├── README.md
├── data
└── data-1.txt
├── plots
├── Lidar.png
└── Radar.png
└── src
├── KF.py
├── datapoint.py
├── helpers.py
├── sensor_fusion.py
└── tools.py
/README.md:
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1 | # Object-Tracking-and-State-Prediction-with-Unscented-and-Extended-Kalman-Filters
2 | Radar and Lidar Sensor Fusion using Extended, and Unscented Kalman Filter for Object Tracking and State Prediction.
3 |
4 | # Understanding the data
5 |
6 | ```
7 | [L(for lidar)] [m_x] [m_y] [t] [r_x] [r_y] [r_vx] [r_vy]
8 | [R(for radar)] [m_rho] [m_phi] [m_drho] [t] [r_px] [r_py] [r_vx] [r_vy]
9 |
10 | Where:
11 | (m_x, m_y) - measurements by the lidar
12 | (m_rho, m_phi, m_dho) - measurements by the radar in polar coordinates
13 | (t) - timestamp in unix/epoch time the measurements were taken
14 | (r_x, r_y, r_vx, r_vy) - the real ground truth state of the system
15 |
16 | Example:
17 | R 8.60363 0.0290616 -2.99903 1477010443399637 8.6 0.25 -3.00029 0
18 | L 8.45 0.25 1477010443349642 8.45 0.25 -3.00027 0
19 | ```
20 | # Extended KF
21 | Since here for fusing Radar and Lidar data, we need to have Extended KF for working with Radar data since the position is obtained by converting the polar co-ordinates using non-linear equations to (x,y) position.
22 |
23 | Lidar data can be treated via simple Kalman Filter.
24 |
25 | # References:
26 |
27 | Helper methods and input data file adapted from https://github.com/mithi/Fusion-EKF-Python
28 |
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/data/data-1.txt:
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1 | R 8.46642 0.0287602 -3.04035 1477010443399637 8.6 0.25 -3.00029 0
2 | L 8.44818 0.251553 1477010443449633 8.45 0.25 -3.00027 0
3 | R 8.57101 0.0282318 -0.0105258 1477010443499690 8.45 0.25 0 0
4 | L 8.45582 0.253997 1477010443549747 8.45 0.25 0 0
5 | R 8.42927 0.0301427 -1.85813 1477010443604698 8.35 0.25 -1.81979 0
6 | L 8.23962 0.24916 1477010443659650 8.25 0.25 -1.81978 0
7 | R 7.9351 0.0237437 -3.81077 1477010443709653 8.05 0.2 -3.99976 -0.99994
8 | L 7.84073 0.159858 1477010443759657 7.85 0.15 -3.99972 -0.99993
9 | R 7.61428 0.0204653 -3.22052 1477010443809660 7.7 0.15 -2.99982 0
10 | L 7.54016 0.159641 1477010443859663 7.55 0.15 -2.99982 0
11 | R 7.50876 0.0127032 -1.89765 1477010443914714 7.45 0.100001 -1.8165 -0.908239
12 | L 7.3426 0.0345312 1477010443969766 7.35 0.0500011 -1.81648 -0.90823
13 | R 7.20598 0.000683772 -2.79536 1477010444024741 7.2 9.49949e-07 -2.72851 -0.909507
14 | L 7.05406 -0.0533413 1477010444079717 7.05 -0.0499992 -2.72849 -0.909499
15 | R 6.74737 -0.0212224 -1.67069 1477010444134693 6.95 -0.15 -1.81898 -1.81898
16 | L 6.84972 -0.258748 1477010444189670 6.85 -0.25 -1.81896 -1.81897
17 | R 6.67866 -0.0519302 -2.02379 1477010444239651 6.75 -0.349999 -2.00076 -2.00075
18 | L 6.64329 -0.462604 1477010444289632 6.65 -0.449999 -2.00076 -2.00075
19 | R 6.63205 -0.0840904 -0.81497 1477010444339690 6.6 -0.549999 -0.998836 -1.99769
20 | L 6.56029 -0.650892 1477010444389748 6.55 -0.65 -0.99884 -1.99769
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22 | L 6.45805 -1.05268 1477010444499791 6.45 -1.05 -0.908739 -3.63494
23 | R 6.49021 -0.179219 -1.23944 1477010444554767 6.35 -1.15 -1.81897 -1.81898
24 | L 6.25499 -1.25007 1477010444609744 6.25 -1.25 -1.81896 -1.81896
25 | R 6.59968 -0.226407 1.73835 1477010444664705 6.3 -1.45 0.909739 -3.63894
26 | L 6.36328 -1.65656 1477010444719667 6.35 -1.65 0.909727 -3.63891
27 | R 6.57796 -0.279439 -0.211523 1477010444769692 6.3 -1.8 -0.999494 -2.9985
28 | L 6.24641 -1.95075 1477010444819718 6.25 -1.95 -0.999489 -2.99847
29 | R 6.66141 -0.322252 1.75689 1477010444869719 6.3 -2.1 0.999984 -2.99994
30 | L 6.34644 -2.23829 1477010444919720 6.35 -2.25 0.999979 -2.99994
31 | R 6.76596 -0.36264 0.855484 1477010444974743 6.35 -2.4 0 -2.72613
32 | L 6.34927 -2.55175 1477010445029767 6.35 -2.55 0 -2.72611
33 | R 6.98232 -0.399959 1.89805 1477010445084742 6.4 -2.7 0.909499 -2.72851
34 | L 6.43257 -2.84245 1477010445139717 6.45 -2.85 0.909503 -2.72851
35 | R 7.12938 -0.43502 1.4149 1477010445189758 6.45 -3 0 -2.99754
36 | L 6.44119 -3.14955 1477010445239799 6.45 -3.15 0 -2.99754
37 | R 7.31886 -0.470836 2.21289 1477010445294720 6.5 -3.3 0.910402 -2.7312
38 | L 6.5453 -3.45745 1477010445349641 6.55 -3.45 0.910402 -2.7312
39 | R 7.5456 -0.49614 2.70911 1477010445404670 6.65 -3.6 1.81722 -2.72583
40 | L 6.76011 -3.75457 1477010445459700 6.75 -3.75 1.81721 -2.72581
41 | R 7.884 -0.521437 2.20619 1477010445514767 6.8 -3.9 0.907988 -2.72396
42 | L 6.85997 -4.05805 1477010445569835 6.85 -4.05 0.907976 -2.72393
43 | R 8.04387 -0.547179 2.23987 1477010445624820 6.9 -4.2 0.909334 -2.72801
44 | L 6.94332 -4.34473 1477010445679806 6.95 -4.35 0.90933 -2.72799
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46 | L 7.04099 -4.55018 1477010445789690 7.05 -4.55 0.910054 -1.8201
47 | R 8.50541 -0.582556 2.23409 1477010445844738 7.1 -4.7 0.908302 -2.72489
48 | L 7.14735 -4.86125 1477010445899786 7.15 -4.85 0.908297 -2.72489
49 | R 8.88902 -0.604494 3.05765 1477010445954759 7.25 -5 1.81907 -2.72861
50 | L 7.30424 -5.15404 1477010446009732 7.35 -5.15 1.81907 -2.72861
51 | R 8.97814 -0.622421 2.42827 1477010446064786 7.4 -5.3 0.908194 -2.7246
52 | L 7.44737 -5.45308 1477010446119841 7.45 -5.45 0.90819 -2.72457
53 | R 9.4762 -0.63468 2.54048 1477010446174794 7.55 -5.55 1.81974 -1.81974
54 | L 7.64356 -5.66706 1477010446229748 7.65 -5.65 1.81972 -1.81972
55 | R 9.53172 -0.647271 2.40306 1477010446284703 7.7 -5.8 0.90983 -2.72951
56 | L 7.7342 -5.95374 1477010446339659 7.75 -5.95 0.909826 -2.72948
57 | R 9.89957 -0.659824 3.5853 1477010446389694 7.85 -6.1 1.9986 -2.9979
58 | L 7.95703 -6.24328 1477010446439730 7.95 -6.25 1.99858 -2.99787
59 | R 10.4099 -0.66785 2.53218 1477010446494754 8.05 -6.35 1.81738 -1.81739
60 | L 8.15891 -6.45335 1477010446549779 8.15 -6.45 1.81737 -1.81737
61 | R 10.3865 -0.678464 2.50771 1477010446604767 8.2 -6.6 0.909293 -2.72787
62 | L 8.25411 -6.74791 1477010446659755 8.25 -6.75 0.909293 -2.72787
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64 | L 8.44494 -6.95805 1477010446769649 8.45 -6.95 1.81993 -1.81993
65 | R 10.9406 -0.696337 2.56416 1477010446819646 8.5 -7.1 1.00006 -3.00018
66 | L 8.54925 -7.24433 1477010446869643 8.55 -7.25 1.00006 -3.00018
67 | R 11.3727 -0.707378 3.44956 1477010446919691 8.65 -7.4 1.99807 -2.99712
68 | L 8.73559 -7.5421 1477010446969739 8.75 -7.55 1.99808 -2.99712
69 | R 11.7598 -0.716035 2.04742 1477010447024687 8.8 -7.65 0.909955 -1.8199
70 | L 8.84538 -7.75497 1477010447079636 8.85 -7.75 0.909946 -1.81988
71 | R 11.8789 -0.726017 2.80161 1477010447129681 8.9 -7.9 0.999086 -2.9973
72 | L 8.95966 -8.0381 1477010447179726 8.95 -8.05 0.999095 -2.9973
73 | R 11.9442 -0.735521 1.86678 1477010447234713 9 -8.15 0.909309 -1.8186
74 | L 9.05896 -8.24314 1477010447289701 9.05 -8.25 0.909301 -1.81859
75 | R 12.4893 -0.745345 2.79706 1477010447339681 9.1 -8.4 1.0004 -3.00119
76 | L 9.15332 -8.54857 1477010447389662 9.15 -8.55 1.00038 -3.00117
77 | R 12.6764 -0.755304 1.92258 1477010447444637 9.2 -8.65 0.909508 -1.819
78 | L 9.24541 -8.76863 1477010447499613 9.25 -8.75 0.9095 -1.81899
79 | R 13.0018 -0.765199 4.2891 1477010447549622 9.35 -8.95 1.99965 -3.99928
80 | L 9.44703 -9.15328 1477010447599631 9.45 -9.15 1.99964 -3.99928
81 | R 13.2646 -0.772362 2.209 1477010447649652 9.5 -9.25 0.999584 -1.99917
82 | L 9.54157 -9.35195 1477010447699673 9.55 -9.35 0.999584 -1.99917
83 | R 13.3655 -0.782802 2.24797 1477010447749669 9.55 -9.5 0 -3.00023
84 | L 9.54779 -9.64959 1477010447799666 9.55 -9.65 0 -3.0002
85 | R 13.8483 -0.794831 2.85181 1477010447849716 9.6 -9.8 0.999005 -2.997
86 | L 9.64606 -9.9341 1477010447899766 9.65 -9.95 0.998995 -2.997
87 | R 13.8368 -0.803954 1.95724 1477010447954720 9.7 -10.05 0.909855 -1.81969
88 | L 9.73801 -10.1396 1477010448009675 9.75 -10.15 0.909847 -1.81969
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90 | L 9.75524 -10.4732 1477010448109654 9.75 -10.45 0 -3.00063
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92 | L 9.85048 -10.7526 1477010448219767 9.85 -10.75 0.908161 -2.72448
93 | R 14.7429 -0.835956 1.92166 1477010448274697 9.85 -10.9 0 -2.73074
94 | L 9.83614 -11.041 1477010448329628 9.85 -11.05 0 -2.73073
95 | R 14.8851 -0.848604 2.27417 1477010448379634 9.85 -11.2 0 -2.99965
96 | L 9.84568 -11.3496 1477010448429641 9.85 -11.35 0 -2.99961
97 | R 15.2094 -0.863774 2.26264 1477010448479667 9.85 -11.5 0 -2.99843
98 | L 9.83206 -11.6307 1477010448529693 9.85 -11.65 0 -2.99843
99 | R 15.413 -0.877244 0.866545 1477010448579678 9.8 -11.75 -1.0003 -2.00061
100 | L 9.74928 -11.8442 1477010448629664 9.75 -11.85 -1.00029 -2.00059
101 | R 15.4054 -0.88995 1.77828 1477010448679644 9.7 -12 -1.0004 -3.00119
102 | L 9.65354 -12.1497 1477010448729625 9.65 -12.15 -1.00039 -3.00116
103 | R 15.7547 -0.905673 1.01994 1477010448779630 9.6 -12.25 -0.999885 -1.99981
104 | L 9.54859 -12.3478 1477010448829636 9.55 -12.35 -0.999884 -1.99979
105 | R 15.5466 -0.922266 0.369379 1477010448879685 9.45 -12.45 -1.99803 -1.99805
106 | L 9.36086 -12.5501 1477010448929734 9.35 -12.55 -1.99804 -1.99804
107 | R 15.7759 -0.936847 0.912459 1477010448984693 9.3 -12.65 -0.909773 -1.81953
108 | L 9.22134 -12.7613 1477010449039652 9.25 -12.75 -0.909773 -1.81954
109 | R 15.9017 -0.95744 -0.700602 1477010449089745 9.05 -12.85 -3.99257 -1.99629
110 | L 8.84161 -12.9519 1477010449139839 8.85 -12.95 -3.99253 -1.99627
111 | R 15.6195 -0.978773 -0.291637 1477010449194745 8.75 -13 -1.8213 -0.910651
112 | L 8.66358 -13.0431 1477010449249651 8.65 -13.05 -1.8213 -0.910651
113 | R 15.6995 -0.995246 -0.795374 1477010449299701 8.5 -13.1 -2.997 -0.999005
114 | L 8.35763 -13.1557 1477010449349751 8.35 -13.15 -2.997 -0.998995
115 | R 15.5295 -1.0123 -1.48799 1477010449404748 8.2 -13.15 -2.72741 0
116 | L 8.03583 -13.1513 1477010449459745 8.05 -13.15 -2.72742 0
117 | R 15.2205 -1.03051 -1.3479 1477010449514693 7.9 -13.15 -2.72986 0
118 | L 7.76538 -13.1647 1477010449569641 7.75 -13.15 -2.72986 0
119 | R 15.3557 -1.04843 -0.489357 1477010449619676 7.6 -13.2 -2.9979 -0.999304
120 | L 7.44786 -13.2517 1477010449669711 7.45 -13.25 -2.9979 -0.999304
121 | R 14.9523 -1.06755 -1.47147 1477010449724687 7.3 -13.25 -2.72846 0
122 | L 7.16011 -13.2382 1477010449779664 7.15 -13.25 -2.72844 0
123 | R 14.6741 -1.08244 -2.33868 1477010449829700 7 -13.2 -2.99784 0.999284
124 | L 6.84891 -13.1493 1477010449879736 6.85 -13.15 -2.99784 0.999284
125 | R 14.8668 -1.09768 -2.01344 1477010449934724 6.7 -13.1 -2.72787 0.909275
126 | L 6.55759 -13.0679 1477010449989712 6.55 -13.05 -2.72787 0.909284
127 | R 14.5623 -1.11566 -1.26682 1477010450044722 6.4 -13.05 -2.72678 0
128 | L 6.26174 -13.0534 1477010450099732 6.25 -13.05 -2.72678 0
129 | R 14.3675 -1.1322 -1.86783 1477010450154684 6.1 -13 -2.72966 0.909888
130 | L 5.94198 -12.9494 1477010450209637 5.95 -12.95 -2.72963 0.90988
131 | R 14.0957 -1.14684 -3.18942 1477010450259674 5.8 -12.85 -2.99777 1.99851
132 | L 5.65107 -12.7365 1477010450309711 5.65 -12.75 -2.99778 1.99852
133 | R 13.7247 -1.15804 -1.68433 1477010450364717 5.55 -12.7 -1.81798 0.908995
134 | L 5.43839 -12.652 1477010450419723 5.45 -12.65 -1.81799 0.908995
135 | R 13.5474 -1.17084 -2.84649 1477010450474743 5.3 -12.55 -2.72627 1.81753
136 | L 5.1307 -12.4684 1477010450529764 5.15 -12.45 -2.72625 1.8175
137 | R 13.369 -1.18389 -2.36817 1477010450584712 5.05 -12.35 -1.8199 1.81989
138 | L 4.92985 -12.2499 1477010450639660 4.95 -12.25 -1.81991 1.8199
139 | R 12.8878 -1.19328 -2.72149 1477010450689678 4.85 -12.15 -1.99928 1.99929
140 | L 4.75915 -12.0666 1477010450739696 4.75 -12.05 -1.99928 1.99928
141 | R 12.7991 -1.19553 -2.86727 1477010450794748 4.7 -11.9 -0.908236 2.72471
142 | L 4.64378 -11.7442 1477010450849800 4.65 -11.75 -0.908231 2.7247
143 | R 12.4969 -1.19406 -2.03943 1477010450904754 4.6 -11.65 -0.909847 1.81971
144 | L 4.54975 -11.5706 1477010450959709 4.55 -11.55 -0.909843 1.81969
145 | R 12.2162 -1.1958 -4.32322 1477010451009669 4.5 -11.35 -1.0008 4.0032
146 | L 4.44059 -11.1496 1477010451059629 4.45 -11.15 -1.0008 4.00321
147 | R 11.8835 -1.18814 -1.82178 1477010451114691 4.45 -11.05 0 1.81614
148 | L 4.45136 -10.93 1477010451169754 4.45 -10.95 0 1.81612
149 | R 11.6669 -1.1797 -2.64222 1477010451224789 4.45 -10.8 0 2.72555
150 | L 4.45788 -10.6457 1477010451279824 4.45 -10.65 0 2.72554
151 | R 11.4789 -1.17144 -2.50005 1477010451334789 4.45 -10.5 0 2.729
152 | L 4.44057 -10.3382 1477010451389755 4.45 -10.35 0 2.72898
153 | R 11.1898 -1.15508 -2.26227 1477010451444763 4.5 -10.2 0.908962 2.72687
154 | L 4.54925 -10.0439 1477010451499772 4.55 -10.05 0.908954 2.72685
155 | R 10.9942 -1.13588 -2.23426 1477010451554732 4.6 -9.9 0.909756 2.72927
156 | L 4.63755 -9.74952 1477010451609692 4.65 -9.75 0.909752 2.72926
157 | R 10.4986 -1.11645 -2.34038 1477010451664722 4.7 -9.6 0.90859 2.72578
158 | L 4.74589 -9.4609 1477010451719753 4.75 -9.45 0.908586 2.72576
159 | R 10.592 -1.09073 -1.62445 1477010451774708 4.85 -9.3 1.81967 2.72952
160 | L 4.94849 -9.13867 1477010451829663 4.95 -9.15 1.81967 2.72951
161 | R 10.2398 -1.06602 -1.22863 1477010451879669 5 -9.05 0.999884 1.99977
162 | L 5.04121 -8.94979 1477010451929676 5.05 -8.95 0.999874 1.99974
163 | R 10.3027 -1.04083 -1.33508 1477010451984680 5.15 -8.8 1.81805 2.72708
164 | L 5.26887 -8.63564 1477010452039685 5.25 -8.65 1.81803 2.72705
165 | R 10.116 -1.01021 -0.66468 1477010452089696 5.35 -8.55 1.99956 1.99957
166 | L 5.44864 -8.42738 1477010452139708 5.45 -8.45 1.99954 1.99954
167 | R 10.1164 -0.988358 -0.754614 1477010452194711 5.5 -8.35 0.909045 1.81807
168 | L 5.53862 -8.24357 1477010452249714 5.55 -8.25 0.909045 1.81808
169 | R 9.81408 -0.957431 -0.747175 1477010452304738 5.7 -8.1 2.72608 2.72608
170 | L 5.85094 -7.95601 1477010452359763 5.85 -7.95 2.72606 2.72606
171 | R 9.834 -0.9229 -0.425811 1477010452414768 5.95 -7.85 1.81801 1.81801
172 | L 6.05986 -7.73559 1477010452469773 6.05 -7.75 1.81802 1.81801
173 | R 9.79397 -0.893547 -0.362809 1477010452524779 6.15 -7.65 1.81798 1.81798
174 | L 6.25659 -7.54336 1477010452579785 6.25 -7.55 1.81798 1.81798
175 | R 9.82626 -0.862628 -0.124766 1477010452634722 6.35 -7.45 1.82027 1.82027
176 | L 6.452 -7.36418 1477010452689660 6.45 -7.35 1.82025 1.82025
177 | R 9.61034 -0.830934 0.570221 1477010452739683 6.6 -7.25 2.99862 1.99908
178 | L 6.7556 -7.14598 1477010452789706 6.75 -7.15 2.99862 1.99908
179 | R 9.63222 -0.803469 -0.456175 1477010452844772 6.8 -7.05 0.908005 1.816
180 | L 6.86356 -6.94732 1477010452899838 6.85 -6.95 0.908 1.81601
181 | R 9.90965 -0.773882 0.725797 1477010452954812 7 -6.85 2.72856 1.81904
182 | L 7.15695 -6.75136 1477010453009787 7.15 -6.75 2.72854 1.81902
183 | R 9.90034 -0.741696 -0.0555249 1477010453064726 7.25 -6.65 1.8202 1.8202
184 | L 7.35505 -6.56058 1477010453119666 7.35 -6.55 1.82018 1.82018
185 | R 9.97586 -0.714582 1.59387 1477010453169672 7.5 -6.5 2.99964 0.999884
186 | L 7.65664 -6.46417 1477010453219678 7.65 -6.45 2.99964 0.999884
187 | R 9.95941 -0.690878 0.851221 1477010453274711 7.75 -6.4 1.81709 0.908549
188 | L 7.85193 -6.35845 1477010453329744 7.85 -6.35 1.81709 0.908545
189 | R 10.1984 -0.663884 0.962109 1477010453384703 8 -6.25 2.72931 1.81954
190 | L 8.13749 -6.14586 1477010453439663 8.15 -6.15 2.72928 1.81952
191 | R 10.1836 -0.632288 0.481433 1477010453489669 8.25 -6.05 1.99977 1.99976
192 | L 8.33626 -5.94674 1477010453539675 8.35 -5.95 1.99977 1.99976
193 | R 10.3458 -0.606064 1.70327 1477010453594661 8.5 -5.9 2.72796 0.909326
194 | L 8.65894 -5.86309 1477010453649647 8.65 -5.85 2.72796 0.909321
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287 | R 7.57641 -0.485339 2.0947 1477010458684825 6.6 -3.5 0.908714 -2.72613
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289 | R 7.7195 -0.517274 2.30516 1477010458794820 6.7 -3.8 0.909565 -2.72871
290 | L 6.74885 -3.96368 1477010458849792 6.75 -3.95 0.909561 -2.72869
291 | R 7.86967 -0.543033 2.16903 1477010458904824 6.8 -4.1 0.908566 -2.72568
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293 | R 8.40442 -0.563739 3.04353 1477010459014855 6.95 -4.4 1.81821 -2.72732
294 | L 7.04679 -4.55716 1477010459069854 7.05 -4.55 1.81822 -2.72732
295 | R 8.42068 -0.584245 2.43749 1477010459124748 7.1 -4.7 0.91085 -2.73253
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297 | R 8.79053 -0.601801 2.18989 1477010459229626 7.2 -4.95 1.00033 -2.00068
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299 | R 8.83775 -0.614588 3.42131 1477010459329670 7.35 -5.2 1.9976 -2.9964
300 | L 7.43604 -5.34825 1477010459379730 7.45 -5.35 1.9976 -2.9964
301 | R 9.16064 -0.628545 1.87661 1477010459434781 7.5 -5.45 0.908252 -1.8165
302 | L 7.55662 -5.56714 1477010459489833 7.55 -5.55 0.908244 -1.81648
303 | R 9.23072 -0.641835 2.97566 1477010459544843 7.65 -5.7 1.81785 -2.72677
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306 | L 7.85921 -6.04502 1477010459709855 7.85 -6.05 0.909073 -1.81815
307 | R 10.0026 -0.661119 2.97834 1477010459764809 7.95 -6.2 1.8197 -2.72955
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309 | R 10.5053 -0.671666 3.05024 1477010459874748 8.15 -6.5 1.8187 -2.72807
310 | L 8.25446 -6.64851 1477010459929733 8.25 -6.65 1.81869 -2.72804
311 | R 10.7287 -0.683001 1.92616 1477010459984740 8.3 -6.75 0.908979 -1.81795
312 | L 8.31728 -6.8399 1477010460039747 8.35 -6.85 0.908979 -1.81795
313 | R 10.7785 -0.696037 2.49181 1477010460094746 8.4 -7 0.909094 -2.72732
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315 | R 11.2099 -0.704456 3.11293 1477010460204765 8.55 -7.3 1.81751 -2.72628
316 | L 8.65899 -7.4699 1477010460259785 8.65 -7.45 1.81752 -2.72628
317 | R 11.7082 -0.715625 1.98212 1477010460314814 8.7 -7.55 0.908615 -1.81723
318 | L 8.76769 -7.64094 1477010460369844 8.75 -7.65 0.908607 -1.81721
319 | R 11.6937 -0.721585 3.12809 1477010460424744 8.85 -7.8 1.8215 -2.73224
320 | L 8.95669 -7.95155 1477010460479645 8.95 -7.95 1.82148 -2.73221
321 | R 12.2718 -0.734453 2.87621 1477010460529694 9 -8.1 0.999025 -2.99707
322 | L 9.05476 -8.24211 1477010460579744 9.05 -8.25 0.999015 -2.99704
323 | R 12.4149 -0.743549 1.7167 1477010460634793 9.1 -8.35 0.908285 -1.81657
324 | L 9.14025 -8.44981 1477010460689843 9.15 -8.45 0.908268 -1.81655
325 | R 12.6031 -0.750086 2.60268 1477010460744781 9.2 -8.6 0.91012 -2.73036
326 | L 9.25031 -8.76071 1477010460799720 9.25 -8.75 0.910112 -2.73033
327 | R 12.8797 -0.763778 2.57907 1477010460854694 9.3 -8.9 0.909524 -2.72856
328 | L 9.34431 -9.04587 1477010460909668 9.35 -9.05 0.909524 -2.72856
329 | R 13.1448 -0.77402 2.8177 1477010460959714 9.4 -9.2 0.999066 -2.99725
330 | L 9.4452 -9.37195 1477010461009760 9.45 -9.35 0.999075 -2.99724
331 | R 13.4009 -0.784169 2.57406 1477010461064723 9.5 -9.5 0.909706 -2.7291
332 | L 9.55098 -9.64789 1477010461119686 9.55 -9.65 0.909706 -2.7291
333 | R 13.6665 -0.79492 2.7101 1477010461169662 9.6 -9.8 1.00048 -3.00143
334 | L 9.62408 -9.94149 1477010461219639 9.65 -9.95 1.00046 -3.00141
335 | R 13.8463 -0.803265 2.26838 1477010461269700 9.7 -10.05 0.998785 -1.99755
336 | L 9.7457 -10.1502 1477010461319762 9.75 -10.15 0.998775 -1.99754
337 | R 14.223 -0.812853 1.76448 1477010461374737 9.75 -10.3 0 -2.72851
338 | L 9.76557 -10.4501 1477010461429713 9.75 -10.45 0 -2.72849
339 | R 14.5847 -0.825059 2.61823 1477010461484718 9.8 -10.6 0.909012 -2.72704
340 | L 9.85066 -10.7537 1477010461539723 9.85 -10.75 0.909012 -2.72703
341 | R 14.7085 -0.837569 2.00573 1477010461589670 9.85 -10.9 0 -3.00318
342 | L 9.85693 -11.0512 1477010461639618 9.85 -11.05 0 -3.00316
343 | R 14.7337 -0.847854 2.31145 1477010461689616 9.85 -11.2 0 -3.00013
344 | L 9.84407 -11.3565 1477010461739614 9.85 -11.35 0 -3.00012
345 | R 15.0821 -0.863946 2.42247 1477010461789682 9.85 -11.5 0 -2.99592
346 | L 9.83943 -11.6467 1477010461839750 9.85 -11.65 0 -2.99592
347 | R 15.2386 -0.875133 0.900904 1477010461894772 9.8 -11.75 -0.908731 -1.81746
348 | L 9.74774 -11.8511 1477010461949794 9.75 -11.85 -0.908731 -1.81746
349 | R 15.5821 -0.891812 1.52511 1477010462004764 9.7 -12 -0.909591 -2.72875
350 | L 9.64482 -12.1455 1477010462059735 9.65 -12.15 -0.909582 -2.72873
351 | R 15.5818 -0.909263 1.55806 1477010462114747 9.6 -12.3 -0.908879 -2.72667
352 | L 9.55167 -12.4583 1477010462169760 9.55 -12.45 -0.908879 -2.72665
353 | R 15.6094 -0.92684 0.530042 1477010462224703 9.45 -12.55 -1.82006 -1.82006
354 | L 9.359 -12.6499 1477010462279647 9.35 -12.65 -1.82005 -1.82005
355 | R 15.647 -0.945318 -0.172995 1477010462329639 9.2 -12.75 -3.00047 -2.00033
356 | L 9.028 -12.8337 1477010462379631 9.05 -12.85 -3.00048 -2.00033
357 | R 15.5629 -0.965203 -0.227325 1477010462434714 8.95 -12.9 -1.81543 -0.907707
358 | L 8.84257 -12.9684 1477010462489798 8.85 -12.95 -1.81542 -0.907708
359 | R 15.6346 -0.983091 -0.00852268 1477010462544716 8.7 -13.05 -2.73134 -1.82089
360 | L 8.55319 -13.1445 1477010462599634 8.55 -13.15 -2.73135 -1.82089
361 | R 15.4056 -1.00393 -1.71054 1477010462649687 8.4 -13.15 -2.99683 0
362 | L 8.25965 -13.1488 1477010462699741 8.25 -13.15 -2.9968 0
363 | R 15.3433 -1.01881 -0.506483 1477010462754745 8.1 -13.2 -2.72707 -0.909028
364 | L 7.95841 -13.2508 1477010462809750 7.95 -13.25 -2.72705 -0.90902
365 | R 15.5514 -1.04048 -1.31109 1477010462864705 7.8 -13.25 -2.7295 0
366 | L 7.62489 -13.249 1477010462919660 7.65 -13.25 -2.7295 0
367 | R 15.2679 -1.05671 -1.35977 1477010462969661 7.5 -13.25 -2.99994 0
368 | L 7.3419 -13.2474 1477010463019662 7.35 -13.25 -2.99994 0
369 | R 15.0325 -1.07149 -2.14048 1477010463074668 7.2 -13.2 -2.72698 0.908995
370 | L 7.04935 -13.1606 1477010463129675 7.05 -13.15 -2.72695 0.908987
371 | R 14.707 -1.09054 -1.80307 1477010463184664 6.85 -13.15 -3.63709 0
372 | L 6.65123 -13.1604 1477010463239653 6.65 -13.15 -3.63709 0
373 | R 14.7389 -1.11096 -2.29967 1477010463289643 6.5 -13.1 -3.0006 1.00018
374 | L 6.35317 -13.0396 1477010463339633 6.35 -13.05 -3.0006 1.00019
375 | R 14.5358 -1.1248 -0.815348 1477010463394660 6.25 -13.05 -1.81729 0
376 | L 6.14459 -13.0471 1477010463449687 6.15 -13.05 -1.81729 0
377 | R 14.2651 -1.13707 -2.63142 1477010463504660 6 -12.95 -2.72861 1.81906
378 | L 5.86379 -12.8408 1477010463559633 5.85 -12.85 -2.72861 1.81907
379 | R 14.0885 -1.15135 -2.17259 1477010463609635 5.7 -12.8 -2.99988 0.999964
380 | L 5.55118 -12.7486 1477010463659638 5.55 -12.75 -2.99985 0.999954
381 | R 13.857 -1.16639 -1.77211 1477010463709638 5.45 -12.7 -2.00001 1
382 | L 5.35556 -12.6423 1477010463759639 5.35 -12.65 -1.99998 0.999994
383 | R 13.6105 -1.17364 -2.55514 1477010463809706 5.25 -12.55 -1.99732 1.99733
384 | L 5.13769 -12.4355 1477010463859774 5.15 -12.45 -1.9973 1.9973
385 | R 13.5812 -1.18332 -2.44489 1477010463914701 5.05 -12.35 -1.8206 1.82059
386 | L 4.94432 -12.2438 1477010463969629 4.95 -12.25 -1.82058 1.82058
387 | R 13.1093 -1.18836 -3.51972 1477010464019634 4.85 -12.1 -1.9998 2.99969
388 | L 4.75047 -11.9578 1477010464069639 4.75 -11.95 -1.9998 2.9997
389 | R 12.6605 -1.19538 -2.32022 1477010464119638 4.7 -11.85 -1.00002 2.00003
390 | L 4.63967 -11.7513 1477010464169638 4.65 -11.75 -1.00001 2.00002
391 | R 12.5025 -1.19544 -3.28171 1477010464219690 4.6 -11.6 -0.998955 2.99688
392 | L 4.56619 -11.4576 1477010464269742 4.55 -11.45 -0.99896 2.99689
393 | R 12.067 -1.19146 -3.21867 1477010464319689 4.5 -11.3 -1.00106 3.00319
394 | L 4.46017 -11.1425 1477010464369636 4.45 -11.15 -1.00106 3.00319
395 | R 11.9516 -1.19047 -1.86393 1477010464424678 4.45 -11.05 0 1.8168
396 | L 4.43803 -10.9525 1477010464479720 4.45 -10.95 0 1.81679
397 | R 11.5778 -1.18044 -2.7544 1477010464529697 4.45 -10.8 0 3.00139
398 | L 4.45507 -10.6618 1477010464579674 4.45 -10.65 0 3.00138
399 | R 11.4567 -1.17215 -1.64771 1477010464629664 4.45 -10.55 0 2.00041
400 | L 4.45187 -10.4417 1477010464679655 4.45 -10.45 0 2.00038
401 | R 11.1439 -1.15943 -2.22282 1477010464729676 4.5 -10.3 0.999584 2.99875
402 | L 4.55349 -10.1343 1477010464779698 4.55 -10.15 0.999574 2.99871
403 | R 10.9977 -1.14265 -2.49001 1477010464834700 4.55 -10 0 2.72717
404 | L 4.55922 -9.84124 1477010464889703 4.55 -9.85 0 2.72714
405 | R 10.7057 -1.12424 -1.78171 1477010464944669 4.65 -9.7 1.8193 2.72895
406 | L 4.7439 -9.53522 1477010464999636 4.75 -9.55 1.81929 2.72894
407 | R 10.4189 -1.09757 -2.33568 1477010465049633 4.8 -9.4 1.00006 3.00019
408 | L 4.8446 -9.24095 1477010465099631 4.85 -9.25 1.00005 3.00015
409 | R 10.4688 -1.07438 -0.809142 1477010465149630 4.95 -9.15 2.00004 2.00005
410 | L 5.02283 -9.03066 1477010465199629 5.05 -9.05 2.00004 2.00004
411 | R 10.323 -1.05092 -2.02017 1477010465249644 5.1 -8.9 0.999704 2.99911
412 | L 5.14109 -8.73073 1477010465299660 5.15 -8.75 0.999689 2.99907
413 | R 10.3067 -1.02629 -0.584474 1477010465354651 5.25 -8.65 1.81848 1.81849
414 | L 5.36408 -8.56805 1477010465409643 5.35 -8.55 1.81846 1.81846
415 | R 9.90444 -0.995544 -1.4781 1477010465459626 5.45 -8.4 2.00068 3.00103
416 | L 5.53874 -8.25196 1477010465509610 5.55 -8.25 2.00066 3.00099
417 | R 9.78462 -0.965647 -0.333006 1477010465559622 5.65 -8.15 1.99952 1.99953
418 | L 5.7459 -8.05196 1477010465609635 5.75 -8.05 1.9995 1.9995
419 | R 9.80473 -0.939805 -0.960882 1477010465659638 5.8 -7.95 0.999944 1.99989
420 | L 5.84978 -7.88144 1477010465709641 5.85 -7.85 0.999939 1.99988
421 | R 9.71238 -0.915003 -0.433286 1477010465759698 5.95 -7.75 1.99772 1.99772
422 | L 6.04031 -7.64732 1477010465809756 6.05 -7.65 1.99771 1.9977
423 | R 9.7524 -0.886108 -0.238878 1477010465864704 6.15 -7.55 1.8199 1.8199
424 | L 6.23866 -7.45306 1477010465919652 6.25 -7.45 1.8199 1.81991
425 | R 9.5193 -0.854097 0.571373 1477010465969718 6.4 -7.35 2.99605 1.99736
426 | L 6.55461 -7.24907 1477010466019784 6.55 -7.25 2.99605 1.99736
427 | R 9.83822 -0.825564 0.654754 1477010466074711 6.65 -7.2 1.8206 0.910303
428 | L 6.74664 -7.15616 1477010466129638 6.75 -7.15 1.8206 0.910298
429 | R 9.75541 -0.799682 -0.0700368 1477010466179676 6.85 -7.05 1.99848 1.99848
430 | L 6.95342 -6.96092 1477010466229715 6.95 -6.95 1.99846 1.99846
431 | R 9.98866 -0.768765 0.81251 1477010466284723 7.1 -6.85 2.72688 1.81792
432 | L 7.24929 -6.7261 1477010466339731 7.25 -6.75 2.72688 1.81792
433 | R 10.0188 -0.735659 0.0377937 1477010466394730 7.35 -6.65 1.81821 1.81821
434 | L 7.46332 -6.54842 1477010466449729 7.45 -6.55 1.81821 1.81821
435 | R 9.96291 -0.711687 0.751952 1477010466504757 7.55 -6.5 1.81726 0.908632
436 | L 7.65373 -6.46481 1477010466559785 7.65 -6.45 1.81726 0.908632
437 | R 10.1729 -0.687264 1.46825 1477010466614740 7.8 -6.4 2.72951 0.909839
438 | L 7.94162 -6.34466 1477010466669696 7.95 -6.35 2.72948 0.909826
439 | R 9.98859 -0.65929 0.432862 1477010466719684 8.05 -6.25 2.00047 2.00048
440 | L 8.12355 -6.14389 1477010466769672 8.15 -6.15 2.00048 2.00048
441 | R 10.2866 -0.629609 1.03301 1477010466824677 8.3 -6.05 2.72702 1.81801
442 | L 8.45315 -5.97158 1477010466879683 8.45 -5.95 2.727 1.818
443 | R 10.375 -0.605303 0.959092 1477010466929651 8.55 -5.9 2.00127 1.00064
444 | L 8.64207 -5.83392 1477010466979619 8.65 -5.85 2.00128 1.00064
445 | R 10.625 -0.579961 1.49575 1477010467029625 8.8 -5.75 2.99963 1.99976
446 | L 8.94256 -5.64534 1477010467079632 8.95 -5.65 2.99961 1.99974
447 | R 10.8081 -0.549553 0.759227 1477010467129707 9.05 -5.55 1.99699 1.997
448 | L 9.15342 -5.45253 1477010467179782 9.15 -5.45 1.997 1.99701
449 | R 10.5876 -0.523958 0.708015 1477010467234830 9.25 -5.35 1.8166 1.81659
450 | L 9.34852 -5.23619 1477010467289878 9.35 -5.25 1.8166 1.81659
451 | R 10.6878 -0.497879 0.724693 1477010467344757 9.45 -5.15 1.8222 1.82219
452 | L 9.54207 -5.04728 1477010467399636 9.55 -5.05 1.82219 1.82219
453 | R 10.8597 -0.480893 1.35607 1477010467449735 9.65 -5 1.99604 0.998028
454 | L 9.75 -4.94288 1477010467499834 9.75 -4.95 1.99605 0.998028
455 | R 11.0125 -0.457284 0.645184 1477010467554811 9.85 -4.85 1.81895 1.81894
456 | L 9.94502 -4.76247 1477010467609788 9.95 -4.75 1.81894 1.81894
457 | R 11.0854 -0.430334 0.501275 1477010467664743 10.05 -4.6 1.81966 2.72951
458 | L 10.1384 -4.4533 1477010467719699 10.15 -4.45 1.81965 2.72948
459 | R 11.0567 -0.402233 0.823486 1477010467774672 10.25 -4.35 1.81908 1.81907
460 | L 10.3492 -4.25956 1477010467829645 10.35 -4.25 1.81908 1.81907
461 | R 11.3494 -0.375162 0.778181 1477010467879630 10.45 -4.1 2.00061 3.0009
462 | L 10.5557 -3.96034 1477010467929615 10.55 -3.95 2.0006 3.0009
463 | R 11.455 -0.3466 1.31515 1477010467979624 10.65 -3.85 1.99963 1.99964
464 | L 10.7394 -3.74821 1477010468029634 10.75 -3.75 1.99962 1.99962
465 | R 11.3478 -0.321223 -0.147548 1477010468079642 10.8 -3.6 0.999844 2.99952
466 | L 10.8596 -3.4348 1477010468129651 10.85 -3.45 0.999834 2.99949
467 | R 11.4637 -0.297497 1.34361 1477010468179635 10.95 -3.35 2.00065 2.00064
468 | L 11.0504 -3.25128 1477010468229619 11.05 -3.25 2.00064 2.00064
469 | R 11.4488 -0.271915 0.00655611 1477010468279692 11.1 -3.1 0.998546 2.99563
470 | L 11.1569 -2.944 1477010468329766 11.15 -2.95 0.998526 2.9956
471 | R 11.4825 -0.245369 -0.702556 1477010468384690 11.15 -2.8 0 2.73104
472 | L 11.141 -2.65084 1477010468439615 11.15 -2.65 0 2.73102
473 | R 11.3918 -0.225176 -0.449848 1477010468494625 11.15 -2.55 0 1.81785
474 | L 11.1497 -2.45904 1477010468549635 11.15 -2.45 0 1.81785
475 | R 11.3915 -0.199406 1.24065 1477010468599702 11.25 -2.25 1.99733 3.99465
476 | L 11.3591 -2.05184 1477010468649769 11.35 -2.05 1.99733 3.99465
477 | R 11.4735 -0.168155 -0.239169 1477010468704758 11.35 -1.95 0 1.81854
478 | L 11.3504 -1.8663 1477010468759748 11.35 -1.85 0 1.81853
479 | R 11.5444 -0.147849 -0.188915 1477010468814741 11.35 -1.7 0 2.72762
480 | L 11.3568 -1.53205 1477010468869734 11.35 -1.55 0 2.72762
481 | R 11.3388 -0.12234 -1.27743 1477010468924749 11.3 -1.4 -0.908846 2.72653
482 | L 11.2429 -1.24238 1477010468979764 11.25 -1.25 -0.908846 2.72653
483 | R 11.1287 -0.0978734 -1.86279 1477010469034705 11.15 -1.1 -1.82014 2.73021
484 | L 11.0544 -0.927069 1477010469089646 11.05 -0.949999 -1.82013 2.73021
485 | R 10.9533 -0.0723018 -1.45714 1477010469139628 11 -0.799999 -1.00036 3.00107
486 | L 10.9434 -0.640459 1477010469189611 10.95 -0.65 -1.00035 3.00104
487 | R 10.7256 -0.0453229 -2.2716 1477010469239610 10.85 -0.5 -2.00003 3.00006
488 | L 10.7471 -0.362528 1477010469289610 10.75 -0.35 -2.00002 3.00003
489 | R 10.6938 -0.0284812 -1.8134 1477010469344622 10.65 -0.3 -1.81779 0.908892
490 | L 10.547 -0.266677 1477010469399634 10.55 -0.25 -1.81778 0.908893
491 | R 10.5043 -0.0147089 -2.93008 1477010469449624 10.4 -0.15 -3.00061 2.00041
492 | L 10.2485 -0.0475524 1477010469499614 10.25 -0.0499992 -3.0006 2.00041
493 | R 10.141 -0.00711558 -1.90917 1477010469549703 10.15 -0.0499992 -1.99645 0
494 | L 10.0313 -0.0518611 1477010469599792 10.05 -0.0499992 -1.99644 0
495 | R 10.0898 -0.000164915 -2.6309 1477010469654702 9.9 9.49949e-07 -2.73175 0.910584
496 | L 9.74695 0.0464954 1477010469709612 9.75 0.0500011 -2.73174 0.910584
497 | R 9.50438 0.00876647 -3.02557 1477010469759688 9.6 0.100001 -2.99544 0.998471
498 | L 9.45825 0.149456 1477010469809764 9.45 0.15 -2.99545 0.998471
499 | R 9.22961 0.0175293 -1.86965 1477010469864720 9.35 0.15 -1.81963 0
500 | L 9.24456 0.139817 1477010469919677 9.25 0.15 -1.81962 0
501 | R 9.07524 0.0226968 -2.96695 1477010469969667 9.1 0.2 -3.00059 1.0002
502 | L 8.96281 0.250937 1477010470019657 8.95 0.25 -3.0006 1.0002
503 | R 8.84543 0.0291284 -2.90258 1477010470069658 8.8 0.25 -2.99995 0
504 | L 8.65606 0.24536 1477010470119659 8.65 0.25 -2.99994 0
505 | R 8.43911 0.029295 -2.8363 1477010470169772 8.5 0.25 -2.99323 0
506 | L 8.34184 0.250769 1477010470219886 8.35 0.25 -2.9932 0
507 | R 8.27433 0.0294916 -2.5969 1477010470274784 8.2 0.25 -2.73233 0
508 | L 8.03847 0.260731 1477010470329682 8.05 0.25 -2.73234 0
509 | R 7.98315 0.0256301 -2.85879 1477010470379643 7.9 0.2 -3.00234 -1.00078
510 | L 7.73327 0.149199 1477010470429605 7.75 0.15 -3.00231 -1.00077
511 | R 7.46312 0.019266 -3.11504 1477010470479672 7.6 0.15 -2.99599 0
512 | L 7.45455 0.149294 1477010470529740 7.45 0.15 -2.99596 0
513 | R 7.38047 0.0128858 -1.78619 1477010470584745 7.35 0.100001 -1.81801 -0.908998
514 | L 7.26713 0.055036 1477010470639750 7.25 0.0500011 -1.81801 -0.908998
515 | R 7.32563 -0.00630343 -2.78329 1477010470694797 7.1 -0.0499995 -2.72495 -1.81664
516 | L 6.93127 -0.133333 1477010470749845 6.95 -0.15 -2.72492 -1.81662
517 | R 7.10159 -0.0371997 -0.867081 1477010470804752 6.9 -0.25 -0.910634 -1.82126
518 | L 6.84796 -0.34284 1477010470859660 6.85 -0.35 -0.910622 -1.82124
519 | R 6.66164 -0.0676942 -3.03601 1477010470909720 6.7 -0.45 -2.99641 -1.99759
520 | L 6.54772 -0.536719 1477010470959781 6.55 -0.549999 -2.99637 -1.99757
521 | R 6.54413 -0.109938 -0.762244 1477010471014729 6.5 -0.7 -0.909955 -2.72986
522 | L 6.45451 -0.851695 1477010471069677 6.45 -0.85 -0.909955 -2.72986
523 | R 6.50274 -0.154318 -0.418379 1477010471119716 6.4 -1 -0.999224 -2.99766
524 | L 6.3554 -1.1624 1477010471169756 6.35 -1.15 -0.99921 -2.99763
525 | R 6.27693 -0.205206 -0.50992 1477010471224778 6.3 -1.3 -0.908722 -2.72618
526 | L 6.2299 -1.45381 1477010471279801 6.25 -1.45 -0.908718 -2.72616
527 | R 6.57101 -0.247004 1.50277 1477010471334717 6.3 -1.6 0.910485 -2.73144
528 | L 6.34473 -1.75136 1477010471389633 6.35 -1.75 0.910481 -2.73144
529 | R 6.61568 -0.2897 0.888256 1477010471439714 6.35 -1.9 0 -2.99515
530 | L 6.35828 -2.03886 1477010471489795 6.35 -2.05 0 -2.99515
531 | R 6.82616 -0.333515 0.963947 1477010471544769 6.35 -2.2 0 -2.72856
532 | L 6.34057 -2.34076 1477010471599744 6.35 -2.35 0 -2.72854
533 | R 6.98476 -0.375575 1.93359 1477010471654796 6.4 -2.5 0.908227 -2.7247
534 | L 6.45265 -2.65056 1477010471709849 6.45 -2.65 0.908223 -2.72467
535 | R 7.04974 -0.410031 0.8694 1477010471764848 6.45 -2.8 0 -2.72732
536 | L 6.45762 -2.94003 1477010471819848 6.45 -2.95 0 -2.7273
537 | R 7.07693 -0.451697 2.31494 1477010471874849 6.5 -3.15 0.909078 -3.6363
538 | L 6.54349 -3.33906 1477010471929851 6.55 -3.35 0.90907 -3.63626
539 | R 7.52682 -0.488801 2.06302 1477010471984794 6.6 -3.5 0.910038 -2.7301
540 | L 6.65608 -3.67065 1477010472039737 6.65 -3.65 0.910033 -2.7301
541 | R 7.721 -0.51755 2.03882 1477010472094797 6.7 -3.8 0.908095 -2.7243
542 | L 6.74422 -3.95901 1477010472149858 6.75 -3.95 0.908091 -2.72428
543 | R 7.85227 -0.5424 1.92133 1477010472204746 6.8 -4.1 0.910949 -2.73284
544 | L 6.83817 -4.25449 1477010472259634 6.85 -4.25 0.910945 -2.73284
545 | R 8.11508 -0.562462 1.69022 1477010472309642 6.9 -4.35 0.999834 -1.99968
546 | L 6.94879 -4.45387 1477010472359651 6.95 -4.45 0.999829 -1.99966
547 | R 8.35836 -0.579578 3.03057 1477010472409738 7.05 -4.6 1.99653 -2.99479
548 | L 7.14339 -4.76622 1477010472459825 7.15 -4.75 1.99653 -2.99479
549 | R 8.58227 -0.592606 2.07227 1477010472509776 7.2 -4.85 1.00098 -2.00196
550 | L 7.25883 -4.94719 1477010472559728 7.25 -4.95 1.00097 -2.00194
551 | R 8.88891 -0.610082 2.25633 1477010472614692 7.3 -5.1 0.90969 -2.72906
552 | L 7.33925 -5.24908 1477010472669657 7.35 -5.25 0.909677 -2.72904
553 | R 9.05785 -0.626214 3.44331 1477010472719663 7.45 -5.4 1.99976 -2.99964
554 | L 7.5499 -5.55354 1477010472769669 7.55 -5.55 1.99976 -2.99964
555 | R 9.4601 -0.639334 2.07117 1477010472819650 7.6 -5.65 1.00038 -2.00076
556 | L 7.64483 -5.73357 1477010472869632 7.65 -5.75 1.00037 -2.00074
557 | R 9.60303 -0.650794 2.99934 1477010472924680 7.75 -5.9 1.81659 -2.7249
558 | L 7.84396 -6.051 1477010472979728 7.85 -6.05 1.81659 -2.7249
559 | R 10.0235 -0.660739 1.82872 1477010473034718 7.9 -6.15 0.909251 -1.81851
560 | L 7.95018 -6.25957 1477010473089709 7.95 -6.25 0.909247 -1.81849
561 | R 10.3121 -0.667107 2.66075 1477010473144782 8.05 -6.35 1.81576 -1.81577
562 | L 8.15891 -6.45832 1477010473199855 8.15 -6.45 1.81577 -1.81577
563 | R 10.6726 -0.673465 3.0068 1477010473254746 8.25 -6.6 1.8218 -2.73269
564 | L 8.33273 -6.74641 1477010473309637 8.35 -6.75 1.8218 -2.73269
565 | R 10.9601 -0.688074 2.42458 1477010473364703 8.4 -6.9 0.907987 -2.72401
566 | L 8.44383 -7.03914 1477010473419770 8.45 -7.05 0.907988 -2.72398
567 | R 11.191 -0.703148 2.55121 1477010473474712 8.5 -7.2 0.910054 -2.73014
568 | L 8.55761 -7.36836 1477010473529655 8.55 -7.35 0.910046 -2.73012
569 | R 11.4834 -0.710496 2.74755 1477010473579698 8.65 -7.45 1.99827 -1.99828
570 | L 8.7565 -7.55536 1477010473629741 8.75 -7.55 1.99828 -1.99828
571 | R 11.6331 -0.719838 2.5798 1477010473684743 8.8 -7.7 0.909061 -2.72717
572 | L 8.85031 -7.85391 1477010473739746 8.85 -7.85 0.909053 -2.72715
573 | R 11.9356 -0.731085 2.49207 1477010473794754 8.9 -8 0.908945 -2.72688
574 | L 8.95085 -8.16397 1477010473849762 8.95 -8.15 0.908953 -2.72687
575 | R 12.2284 -0.739285 2.50621 1477010473904699 9.05 -8.25 1.82026 -1.82027
576 | L 9.15149 -8.34807 1477010473959637 9.15 -8.35 1.82025 -1.82026
577 | R 12.4412 -0.745624 2.97957 1477010474009643 9.2 -8.5 0.999884 -2.99963
578 | L 9.22902 -8.65053 1477010474059650 9.25 -8.65 0.999874 -2.9996
579 | R 12.7039 -0.756748 2.71995 1477010474109686 9.3 -8.8 0.999284 -2.99783
580 | L 9.36398 -8.95164 1477010474159722 9.35 -8.95 0.999284 -2.99784
581 | R 13.1658 -0.767119 1.91016 1477010474214714 9.4 -9.05 0.909209 -1.81844
582 | L 9.43696 -9.1575 1477010474269707 9.45 -9.15 0.90921 -1.81843
583 | R 13.2358 -0.776088 2.49057 1477010474324744 9.5 -9.3 0.908483 -2.72543
584 | L 9.53884 -9.45193 1477010474379782 9.55 -9.45 0.908475 -2.72542
585 | R 13.696 -0.785123 2.56399 1477010474434742 9.6 -9.6 0.909756 -2.72927
586 | L 9.67284 -9.75791 1477010474489703 9.65 -9.75 0.909739 -2.72923
587 | R 14.0976 -0.795739 2.84598 1477010474539772 9.7 -9.9 0.998626 -2.99586
588 | L 9.74713 -10.0467 1477010474589842 9.75 -10.05 0.998616 -2.99584
589 | R 14.1089 -0.807364 1.87476 1477010474644816 9.75 -10.2 0 -2.72857
590 | L 9.74697 -10.3629 1477010474699791 9.75 -10.35 0 -2.72854
591 | R 14.3022 -0.818543 2.54971 1477010474754750 9.8 -10.5 0.909773 -2.7293
592 | L 9.86261 -10.6462 1477010474809709 9.85 -10.65 0.909773 -2.7293
593 | R 14.6457 -0.831495 1.87281 1477010474864685 9.85 -10.8 0 -2.72846
594 | L 9.8455 -10.9408 1477010474919662 9.85 -10.95 0 -2.72844
595 | R 14.8355 -0.845076 2.08863 1477010474974658 9.85 -11.1 0 -2.72748
596 | L 9.83549 -11.2481 1477010475029655 9.85 -11.25 0 -2.72745
597 | R 15.1415 -0.858804 2.28971 1477010475079710 9.85 -11.4 0 -2.9967
598 | L 9.87136 -11.5692 1477010475129765 9.85 -11.55 0 -2.99671
599 | R 15.3827 -0.87173 1.57763 1477010475184757 9.8 -11.7 -0.909227 -2.72768
600 | L 9.74749 -11.8623 1477010475239750 9.75 -11.85 -0.909218 -2.72765
601 | R 15.4788 -0.888766 1.45861 1477010475294773 9.7 -12 -0.908714 -2.72613
602 | L 9.65408 -12.1513 1477010475349797 9.65 -12.15 -0.908706 -2.7261
603 | R 15.5601 -0.905614 0.938636 1477010475404763 9.6 -12.25 -0.909639 -1.81931
604 | L 9.54006 -12.3354 1477010475459730 9.55 -12.35 -0.90964 -1.8193
605 | R 15.5217 -0.921231 0.267302 1477010475514728 9.45 -12.45 -1.81824 -1.81825
606 | L 9.35049 -12.5377 1477010475569727 9.35 -12.55 -1.81823 -1.81823
607 | R 15.8729 -0.942024 -0.053447 1477010475624757 9.2 -12.65 -2.72578 -1.81718
608 | L 9.04708 -12.7528 1477010475679788 9.05 -12.75 -2.72576 -1.81717
609 | R 15.6473 -0.959771 0.301972 1477010475734757 8.95 -12.85 -1.8192 -1.81921
610 | L 8.84904 -12.9423 1477010475789726 8.85 -12.95 -1.81921 -1.81921
611 | R 15.5759 -0.982069 0.0451108 1477010475844715 8.7 -13.05 -2.72781 -1.81854
612 | L 8.5479 -13.1496 1477010475899705 8.55 -13.15 -2.7278 -1.81853
613 | R 15.7178 -1.00436 -1.56759 1477010475954722 8.4 -13.15 -2.72644 0
614 | L 8.26001 -13.1483 1477010476009740 8.25 -13.15 -2.72641 0
615 | R 15.4407 -1.01846 -1.48784 1477010476059726 8.1 -13.15 -3.00083 0
616 | L 7.95138 -13.1593 1477010476109712 7.95 -13.15 -3.00084 0
617 | R 15.4191 -1.03651 -0.716977 1477010476164747 7.8 -13.2 -2.72553 -0.908516
618 | L 7.64076 -13.2654 1477010476219782 7.65 -13.25 -2.72554 -0.908516
619 | R 15.2661 -1.05621 -2.11606 1477010476274819 7.5 -13.2 -2.72544 0.908483
620 | L 7.35762 -13.1417 1477010476329856 7.35 -13.15 -2.72544 0.908483
621 | R 15.0349 -1.07175 -1.34238 1477010476384794 7.2 -13.15 -2.73035 0
622 | L 7.05296 -13.1563 1477010476439732 7.05 -13.15 -2.73035 0
623 | R 14.893 -1.09086 -1.71646 1477010476494735 6.85 -13.15 -3.63616 0
624 | L 6.672 -13.1364 1477010476549738 6.65 -13.15 -3.63617 0
625 | R 14.6699 -1.10884 -0.875032 1477010476604787 6.55 -13.15 -1.81656 0
626 | L 6.46904 -13.1587 1477010476659836 6.45 -13.15 -1.81657 0
627 | R 14.4304 -1.12445 -1.88305 1477010476714759 6.3 -13.1 -2.73109 0.910352
628 | L 6.15723 -13.0319 1477010476769682 6.15 -13.05 -2.73109 0.91036
629 | R 14.1864 -1.13755 -2.90062 1477010476824762 6 -12.95 -2.72331 1.81553
630 | L 5.85711 -12.8423 1477010476879842 5.85 -12.85 -2.72331 1.81554
631 | R 14.1065 -1.15181 -1.95986 1477010476934819 5.7 -12.8 -2.72842 0.909475
632 | L 5.5559 -12.7556 1477010476989797 5.55 -12.75 -2.72839 0.909466
633 | R 13.8068 -1.16747 -2.70953 1477010477044722 5.4 -12.65 -2.731 1.82067
634 | L 5.23681 -12.5467 1477010477099647 5.25 -12.55 -2.731 1.82066
635 | R 13.5269 -1.17869 -2.4779 1477010477154646 5.15 -12.45 -1.81821 1.8182
636 | L 5.04959 -12.3548 1477010477209646 5.05 -12.35 -1.8182 1.8182
637 | R 13.3273 -1.18622 -2.53566 1477010477259657 4.95 -12.25 -1.99957 1.99957
638 | L 4.86969 -12.1416 1477010477309669 4.85 -12.15 -1.99954 1.99955
639 | R 12.9798 -1.18873 -2.09258 1477010477359671 4.8 -12.05 -0.999954 1.99993
640 | L 4.77326 -11.9345 1477010477409673 4.75 -11.95 -0.999959 1.99992
641 | R 12.539 -1.19655 -3.42819 1477010477459668 4.65 -11.8 -2.0002 3.00031
642 | L 4.54148 -11.6336 1477010477509663 4.55 -11.65 -2.0002 3.0003
643 | R 12.4176 -1.19677 -1.98844 1477010477559726 4.55 -11.55 0 1.99749
644 | L 4.55238 -11.4548 1477010477609790 4.55 -11.45 0 1.99746
645 | R 12.0696 -1.19087 -3.81549 1477010477664786 4.5 -11.25 -0.90916 3.63662
646 | L 4.45629 -11.0538 1477010477719783 4.45 -11.05 -0.909152 3.63659
647 | R 11.7368 -1.18684 -1.55765 1477010477774786 4.45 -10.95 0 1.81807
648 | L 4.45899 -10.8664 1477010477829789 4.45 -10.85 0 1.81808
649 | R 11.6867 -1.17618 -2.47213 1477010477884761 4.45 -10.7 0 2.72865
650 | L 4.44058 -10.5544 1477010477939734 4.45 -10.55 0 2.72864
651 | R 11.3043 -1.16677 -2.36037 1477010477994802 4.45 -10.4 0 2.72392
652 | L 4.44086 -10.2469 1477010478049870 4.45 -10.25 0 2.72391
653 | R 11.1445 -1.152 -2.18731 1477010478104838 4.5 -10.1 0.909624 2.72885
654 | L 4.54793 -9.94716 1477010478159807 4.55 -9.95 0.909615 2.72884
655 | R 10.7341 -1.13037 -1.9859 1477010478214806 4.6 -9.8 0.909111 2.72733
656 | L 4.64364 -9.63888 1477010478269805 4.65 -9.65 0.909107 2.72732
657 | R 10.6729 -1.10786 -1.7483 1477010478324774 4.75 -9.5 1.81921 2.7288
658 | L 4.82761 -9.37054 1477010478379743 4.85 -9.35 1.81921 2.7288
659 | R 10.4052 -1.08079 -1.92895 1477010478434764 4.9 -9.2 0.908739 2.72622
660 | L 4.94177 -9.04086 1477010478489785 4.95 -9.05 0.908743 2.72623
661 | R 10.3962 -1.05915 -0.886898 1477010478544824 5.05 -8.95 1.8169 1.81688
662 | L 5.1385 -8.8446 1477010478599864 5.15 -8.85 1.81688 1.81688
663 | R 10.09 -1.02781 -1.24596 1477010478654806 5.25 -8.7 1.8201 2.73014
664 | L 5.33853 -8.54729 1477010478709749 5.35 -8.55 1.82008 2.73013
665 | R 9.95856 -1.00261 -0.973357 1477010478764679 5.4 -8.45 0.910244 1.82049
666 | L 5.46213 -8.36055 1477010478819609 5.45 -8.35 0.910249 1.8205
667 | R 10.0625 -0.976143 -1.42162 1477010478869695 5.55 -8.2 1.99657 2.99484
668 | L 5.63863 -8.05978 1477010478919781 5.65 -8.05 1.99657 2.99485
669 | R 9.6935 -0.942624 -0.325581 1477010478974765 5.75 -7.95 1.81871 1.81872
670 | L 5.84353 -7.85177 1477010479029749 5.85 -7.85 1.81871 1.81871
671 | R 9.73588 -0.912814 -0.00486679 1477010479084702 6 -7.75 2.72961 1.81974
672 | L 6.14453 -7.66253 1477010479139655 6.15 -7.65 2.72961 1.81974
673 | R 9.82092 -0.878335 -0.277344 1477010479189675 6.25 -7.55 1.9992 1.9992
674 | L 6.3386 -7.44193 1477010479239696 6.35 -7.45 1.99918 1.99918
675 | R 9.75766 -0.849487 -0.0978727 1477010479294668 6.45 -7.35 1.81911 1.81911
676 | L 6.54947 -7.257 1477010479349641 6.55 -7.25 1.81909 1.81909
677 | R 9.86809 -0.822197 0.120418 1477010479399642 6.65 -7.15 1.99996 1.99996
678 | L 6.7522 -7.03215 1477010479449643 6.75 -7.05 1.99996 1.99996
679 | R 9.74178 -0.796215 0.681366 1477010479499705 6.85 -7 1.99752 0.998765
680 | L 6.95051 -6.93564 1477010479549767 6.95 -6.95 1.99752 0.998765
681 | R 9.73646 -0.767898 0.636735 1477010479604705 7.1 -6.85 2.73035 1.82023
682 | L 7.25571 -6.75585 1477010479659643 7.25 -6.75 2.73035 1.82023
683 | R 9.82669 -0.735388 0.310058 1477010479709628 7.35 -6.65 2.0006 2.0006
684 | L 7.42947 -6.54884 1477010479759614 7.45 -6.55 2.00058 2.00058
685 | R 10.0155 -0.711196 0.937302 1477010479809654 7.55 -6.5 1.99841 0.999204
686 | L 7.65263 -6.44325 1477010479859695 7.65 -6.45 1.99838 0.999195
687 | R 10.0688 -0.686191 1.79052 1477010479909703 7.8 -6.4 2.99952 0.999844
688 | L 7.95705 -6.33998 1477010479959712 7.95 -6.35 2.99949 0.999829
689 | R 10.2955 -0.660707 0.133278 1477010480014777 8.05 -6.25 1.81603 1.81603
690 | L 8.15184 -6.14215 1477010480069842 8.15 -6.15 1.81603 1.81603
691 | R 10.0748 -0.631835 0.423922 1477010480124811 8.25 -6.05 1.81921 1.81921
692 | L 8.34844 -5.95288 1477010480179780 8.35 -5.95 1.81921 1.81921
693 | R 10.2941 -0.606816 1.81063 1477010480234745 8.5 -5.9 2.729 0.909673
694 | L 8.64546 -5.83668 1477010480289711 8.65 -5.85 2.72898 0.909661
695 | R 10.3761 -0.579463 1.35126 1477010480344702 8.8 -5.75 2.72771 1.81848
696 | L 8.95179 -5.66017 1477010480399693 8.95 -5.65 2.72772 1.81848
697 | R 10.5509 -0.550533 0.683611 1477010480449708 9.05 -5.55 1.99939 1.9994
698 | L 9.15234 -5.45358 1477010480499723 9.15 -5.45 1.9994 1.9994
699 | R 10.7242 -0.529443 1.0617 1477010480554736 9.25 -5.4 1.81776 0.90888
700 | L 9.3462 -5.33648 1477010480609749 9.35 -5.35 1.81776 0.908875
701 | R 10.6867 -0.503878 0.00801584 1477010480664832 9.45 -5.2 1.81545 2.72317
702 | L 9.55586 -5.07162 1477010480719916 9.55 -5.05 1.81542 2.72314
703 | R 10.7882 -0.472652 0.842123 1477010480774817 9.65 -4.95 1.82145 1.82147
704 | L 9.75218 -4.8384 1477010480829718 9.75 -4.85 1.82146 1.82146
705 | R 11.1251 -0.450929 0.916652 1477010480884675 9.85 -4.75 1.81961 1.8196
706 | L 9.9571 -4.66087 1477010480939632 9.95 -4.65 1.8196 1.8196
707 | R 10.9735 -0.424449 0.969135 1477010480989675 10.05 -4.55 1.99827 1.99828
708 | L 10.1558 -4.43582 1477010481039719 10.15 -4.45 1.99826 1.99826
709 | R 11.012 -0.396725 0.528506 1477010481094757 10.25 -4.3 1.81693 2.72538
710 | L 10.3312 -4.16698 1477010481149796 10.35 -4.15 1.81692 2.72536
711 | R 11.1324 -0.37187 1.16702 1477010481204756 10.45 -4.05 1.81951 1.8195
712 | L 10.5551 -3.94162 1477010481259716 10.55 -3.95 1.8195 1.81951
713 | R 11.3205 -0.346977 1.07019 1477010481314716 10.65 -3.85 1.81817 1.81818
714 | L 10.746 -3.7455 1477010481369717 10.75 -3.75 1.81816 1.81817
715 | R 11.5288 -0.321428 0.958754 1477010481424777 10.85 -3.6 1.81621 2.7243
716 | L 10.9457 -3.45027 1477010481479837 10.95 -3.45 1.8162 2.7243
717 | R 11.5968 -0.290931 0.0529376 1477010481534865 11 -3.3 0.908632 2.72588
718 | L 11.0435 -3.16012 1477010481589893 11.05 -3.15 0.908632 2.72588
719 | R 11.3751 -0.262609 0.248627 1477010481644844 11.1 -3 0.909905 2.72971
720 | L 11.1542 -2.85724 1477010481699796 11.15 -2.85 0.909888 2.72968
721 | R 11.3429 -0.237942 -0.657875 1477010481754772 11.15 -2.7 0 2.72847
722 | L 11.1324 -2.55807 1477010481809749 11.15 -2.55 0 2.72844
723 | R 11.5121 -0.212107 -0.543635 1477010481864748 11.15 -2.4 0 2.72732
724 | L 11.1569 -2.2658 1477010481919747 11.15 -2.25 0 2.72732
725 | R 11.4133 -0.184157 1.27014 1477010481974721 11.25 -2.1 1.81905 2.72856
726 | L 11.3482 -1.94557 1477010482029695 11.35 -1.95 1.81905 2.72856
727 | R 11.5354 -0.157397 -1.33198 1477010482084691 11.3 -1.8 -0.90916 2.72747
728 | L 11.2389 -1.64141 1477010482139688 11.25 -1.65 -0.909152 2.72745
729 | R 11.4353 -0.133106 -0.409532 1477010482194770 11.25 -1.5 0 2.72321
730 | L 11.2625 -1.34833 1477010482249852 11.25 -1.35 0 2.72321
731 | R 11.3746 -0.104867 -1.32078 1477010482304826 11.2 -1.2 -0.909524 2.72856
732 | L 11.1556 -1.0549 1477010482359800 11.15 -1.05 -0.909524 2.72856
733 | R 11.0344 -0.0810376 -2.19048 1477010482414762 11.05 -0.9 -1.81945 2.72916
734 | L 10.9445 -0.73319 1477010482469725 10.95 -0.75 -1.81942 2.72913
735 | R 10.852 -0.0540255 -1.26188 1477010482524680 10.9 -0.599999 -0.909839 2.72952
736 | L 10.8388 -0.441032 1477010482579636 10.85 -0.449999 -0.909822 2.72949
737 | R 10.637 -0.0338463 -3.16697 1477010482629713 10.7 -0.349999 -2.99538 1.99692
738 | L 10.5529 -0.247121 1477010482679790 10.55 -0.25 -2.99539 1.99691
739 | R 10.434 -0.0187044 -1.67055 1477010482734821 10.45 -0.2 -1.81715 0.908579
740 | L 10.3505 -0.143257 1477010482789852 10.35 -0.15 -1.81716 0.908579
741 | R 10.2074 -0.0106801 -2.6148 1477010482844758 10.2 -0.0999996 -2.73194 0.910655
742 | L 10.0541 -0.0441275 1477010482899664 10.05 -0.0499992 -2.73194 0.910655
743 | R 9.89001 0.000825869 -2.58728 1477010482954714 9.9 9.49949e-07 -2.72481 0.908268
744 | L 9.75876 0.0560308 1477010483009765 9.75 0.0500011 -2.72477 0.90826
745 | R 9.63573 0.00964489 -1.81622 1477010483064783 9.65 0.100001 -1.81759 0.908784
746 | L 9.56967 0.149506 1477010483119802 9.55 0.15 -1.81757 0.908775
747 | R 9.5634 0.0155366 -2.61454 1477010483174816 9.4 0.15 -2.72659 0
748 | L 9.25348 0.150166 1477010483229830 9.25 0.15 -2.72658 0
749 | R 9.08448 0.0228764 -2.61295 1477010483284790 9.1 0.2 -2.72925 0.909752
750 | L 8.93575 0.243169 1477010483339750 8.95 0.25 -2.72926 0.909752
751 | R 8.95026 0.0295912 -2.79773 1477010483394739 8.8 0.25 -2.72783 0
752 | L 8.64483 0.254352 1477010483449729 8.65 0.25 -2.7278 0
753 | R 8.47238 0.0296451 -2.72052 1477010483504796 8.5 0.25 -2.72395 0
754 | L 8.35775 0.250149 1477010483559863 8.35 0.25 -2.72395 0
755 | R 8.28345 0.0237815 -2.75499 1477010483614752 8.2 0.2 -2.73278 -0.910929
756 | L 8.03004 0.154113 1477010483669641 8.05 0.15 -2.73279 -0.910929
757 | R 7.74832 0.0199538 -4.06511 1477010483719651 7.85 0.15 -3.9992 0
758 | L 7.63715 0.143302 1477010483769662 7.65 0.15 -3.99916 0
759 | R 7.62144 0.020608 -1.92459 1477010483819695 7.55 0.15 -1.99868 0
760 | L 7.43926 0.160393 1477010483869729 7.45 0.15 -1.99866 0
761 | R 7.34552 0.013889 -2.79671 1477010483924750 7.3 0.100001 -2.72622 -0.908734
762 | L 7.16771 0.0463372 1477010483979771 7.15 0.0500011 -2.72623 -0.908734
763 | R 7.05739 -0.00713726 -1.92312 1477010484034750 7.05 -0.0499995 -1.81887 -1.81889
764 | L 6.95035 -0.144849 1477010484089729 6.95 -0.15 -1.81888 -1.81889
765 | R 6.87642 -0.0353825 -1.74942 1477010484144737 6.85 -0.25 -1.81792 -1.81792
766 | L 6.76317 -0.34818 1477010484199745 6.75 -0.35 -1.81792 -1.81792
767 | R 6.77279 -0.0670127 -1.80294 1477010484254726 6.65 -0.45 -1.81881 -1.8188
768 | L 6.56412 -0.553924 1477010484309708 6.55 -0.549999 -1.81879 -1.81878
769 | R 6.64863 -0.115431 -1.34231 1477010484364662 6.45 -0.749999 -1.81971 -3.63941
770 | L 6.36344 -0.942514 1477010484419617 6.35 -0.949999 -1.81969 -3.63937
771 | R 6.45072 -0.171362 0.516744 1477010484469697 6.35 -1.1 0 -2.99522
772 | L 6.36321 -1.25111 1477010484519778 6.35 -1.25 0 -2.99519
773 | R 6.46485 -0.219372 0.384586 1477010484574760 6.35 -1.4 0 -2.72817
774 | L 6.35537 -1.55723 1477010484629742 6.35 -1.55 0 -2.72817
775 | R 6.74008 -0.262061 0.70842 1477010484684762 6.35 -1.7 0 -2.72628
776 | L 6.35448 -1.83673 1477010484739782 6.35 -1.85 0 -2.72628
777 | R 6.70101 -0.306612 0.641725 1477010484794696 6.35 -2 0 -2.73154
778 | L 6.35078 -2.13767 1477010484849611 6.35 -2.15 0 -2.73152
779 | R 6.68883 -0.347417 0.876202 1477010484899694 6.35 -2.3 0 -2.99503
780 | L 6.33355 -2.43768 1477010484949777 6.35 -2.45 0 -2.99503
781 | R 6.81201 -0.386367 1.94865 1477010485004722 6.4 -2.6 0.909996 -2.73
782 | L 6.45623 -2.7536 1477010485059667 6.45 -2.75 0.91 -2.73
783 | R 7.01896 -0.421439 1.97259 1477010485114758 6.5 -2.9 0.907593 -2.72277
784 | L 6.54629 -3.01951 1477010485169850 6.55 -3.05 0.907584 -2.72274
785 | R 7.4891 -0.460405 1.56 1477010485224853 6.55 -3.25 0 -3.63617
786 | L 6.54763 -3.45513 1477010485279857 6.55 -3.45 0 -3.63613
787 | R 7.42685 -0.499304 2.20461 1477010485334822 6.6 -3.6 0.909673 -2.72901
788 | L 6.64571 -3.73674 1477010485389788 6.65 -3.75 0.909661 -2.72898
789 | R 7.73369 -0.519546 2.24171 1477010485444795 6.75 -3.85 1.81795 -1.81795
790 | L 6.85201 -3.93663 1477010485499803 6.85 -3.95 1.81793 -1.81793
791 | R 8.32239 -0.540957 2.69981 1477010485554749 6.9 -4.15 0.909979 -3.63994
792 | L 6.9548 -4.32855 1477010485609696 6.95 -4.35 0.909975 -3.6399
793 | R 8.30107 -0.565342 1.79442 1477010485664769 7 -4.45 0.907889 -1.81577
794 | L 7.05586 -4.55105 1477010485719843 7.05 -4.55 0.907881 -1.81576
795 | R 8.59318 -0.585948 2.51996 1477010485774801 7.1 -4.7 0.909789 -2.72935
796 | L 7.13261 -4.82718 1477010485829760 7.15 -4.85 0.909777 -2.72933
797 | R 8.84525 -0.603476 3.20216 1477010485884747 7.25 -5 1.81861 -2.72792
798 | L 7.34301 -5.14642 1477010485939735 7.35 -5.15 1.81859 -2.72789
799 | R 9.02066 -0.622827 2.45726 1477010485989735 7.4 -5.3 0.999994 -3
800 | L 7.46174 -5.44979 1477010486039735 7.45 -5.45 0.999999 -3
801 | R 9.26753 -0.633155 2.46174 1477010486094756 7.55 -5.55 1.81749 -1.81749
802 | L 7.65989 -5.6552 1477010486149778 7.65 -5.65 1.81747 -1.81747
803 | R 9.74447 -0.645201 2.28392 1477010486204747 7.7 -5.8 0.909598 -2.72881
804 | L 7.75276 -5.95823 1477010486259717 7.75 -5.95 0.909594 -2.72878
805 | R 9.84383 -0.656665 2.49108 1477010486314673 7.85 -6.05 1.81964 -1.81964
806 | L 7.95034 -6.15505 1477010486369630 7.95 -6.15 1.81962 -1.81962
807 | R 10.2229 -0.664707 3.50849 1477010486419697 8.05 -6.3 1.99731 -2.99599
808 | L 8.14855 -6.44643 1477010486469764 8.15 -6.45 1.99732 -2.99598
809 | R 10.5783 -0.673972 1.89866 1477010486524763 8.2 -6.55 0.909111 -1.81822
810 | L 8.25279 -6.64996 1477010486579762 8.25 -6.65 0.909111 -1.81822
811 | R 10.6184 -0.686812 2.56302 1477010486634801 8.3 -6.8 0.90845 -2.72534
812 | L 8.35448 -6.94719 1477010486689841 8.35 -6.95 0.908442 -2.72531
813 | R 11.2448 -0.697694 3.19734 1477010486744809 8.45 -7.1 1.81925 -2.72886
814 | L 8.54666 -7.24803 1477010486799777 8.55 -7.25 1.81924 -2.72886
815 | R 11.4023 -0.703311 2.66814 1477010486854698 8.65 -7.35 1.82079 -1.8208
816 | L 8.74711 -7.43338 1477010486909619 8.75 -7.45 1.8208 -1.8208
817 | R 11.4784 -0.712179 2.92582 1477010486959738 8.8 -7.6 0.997629 -2.99288
818 | L 8.84984 -7.75349 1477010487009857 8.85 -7.75 0.997629 -2.99288
819 | R 11.741 -0.726703 2.18954 1477010487064854 8.9 -7.9 0.909127 -2.72742
820 | L 8.94604 -8.07116 1477010487119852 8.95 -8.05 0.909127 -2.7274
821 | R 12.0578 -0.738017 1.97437 1477010487174826 9 -8.15 0.909524 -1.81903
822 | L 9.01893 -8.23506 1477010487229801 9.05 -8.25 0.909516 -1.81902
823 | R 12.3259 -0.7466 2.46112 1477010487284751 9.1 -8.4 0.909922 -2.72975
824 | L 9.1628 -8.53304 1477010487339701 9.15 -8.55 0.909913 -2.72976
825 | R 12.4872 -0.755033 3.12856 1477010487394740 9.25 -8.7 1.8169 -2.72535
826 | L 9.3508 -8.85468 1477010487449780 9.35 -8.85 1.81688 -2.72532
827 | R 12.9973 -0.761667 2.54796 1477010487504711 9.4 -9 0.910219 -2.73069
828 | L 9.45338 -9.162 1477010487559642 9.45 -9.15 0.910228 -2.73069
829 | R 13.3492 -0.7773 2.64735 1477010487609747 9.5 -9.3 0.997908 -2.99371
830 | L 9.56994 -9.45148 1477010487659852 9.55 -9.45 0.997908 -2.99372
831 | R 13.5895 -0.783535 1.80157 1477010487714799 9.6 -9.55 0.909971 -1.81993
832 | L 9.64603 -9.66938 1477010487769747 9.65 -9.65 0.909954 -1.81992
833 | R 13.7362 -0.793586 1.9961 1477010487824681 9.65 -9.8 0 -2.73054
834 | L 9.64842 -9.95866 1477010487879616 9.65 -9.95 0 -2.73053
835 | R 13.8624 -0.804437 2.82352 1477010487929707 9.7 -10.1 0.998187 -2.99456
836 | L 9.7516 -10.2483 1477010487979798 9.75 -10.25 0.998187 -2.99455
837 | R 14.2377 -0.814555 2.56532 1477010488034755 9.8 -10.4 0.909806 -2.7294
838 | L 9.8633 -10.5382 1477010488089713 9.85 -10.55 0.909797 -2.72938
839 | R 14.5453 -0.825759 1.93079 1477010488144715 9.85 -10.7 0 -2.72718
840 | L 9.84674 -10.8547 1477010488199718 9.85 -10.85 0 -2.72715
841 | R 14.6206 -0.839985 1.93631 1477010488254785 9.85 -11 0 -2.72395
842 | L 9.86442 -11.1448 1477010488309852 9.85 -11.15 0 -2.72395
843 | R 15.0172 -0.853421 2.11639 1477010488364782 9.85 -11.3 0 -2.73074
844 | L 9.85036 -11.4464 1477010488419712 9.85 -11.45 0 -2.73075
845 | R 15.3302 -0.866059 2.06556 1477010488474779 9.85 -11.6 0 -2.72396
846 | L 9.83174 -11.7336 1477010488529846 9.85 -11.75 0 -2.72396
847 | R 15.3983 -0.88022 1.79987 1477010488579821 9.8 -11.9 -1.0005 -3.00149
848 | L 9.75331 -12.0506 1477010488629796 9.75 -12.05 -1.0005 -3.0015
849 | R 15.5297 -0.900676 0.234171 1477010488684802 9.65 -12.15 -1.81799 -1.81797
850 | L 9.55524 -12.2329 1477010488739809 9.55 -12.25 -1.81797 -1.81797
851 | R 15.6284 -0.919828 1.29552 1477010488794741 9.45 -12.4 -1.82042 -2.73064
852 | L 9.34172 -12.5587 1477010488849674 9.35 -12.55 -1.82041 -2.73063
853 | R 15.6571 -0.941485 0.291516 1477010488899701 9.25 -12.65 -1.99893 -1.99891
854 | L 9.16462 -12.7506 1477010488949729 9.15 -12.75 -1.99891 -1.9989
855 | R 15.7268 -0.954682 0.414822 1477010489004723 9.05 -12.85 -1.81839 -1.81839
856 | L 8.95936 -12.9426 1477010489059717 8.95 -12.95 -1.81838 -1.81838
857 | R 15.8476 -0.977762 -0.972751 1477010489114674 8.8 -13 -2.72942 -0.909806
858 | L 8.65422 -13.0444 1477010489169631 8.65 -13.05 -2.72941 -0.909806
859 | R 15.6101 -0.995616 -0.116796 1477010489219633 8.5 -13.15 -2.99987 -1.99991
860 | L 8.3436 -13.2381 1477010489269636 8.35 -13.25 -2.99984 -1.9999
861 | R 15.432 -1.01103 -1.74358 1477010489319687 8.25 -13.2 -1.99797 0.998985
862 | L 8.15674 -13.1486 1477010489369738 8.15 -13.15 -1.99797 0.998985
863 | R 15.4246 -1.02553 -0.658323 1477010489424683 8 -13.2 -2.73 -0.910004
864 | L 7.85314 -13.2533 1477010489479628 7.85 -13.25 -2.73 -0.910004
865 | R 15.1639 -1.04748 -1.86286 1477010489534731 7.65 -13.25 -3.62957 0
866 | L 7.45422 -13.2433 1477010489589835 7.45 -13.25 -3.62953 0
867 | R 15.1626 -1.06605 -1.33622 1477010489644778 7.3 -13.25 -2.7301 0
868 | L 7.13166 -13.2372 1477010489699722 7.15 -13.25 -2.73007 0
869 | R 14.8978 -1.08358 -1.4991 1477010489754727 7 -13.25 -2.72703 0
870 | L 6.84852 -13.2404 1477010489809733 6.85 -13.25 -2.727 0
871 | R 14.8615 -1.10225 -2.03746 1477010489864730 6.7 -13.2 -2.72742 0.909144
872 | L 6.55626 -13.1584 1477010489919727 6.55 -13.15 -2.72742 0.909144
873 | R 14.3946 -1.11583 -2.23409 1477010489974732 6.4 -13.1 -2.72703 0.908994
874 | L 6.25112 -13.0473 1477010490029738 6.25 -13.05 -2.727 0.908995
875 | R 14.3607 -1.13231 -1.93156 1477010490084683 6.1 -13 -2.73 0.910004
876 | L 5.95513 -12.9613 1477010490139629 5.95 -12.95 -2.72998 0.909996
877 | R 14.3815 -1.14857 -2.05973 1477010490189750 5.8 -12.9 -2.99275 0.99759
878 | L 5.6635 -12.8347 1477010490239871 5.65 -12.85 -2.99275 0.99758
879 | R 14.0268 -1.15873 -2.36004 1477010490294818 5.55 -12.75 -1.81993 1.81994
880 | L 5.45219 -12.6228 1477010490349765 5.45 -12.65 -1.81994 1.81994
881 | R 13.5149 -1.16708 -2.47916 1477010490404794 5.35 -12.55 -1.81722 1.81723
882 | L 5.25069 -12.4448 1477010490459824 5.25 -12.45 -1.81721 1.81721
883 | R 13.4552 -1.17936 -2.79199 1477010490514793 5.1 -12.35 -2.72881 1.8192
884 | L 4.9499 -12.2734 1477010490569762 4.95 -12.25 -2.72881 1.81921
885 | R 13.1387 -1.18616 -2.10019 1477010490624800 4.9 -12.15 -0.908467 1.81693
886 | L 4.84027 -12.0359 1477010490679838 4.85 -12.05 -0.908462 1.81692
887 | R 12.8997 -1.1896 -3.33726 1477010490734847 4.75 -11.9 -1.81788 2.72684
888 | L 4.63814 -11.758 1477010490789856 4.65 -11.75 -1.81788 2.72683
889 | R 12.3042 -1.19578 -2.07279 1477010490844803 4.6 -11.65 -0.909963 1.81994
890 | L 4.5399 -11.541 1477010490899751 4.55 -11.55 -0.909959 1.81992
891 | R 12.1226 -1.19333 -3.59435 1477010490954743 4.5 -11.35 -0.909227 3.63689
892 | L 4.45518 -11.1556 1477010491009736 4.45 -11.15 -0.909218 3.63686
893 | R 12.0979 -1.19164 -1.93426 1477010491064728 4.4 -11.05 -0.909227 1.81845
894 | L 4.36691 -10.9529 1477010491119720 4.35 -10.95 -0.909222 1.81844
895 | R 11.5336 -1.18409 -2.00293 1477010491174701 4.4 -10.8 0.9094 2.72823
896 | L 4.42489 -10.6572 1477010491229683 4.45 -10.65 0.909396 2.72819
897 | R 11.4356 -1.17086 -2.81654 1477010491279664 4.45 -10.5 0 3.00113
898 | L 4.44692 -10.3512 1477010491329645 4.45 -10.35 0 3.00113
899 | R 10.9254 -1.15506 -2.25117 1477010491379669 4.5 -10.2 0.999524 2.99855
900 | L 4.5556 -10.0446 1477010491429693 4.55 -10.05 0.999524 2.99856
901 | R 10.8604 -1.13609 -2.26342 1477010491479780 4.6 -9.9 0.998267 2.9948
902 | L 4.65841 -9.75093 1477010491529867 4.65 -9.75 0.998262 2.99479
903 | R 10.5329 -1.11857 -1.07221 1477010491584756 4.7 -9.65 0.910924 1.82187
904 | L 4.74425 -9.53907 1477010491639645 4.75 -9.55 0.910928 1.82186
905 | R 10.5106 -1.09824 -1.95487 1477010491694709 4.8 -9.4 0.908038 2.72411
906 | L 4.87523 -9.25457 1477010491749773 4.85 -9.25 0.908033 2.7241
907 | R 10.2807 -1.07104 -1.598 1477010491804763 4.95 -9.1 1.81851 2.72776
908 | L 5.04624 -8.96378 1477010491859753 5.05 -8.95 1.81852 2.72777
909 | R 10.1055 -1.04116 -1.48391 1477010491914804 5.15 -8.8 1.8165 2.72476
910 | L 5.26299 -8.64398 1477010491969855 5.25 -8.65 1.8165 2.72475
911 | R 10.0829 -1.0125 -0.557198 1477010492024752 5.35 -8.55 1.82159 1.8216
912 | L 5.43218 -8.43885 1477010492079650 5.45 -8.45 1.82157 1.82157
913 | R 10.0189 -0.988216 -1.11875 1477010492134713 5.5 -8.35 0.908054 1.81609
914 | L 5.54143 -8.23537 1477010492189776 5.55 -8.25 0.908054 1.8161
915 | R 10.0003 -0.961796 0.253973 1477010492244751 5.7 -8.15 2.72851 1.81902
916 | L 5.85207 -8.0316 1477010492299727 5.85 -8.05 2.72849 1.81899
917 | R 9.9687 -0.926042 -1.09038 1477010492354717 5.95 -7.9 1.81851 2.72777
918 | L 6.04955 -7.75139 1477010492409707 6.05 -7.75 1.81852 2.72777
919 | R 9.92266 -0.895106 -0.251318 1477010492464731 6.15 -7.65 1.81739 1.81739
920 | L 6.2473 -7.56333 1477010492519755 6.25 -7.55 1.81739 1.81739
921 | R 9.84317 -0.864096 -0.225876 1477010492574809 6.35 -7.45 1.8164 1.81641
922 | L 6.44526 -7.36223 1477010492629864 6.45 -7.35 1.81638 1.81638
923 | R 9.89056 -0.831754 0.387467 1477010492684838 6.6 -7.25 2.72856 1.81904
924 | L 6.74856 -7.14204 1477010492739812 6.75 -7.15 2.72856 1.81904
925 | R 9.82091 -0.80111 0.0111151 1477010492794737 6.85 -7.05 1.82066 1.82066
926 | L 6.97717 -6.96144 1477010492849663 6.95 -6.95 1.82065 1.82065
927 | R 9.91553 -0.767345 0.813798 1477010492899667 7.1 -6.85 2.99976 1.99984
928 | L 7.2552 -6.74652 1477010492949671 7.25 -6.75 2.99976 1.99984
929 | R 9.78918 -0.739243 -0.48358 1477010492999702 7.3 -6.65 0.999384 1.99876
930 | L 7.33491 -6.53948 1477010493049734 7.35 -6.55 0.999369 1.99874
931 | R 10.1874 -0.716878 2.05987 1477010493104730 7.5 -6.55 2.72747 0
932 | L 7.64389 -6.55777 1477010493159727 7.65 -6.55 2.72745 0
933 | R 10.3287 -0.693472 0.298687 1477010493214786 7.75 -6.45 1.81623 1.81624
934 | L 7.84566 -6.34021 1477010493269845 7.85 -6.35 1.81623 1.81624
935 | R 10.0811 -0.66755 1.53364 1477010493324861 8 -6.3 2.72648 0.908821
936 | L 8.14601 -6.23101 1477010493379878 8.15 -6.25 2.72645 0.908817
937 | R 10.4109 -0.636269 -0.146262 1477010493434854 8.25 -6.1 1.81898 2.72847
938 | L 8.35502 -5.95811 1477010493489831 8.35 -5.95 1.81897 2.72844
939 | R 10.2804 -0.607939 1.90025 1477010493544847 8.5 -5.9 2.72647 0.90883
940 | L 8.64201 -5.86567 1477010493599864 8.65 -5.85 2.72645 0.908817
941 | R 10.4873 -0.580994 0.616103 1477010493654873 8.75 -5.75 1.81789 1.81788
942 | L 8.85198 -5.65134 1477010493709882 8.85 -5.65 1.81789 1.81788
943 | R 10.4588 -0.551115 1.48103 1477010493764805 9 -5.55 2.73109 1.82073
944 | L 9.13482 -5.45989 1477010493819729 9.15 -5.45 2.73106 1.82072
945 | R 10.7747 -0.524384 0.767143 1477010493874736 9.25 -5.35 1.81796 1.81795
946 | L 9.33045 -5.26019 1477010493929744 9.35 -5.25 1.81794 1.81793
947 | R 10.7517 -0.497219 1.45792 1477010493984708 9.5 -5.15 2.72905 1.81937
948 | L 9.63754 -5.04328 1477010494039673 9.65 -5.05 2.72903 1.81935
949 | R 11.0341 -0.470968 0.0453641 1477010494089740 9.7 -4.95 0.998666 1.99733
950 | L 9.74856 -4.84745 1477010494139808 9.75 -4.85 0.998656 1.99731
951 | R 11.0612 -0.449999 0.869401 1477010494194838 9.85 -4.75 1.8172 1.81719
952 | L 9.93394 -4.66432 1477010494249869 9.95 -4.65 1.81717 1.81717
953 | R 11.125 -0.424919 0.928876 1477010494304801 10.05 -4.55 1.82042 1.82043
954 | L 10.1444 -4.44253 1477010494359734 10.15 -4.45 1.82041 1.82042
955 | R 11.1995 -0.399294 1.77981 1477010494414745 10.3 -4.35 2.72672 1.81782
956 | L 10.4496 -4.24828 1477010494469757 10.45 -4.25 2.7267 1.8178
957 | R 11.3545 -0.370848 -0.30287 1477010494524777 10.5 -4.1 0.908764 2.72628
958 | L 10.5542 -3.95161 1477010494579797 10.55 -3.95 0.908764 2.72628
959 | R 11.3211 -0.342958 0.712047 1477010494634760 10.65 -3.8 1.8194 2.72911
960 | L 10.7602 -3.65417 1477010494689724 10.75 -3.65 1.81939 2.72908
961 | R 11.4312 -0.317652 1.20517 1477010494744718 10.85 -3.55 1.81839 1.81838
962 | L 10.9375 -3.46792 1477010494799712 10.95 -3.45 1.81838 1.81838
963 | R 11.5021 -0.29106 0.041231 1477010494854745 11 -3.3 0.908549 2.72563
964 | L 11.0672 -3.14607 1477010494909778 11.05 -3.15 0.908549 2.72564
965 | R 11.5069 -0.264934 0.216416 1477010494964791 11.1 -3 0.90888 2.72663
966 | L 11.1428 -2.8481 1477010495019805 11.15 -2.85 0.908863 2.72661
967 | R 11.4972 -0.237002 0.259454 1477010495074751 11.2 -2.7 0.909988 2.72995
968 | L 11.2495 -2.5409 1477010495129697 11.25 -2.55 0.909988 2.72995
969 | R 11.7171 -0.211967 -1.46601 1477010495184782 11.2 -2.4 -0.907692 2.72306
970 | L 11.1705 -2.24061 1477010495239868 11.15 -2.25 -0.907683 2.72304
971 | R 11.4117 -0.183451 1.44344 1477010495294756 11.25 -2.1 1.8219 2.73284
972 | L 11.3536 -1.97104 1477010495349645 11.35 -1.95 1.82188 2.73281
973 | R 11.3633 -0.156728 -0.454345 1477010495404793 11.35 -1.8 0 2.71996
974 | L 11.3661 -1.64386 1477010495459942 11.35 -1.65 0 2.71993
975 | R 11.3658 -0.130565 -0.347156 1477010495514797 11.35 -1.5 0 2.73448
976 | L 11.3391 -1.34511 1477010495569653 11.35 -1.35 0 2.73446
977 | R 11.3141 -0.10593 -2.15196 1477010495619661 11.25 -1.2 -1.99969 2.99952
978 | L 11.1532 -1.04324 1477010495669670 11.15 -1.05 -1.99967 2.99949
979 | R 11.0059 -0.0810972 -1.38138 1477010495719666 11.1 -0.9 -1.00006 3.00024
980 | L 11.0654 -0.742685 1477010495769663 11.05 -0.75 -1.00006 3.00021
981 | R 10.911 -0.0601231 -1.96694 1477010495824677 10.95 -0.649999 -1.81771 1.81773
982 | L 10.8418 -0.549005 1477010495879691 10.85 -0.549999 -1.81772 1.81773
983 | R 10.8361 -0.0408851 -1.78777 1477010495934684 10.75 -0.45 -1.81842 1.8184
984 | L 10.6642 -0.353413 1477010495989678 10.65 -0.35 -1.8184 1.81839
985 | R 10.5532 -0.0235621 -3.26318 1477010496039672 10.5 -0.25 -3.00035 2.00024
986 | L 10.3487 -0.171899 1477010496089667 10.35 -0.15 -3.00032 2.00022
987 | R 10.2565 -0.0112412 -2.08683 1477010496139654 10.25 -0.0999996 -2.00053 1.00027
988 | L 10.137 -0.0462889 1477010496189641 10.15 -0.0499992 -2.00053 1.00027
989 | R 10.0717 0.00163858 -1.97994 1477010496239639 10.05 9.49949e-07 -2.00009 1.00004
990 | L 9.92833 0.0556031 1477010496289638 9.95 0.0500011 -2.00006 1.00003
991 | R 9.84784 0.00284182 -2.93313 1477010496339634 9.8 0.0500011 -3.00025 0
992 | L 9.65029 0.0372757 1477010496389630 9.65 0.0500011 -3.00024 0
993 | R 9.54734 0.0104496 -2.97688 1477010496439659 9.5 0.100001 -2.99825 0.999409
994 | L 9.33515 0.138463 1477010496489688 9.35 0.15 -2.99825 0.999409
995 | R 9.46208 0.0141913 -1.83274 1477010496544659 9.25 0.15 -1.81915 0
996 | L 9.13787 0.161547 1477010496599631 9.15 0.15 -1.81913 0
997 | R 9.02937 0.0217632 -3.05478 1477010496649643 9 0.2 -2.99927 0.99976
998 | L 8.82128 0.250523 1477010496699655 8.85 0.25 -2.99927 0.99976
999 | R 8.6796 0.0285204 -3.02363 1477010496749676 8.7 0.25 -2.99873 0
1000 | L 8.54916 0.247046 1477010496799698 8.55 0.25 -2.99871 0
1001 | R 8.44466 0.0300046 -2.58343 1477010496854669 8.4 0.25 -2.72872 0
1002 | L 8.2302 0.258614 1477010496909640 8.25 0.25 -2.72871 0
1003 | R 8.10918 0.0252484 -2.97538 1477010496959655 8.1 0.2 -2.99909 -0.9997
1004 | L 7.94777 0.127954 1477010497009670 7.95 0.15 -2.9991 -0.9997
1005 | R 7.82356 0.0191399 -3.06134 1477010497059653 7.8 0.15 -3.00101 0
1006 | L 7.63383 0.15164 1477010497109637 7.65 0.15 -3.00099 0
1007 | R 7.49759 0.0134815 -2.56198 1477010497164643 7.5 0.100001 -2.72698 -0.908982
1008 | L 7.35046 0.0656199 1477010497219650 7.35 0.0500011 -2.72695 -0.908974
1009 | R 7.13188 -0.000633743 -2.02086 1477010497269642 7.25 9.49949e-07 -2.00032 -1.00016
1010 | L 7.14965 -0.0559868 1477010497319635 7.15 -0.0499992 -2.0003 -1.00015
1011 | R 6.77401 -0.0222792 -2.89458 1477010497374674 7 -0.15 -2.72534 -1.8169
1012 | L 6.85872 -0.227918 1477010497429714 6.85 -0.25 -2.72532 -1.81688
1013 | R 6.7132 -0.0517516 -0.920895 1477010497479687 6.8 -0.349999 -1.00053 -2.00107
1014 | L 6.74687 -0.441006 1477010497529660 6.75 -0.449999 -1.00054 -2.00107
1015 | R 6.74335 -0.0817351 -1.87538 1477010497579656 6.65 -0.549999 -2.00016 -2.00017
1016 | L 6.54837 -0.624094 1477010497629652 6.55 -0.65 -2.00016 -2.00017
1017 | R 6.44839 -0.121462 -0.595118 1477010497679658 6.5 -0.799999 -0.999884 -2.99963
1018 | L 6.44859 -0.960126 1477010497729664 6.45 -0.949999 -0.999884 -2.99963
1019 | R 6.46919 -0.16842 -0.399597 1477010497779665 6.4 -1.1 -0.999984 -2.99995
1020 | L 6.35596 -1.24356 1477010497829667 6.35 -1.25 -0.999969 -2.99992
1021 | R 6.70803 -0.215617 0.545837 1477010497879645 6.35 -1.4 0 -3.00132
1022 | L 6.35503 -1.55184 1477010497929624 6.35 -1.55 0 -3.00129
1023 | R 6.54119 -0.254276 0.351234 1477010497984639 6.35 -1.65 0 -1.81769
1024 | L 6.34356 -1.7483 1477010498039654 6.35 -1.75 0 -1.81769
1025 | R 6.64128 -0.299712 1.27178 1477010498089657 6.35 -1.95 0 -3.99976
1026 | L 6.34095 -2.12276 1477010498139661 6.35 -2.15 0 -3.99972
1027 | R 6.6916 -0.340776 0.695528 1477010498189661 6.35 -2.25 0 -2
1028 | L 6.34785 -2.35274 1477010498239662 6.35 -2.35 0 -1.99998
1029 | R 6.73182 -0.375521 1.10962 1477010498289662 6.35 -2.5 0 -3
1030 | L 6.36265 -2.64414 1477010498339662 6.35 -2.65 0 -3
1031 | R 6.95809 -0.410128 2.11301 1477010498389660 6.4 -2.8 1.00003 -3.00012
1032 | L 6.461 -2.95066 1477010498439659 6.45 -2.95 1.00003 -3.00009
1033 | R 7.23519 -0.451689 2.60049 1477010498489695 6.5 -3.15 0.999284 -3.99712
1034 | L 6.55345 -3.36165 1477010498539731 6.55 -3.35 0.999284 -3.99712
1035 | R 7.4921 -0.481382 1.63266 1477010498594681 6.6 -3.45 0.909922 -1.81983
1036 | L 6.63536 -3.55462 1477010498649631 6.65 -3.55 0.909917 -1.81984
1037 | R 7.65601 -0.504076 2.5156 1477010498699664 6.7 -3.7 0.999335 -2.99802
1038 | L 6.75151 -3.84595 1477010498749697 6.75 -3.85 0.99934 -2.99802
1039 | R 7.7202 -0.532089 2.13592 1477010498804730 6.8 -4 0.908549 -2.72564
1040 | L 6.85764 -4.15466 1477010498859764 6.85 -4.15 0.908537 -2.72561
1041 | R 8.16769 -0.554308 1.6278 1477010498914716 6.9 -4.25 0.90988 -1.81977
1042 | L 6.94846 -4.34203 1477010498969669 6.95 -4.35 0.909876 -1.81975
1043 | R 8.43143 -0.57064 2.47219 1477010499019665 7 -4.5 1.00008 -3.00024
1044 | L 7.03506 -4.6461 1477010499069661 7.05 -4.65 1.00008 -3.00024
1045 | R 8.74234 -0.592929 3.2723 1477010499119665 7.15 -4.8 1.99984 -2.99976
1046 | L 7.26242 -4.9318 1477010499169669 7.25 -4.95 1.99984 -2.99976
1047 | R 8.92451 -0.606477 1.91642 1477010499219655 7.3 -5.05 1.00028 -2.00057
1048 | L 7.3521 -5.15533 1477010499269641 7.35 -5.15 1.00028 -2.00056
1049 | R 9.0968 -0.620096 2.43519 1477010499319639 7.4 -5.3 1.00003 -3.00012
1050 | L 7.44098 -5.44336 1477010499369637 7.45 -5.45 1.00004 -3.00012
1051 | R 9.27969 -0.635369 2.73629 1477010499419647 7.55 -5.55 1.99961 -1.99961
1052 | L 7.66561 -5.647 1477010499469657 7.65 -5.65 1.9996 -1.9996
1053 | R 9.6591 -0.646603 2.41719 1477010499519643 7.7 -5.8 1.00027 -3.00084
1054 | L 7.75724 -5.96146 1477010499569629 7.75 -5.95 1.00028 -3.00084
1055 | R 9.7353 -0.656464 2.7844 1477010499619634 7.85 -6.05 1.9998 -1.99981
1056 | L 7.95005 -6.14609 1477010499669639 7.95 -6.15 1.9998 -1.9998
1057 | R 10.2057 -0.66388 2.032 1477010499719637 8 -6.25 1.00004 -2.00008
1058 | L 8.04346 -6.35767 1477010499769635 8.05 -6.35 1.00004 -2.00008
1059 | R 10.382 -0.674723 3.36501 1477010499819647 8.15 -6.5 1.99951 -2.99928
1060 | L 8.25742 -6.66007 1477010499869660 8.25 -6.65 1.9995 -2.99925
1061 | R 10.7955 -0.680973 1.7607 1477010499924653 8.3 -6.75 0.90921 -1.81841
1062 | L 8.33159 -6.8699 1477010499979646 8.35 -6.85 0.90921 -1.81841
1063 | R 11.1716 -0.694584 2.79064 1477010500029640 8.4 -7 1.0001 -3.00036
1064 | L 8.45779 -7.15285 1477010500079634 8.45 -7.15 1.00011 -3.00036
1065 | R 10.9872 -0.703397 2.84011 1477010500129657 8.55 -7.25 1.99907 -1.99908
1066 | L 8.647 -7.3454 1477010500179680 8.65 -7.35 1.99908 -1.99908
1067 | R 11.5795 -0.712791 2.85508 1477010500229651 8.7 -7.5 1.00058 -3.00174
1068 | L 8.76121 -7.66039 1477010500279623 8.75 -7.65 1.00057 -3.00171
1069 | R 11.7383 -0.721773 1.98214 1477010500329631 8.8 -7.75 0.999844 -1.99968
1070 | L 8.84363 -7.83628 1477010500379639 8.85 -7.85 0.999844 -1.99968
1071 | R 12.1699 -0.728905 3.6081 1477010500429635 8.95 -8 2.00017 -3.00024
1072 | L 9.05238 -8.15838 1477010500479632 9.05 -8.15 2.00014 -3.00021
1073 | R 12.2342 -0.737451 2.08465 1477010500529687 9.1 -8.25 0.998905 -1.99781
1074 | L 9.15492 -8.35136 1477010500579743 9.15 -8.35 0.998886 -1.99779
1075 | R 12.6512 -0.744494 2.40551 1477010500634757 9.2 -8.5 0.908863 -2.72657
1076 | L 9.2543 -8.64461 1477010500689772 9.25 -8.65 0.908855 -2.72655
1077 | R 12.6125 -0.757959 2.41541 1477010500744749 9.3 -8.8 0.909475 -2.72841
1078 | L 9.35164 -8.95148 1477010500799727 9.35 -8.95 0.909466 -2.72839
1079 | R 13.1952 -0.769299 3.02289 1477010500849738 9.4 -9.1 0.999765 -2.99935
1080 | L 9.44272 -9.24111 1477010500899749 9.45 -9.25 0.999774 -2.99934
1081 | R 13.3758 -0.777761 1.84184 1477010500954757 9.5 -9.35 0.908962 -1.81792
1082 | L 9.5752 -9.43724 1477010501009766 9.55 -9.45 0.908954 -1.8179
1083 | R 13.5831 -0.785718 2.38885 1477010501064712 9.6 -9.6 0.909988 -2.72996
1084 | L 9.62903 -9.75552 1477010501119659 9.65 -9.75 0.909971 -2.72993
1085 | R 13.8538 -0.796016 2.63696 1477010501174727 9.7 -9.9 0.907972 -2.7239
1086 | L 9.74482 -10.046 1477010501229795 9.75 -10.05 0.907972 -2.72391
1087 | R 14.0227 -0.807641 1.93977 1477010501284719 9.75 -10.2 0 -2.73106
1088 | L 9.75264 -10.3488 1477010501339643 9.75 -10.35 0 -2.73105
1089 | R 14.3811 -0.818753 3.01654 1477010501389643 9.8 -10.5 1 -2.99999
1090 | L 9.83234 -10.6514 1477010501439644 9.85 -10.65 0.999994 -2.99996
1091 | R 14.5653 -0.830443 2.32942 1477010501489636 9.85 -10.8 0 -3.00047
1092 | L 9.85033 -10.9603 1477010501539628 9.85 -10.95 0 -3.00048
1093 | R 14.9194 -0.843535 1.3418 1477010501589738 9.85 -11.05 0 -1.9956
1094 | L 9.86057 -11.1502 1477010501639849 9.85 -11.15 0 -1.99559
1095 | R 14.9674 -0.853527 1.98082 1477010501694757 9.85 -11.3 0 -2.73184
1096 | L 9.83839 -11.4483 1477010501749666 9.85 -11.45 0 -2.73182
1097 | R 15.1773 -0.869154 1.8979 1477010501799738 9.8 -11.6 -0.998566 -2.9957
1098 | L 9.75592 -11.7487 1477010501849810 9.75 -11.75 -0.998566 -2.99569
1099 | R 15.4203 -0.884038 2.12451 1477010501904738 9.75 -11.9 0 -2.73084
1100 | L 9.75861 -12.0491 1477010501959666 9.75 -12.05 0 -2.73085
1101 | R 15.5366 -0.901665 1.06967 1477010502014733 9.65 -12.2 -1.81598 -2.72396
1102 | L 9.56514 -12.3446 1477010502069800 9.55 -12.35 -1.81597 -2.72396
1103 | R 15.6711 -0.921787 0.222425 1477010502124705 9.45 -12.45 -1.82132 -1.82133
1104 | L 9.34752 -12.5408 1477010502179611 9.35 -12.55 -1.82131 -1.82131
1105 | R 15.7693 -0.93835 0.863191 1477010502234675 9.3 -12.65 -0.908038 -1.81606
1106 | L 9.24151 -12.7411 1477010502289740 9.25 -12.75 -0.908029 -1.81605
1107 | R 15.6491 -0.954916 -0.702749 1477010502344684 9.05 -12.85 -3.64007 -1.82004
1108 | L 8.83995 -12.9425 1477010502399628 8.85 -12.95 -3.64007 -1.82003
1109 | R 15.6757 -0.979734 -0.475503 1477010502449731 8.75 -13 -1.9959 -0.997948
1110 | L 8.6395 -13.0341 1477010502499834 8.65 -13.05 -1.9959 -0.997948
1111 | R 15.5584 -0.994038 -0.821157 1477010502554749 8.5 -13.1 -2.73149 -0.910502
1112 | L 8.34268 -13.1451 1477010502609665 8.35 -13.15 -2.73146 -0.910485
1113 | R 15.5362 -1.01517 -0.66709 1477010502664734 8.2 -13.2 -2.72385 -0.907955
1114 | L 8.06107 -13.2364 1477010502719804 8.05 -13.25 -2.72383 -0.907947
1115 | R 15.36 -1.03139 -1.35298 1477010502774815 7.9 -13.25 -2.72673 0
1116 | L 7.75593 -13.2425 1477010502829826 7.75 -13.25 -2.72673 0
1117 | R 15.1473 -1.05087 -1.53492 1477010502884773 7.6 -13.25 -2.72991 0
1118 | L 7.44992 -13.2329 1477010502939720 7.45 -13.25 -2.72991 0
1119 | R 14.9795 -1.06989 -2.58841 1477010502994682 7.25 -13.2 -3.63887 0.909723
1120 | L 7.05468 -13.1604 1477010503049644 7.05 -13.15 -3.63887 0.909723
1121 | R 14.8543 -1.08728 -1.23077 1477010503104728 6.9 -13.15 -2.72312 0
1122 | L 6.73255 -13.1364 1477010503159812 6.75 -13.15 -2.72312 0
1123 | R 14.9527 -1.10661 -1.43926 1477010503209733 6.6 -13.15 -3.00475 0
1124 | L 6.42749 -13.1436 1477010503259655 6.45 -13.15 -3.00472 0
1125 | R 14.6848 -1.11798 -1.7778 1477010503309715 6.35 -13.1 -1.9976 0.998786
1126 | L 6.2503 -13.0552 1477010503359776 6.25 -13.05 -1.99758 0.998786
1127 | R 14.5284 -1.13279 -1.95691 1477010503414775 6.1 -13 -2.72732 0.909111
1128 | L 5.95166 -12.9396 1477010503469774 5.95 -12.95 -2.72732 0.909111
1129 | R 14.1205 -1.14893 -2.0284 1477010503524740 5.8 -12.9 -2.72895 0.909657
1130 | L 5.64785 -12.8416 1477010503579706 5.65 -12.85 -2.72896 0.909648
1131 | R 13.9407 -1.16209 -2.51174 1477010503634709 5.55 -12.75 -1.81808 1.81809
1132 | L 5.43842 -12.6552 1477010503689712 5.45 -12.65 -1.81809 1.81809
1133 | R 13.6781 -1.17045 -2.71295 1477010503744786 5.3 -12.55 -2.7236 1.81575
1134 | L 5.12059 -12.4527 1477010503799861 5.15 -12.45 -2.72358 1.81572
1135 | R 13.5987 -1.18335 -2.35186 1477010503854816 5.05 -12.35 -1.81967 1.81966
1136 | L 4.95241 -12.2277 1477010503909771 4.95 -12.25 -1.81967 1.81967
1137 | R 13.0294 -1.1923 -3.09079 1477010503964763 4.85 -12.1 -1.81844 2.72766
1138 | L 4.75144 -11.9561 1477010504019755 4.75 -11.95 -1.81844 2.72767
1139 | R 12.6426 -1.19135 -2.14426 1477010504074727 4.7 -11.85 -0.909557 1.8191
1140 | L 4.67603 -11.7496 1477010504129699 4.65 -11.75 -0.909553 1.81911
1141 | R 12.532 -1.19356 -2.71022 1477010504184778 4.6 -11.6 -0.907782 2.72335
1142 | L 4.5432 -11.4431 1477010504239858 4.55 -11.45 -0.907778 2.72334
1143 | R 12.0277 -1.18903 -3.7137 1477010504294788 4.5 -11.25 -0.910253 3.64099
1144 | L 4.44374 -11.0696 1477010504349718 4.45 -11.05 -0.910253 3.64099
1145 | R 11.8559 -1.18496 -1.62718 1477010504404722 4.45 -10.95 0 1.81804
1146 | L 4.4464 -10.8663 1477010504459726 4.45 -10.85 0 1.81805
1147 | R 12.0444 -1.17805 -1.73855 1477010504514788 4.45 -10.75 0 1.81614
1148 | L 4.43858 -10.6346 1477010504569850 4.45 -10.65 0 1.81614
1149 | R 11.3269 -1.16887 -3.27343 1477010504624801 4.45 -10.45 0 3.6396
1150 | L 4.44616 -10.2449 1477010504679752 4.45 -10.25 0 3.6396
1151 | R 11.0834 -1.15058 -2.17109 1477010504734696 4.5 -10.1 0.910021 2.73005
1152 | L 4.55225 -9.94382 1477010504789641 4.55 -9.95 0.910013 2.73003
1153 | R 10.7623 -1.13022 -2.44174 1477010504839687 4.6 -9.8 0.999085 2.99725
1154 | L 4.64586 -9.65356 1477010504889733 4.65 -9.65 0.99908 2.99724
1155 | R 10.4579 -1.11412 -1.35603 1477010504944672 4.7 -9.55 0.910095 1.82021
1156 | L 4.7442 -9.45683 1477010504999612 4.75 -9.45 0.910091 1.82018
1157 | R 10.4108 -1.0908 -1.78308 1477010505049655 4.85 -9.3 1.99828 2.99743
1158 | L 4.94202 -9.15915 1477010505099699 4.95 -9.15 1.99826 2.99739
1159 | R 10.3405 -1.06237 -1.95528 1477010505154742 5 -9 0.908384 2.72514
1160 | L 5.06039 -8.84271 1477010505209785 5.05 -8.85 0.908384 2.72514
1161 | R 10.2601 -1.03811 -0.803907 1477010505264827 5.15 -8.75 1.81679 1.8168
1162 | L 5.26598 -8.6555 1477010505319869 5.25 -8.65 1.81679 1.8168
1163 | R 10.037 -1.01005 -1.20349 1477010505374809 5.35 -8.5 1.82017 2.73024
1164 | L 5.43516 -8.36367 1477010505429750 5.45 -8.35 1.82015 2.73022
1165 | R 9.99123 -0.978202 -0.644215 1477010505484740 5.55 -8.25 1.81852 1.81852
1166 | L 5.65225 -8.1686 1477010505539731 5.65 -8.15 1.8185 1.8185
1167 | R 10.0235 -0.949248 -0.364583 1477010505594751 5.75 -8.05 1.81752 1.81753
1168 | L 5.81186 -7.95139 1477010505649771 5.85 -7.95 1.81752 1.81752
1169 | R 9.93472 -0.92343 -0.302353 1477010505704734 5.95 -7.85 1.8194 1.8194
1170 | L 6.04627 -7.74932 1477010505759697 6.05 -7.75 1.81941 1.8194
1171 | R 9.62546 -0.88994 -0.832488 1477010505814720 6.15 -7.6 1.81742 2.72613
1172 | L 6.26179 -7.43797 1477010505869744 6.25 -7.45 1.8174 2.72611
1173 | R 9.67107 -0.855073 0.418138 1477010505924794 6.4 -7.35 2.7248 1.81653
1174 | L 6.55234 -7.24441 1477010505979845 6.55 -7.25 2.72477 1.81651
1175 | R 9.76103 -0.825526 0.489049 1477010506034803 6.65 -7.2 1.81957 0.909789
1176 | L 6.74556 -7.15305 1477010506089761 6.75 -7.15 1.81957 0.909785
1177 | R 9.87674 -0.794649 0.607961 1477010506144717 6.9 -7.05 2.72946 1.81964
1178 | L 7.06886 -6.94012 1477010506199673 7.05 -6.95 2.72946 1.81964
1179 | R 9.86232 -0.763353 0.0351385 1477010506254753 7.15 -6.85 1.81554 1.81554
1180 | L 7.25262 -6.73852 1477010506309833 7.25 -6.75 1.81554 1.81554
1181 | R 9.85837 -0.736116 0.148173 1477010506364776 7.35 -6.65 1.82007 1.82007
1182 | L 7.45762 -6.53827 1477010506419720 7.45 -6.55 1.82005 1.82005
1183 | R 9.98613 -0.706558 1.37208 1477010506474740 7.6 -6.5 2.72628 0.908764
1184 | L 7.74315 -6.44913 1477010506529760 7.75 -6.45 2.72628 0.908764
1185 | R 10.1317 -0.684623 0.777791 1477010506584809 7.85 -6.4 1.81656 0.908285
1186 | L 7.9584 -6.34152 1477010506639858 7.95 -6.35 1.81656 0.908281
1187 | R 10.1837 -0.658568 0.969682 1477010506694802 8.1 -6.25 2.73006 1.82003
1188 | L 8.24773 -6.1485 1477010506749746 8.25 -6.15 2.73005 1.82003
1189 | R 10.2499 -0.623097 1.26912 1477010506804753 8.4 -6.05 2.72692 1.81795
1190 | L 8.55156 -5.93992 1477010506859761 8.55 -5.95 2.7269 1.81794
1191 | R 10.4301 -0.594934 0.657942 1477010506914753 8.65 -5.85 1.81844 1.81844
1192 | L 8.7543 -5.75242 1477010506969746 8.75 -5.75 1.81843 1.81843
1193 | R 10.6686 -0.565194 1.34315 1477010507024799 8.9 -5.65 2.72464 1.81643
1194 | L 9.06113 -5.54128 1477010507079852 9.05 -5.55 2.72465 1.81643
1195 | R 10.6386 -0.537333 0.800178 1477010507134822 9.15 -5.45 1.81916 1.81918
1196 | L 9.24452 -5.3352 1477010507189793 9.25 -5.35 1.81916 1.81916
1197 | R 10.6468 -0.515691 1.14332 1477010507244770 9.35 -5.3 1.81895 0.909466
1198 | L 9.43645 -5.25801 1477010507299747 9.45 -5.25 1.81894 0.90947
1199 | R 11.0213 -0.491273 0.378567 1477010507354696 9.55 -5.1 1.81986 2.72981
1200 | L 9.64908 -4.9531 1477010507409645 9.65 -4.95 1.81987 2.72981
1201 | R 10.8076 -0.462569 0.808191 1477010507464670 9.75 -4.85 1.81736 1.81735
1202 | L 9.86682 -4.74721 1477010507519695 9.85 -4.75 1.81736 1.81735
1203 | R 10.9769 -0.436747 0.773696 1477010507574699 9.95 -4.65 1.81806 1.81805
1204 | L 10.0706 -4.54316 1477010507629703 10.05 -4.55 1.81805 1.81805
1205 | R 11.3501 -0.410455 1.79691 1477010507684727 10.2 -4.45 2.72609 1.8174
1206 | L 10.3431 -4.35107 1477010507739752 10.35 -4.35 2.72606 1.81737
1207 | R 11.1934 -0.382691 -0.210545 1477010507794760 10.4 -4.2 0.908945 2.72688
1208 | L 10.4578 -4.07071 1477010507849768 10.45 -4.05 0.908953 2.72687
1209 | R 11.2679 -0.358104 1.20807 1477010507904742 10.55 -3.95 1.81903 1.81904
1210 | L 10.6424 -3.84126 1477010507959717 10.65 -3.85 1.81902 1.81903
1211 | R 11.2214 -0.330063 0.887286 1477010508014683 10.75 -3.7 1.81931 2.72896
1212 | L 10.8572 -3.53957 1477010508069649 10.85 -3.55 1.81931 2.72896
1213 | R 11.3235 -0.30085 0.302095 1477010508119661 10.9 -3.4 0.999745 2.99928
1214 | L 10.9372 -3.24816 1477010508169673 10.95 -3.25 0.999754 2.99928
1215 | R 11.3417 -0.277495 1.37716 1477010508224689 11.05 -3.15 1.81764 1.81765
1216 | L 11.135 -3.05697 1477010508279706 11.15 -3.05 1.81763 1.81764
1217 | R 11.5166 -0.252737 0.253884 1477010508334672 11.2 -2.9 0.909657 2.72896
1218 | L 11.2571 -2.74346 1477010508389639 11.25 -2.75 0.909648 2.72893
1219 | R 11.5547 -0.225803 -1.5427 1477010508439703 11.2 -2.6 -0.998725 2.99617
1220 | L 11.1393 -2.45312 1477010508489767 11.15 -2.45 -0.998725 2.99616
1221 | R 11.4655 -0.201037 0.457362 1477010508544784 11.2 -2.3 0.908813 2.72643
1222 | L 11.239 -2.16508 1477010508599802 11.25 -2.15 0.908805 2.7264
1223 | R 11.3365 -0.175531 0.442173 1477010508654756 11.3 -2 0.909855 2.72956
1224 | L 11.3424 -1.85113 1477010508709711 11.35 -1.85 0.909847 2.72953
1225 |
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/plots/Lidar.png:
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https://raw.githubusercontent.com/srnand/Object-Tracking-and-State-Prediction-with-Unscented-and-Extended-Kalman-Filters/e51a52153a1ca2dfd26a2c7dfc9da126afa333b2/plots/Lidar.png
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/plots/Radar.png:
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https://raw.githubusercontent.com/srnand/Object-Tracking-and-State-Prediction-with-Unscented-and-Extended-Kalman-Filters/e51a52153a1ca2dfd26a2c7dfc9da126afa333b2/plots/Radar.png
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/src/KF.py:
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1 | import numpy as np
2 |
3 | class KF():
4 | def __init__(self,n):
5 | self.n = n
6 | self.I = np.matrix(np.eye(n))
7 | self.x = None
8 | self.P = None
9 | self.F = None
10 | self.Q = None
11 |
12 | def predict(self):
13 |
14 | self.x = self.F * self.x
15 | self.P = self.F * self.P * self.F.T + self.Q
16 |
17 | def update(self, z, H, Hx, R):
18 |
19 | y = z - Hx
20 | PHt = self.P * H.T
21 | S = H * PHt + R
22 | K = PHt * (S.I)
23 |
24 | self.x = self.x + K * y
25 | self.P = (self.I - K * H) * self.P
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/src/datapoint.py:
--------------------------------------------------------------------------------
1 | from tools import polar_to_cartesian
2 | import copy
3 |
4 | class DataPoint:
5 | """
6 | A set of derived information from measurements of known sensors
7 | NOTE: Upon instantiation of a "radar" DataPoint, state variables are computed from raw data
8 | """
9 |
10 | def __init__(self, d):
11 | self.timestamp = d['timestamp']
12 | self.name = d['name']
13 | self.all = d
14 | self.raw = []
15 | self.data = []
16 |
17 | if self.name == 'state':
18 | self.data = [d['x'], d['y'], d['vx'], d['vy']]
19 | self.raw = copy.deepcopy(self.data)
20 |
21 | elif self.name == 'lidar':
22 | self.data = [d['x'], d['y'], 0, 0]
23 | self.raw = [d['x'], d['y']]
24 |
25 | elif self.name == 'radar':
26 | x, y, vx, vy = polar_to_cartesian(d['rho'], d['phi'], d['drho'])
27 | self.data = [x, y, vx, vy]
28 | self.raw = [d['rho'], d['phi'], d['drho']]
29 |
30 | self.all['processed_data'] = self.data
31 | self.all['raw'] = self.raw
32 |
33 | def get_dict(self):
34 | return self.all
35 |
36 | def get_raw(self):
37 | return self.raw
38 |
39 | def get(self):
40 | return self.data
41 |
42 | def get_timestamp(self):
43 | return self.timestamp
44 |
45 | def get_name(self):
46 | return self.name
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/src/helpers.py:
--------------------------------------------------------------------------------
1 | from datapoint import DataPoint
2 | from KF import KF
3 |
4 | def parse_data(file_path):
5 | """
6 | Args:
7 | file_path
8 | - path to a text file with all data.
9 | - each line should have the following format:
10 | [SENSOR ID] [SENSOR RAW VALUES] [TIMESTAMP] [GROUND TRUTH VALUES]
11 | Whereas radar has three measurements (rho, phi, rhodot), lidar has two measurements (x, y).
12 |
13 | Specifically:
14 |
15 | For a row containing radar data, the columns are:
16 | sensor_type, rho_measured, phi_measured, rhodot_measured, timestamp, x_groundtruth, y_groundtruth, vx_groundtruth, vy_groundtruth
17 |
18 | For a row containing lidar data, the columns are:
19 | sensor_type, x_measured, y_measured, timestamp, x_groundtruth, y_groundtruth, vx_groundtruth, vy_groundtruth
20 |
21 | Example 1:
22 | line with three measurements from a radar sensor in polar coordinate
23 | followed by a timestamp in unix time
24 | followed by the the "ground truth" which is
25 | actual real position and velocity in cartesian coordinates (four state variables)
26 |
27 | R 8.46642 0.0287602 -3.04035 1477010443399637 8.6 0.25 -3.00029 0
28 | (R) (rho) (phi) (drho) (timestamp) (real x) (real y) (real vx) (real vy)
29 |
30 | Example 2:
31 | line with two measurements from a lidar sensor in cartesian coordinates
32 | followed by a timestamp in unix time
33 | followed by the the "ground truth" which is
34 | the actual real position and velocity in cartesian coordinates (four state variables)
35 |
36 | L 8.44818 0.251553 1477010443449633 8.45 0.25 -3.00027 0
37 |
38 | Returns:
39 | all_sensor_data, all_ground_truths
40 | - two lists of DataPoint() instances
41 |
42 | """
43 |
44 | all_sensor_data = []
45 | all_ground_truths = []
46 |
47 | with open(file_path) as f:
48 |
49 | for line in f:
50 | data = line.split()
51 |
52 | if data[0] == 'L':
53 |
54 | sensor_data = DataPoint({
55 | 'timestamp': int(data[3]),
56 | 'name': 'lidar',
57 | 'x': float(data[1]),
58 | 'y': float(data[2])
59 | })
60 |
61 | g = {'timestamp': int(data[3]),
62 | 'name': 'state',
63 | 'x': float(data[4]),
64 | 'y': float(data[5]),
65 | 'vx': float(data[6]),
66 | 'vy': float(data[7])
67 | }
68 |
69 | ground_truth = DataPoint(g)
70 |
71 | elif data[0] == 'R':
72 |
73 | sensor_data = DataPoint({
74 | 'timestamp': int(data[4]),
75 | 'name': 'radar',
76 | 'rho': float(data[1]),
77 | 'phi': float(data[2]),
78 | 'drho': float(data[3])
79 | })
80 |
81 | g = {'timestamp': int(data[4]),
82 | 'name': 'state',
83 | 'x': float(data[5]),
84 | 'y': float(data[6]),
85 | 'vx': float(data[7]),
86 | 'vy': float(data[8])
87 | }
88 | ground_truth = DataPoint(g)
89 |
90 | all_sensor_data.append(sensor_data)
91 | all_ground_truths.append(ground_truth)
92 |
93 | return all_sensor_data, all_ground_truths
94 |
95 |
96 | def get_state_estimations(EKF, all_sensor_data):
97 | """
98 | Calculates all state estimations given a FusionEKF instance() and sensor measurements
99 |
100 | Args:
101 | EKF - an instance of a FusionEKF() class
102 | all_sensor_data - a list of sensor measurements as a DataPoint() instance
103 |
104 | Returns:
105 | all_state_estimations
106 | - a list of all state estimations as predicted by the EKF instance
107 | - each state estimation is wrapped in DataPoint() instance
108 | """
109 |
110 | all_state_estimations = []
111 |
112 | for data in all_sensor_data:
113 |
114 | EKF.process(data)
115 |
116 | x = EKF.get()
117 | px, py, vx, vy = x[0, 0], x[1, 0], x[2, 0], x[3, 0]
118 |
119 | g = {'timestamp': data.get_timestamp(),
120 | 'name': 'state',
121 | 'x': px,
122 | 'y': py,
123 | 'vx': vx,
124 | 'vy': vy }
125 |
126 | state_estimation = DataPoint(g)
127 | all_state_estimations.append(state_estimation)
128 |
129 | return all_state_estimations
130 |
131 | def print_EKF_data(all_sensor_data, all_ground_truths, all_state_estimations, RMSE):
132 | """
133 | Prints all relevant EKF data in a nice formal
134 |
135 | Args:
136 | all_sensor_data
137 | - a list of sensor measurements as DataPoint() instances
138 | all_state_estimations
139 | - a list of state estimations as DataPoint() instances
140 | all_ground_truths
141 | - a list of ground truths as DataPoint instances
142 | RMSE
143 | - a list of the four computed root-mean-square error of the four state variables considered
144 |
145 | Returns: None
146 | """
147 |
148 | px, py, vx, vy = RMSE
149 |
150 | print("-----------------------------------------------------------")
151 | print('{:10s} | {:8.3f} | {:8.3f} | {:8.3f} | {:8.3f} |'.format("RMSE:", px, py, vx, vy))
152 | print("-----------------------------------------------------------")
153 | print("NUMBER OF DATA POINTS:", len(all_sensor_data))
154 | print("-----------------------------------------------------------")
155 |
156 | i = 1
157 | for s, p, t in zip(all_sensor_data, all_state_estimations, all_ground_truths):
158 |
159 | print("-----------------------------------------------------------")
160 | print("#", i, ":", s.get_timestamp())
161 | print("-----------------------------------------------------------")
162 |
163 | if s.get_name() == 'lidar':
164 | x, y = s.get_raw()
165 | print('{:15s} | {:8.3f} | {:8.3f} |'.format("LIDAR:", x, y))
166 | else:
167 | rho, phi, drho = s.get_raw()
168 | print('{:15s} | {:8.3f} | {:8.3f} | {:8.3f} |'.format("RADAR:", rho, phi, drho))
169 |
170 | x, y, vx, vy = p.get()
171 | print('{:15s} | {:8.3f} | {:8.3f} | {:8.3f} | {:8.3f} |'.format("PREDICTION:", x, y, x, y))
172 |
173 | x, y, vx, vy = t.get()
174 | print('{:15s} | {:8.3f} | {:8.3f} | {:8.3f} | {:8.3f} |'.format("TRUTH:", x, y, x, y))
175 |
176 | i += 1
177 |
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/src/sensor_fusion.py:
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1 | from KF import KF
2 | from datapoint import DataPoint
3 | from tools import calculate_jacobian, cartesian_to_polar, time_difference
4 | import numpy as np
5 | from tools import get_RMSE
6 | import matplotlib.pyplot as plt
7 | # from helpers import print_EKF_data
8 |
9 | def parse_data(file):
10 | sensor_data=[]
11 | true_data=[]
12 |
13 | with open(file, 'r') as dat:
14 | lines = dat.readlines()
15 |
16 | for line in lines:
17 | x= line.split()
18 |
19 | if x[0] == 'L':
20 | sensor_data_point = DataPoint({ 'timestamp': int(x[3]), 'name': 'lidar', 'x': float(x[1]), 'y': float(x[2])})
21 |
22 | true_data_point = DataPoint({ 'timestamp': int(x[3]), 'name': 'state', 'x': float(x[4]), 'y': float(x[5]), 'vx': float(x[6]), 'vy': float(x[7])})
23 |
24 | elif x[0] == 'R':
25 | sensor_data_point = DataPoint({ 'timestamp': int(x[4]), 'name': 'radar', 'rho': float(x[1]), 'phi': float(x[2]), 'drho': float(x[3])})
26 |
27 | true_data_point = DataPoint({ 'timestamp': int(x[4]), 'name': 'state', 'x': float(x[5]), 'y': float(x[6]), 'vx': float(x[7]), 'vy': float(x[8])})
28 |
29 | sensor_data.append(sensor_data_point)
30 | true_data.append(true_data_point)
31 |
32 | return sensor_data,true_data
33 |
34 | def start(data,kalmanFilter):
35 | global first_time
36 | timestamp = data.get_timestamp()
37 | x = np.matrix([data.get()]).T
38 | kalmanFilter.x=x
39 | kalmanFilter.P=P
40 | kalmanFilter.F=F
41 | kalmanFilter.Q=Q
42 | first_time=False
43 | return timestamp
44 |
45 | def updateQ(dt):
46 |
47 | dt2 = dt * dt
48 | dt3 = dt * dt2
49 | dt4 = dt * dt3
50 |
51 | x, y = a
52 |
53 | r11 = dt4 * x / 4
54 | r13 = dt3 * x / 2
55 | r22 = dt4 * y / 4
56 | r24 = dt3 * y / 2
57 | r31 = dt3 * x / 2
58 | r33 = dt2 * x
59 | r42 = dt3 * y / 2
60 | r44 = dt2 * y
61 |
62 | Q = np.matrix([[r11, 0, r13, 0],[0, r22, 0, r24],[r31, 0, r33, 0], [0, r42, 0, r44]])
63 |
64 | return Q
65 |
66 | def kalman(data,kalmanFilter,timestamp):
67 | dt = time_difference(timestamp, data.get_timestamp())
68 | timestamp = data.get_timestamp()
69 |
70 | kalmanFilter.F[0,2],kalmanFilter.F[1,3]=dt,dt
71 | kalmanFilter.Q = updateQ(dt)
72 | kalmanFilter.predict()
73 |
74 | z = np.matrix(data.get_raw()).T
75 | x = kalmanFilter.x
76 |
77 | if data.get_name() == 'radar':
78 |
79 | px, py, vx, vy = x[0, 0], x[1, 0], x[2, 0], x[3, 0]
80 | rho, phi, drho = cartesian_to_polar(px, py, vx, vy)
81 | H = calculate_jacobian(px, py, vx, vy)
82 | Hx = (np.matrix([[rho, phi, drho]])).T
83 | R = radar_R
84 |
85 | elif data.get_name() == 'lidar':
86 |
87 | H = lidar_H
88 | Hx = lidar_H * x
89 | R = lidar_R
90 |
91 | kalmanFilter.update(z, H, Hx, R)
92 |
93 | # if data.get_name() == 'lidar':
94 | # print x, kalmanFilter.x, "lidar"
95 | # elif data.get_name() == 'radar':
96 | # print x, kalmanFilter.x, "radar"
97 |
98 | return timestamp
99 |
100 | def run(sensor_data,kalmanFilter):
101 |
102 | state_estimations=[]
103 | for data in sensor_data:
104 | if first_time:
105 | timestamp = start(data,kalmanFilter)
106 | else:
107 | timestamp = kalman(data,kalmanFilter,timestamp)
108 |
109 | x = kalmanFilter.x
110 |
111 | px, py, vx, vy = x[0, 0], x[1, 0], x[2, 0], x[3, 0]
112 |
113 | g = {'timestamp': data.get_timestamp(),
114 | 'name': 'state',
115 | 'x': px,
116 | 'y': py,
117 | 'vx': vx,
118 | 'vy': vy }
119 |
120 | state_estimation = DataPoint(g)
121 | state_estimations.append(state_estimation)
122 |
123 | return state_estimations
124 |
125 | lidar_R = np.matrix([[0.01, 0],
126 | [0, 0.01]])
127 |
128 | radar_R = np.matrix([[0.01, 0, 0],
129 | [0, 1.0e-6, 0],
130 | [0, 0, 0.01]])
131 |
132 | lidar_H = np.matrix([[1, 0, 0, 0],
133 | [0, 1, 0, 0]])
134 |
135 | P = np.matrix([[1, 0, 0, 0],
136 | [0, 1, 0, 0],
137 | [0, 0, 1000, 0],
138 | [0, 0, 0, 1000]])
139 |
140 | Q = np.matrix(np.zeros([4, 4]))
141 | F = np.matrix(np.eye(4))
142 |
143 | sensor_data,true_data = parse_data("data/data-1.txt")
144 |
145 | n = 4
146 | a = (5, 5) #accelerationNoise
147 |
148 | kalmanFilter = KF(n)
149 |
150 | first_time=True
151 | state_estimations=run(sensor_data,kalmanFilter)
152 |
153 | px, py, vx, vy = get_RMSE(state_estimations, true_data)
154 |
155 | # print_EKF_data(sensor_data, true_data, state_estimations,
156 | # RMSE = [px, py, vx, vy])
157 |
158 | lidar_x_sensor=[]
159 | lidar_y_sensor=[]
160 | x_pred=[]
161 | y_pred=[]
162 | x_true=[]
163 | y_true=[]
164 |
165 | radar_r_sensor=[]
166 | radar_p_sensor=[]
167 | radar_d_sensor=[]
168 |
169 | i=0
170 | for s, p, t in zip(sensor_data, state_estimations, true_data):
171 | i+=1
172 | if i%4==0:
173 | continue
174 | if s.get_name() == 'lidar':
175 | x, y = s.get_raw()
176 | lidar_x_sensor.append(x)
177 | lidar_y_sensor.append(y)
178 | # x,y, vx, vy=p.get()
179 | # x_pred.append(x)
180 | # y_pred.append(y)
181 |
182 | # x,y, vx, vy=t.get()
183 | # x_true.append(x)
184 | # y_true.append(y)
185 | else:
186 | rho, phi, drho = s.get_raw()
187 | radar_r_sensor.append(rho)
188 | radar_p_sensor.append(phi)
189 | radar_d_sensor.append(drho)
190 | x,y, vx, vy=p.get()
191 | x_pred.append(x)
192 | y_pred.append(y)
193 |
194 | x,y, vx, vy=t.get()
195 | x_true.append(x)
196 | y_true.append(y)
197 |
198 |
199 | # plt.scatter(lidar_x_sensor,lidar_y_sensor,label="Sensor-Data",marker="o")
200 | plt.scatter(x_pred,y_pred,label="Predicted-Data",marker="*")
201 | plt.scatter(x_true,y_true,label="True-Data",marker=".")
202 | plt.legend()
203 | plt.text(10,-13,"Radar Data Plot")
204 | plt.show()
205 |
206 |
207 |
208 |
209 |
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/src/tools.py:
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1 | import numpy as np
2 | from math import sin, cos, sqrt
3 |
4 | def cartesian_to_polar(x, y, vx, vy, THRESH = 0.0001):
5 | """
6 | Converts 2d cartesian position and velocity coordinates to polar coordinates
7 |
8 | Args:
9 | x, y, vx, vy : floats - position and velocity components in cartesian respectively
10 | THRESH : float - minimum value of rho to return non-zero values
11 |
12 | Returns:
13 | rho, drho : floats - radius and velocity magnitude respectively
14 | phi : float - angle in radians
15 | """
16 |
17 | rho = sqrt(x * x + y * y)
18 | phi = np.arctan2(y, x)
19 |
20 |
21 | if rho < THRESH:
22 | print("WARNING: in cartesian_to_polar(): d_squared < THRESH")
23 | rho, phi, drho = 0, 0, 0
24 | else:
25 | drho = (x * vx + y * vy) / rho
26 |
27 | return rho, phi, drho
28 |
29 | def polar_to_cartesian(rho, phi, drho):
30 | """
31 | Converts 2D polar coordinates into cartesian coordinates
32 |
33 | Args:
34 | rho. drho : floats - radius magnitude and velocity magnitudes respectively
35 | phi : float - angle in radians
36 |
37 | Returns:
38 | x, y, vx, vy : floats - position and velocity components in cartesian respectively
39 | """
40 |
41 | x, y = rho * cos(phi), rho * sin(phi)
42 | vx, vy = drho * cos(phi) , drho * sin(phi)
43 |
44 | return x, y, vx, vy
45 |
46 | def time_difference(t1, t2):
47 | """
48 | Computes the time difference in microseconds (type: float) of two epoch times values in seconds (type: int)
49 |
50 | Args:
51 | t1 : int - previous epoch time in seconds
52 | t2 : int - current epoch time in seconds
53 |
54 | Returns: a float - the time difference in seconds
55 | """
56 | return (t2 - t1) / 1000000.0
57 |
58 |
59 | def get_RMSE(predictions, truths):
60 | """
61 | Computes the root mean square errors (RMSE) of the attributes of two lists of DataPoint() instances
62 |
63 | Args:
64 | predictions - a list of DataPoint() instances
65 | truths - a list of DataPoint() instances
66 |
67 | Returns:
68 | px, py, vx, vy - The RMSE of each respective DataPoint() attributes (type: float)
69 | """
70 | pxs, pys, vxs, vys = [], [], [], []
71 |
72 | for p, t in zip(predictions, truths):
73 |
74 | ppx, ppy, pvx, pvy = p.get()
75 | tpx, tpy, tvx, tvy = t.get()
76 |
77 | pxs += [(ppx - tpx) * (ppx - tpx)]
78 | pys += [(ppy - tpy) * (ppy - tpy)]
79 | vxs += [(pvx - tvx) * (pvx - tvx)]
80 | vys += [(pvy - tvy) * (pvy - tvy)]
81 |
82 | px, py = sqrt(np.mean(pxs)), sqrt(np.mean(pys))
83 | vx, vy = sqrt(np.mean(vxs)), sqrt(np.mean(vys))
84 |
85 | return px, py, vx, vy
86 |
87 | def calculate_jacobian(px, py, vx, vy, THRESH = 0.0001, ZERO_REPLACEMENT = 0.0001):
88 | """
89 | Calculates the Jacobian given for four state variables
90 |
91 | Args:
92 | px, py, vx, vy : floats - four state variables in the system
93 | THRESH - minimum value of squared distance to return a non-zero matrix
94 | ZERO_REPLACEMENT - value to replace zero to avoid division by zero error
95 |
96 | Returns:
97 | H : the jacobian matrix expressed as a 4 x 4 numpy matrix with float values
98 | """
99 |
100 | d_squared = px * px + py * py
101 | d = sqrt(d_squared)
102 | d_cubed = d_squared * d
103 |
104 | if d_squared < THRESH:
105 |
106 | print("WARNING: in calculate_jacobian(): d_squared < THRESH")
107 | H = np.matrix(np.zeros([3, 4]))
108 |
109 | else:
110 |
111 | r11 = px / d
112 | r12 = py / d
113 | r21 = -py / d_squared
114 | r22 = px / d_squared
115 | r31 = py * (vx * py - vy * px) / d_cubed
116 | r32 = px * (vy * px - vx * py) / d_cubed
117 |
118 | H = np.matrix([[r11, r12, 0, 0],
119 | [r21, r22, 0, 0],
120 | [r31, r32, r11, r12]])
121 |
122 | return H
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