir(output_folder):
        print(f"ERROR: Ms_RN.combine_output_to_plot: given folder ({output_folder}) doesn't exist or is not a folder!")
        exit(1)
    if output_folder[-1] is not os.sep:
        output_folder = f"{output_folder}{os.sep}"
    # prepare the data-structure to fill in all the data-points
    temp_data_list = []  # [[atk1_data[[x1], [x2], ...], [atk2_data[[x1], [x2], ...], ...]
    for j in range(len(atk_types)):
        type_list = []
        for k in range(len(x_axis)):
            type_list.append([])
        temp_data_list.append(type_list)
    # get each file and fill the data-structure
    for entry in output_mapping:
        temp_x_index, temp_atk_type, temp_file = entry
        print(f"open: {temp_file}")
        some_data = read_output_csv(output_folder + temp_file, old_output_setting=False)
        for i in range(len(some_data)):
            loaded_column = some_data[i, :]
            temp_data_list[temp_atk_type + i][temp_x_index] = loaded_column
    # create the plot
    print_dict = {}  # {atk_type: [[x1], [x2], ...] }
    for i in range(len(atk_types)):
        print_dict[atk_types[i]] = temp_data_list[i]
    # customization
    del print_dict[atk_types[4]]
    del print_dict[atk_types[2]]
    plot_measurement_row.plot_attack_area_analysis_selfmade_quantiles(print_dict, x_axis, x_label=x_label, figure_name=figure_name, real_world_data=real_world_data)

if __name__ == '__main__':
    # real_world_times = [288, 683, 1028, 1463, 1715, 2085, 2318, 4044, 4237, 6190]
    real_world_times = ['3 min', '10 min', '10 min', '10 min', '30 min', '30 min', '30 min', '1h', '1h', '2h' ]
    real_world_areas = [102534, 81437, 81337, 17118, 88292, 3240, 47704, 8654, 43152, 383]
    real_world_data = {'times': real_world_times, 'estimations': real_world_areas}

    fig_name = f"Comparison of different attacker types vs. real-world measurements"
    folder_name = "simulation_data/duration_and_type/"

    x_values = ['1 min', '3 min', '10 min', '30 min', '1 h', '2 h', '4 h']
    x_text = f"observation duration"

    attack_types = [
        "weak noisy (1rec)", "weak noisy (3rec)",
        "weak (1rec)", "weak (3rec)",
        "strong (1rec)", "strong (3rec)",
    ]
    mapping = [
        (0, 0, "100kmFibo_cont_60sec_3eves_weakEvents_noisyPrediction.csv"),
        (0, 2, "100kmFibo_cont_60sec_3eves_weakEvents.csv"),
        (0, 4, "100kmFibo_cont_60sec_3eves.csv"),
        (1, 0, "100kmFibo_cont_180sec_3eves_weakEvents_noisyPrediction.csv"),
        (1, 2, "100kmFibo_cont_180sec_3eves_weakEvents.csv"),
        (1, 4, "100kmFibo_cont_180sec_3eves.csv"),
        (2, 0, "100kmFibo_cont_600sec_3eves_weakEvents_noisyPrediction.csv"),
        (2, 2, "100kmFibo_cont_600sec_3eves_weakEvents.csv"),
        (2, 4, "100kmFibo_cont_600sec_3eves.csv"),
        (3, 0, "100kmFibo_cont_1800sec_3eves_weakEvents_noisyPrediction.csv"),
        (3, 2, "100kmFibo_cont_1800sec_3eves_weakEvents.csv"),
        (3, 4, "100kmFibo_cont_1800sec_3eves.csv"),
        (4, 0, "100kmFibo_cont_3600sec_3eves_weakEvents_noisyPrediction.csv"),
        (4, 2, "100kmFibo_cont_3600sec_3eves_weakEvents.csv"),
        (4, 4, "100kmFibo_cont_3600sec_3eves.csv"),
        (5, 0, "100kmFibo_cont_7200sec_3eves_weakEvents_noisyPrediction.csv"),
        (5, 2, "100kmFibo_cont_7200sec_3eves_weakEvents.csv"),
        (5, 4, "100kmFibo_cont_7200sec_3eves.csv"),
        (6, 0, "100kmFibo_cont_14400sec_3eves_weakEvents_noisyPrediction.csv"),
        (6, 2, "100kmFibo_cont_14400sec_3eves_weakEvents.csv"),
        (6, 4, "100kmFibo_cont_14400sec_3eves.csv")
    ]

    combine_rec_outputs_to_plot(x_axis=x_values, x_label=x_text, figure_name=fig_name, output_folder=folder_name,
                                output_mapping=mapping, atk_types=attack_types, real_world_data=real_world_data)