From 526c9b418bd0d9209844265eb3f6ded97ab64b5d Mon Sep 17 00:00:00 2001 From: Emma Templeton Date: Tue, 9 Aug 2022 19:41:32 -0400 Subject: [PATCH] updated files from manuscript revision --- .DS_Store | Bin 6148 -> 6148 bytes Analyses/.DS_Store | Bin 6148 -> 6148 bytes .../long_gap_connection_all_1000.csv | 46621 +++ .../long_gap_connection_all_1250.csv | 27721 ++ .../long_gap_connection_all_1750.csv | 10641 + .../long_gap_connection_all_2000.csv | 6497 + .../long_gap_connection_all_2250.csv | 4285 + .../long_gap_connection_all_2500.csv | 2955 + .../long_gap_connection_all_2750.csv | 2171 + .../long_gap_connection_all_3000.csv | 1779 + .../long_gap_connection_all_500.csv | 153063 ++++++++ .../long_gap_connection_all_750.csv | 80291 +++++ Analyses/long_gap_connection_all.csv | 1990 +- .../long_gap_connection_all_long_format.csv | 13918 +- ...ap_connection_all_long_format_baseline.csv | 6497 + ...gap_connection_all_long_format_friends.csv | 2829 + 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a common baseline-checkpoint.ipynb | 595 + ...lyze long gap rating task-checkpoint.ipynb | 51 +- ...oratory semantic analyses-checkpoint.ipynb | 315 + ...ntervals impact Figure 2?-checkpoint.ipynb | 652 + ...resholds impact Figure 2?-checkpoint.ipynb | 738 + ...tches (vs speaker stays)?-checkpoint.ipynb | 533 + ...igate laughter in Study 1-checkpoint.ipynb | 232 + ...eria for stranger dataset-checkpoint.ipynb | 6 + .../plot figures-checkpoint.ipynb | 952 +- ... of conversations that are long gaps.ipynb | 134 - ...do friends use more \"long gaps\"?).ipynb" | 347 - .../Study 1 - defining a \"long gap\".ipynb" | 18 +- ...y 1 - do friends use more long gaps?.ipynb | 332 + ...ng gaps and self-reported connection.ipynb | 1385 +- ...nection ratings to a common baseline.ipynb | 595 + ...udy 2 - analyze long gap rating task.ipynb | 10 +- ...ment - exploratory semantic analyses.ipynb | 351 + ...erent bin intervals impact Figure 2?.ipynb | 625 + ...ifferent thresholds impact Figure 2?.ipynb | 746 + ...speaker switches (vs speaker stays)?.ipynb | 541 + ...nt - investigate laughter in Study 1.ipynb | 242 + ...lusion criteria for stranger dataset.ipynb | 172 + Code/plot figures.ipynb | 808 +- Code/statistical_tests.R | 433 +- Data/.DS_Store | Bin 6148 -> 6148 bytes Data/post_convo_survey/.DS_Store | Bin 0 -> 6148 bytes Data/post_convo_survey/friends/clean_data.csv | 131 + .../strangers/clean_data.csv | 645 + .../strangers/data_common_questions.csv | 645 + Data/subject_id_key.csv | 388 + Figures/.DS_Store | Bin 6148 -> 6148 bytes Figures/{Fig1D.pdf => Fig1.pdf} | Bin 12707 -> 12674 bytes Figures/Fig1A_strangers.pdf | Bin 13647 -> 0 bytes Figures/Fig1B.pdf | Bin 15001 -> 0 bytes .../{Fig1C.pdf => Fig2_bottom_entering.pdf} | Bin 12552 -> 10509 bytes .../{Fig2A.pdf => Fig2_bottom_exiting.pdf} | Bin 12579 -> 10510 bytes Figures/{Fig1A_friends.pdf => Fig2_top.pdf} | Bin 14069 -> 14511 bytes Figures/Fig3A.pdf | Bin 12964 -> 12964 bytes Figures/Fig3B.pdf | Bin 12537 -> 12540 bytes 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delete mode 100644 Analyses/turn_taking_friends.csv delete mode 100644 Analyses/turn_taking_strangers.csv delete mode 100644 Code/.ipynb_checkpoints/Discussion - percent of conversations that are long gaps-checkpoint.ipynb delete mode 100644 "Code/.ipynb_checkpoints/Study 1 - chi-square test (do friends use more \"long gaps\"?)-checkpoint.ipynb" delete mode 100644 "Code/.ipynb_checkpoints/Study 1 - defining a \"long gap\"-checkpoint.ipynb" create mode 100644 Code/.ipynb_checkpoints/Study 1 - do friends use more long gaps?-checkpoint.ipynb create mode 100644 Code/.ipynb_checkpoints/Study 1 - reference connection ratings to a common baseline-checkpoint.ipynb create mode 100644 Code/.ipynb_checkpoints/Supplement - exploratory semantic analyses-checkpoint.ipynb create mode 100644 Code/.ipynb_checkpoints/Supplement - how do different bin intervals impact Figure 2?-checkpoint.ipynb create mode 100644 Code/.ipynb_checkpoints/Supplement - how do different thresholds impact Figure 2?-checkpoint.ipynb create mode 100644 Code/.ipynb_checkpoints/Supplement - how often are there speaker switches (vs speaker stays)?-checkpoint.ipynb create mode 100644 Code/.ipynb_checkpoints/Supplement - investigate laughter in Study 1-checkpoint.ipynb create mode 100644 Code/.ipynb_checkpoints/inclusion criteria for stranger dataset-checkpoint.ipynb delete mode 100644 Code/Discussion - percent of conversations that are long gaps.ipynb delete mode 100644 "Code/Study 1 - chi-square test (do friends use more \"long gaps\"?).ipynb" create mode 100644 Code/Study 1 - do friends use more long gaps?.ipynb create mode 100644 Code/Study 1 - reference connection ratings to a common baseline.ipynb create mode 100644 Code/Supplement - exploratory semantic analyses.ipynb create mode 100644 Code/Supplement - how do different bin intervals impact Figure 2?.ipynb create mode 100644 Code/Supplement - how do different thresholds impact Figure 2?.ipynb create mode 100644 Code/Supplement - how often are there speaker switches (vs speaker stays)?.ipynb create mode 100644 Code/Supplement - investigate laughter in Study 1.ipynb create mode 100644 Code/inclusion criteria for stranger dataset.ipynb create mode 100644 Data/post_convo_survey/.DS_Store create mode 100644 Data/post_convo_survey/friends/clean_data.csv create mode 100644 Data/post_convo_survey/strangers/clean_data.csv create mode 100644 Data/post_convo_survey/strangers/data_common_questions.csv create mode 100644 Data/subject_id_key.csv rename Figures/{Fig1D.pdf => Fig1.pdf} (76%) delete mode 100644 Figures/Fig1A_strangers.pdf delete mode 100644 Figures/Fig1B.pdf rename Figures/{Fig1C.pdf => Fig2_bottom_entering.pdf} (67%) rename Figures/{Fig2A.pdf => Fig2_bottom_exiting.pdf} (64%) rename Figures/{Fig1A_friends.pdf => Fig2_top.pdf} (61%) create mode 100644 Figures/Supplement_different_intervals/0.5 seconds.png create mode 100644 Figures/Supplement_different_intervals/1 second.png create mode 100644 Figures/Supplement_different_intervals/10 seconds.png create mode 100644 Figures/Supplement_different_intervals/2 seconds.png create mode 100644 Figures/Supplement_different_intervals/3 seconds.png create mode 100644 Figures/Supplement_different_intervals/4 seconds.png create mode 100644 Figures/Supplement_different_intervals/5 seconds.png create mode 100644 Figures/Supplement_different_intervals/6 seconds.png create mode 100644 Figures/Supplement_different_intervals/7 seconds.png create mode 100644 Figures/Supplement_different_intervals/8 seconds.png create mode 100644 Figures/Supplement_different_intervals/9 seconds.png create mode 100644 Figures/Supplement_different_thresholds/1000.pdf create mode 100644 Figures/Supplement_different_thresholds/1250.pdf create mode 100644 Figures/Supplement_different_thresholds/1750.pdf create mode 100644 Figures/Supplement_different_thresholds/2000.pdf create mode 100644 Figures/Supplement_different_thresholds/2250.pdf create mode 100644 Figures/Supplement_different_thresholds/2500.pdf create mode 100644 Figures/Supplement_different_thresholds/2750.pdf create mode 100644 Figures/Supplement_different_thresholds/3000.pdf rename Figures/{Fig2B.pdf => Supplement_different_thresholds/500.pdf} (70%) create mode 100644 Figures/Supplement_different_thresholds/750.pdf create mode 100644 Supplement/.DS_Store create mode 100644 Supplement/ConversationDatasetDetails.docx create mode 100644 Supplement/SupplementaryMaterials.pdf create mode 100644 Supplement/laughter_annotation.csv diff --git a/.DS_Store b/.DS_Store index bcbb4bb5e9029d39b493c30ee36f3fd6139191e8..a2de2b19fa2b64b29e8742a69d304aa2c9b761b2 100644 GIT binary patch delta 104 zcmZoMXffEZkVW2u0R&hWoEh>NQW#Qm(hY-?^K%Qpq5^NiCm&#uV0~8Gt?+B|T^4C5 s>o3bTW~P4U$=g`vSUR@vHl6&BRc!J*Rvs8fnUQ&8;Zw%V>>Pjj0hJ&l*#H0l delta 105 zcmZoMXffEZkVW28fdL3u7@QgM8B!QhbJ7iilk;;6K!OYcZ-OTuV3A<`yi>F0+vK|} w(ooi4mTk;(o1RSG#wy2h?g9t<`gq;+Fc^zc$xNzdPLNL3I+!lz7V-D+K?S@*$1*)V?uM9P(cei 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-81,88,81_88,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2796.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -88,81,81_88,57.0,57.85,58.0,58.0,58.0,58.7,60.0,2796.0,-1.0,-0.14999999999999858,0.0,0.0,0.7000000000000028,2.0,strangers -41,45,41_45,41.0,41.0,41.0,41.0,41.0,41.0,41.0,2140.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -45,41,41_45,74.0,74.0,74.2,75.0,75.0,75.0,75.0,2140.0,-1.0,-1.0,-0.7999999999999972,0.0,0.0,0.0,strangers -41,45,41_45,50.0,50.0,50.0,50.0,50.0,50.0,50.0,2270.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -45,41,41_45,85.0,85.0,85.0,85.0,85.0,85.0,85.0,2270.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -41,45,41_45,55.0,55.0,55.0,55.0,55.0,55.0,55.0,2638.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -45,41,41_45,78.0,78.0,78.0,78.0,78.0,80.9,86.0,2638.0,0.0,0.0,0.0,0.0,2.9000000000000057,8.0,strangers -21,28,21_28,69.95,70.0,70.0,76.80645161290323,78.6,77.35,73.2,3045.0,-6.8564516129032285,-6.806451612903231,-6.806451612903231,1.793548387096763,0.543548387096763,-3.6064516129032285,strangers -28,21,21_28,72.0,72.0,72.0,34.12903225806452,25.6,28.8,40.45,3045.0,37.87096774193548,37.87096774193548,37.87096774193548,-8.529032258064518,-5.329032258064519,6.320967741935483,strangers -43,40,43_40,89.0,89.0,89.0,89.0,89.0,89.0,89.0,2413.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -40,43,43_40,72.0,72.0,72.0,72.0,72.0,72.0,72.0,2413.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -6,7,6_7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2130.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -7,6,6_7,20.0,20.0,20.0,20.0,20.0,20.0,20.0,2130.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -6,7,6_7,16.0,16.0,16.0,16.0,16.0,16.0,16.0,2972.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -7,6,6_7,22.0,22.0,22.0,22.0,22.0,22.0,22.0,2972.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -6,7,6_7,16.0,16.0,16.0,16.0,16.0,16.0,16.0,2365.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -7,6,6_7,22.0,22.0,22.0,22.0,22.0,25.75,31.0,2365.0,0.0,0.0,0.0,0.0,3.75,9.0,strangers -6,7,6_7,17.15,25.0,25.0,25.0,25.0,25.0,25.0,2399.0,-7.850000000000001,0.0,0.0,0.0,0.0,0.0,strangers 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-67,70,70_67,36.0,36.0,36.0,36.0,36.0,36.0,36.0,4873.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -88,87,88_87,89.0,89.0,89.0,89.0,89.0,89.0,89.0,2415.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -87,88,88_87,90.0,90.0,90.0,90.0,90.0,90.0,90.0,2415.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -88,87,88_87,93.0,93.0,93.0,93.0,93.0,93.0,93.0,2459.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -87,88,88_87,90.0,90.0,90.0,90.0,90.0,90.0,90.0,2459.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -41,46,41_46,23.0,23.0,23.0,23.0,23.0,23.0,23.0,2057.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -46,41,41_46,37.0,37.0,37.0,37.04761904761905,38.0,38.0,38.0,2057.0,-0.047619047619051,-0.047619047619051,-0.047619047619051,0.952380952380949,0.952380952380949,0.952380952380949,strangers -41,46,41_46,27.0,27.0,27.0,27.0,27.0,27.0,27.0,3849.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers -46,41,41_46,35.0,35.0,35.0,35.0,35.0,35.0,35.0,3849.0,0.0,0.0,0.0,0.0,0.0,0.0,strangers 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+107.0,259.0,107_259,3_after,100.0,0.0,2311.0,other,131.0 +259.0,107.0,107_259,3_after,92.0,0.0,2311.0,self,131.0 +107.0,259.0,107_259,long_gap,100.0,0.0,2609.0,other,142.0 +259.0,107.0,107_259,long_gap,92.0,0.0,2609.0,self,142.0 +107.0,259.0,107_259,1_before,100.0,0.0,2609.0,other,142.0 +259.0,107.0,107_259,1_before,92.0,0.0,2609.0,self,142.0 +107.0,259.0,107_259,2_before,100.0,0.0,2609.0,other,142.0 +259.0,107.0,107_259,2_before,92.0,0.0,2609.0,self,142.0 +107.0,259.0,107_259,3_before,100.0,0.0,2609.0,other,142.0 +259.0,107.0,107_259,3_before,92.0,0.0,2609.0,self,142.0 +107.0,259.0,107_259,1_after,100.0,0.0,2609.0,other,142.0 +259.0,107.0,107_259,1_after,92.0,0.0,2609.0,self,142.0 +107.0,259.0,107_259,2_after,100.0,0.0,2609.0,other,142.0 +259.0,107.0,107_259,2_after,92.0,0.0,2609.0,self,142.0 +107.0,259.0,107_259,3_after,100.0,0.0,2609.0,other,142.0 +259.0,107.0,107_259,3_after,92.0,0.0,2609.0,self,142.0 diff --git a/Analyses/long_gap_connection_all_long_format_laughter.csv b/Analyses/long_gap_connection_all_long_format_laughter.csv new file mode 100644 index 0000000..e38d1d9 --- /dev/null +++ b/Analyses/long_gap_connection_all_long_format_laughter.csv @@ -0,0 +1,6497 @@ +clip,gap_length_x,condition_x,dyad,turn_num,turn_start_msec,video_num,laughter,Unnamed: 8,Unnamed: 9,subID,partnerID,timepoint,connection,connection_change,gap_length_y,speaker,condition_y +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,long_gap,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,long_gap,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,1_before,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,1_before,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,2_before,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,2_before,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,3_before,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,3_before,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,1_after,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,1_after,70.7,-0.2999999999999971,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,2_after,63.95,0.9500000000000028,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,2_after,46.05,-24.950000000000003,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,3_after,65.0,2.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,3_after,43.0,-28.0,2388.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,long_gap,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,long_gap,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,1_before,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,1_before,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,2_before,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,2_before,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,3_before,74.35,-4.650000000000006,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,3_before,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,1_after,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,1_after,75.35,-0.6500000000000057,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,2_after,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,2_after,75.0,-1.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,3_after,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,3_after,75.0,-1.0,2183.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,long_gap,82.15384615384616,0.0,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,long_gap,68.42307692307692,0.0,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,1_before,82.0,-0.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,1_before,76.0,7.57692307692308,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,2_before,82.0,-0.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,2_before,76.0,7.57692307692308,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,3_before,82.0,-0.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,3_before,77.85,9.426923076923076,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,1_after,80.0,-2.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,1_after,51.9,-16.52307692307692,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,2_after,80.0,-2.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,2_after,42.0,-26.42307692307692,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,3_after,80.0,-2.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,3_after,42.0,-26.42307692307692,2612.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,long_gap,73.14814814814815,0.0,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,long_gap,51.0,0.0,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,1_before,68.0,-5.148148148148152,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,1_before,51.0,0.0,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,2_before,68.0,-5.148148148148152,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,2_before,51.0,0.0,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,3_before,68.0,-5.148148148148152,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,3_before,49.95,-1.0499999999999972,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,1_after,79.6,6.451851851851842,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,1_after,51.85,0.8500000000000014,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,2_after,81.35,8.201851851851842,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,2_after,70.1,19.099999999999994,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,3_after,80.7,7.55185185185185,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,3_after,75.0,24.0,2747.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,long_gap,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,long_gap,11.74074074074074,0.0,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,1_before,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,1_before,10.6,-1.1407407407407408,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,2_before,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,2_before,9.0,-2.7407407407407405,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,3_before,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,3_before,9.0,-2.7407407407407405,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,1_after,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,1_after,12.0,0.2592592592592595,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,2_after,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,2_after,12.0,0.2592592592592595,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,3_after,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,3_after,21.45,9.70925925925926,2693.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,long_gap,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,long_gap,35.63636363636363,0.0,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,1_before,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,1_before,35.0,-0.6363636363636331,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,2_before,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,2_before,37.4,1.7636363636363654,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,3_before,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,3_before,38.0,2.363636363636367,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,1_after,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,1_after,36.0,0.3636363636363668,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,2_after,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,2_after,36.0,0.3636363636363668,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,3_after,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,3_after,37.9,2.263636363636365,2205.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,long_gap,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,long_gap,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,1_before,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,1_before,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,2_before,75.5,0.5,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,2_before,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,3_before,75.65,0.6500000000000057,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,3_before,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,1_after,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,1_after,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,2_after,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,2_after,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,3_after,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,3_after,44.9,-2.1000000000000014,2217.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,long_gap,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,long_gap,47.4,0.0,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,1_before,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,1_before,61.95,14.550000000000004,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,2_before,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,2_before,63.0,15.6,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,3_before,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,3_before,63.0,15.6,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,1_after,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,1_after,28.45,-18.95,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,2_after,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,2_after,31.7,-15.7,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,3_after,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,3_after,32.4,-15.0,5539.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,long_gap,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,long_gap,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,1_before,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,1_before,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,2_before,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,2_before,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,3_before,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,3_before,74.35,-0.6500000000000057,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,1_after,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,1_after,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,2_after,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,2_after,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,3_after,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,3_after,75.0,0.0,2153.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,long_gap,58.0,0.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,long_gap,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,1_before,58.0,0.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,1_before,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,2_before,57.85,-0.1499999999999985,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,2_before,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,3_before,57.0,-1.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,3_before,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,1_after,58.0,0.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,1_after,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,2_after,58.7,0.7000000000000028,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,2_after,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,3_after,60.0,2.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,3_after,0.0,0.0,2796.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,long_gap,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,long_gap,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,1_before,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,1_before,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,2_before,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,2_before,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,3_before,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,3_before,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,1_after,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,1_after,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,2_after,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,2_after,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,3_after,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,3_after,20.0,0.0,2130.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,long_gap,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,long_gap,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,1_before,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,1_before,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,2_before,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,2_before,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,3_before,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,3_before,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,1_after,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,1_after,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,2_after,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,2_after,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,3_after,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,3_after,22.0,0.0,2972.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,long_gap,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,long_gap,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,1_before,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,1_before,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,2_before,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,2_before,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,3_before,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,3_before,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,1_after,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,1_after,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,2_after,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,2_after,25.75,3.75,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,3_after,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,3_after,31.0,9.0,2365.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,long_gap,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,long_gap,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,1_before,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,1_before,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,2_before,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,2_before,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,3_before,17.15,-7.850000000000001,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,3_before,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,1_after,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,1_after,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,2_after,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,2_after,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,3_after,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,3_after,31.0,0.0,2399.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,long_gap,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,long_gap,56.8,0.0,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,1_before,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,1_before,51.3,-5.5,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,2_before,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,2_before,51.0,-5.799999999999997,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,3_before,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,3_before,51.0,-5.799999999999997,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,1_after,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,1_after,57.0,0.2000000000000028,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,2_after,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,2_after,57.0,0.2000000000000028,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,3_after,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,3_after,57.0,0.2000000000000028,3003.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,long_gap,30.77777777777778,0.0,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,long_gap,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,1_before,48.0,17.22222222222222,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,1_before,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,2_before,48.0,17.22222222222222,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,2_before,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,3_before,48.0,17.22222222222222,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,3_before,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,1_after,30.0,-0.7777777777777786,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,1_after,60.9,3.899999999999999,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,2_after,30.0,-0.7777777777777786,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,2_after,62.0,5.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,3_after,30.0,-0.7777777777777786,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,3_after,62.0,5.0,4523.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,long_gap,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,long_gap,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,1_before,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,1_before,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,2_before,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,2_before,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,3_before,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,3_before,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,1_after,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,1_after,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,2_after,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,2_after,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,3_after,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,3_after,62.0,0.0,2388.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,long_gap,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,long_gap,48.0,0.0,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,1_before,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,1_before,47.65,-0.3500000000000014,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,2_before,49.15,-0.8500000000000014,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,2_before,46.0,-2.0,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,3_before,46.55,-3.450000000000003,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,3_before,46.0,-2.0,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,1_after,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,1_after,40.15,-7.850000000000001,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,2_after,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,2_after,39.05,-8.950000000000003,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,3_after,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,3_after,39.0,-9.0,2066.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,long_gap,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,long_gap,38.96774193548387,0.0,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,1_before,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,1_before,40.0,1.032258064516128,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,2_before,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,2_before,40.0,1.032258064516128,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,3_before,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,3_before,40.0,1.032258064516128,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,1_after,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,1_after,35.0,-3.967741935483872,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,2_after,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,2_after,35.0,-3.967741935483872,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,3_after,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,3_after,35.0,-3.967741935483872,3082.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,long_gap,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,long_gap,81.0,0.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,1_before,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,1_before,81.05,0.0499999999999971,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,2_before,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,2_before,82.0,1.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,3_before,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,3_before,82.0,1.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,1_after,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,1_after,81.0,0.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,2_after,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,2_after,81.0,0.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,3_after,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,3_after,81.0,0.0,2133.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,long_gap,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,long_gap,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,1_before,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,1_before,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,2_before,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,2_before,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,3_before,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,3_before,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,1_after,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,1_after,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,2_after,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,2_after,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,3_after,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,3_after,89.0,0.0,2415.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,long_gap,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,long_gap,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,1_before,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,1_before,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,2_before,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,2_before,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,3_before,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,3_before,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,1_after,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,1_after,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,2_after,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,2_after,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,3_after,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,3_after,93.0,0.0,2459.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,long_gap,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,long_gap,37.04761904761905,0.0,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,1_before,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,1_before,37.0,-0.047619047619051,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,2_before,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,2_before,37.0,-0.047619047619051,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,3_before,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,3_before,37.0,-0.047619047619051,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,1_after,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,1_after,38.0,0.952380952380949,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,2_after,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,2_after,38.0,0.952380952380949,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,3_after,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,3_after,38.0,0.952380952380949,2057.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,long_gap,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,long_gap,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,1_before,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,1_before,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,2_before,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,2_before,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,3_before,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,3_before,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,1_after,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,1_after,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,2_after,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,2_after,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,3_after,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,3_after,35.0,0.0,3849.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,long_gap,92.1818181818182,0.0,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,long_gap,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,1_before,94.0,1.818181818181813,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,1_before,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,2_before,94.0,1.818181818181813,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,2_before,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,3_before,93.0,0.818181818181813,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,3_before,68.25,-0.75,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,1_after,86.7,-5.481818181818184,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,1_after,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,2_after,85.0,-7.181818181818187,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,2_after,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,3_after,85.0,-7.181818181818187,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,3_after,69.0,0.0,2127.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,long_gap,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,long_gap,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,1_before,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,1_before,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,2_before,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,2_before,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,3_before,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,3_before,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,1_after,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,1_after,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,2_after,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,2_after,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,3_after,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,3_after,50.0,0.0,2555.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,long_gap,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,long_gap,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,1_before,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,1_before,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,2_before,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,2_before,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,3_before,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,3_before,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,1_after,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,1_after,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,2_after,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,2_after,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,3_after,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,3_after,49.0,0.0,2174.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,long_gap,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,long_gap,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,1_before,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,1_before,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,2_before,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,2_before,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,3_before,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,3_before,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,1_after,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,1_after,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,2_after,69.8,-0.2000000000000028,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,2_after,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,3_after,67.6,-2.4000000000000057,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,3_after,49.0,0.0,2303.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,long_gap,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,long_gap,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,1_before,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,1_before,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,2_before,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,2_before,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,3_before,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,3_before,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,1_after,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,1_after,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,2_after,63.65,-0.3500000000000014,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,2_after,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,3_after,61.0,-3.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,3_after,41.0,0.0,2094.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,long_gap,63.91428571428571,0.0,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,long_gap,42.62857142857143,0.0,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,1_before,63.0,-0.914285714285711,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,1_before,41.0,-1.6285714285714263,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,2_before,61.8,-2.114285714285714,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,2_before,41.0,-1.6285714285714263,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,3_before,62.0,-1.914285714285711,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,3_before,41.0,-1.6285714285714263,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,1_after,64.0,0.0857142857142889,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,1_after,44.0,1.3714285714285737,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,2_after,64.0,0.0857142857142889,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,2_after,44.0,1.3714285714285737,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,3_after,64.0,0.0857142857142889,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,3_after,44.0,1.3714285714285737,3457.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,long_gap,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,long_gap,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,1_before,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,1_before,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,2_before,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,2_before,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,3_before,63.8,-0.2000000000000028,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,3_before,41.45,-2.549999999999997,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,1_after,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,1_after,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,2_after,63.6,-0.3999999999999986,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,2_after,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,3_after,56.4,-7.600000000000001,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,3_after,44.0,0.0,2390.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,long_gap,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,long_gap,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,1_before,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,1_before,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,2_before,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,2_before,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,3_before,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,3_before,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,1_after,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,1_after,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,2_after,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,2_after,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,3_after,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,3_after,44.0,0.0,2122.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,long_gap,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,long_gap,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,1_before,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,1_before,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,2_before,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,2_before,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,3_before,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,3_before,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,1_after,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,1_after,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,2_after,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,2_after,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,3_after,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,3_after,46.0,0.0,3199.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,long_gap,82.76190476190476,0.0,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,long_gap,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,1_before,82.3,-0.461904761904762,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,1_before,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,2_before,82.7,-0.0619047619047563,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,2_before,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,3_before,83.95,1.1880952380952436,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,3_before,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,1_after,81.4,-1.3619047619047535,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,1_after,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,2_after,76.05,-6.711904761904762,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,2_after,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,3_after,82.9,0.1380952380952465,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,3_after,0.0,0.0,2066.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,long_gap,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,long_gap,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,1_before,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,1_before,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,2_before,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,2_before,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,3_before,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,3_before,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,1_after,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,1_after,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,2_after,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,2_after,23.25,0.25,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,3_after,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,3_after,27.35,4.350000000000001,2343.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,long_gap,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,long_gap,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,1_before,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,1_before,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,2_before,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,2_before,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,3_before,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,3_before,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,1_after,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,1_after,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,2_after,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,2_after,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,3_after,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,3_after,51.0,0.0,2029.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,long_gap,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,long_gap,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,1_before,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,1_before,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,2_before,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,2_before,32.3,9.299999999999995,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,3_before,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,3_before,53.5,30.5,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,1_after,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,1_after,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,2_after,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,2_after,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,3_after,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,3_after,23.0,0.0,2805.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,long_gap,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,long_gap,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,1_before,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,1_before,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,2_before,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,2_before,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,3_before,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,3_before,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,1_after,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,1_after,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,2_after,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,2_after,60.85,2.8500000000000014,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,3_after,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,3_after,64.15,6.150000000000006,2266.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,long_gap,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,long_gap,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,1_before,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,1_before,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,2_before,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,2_before,99.75,-0.25,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,3_before,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,3_before,94.05,-5.950000000000003,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,1_after,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,1_after,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,2_after,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,2_after,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,3_after,60.7,0.7000000000000028,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,3_after,100.0,0.0,2170.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,long_gap,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,long_gap,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,1_before,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,1_before,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,2_before,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,2_before,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,3_before,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,3_before,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,1_after,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,1_after,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,2_after,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,2_after,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,3_after,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,3_after,77.0,0.0,3310.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,long_gap,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,long_gap,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,1_before,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,1_before,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,2_before,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,2_before,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,3_before,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,3_before,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,1_after,82.05,0.0499999999999971,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,1_after,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,2_after,83.0,1.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,2_after,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,3_after,83.3,1.2999999999999972,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,3_after,100.0,0.0,2074.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,long_gap,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,long_gap,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,1_before,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,1_before,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,2_before,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,2_before,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,3_before,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,3_before,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,1_after,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,1_after,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,2_after,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,2_after,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,3_after,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,3_after,52.0,0.0,2077.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,long_gap,56.0,0.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,long_gap,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,1_before,55.1,-0.8999999999999986,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,1_before,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,2_before,55.0,-1.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,2_before,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,3_before,55.0,-1.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,3_before,75.9,-0.0999999999999943,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,1_after,56.0,0.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,1_after,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,2_after,64.55,8.549999999999997,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,2_after,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,3_after,78.2,22.200000000000003,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,3_after,76.0,0.0,2706.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,long_gap,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,long_gap,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,1_before,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,1_before,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,2_before,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,2_before,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,3_before,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,3_before,55.9,0.8999999999999986,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,1_after,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,1_after,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,2_after,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,2_after,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,3_after,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,3_after,55.0,0.0,2837.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,long_gap,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,long_gap,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,1_before,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,1_before,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,2_before,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,2_before,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,3_before,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,3_before,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,1_after,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,1_after,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,2_after,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,2_after,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,3_after,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,3_after,43.0,0.0,2028.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,long_gap,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,long_gap,70.14285714285714,0.0,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,1_before,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,1_before,73.0,2.857142857142861,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,2_before,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,2_before,73.0,2.857142857142861,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,3_before,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,3_before,73.0,2.857142857142861,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,1_after,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,1_after,69.25,-0.8928571428571388,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,2_after,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,2_after,58.75,-11.39285714285714,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,3_after,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,3_after,58.0,-12.14285714285714,2093.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,long_gap,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,long_gap,89.13793103448276,0.0,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,1_before,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,1_before,89.0,-0.1379310344827615,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,2_before,58.9,-3.1000000000000014,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,2_before,89.0,-0.1379310344827615,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,3_before,56.0,-6.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,3_before,89.35,0.2120689655172327,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,1_after,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,1_after,88.15,-0.987931034482756,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,2_after,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,2_after,87.0,-2.1379310344827616,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,3_after,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,3_after,87.0,-2.1379310344827616,2917.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,long_gap,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,long_gap,63.0,0.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,1_before,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,1_before,65.9,2.9000000000000057,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,2_before,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,2_before,74.0,11.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,3_before,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,3_before,74.0,11.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,1_after,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,1_after,63.0,0.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,2_after,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,2_after,63.0,0.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,3_after,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,3_after,63.0,0.0,2719.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,long_gap,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,long_gap,35.9,0.0,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,1_before,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,1_before,72.35,36.45,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,2_before,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,2_before,86.25,50.35,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,3_before,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,3_before,85.0,49.1,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,1_after,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,1_after,20.55,-15.349999999999998,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,2_after,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,2_after,10.55,-25.35,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,3_after,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,3_after,22.4,-13.5,2016.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,long_gap,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,long_gap,41.0,0.0,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,1_before,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,1_before,41.73684210526316,0.7368421052631575,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,2_before,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,2_before,56.05263157894737,15.05263157894737,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,3_before,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,3_before,70.89473684210526,29.89473684210526,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,1_after,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,1_after,41.5,0.5,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,2_after,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,2_after,49.55,8.549999999999997,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,3_after,36.4,0.3999999999999986,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,3_after,48.25,7.25,2352.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,long_gap,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,long_gap,43.125,0.0,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,1_before,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,1_before,39.9,-3.2250000000000014,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,2_before,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,2_before,62.8,19.675,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,3_before,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,3_before,100.0,56.875,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,1_after,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,1_after,49.0,5.875,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,2_after,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,2_after,59.0,15.875,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,3_after,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,3_after,23.95,-19.175,2440.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,long_gap,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,long_gap,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,1_before,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,1_before,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,2_before,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,2_before,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,3_before,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,3_before,58.6,-23.4,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,1_after,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,1_after,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,2_after,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,2_after,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,3_after,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,3_after,82.0,0.0,2886.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,long_gap,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,long_gap,22.0,0.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,1_before,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,1_before,22.15,0.1499999999999985,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,2_before,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,2_before,23.0,1.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,3_before,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,3_before,23.0,1.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,1_after,14.5,-0.5,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,1_after,22.0,0.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,2_after,13.0,-2.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,2_after,22.0,0.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,3_after,13.0,-2.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,3_after,22.0,0.0,2057.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,long_gap,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,long_gap,72.0,0.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,1_before,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,1_before,72.0,0.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,2_before,14.05,1.0500000000000007,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,2_before,57.1,-14.9,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,3_before,15.0,2.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,3_before,20.0,-52.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,1_after,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,1_after,72.0,0.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,2_after,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,2_after,72.35,0.3499999999999943,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,3_after,12.05,-0.9499999999999992,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,3_after,75.75,3.75,2588.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,long_gap,15.0,0.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,long_gap,69.33333333333333,0.0,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,1_before,15.0,0.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,1_before,71.0,1.6666666666666714,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,2_before,15.2,0.1999999999999993,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,2_before,71.0,1.6666666666666714,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,3_before,16.0,1.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,3_before,71.0,1.6666666666666714,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,1_after,15.0,0.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,1_after,68.15,-1.183333333333323,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,2_after,10.55,-4.449999999999999,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,2_after,69.0,-0.3333333333333286,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,3_after,12.45,-2.5500000000000007,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,3_after,75.4,6.066666666666677,2033.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,long_gap,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,long_gap,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,1_before,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,1_before,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,2_before,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,2_before,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,3_before,11.05,0.0500000000000007,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,3_before,63.3,-6.700000000000003,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,1_after,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,1_after,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,2_after,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,2_after,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,3_after,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,3_after,70.0,0.0,2370.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,long_gap,8.0,0.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,long_gap,65.61904761904762,0.0,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,1_before,8.0,0.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,1_before,85.5,19.88095238095238,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,2_before,8.0,0.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,2_before,85.95,20.330952380952382,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,3_before,6.55,-1.4500000000000002,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,3_before,84.0,18.38095238095238,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,1_after,9.2,1.1999999999999993,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,1_after,65.0,-0.6190476190476204,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,2_after,10.0,2.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,2_after,65.0,-0.6190476190476204,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,3_after,10.0,2.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,3_after,64.95,-0.6690476190476176,2139.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,long_gap,43.67857142857143,0.0,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,long_gap,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,1_before,48.9,5.221428571428568,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,1_before,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,2_before,49.0,5.321428571428569,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,2_before,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,3_before,49.0,5.321428571428569,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,3_before,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,1_after,44.0,0.3214285714285694,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,1_after,3.1,3.1,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,2_after,44.0,0.3214285714285694,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,2_after,24.35,24.35,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,3_after,44.05,0.3714285714285665,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,3_after,24.0,24.0,2781.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,long_gap,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,long_gap,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,1_before,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,1_before,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,2_before,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,2_before,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,3_before,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,3_before,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,1_after,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,1_after,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,2_after,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,2_after,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,3_after,49.45,-0.5499999999999972,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,3_after,37.0,0.0,2820.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,long_gap,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,long_gap,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,1_before,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,1_before,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,2_before,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,2_before,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,3_before,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,3_before,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,1_after,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,1_after,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,2_after,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,2_after,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,3_after,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,3_after,71.0,0.0,2304.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,long_gap,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,long_gap,68.0,0.0,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,1_before,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,1_before,68.6,0.5999999999999943,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,2_before,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,2_before,70.7,2.700000000000003,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,3_before,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,3_before,70.3,2.299999999999997,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,1_after,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,1_after,68.0,0.0,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,2_after,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,2_after,68.0,0.0,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,3_after,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,3_after,68.0,0.0,2269.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,long_gap,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,long_gap,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,1_before,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,1_before,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,2_before,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,2_before,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,3_before,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,3_before,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,1_after,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,1_after,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,2_after,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,2_after,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,3_after,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,3_after,39.0,0.0,2224.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,long_gap,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,long_gap,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,1_before,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,1_before,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,2_before,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,2_before,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,3_before,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,3_before,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,1_after,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,1_after,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,2_after,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,2_after,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,3_after,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,3_after,24.0,0.0,5029.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,long_gap,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,long_gap,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,1_before,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,1_before,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,2_before,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,2_before,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,3_before,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,3_before,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,1_after,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,1_after,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,2_after,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,2_after,1.05,1.05,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,3_after,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,3_after,4.0,4.0,2027.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,long_gap,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,long_gap,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,1_before,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,1_before,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,2_before,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,2_before,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,3_before,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,3_before,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,1_after,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,1_after,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,2_after,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,2_after,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,3_after,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,3_after,57.0,0.0,2628.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,long_gap,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,long_gap,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,1_before,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,1_before,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,2_before,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,2_before,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,3_before,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,3_before,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,1_after,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,1_after,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,2_after,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,2_after,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,3_after,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,3_after,57.0,0.0,2128.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,long_gap,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,long_gap,57.81818181818182,0.0,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,1_before,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,1_before,57.0,-0.8181818181818201,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,2_before,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,2_before,57.0,-0.8181818181818201,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,3_before,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,3_before,57.0,-0.8181818181818201,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,1_after,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,1_after,69.0,11.18181818181818,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,2_after,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,2_after,70.0,12.18181818181818,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,3_after,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,3_after,70.0,12.18181818181818,2199.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,long_gap,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,long_gap,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,1_before,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,1_before,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,2_before,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,2_before,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,3_before,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,3_before,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,1_after,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,1_after,59.35,2.3500000000000014,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,2_after,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,2_after,63.35,6.350000000000001,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,3_after,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,3_after,63.0,6.0,2508.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,long_gap,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,long_gap,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,1_before,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,1_before,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,2_before,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,2_before,68.1,-1.9000000000000057,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,3_before,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,3_before,63.0,-7.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,1_after,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,1_after,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,2_after,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,2_after,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,3_after,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,3_after,70.0,0.0,2026.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,long_gap,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,long_gap,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,1_before,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,1_before,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,2_before,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,2_before,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,3_before,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,3_before,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,1_after,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,1_after,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,2_after,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,2_after,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,3_after,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,3_after,91.0,0.0,2100.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,long_gap,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,long_gap,90.0,0.0,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,1_before,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,1_before,87.1,-2.9000000000000057,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,2_before,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,2_before,85.0,-5.0,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,3_before,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,3_before,85.0,-5.0,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,1_after,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,1_after,89.85,-0.1500000000000056,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,2_after,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,2_after,78.05,-11.950000000000005,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,3_after,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,3_after,68.35,-21.65000000000001,3540.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,long_gap,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,long_gap,88.6,0.0,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,1_before,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,1_before,90.0,1.4000000000000057,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,2_before,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,2_before,90.0,1.4000000000000057,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,3_before,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,3_before,86.6,-2.0,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,1_after,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,1_after,72.3,-16.299999999999997,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,2_after,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,2_after,68.0,-20.599999999999994,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,3_after,70.1,0.0999999999999943,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,3_after,68.05,-20.55,2498.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,long_gap,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,long_gap,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,1_before,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,1_before,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,2_before,10.9,-1.0999999999999996,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,2_before,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,3_before,9.0,-3.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,3_before,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,1_after,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,1_after,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,2_after,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,2_after,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,3_after,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,3_after,67.0,0.0,2220.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,long_gap,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,long_gap,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,1_before,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,1_before,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,2_before,15.65,0.6500000000000004,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,2_before,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,3_before,16.0,1.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,3_before,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,1_after,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,1_after,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,2_after,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,2_after,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,3_after,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,3_after,67.0,0.0,3392.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,long_gap,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,long_gap,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,1_before,79.8,-2.200000000000003,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,1_before,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,2_before,80.0,-2.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,2_before,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,3_before,80.0,-2.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,3_before,70.8,1.7999999999999972,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,1_after,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,1_after,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,2_after,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,2_after,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,3_after,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,3_after,69.0,0.0,2322.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,long_gap,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,long_gap,51.130434782608695,0.0,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,1_before,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,1_before,49.25,-1.8804347826086956,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,2_before,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,2_before,49.0,-2.130434782608696,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,3_before,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,3_before,49.0,-2.130434782608696,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,1_after,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,1_after,52.0,0.8695652173913047,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,2_after,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,2_after,52.0,0.8695652173913047,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,3_after,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,3_after,52.0,0.8695652173913047,2281.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,long_gap,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,long_gap,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,1_before,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,1_before,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,2_before,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,2_before,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,3_before,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,3_before,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,1_after,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,1_after,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,2_after,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,2_after,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,3_after,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,3_after,79.0,0.0,2133.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,long_gap,35.77272727272727,0.0,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,long_gap,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,1_before,39.0,3.227272727272726,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,1_before,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,2_before,39.0,3.227272727272726,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,2_before,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,3_before,39.0,3.227272727272726,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,3_before,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,1_after,33.0,-2.7727272727272734,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,1_after,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,2_after,33.0,-2.7727272727272734,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,2_after,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,3_after,33.0,-2.7727272727272734,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,3_after,68.0,0.0,2248.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,long_gap,12.545454545454543,0.0,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,long_gap,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,1_before,9.85,-2.6954545454545453,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,1_before,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,2_before,5.0,-7.545454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,2_before,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,3_before,5.0,-7.545454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,3_before,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,1_after,5.15,-7.395454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,1_after,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,2_after,0.4,-12.145454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,2_after,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,3_after,0.0,-12.545454545454543,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,3_after,72.0,0.0,2256.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,long_gap,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,long_gap,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,1_before,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,1_before,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,2_before,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,2_before,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,3_before,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,3_before,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,1_after,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,1_after,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,2_after,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,2_after,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,3_after,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,3_after,72.0,0.0,2250.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,long_gap,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,long_gap,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,1_before,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,1_before,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,2_before,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,2_before,69.65,-2.3499999999999943,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,3_before,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,3_before,67.0,-5.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,1_after,87.65,0.6500000000000057,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,1_after,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,2_after,89.0,2.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,2_after,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,3_after,89.0,2.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,3_after,72.0,0.0,2007.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,long_gap,45.90909090909091,0.0,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,long_gap,29.0,0.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,1_before,59.75,13.840909090909092,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,1_before,33.45,4.450000000000003,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,2_before,67.85,21.940909090909088,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,2_before,36.0,7.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,3_before,68.05,22.14090909090909,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,3_before,36.0,7.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,1_after,48.2,2.2909090909090963,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,1_after,29.0,0.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,2_after,48.85,2.940909090909095,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,2_after,29.0,0.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,3_after,48.0,2.090909090909093,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,3_after,29.0,0.0,2169.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,long_gap,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,long_gap,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,1_before,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,1_before,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,2_before,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,2_before,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,3_before,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,3_before,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,1_after,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,1_after,60.65,-19.35,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,2_after,46.8,11.799999999999995,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,2_after,48.8,-31.200000000000003,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,3_after,47.0,12.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,3_after,69.55,-10.450000000000005,2011.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,long_gap,44.833333333333336,0.0,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,long_gap,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,1_before,51.0,6.166666666666664,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,1_before,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,2_before,51.0,6.166666666666664,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,2_before,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,3_before,51.0,6.166666666666664,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,3_before,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,1_after,46.85,2.0166666666666657,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,1_after,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,2_after,54.9,10.066666666666665,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,2_after,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,3_after,60.45,15.616666666666667,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,3_after,52.0,0.0,2408.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,long_gap,45.121212121212125,0.0,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,long_gap,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,1_before,53.75,8.628787878787875,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,1_before,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,2_before,54.0,8.878787878787875,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,2_before,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,3_before,54.0,8.878787878787875,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,3_before,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,1_after,40.2,-4.921212121212122,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,1_after,83.55,-16.450000000000003,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,2_after,40.0,-5.121212121212125,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,2_after,70.85,-29.15000000000001,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,3_after,39.2,-5.921212121212122,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,3_after,70.0,-30.0,3275.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,long_gap,61.34782608695652,0.0,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,long_gap,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,1_before,59.4,-1.9478260869565247,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,1_before,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,2_before,53.5,-7.847826086956523,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,2_before,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,3_before,49.9,-11.447826086956525,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,3_before,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,1_after,61.55,0.2021739130434738,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,1_after,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,2_after,60.05,-1.297826086956526,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,2_after,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,3_after,61.2,-0.1478260869565204,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,3_after,77.0,0.0,2285.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,long_gap,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,long_gap,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,1_before,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,1_before,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,2_before,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,2_before,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,3_before,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,3_before,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,1_after,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,1_after,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,2_after,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,2_after,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,3_after,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,3_after,23.0,0.0,2113.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,long_gap,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,long_gap,15.0,0.0,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,1_before,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,1_before,15.0,0.0,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,2_before,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,2_before,11.95,-3.0500000000000007,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,3_before,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,3_before,0.55,-14.45,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,1_after,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,1_after,15.6,0.5999999999999996,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,2_after,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,2_after,16.0,1.0,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,3_after,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,3_after,17.35,2.3500000000000014,3152.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,long_gap,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,long_gap,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,1_before,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,1_before,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,2_before,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,2_before,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,3_before,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,3_before,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,1_after,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,1_after,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,2_after,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,2_after,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,3_after,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,3_after,72.0,0.0,2470.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,long_gap,39.74285714285714,0.0,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,long_gap,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,1_before,41.2,1.4571428571428626,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,1_before,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,2_before,41.95,2.207142857142862,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,2_before,53.05,0.0499999999999971,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,3_before,42.0,2.25714285714286,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,3_before,54.6,1.6000000000000014,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,1_after,39.15,-0.5928571428571416,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,1_after,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,2_after,39.8,0.0571428571428569,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,2_after,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,3_after,25.2,-14.54285714285714,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,3_after,53.0,0.0,3425.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,long_gap,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,long_gap,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,1_before,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,1_before,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,2_before,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,2_before,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,3_before,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,3_before,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,1_after,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,1_after,41.55,6.549999999999997,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,2_after,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,2_after,48.95,13.950000000000005,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,3_after,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,3_after,49.0,14.0,3519.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,long_gap,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,long_gap,63.96296296296296,0.0,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,1_before,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,1_before,53.6,-10.36296296296296,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,2_before,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,2_before,46.0,-17.962962962962962,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,3_before,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,3_before,46.0,-17.962962962962962,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,1_after,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,1_after,64.0,0.037037037037038,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,2_after,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,2_after,64.0,0.037037037037038,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,3_after,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,3_after,64.0,0.037037037037038,2656.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,long_gap,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,long_gap,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,1_before,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,1_before,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,2_before,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,2_before,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,3_before,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,3_before,64.75,0.75,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,1_after,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,1_after,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,2_after,40.0,8.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,2_after,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,3_after,57.15,25.15,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,3_after,64.0,0.0,3549.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,long_gap,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,long_gap,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,1_before,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,1_before,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,2_before,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,2_before,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,3_before,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,3_before,44.6,-0.3999999999999986,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,1_after,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,1_after,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,2_after,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,2_after,47.25,2.25,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,3_after,25.2,4.199999999999999,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,3_after,49.0,4.0,2499.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,long_gap,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,long_gap,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,1_before,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,1_before,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,2_before,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,2_before,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,3_before,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,3_before,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,1_after,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,1_after,50.25,-20.75,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,2_after,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,2_after,50.0,-21.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,3_after,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,3_after,50.0,-21.0,2456.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,long_gap,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,long_gap,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,1_before,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,1_before,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,2_before,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,2_before,88.45,-0.5499999999999972,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,3_before,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,3_before,82.4,-6.599999999999994,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,1_after,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,1_after,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,2_after,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,2_after,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,3_after,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,3_after,89.0,0.0,2542.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,long_gap,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,long_gap,88.0,0.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,1_before,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,1_before,87.55,-0.4500000000000028,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,2_before,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,2_before,85.95,-2.049999999999997,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,3_before,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,3_before,87.0,-1.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,1_after,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,1_after,88.0,0.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,2_after,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,2_after,88.0,0.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,3_after,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,3_after,88.0,0.0,2192.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,long_gap,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,long_gap,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,1_before,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,1_before,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,2_before,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,2_before,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,3_before,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,3_before,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,1_after,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,1_after,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,2_after,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,2_after,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,3_after,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,3_after,93.0,0.0,2464.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,long_gap,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,long_gap,57.81818181818182,0.0,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,1_before,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,1_before,63.0,5.18181818181818,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,2_before,58.65,0.6499999999999986,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,2_before,63.0,5.18181818181818,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,3_before,60.9,2.8999999999999986,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,3_before,63.0,5.18181818181818,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,1_after,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,1_after,45.1,-12.71818181818182,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,2_after,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,2_after,39.35,-18.46818181818182,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,3_after,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,3_after,40.45,-17.368181818181817,2193.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,long_gap,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,long_gap,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,1_before,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,1_before,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,2_before,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,2_before,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,3_before,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,3_before,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,1_after,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,1_after,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,2_after,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,2_after,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,3_after,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,3_after,32.0,0.0,2125.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,long_gap,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,long_gap,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,1_before,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,1_before,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,2_before,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,2_before,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,3_before,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,3_before,42.7,-0.2999999999999971,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,1_after,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,1_after,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,2_after,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,2_after,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,3_after,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,3_after,54.25,11.25,2495.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,long_gap,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,long_gap,70.0,0.0,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,1_before,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,1_before,65.15,-4.849999999999994,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,2_before,72.2,-0.7999999999999972,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,2_before,57.65,-12.35,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,3_before,63.65,-9.35,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,3_before,49.3,-20.700000000000003,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,1_after,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,1_after,69.95,-0.0499999999999971,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,2_after,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,2_after,33.15,-36.85,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,3_after,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,3_after,7.0,-63.0,2421.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,long_gap,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,long_gap,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,1_before,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,1_before,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,2_before,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,2_before,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,3_before,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,3_before,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,1_after,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,1_after,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,2_after,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,2_after,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,3_after,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,3_after,25.0,0.0,2105.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,long_gap,44.0,0.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,long_gap,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,1_before,45.6,1.6000000000000014,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,1_before,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,2_before,49.4,5.399999999999999,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,2_before,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,3_before,61.0,17.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,3_before,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,1_after,44.0,0.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,1_after,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,2_after,44.0,0.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,2_after,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,3_after,41.2,-2.799999999999997,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,3_after,40.0,0.0,2148.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,long_gap,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,long_gap,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,1_before,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,1_before,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,2_before,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,2_before,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,3_before,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,3_before,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,1_after,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,1_after,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,2_after,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,2_after,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,3_after,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,3_after,16.75,-2.25,2207.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,long_gap,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,long_gap,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,1_before,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,1_before,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,2_before,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,2_before,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,3_before,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,3_before,18.8,-0.1999999999999993,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,1_after,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,1_after,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,2_after,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,2_after,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,3_after,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,3_after,19.0,0.0,2434.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,long_gap,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,long_gap,49.869565217391305,0.0,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,1_before,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,1_before,51.0,1.1304347826086951,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,2_before,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,2_before,49.6,-0.2695652173913032,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,3_before,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,3_before,47.8,-2.0695652173913075,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,1_after,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,1_after,47.15,-2.719565217391306,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,2_after,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,2_after,45.0,-4.869565217391305,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,3_after,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,3_after,43.3,-6.569565217391308,2320.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,long_gap,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,long_gap,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,1_before,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,1_before,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,2_before,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,2_before,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,3_before,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,3_before,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,1_after,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,1_after,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,2_after,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,2_after,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,3_after,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,3_after,51.0,0.0,2472.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,long_gap,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,long_gap,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,1_before,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,1_before,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,2_before,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,2_before,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,3_before,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,3_before,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,1_after,52.45,-4.549999999999997,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,1_after,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,2_after,42.0,-15.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,2_after,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,3_after,42.0,-15.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,3_after,43.0,0.0,2169.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,long_gap,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,long_gap,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,1_before,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,1_before,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,2_before,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,2_before,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,3_before,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,3_before,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,1_after,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,1_after,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,2_after,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,2_after,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,3_after,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,3_after,26.0,0.0,2226.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,long_gap,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,long_gap,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,1_before,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,1_before,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,2_before,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,2_before,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,3_before,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,3_before,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,1_after,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,1_after,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,2_after,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,2_after,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,3_after,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,3_after,54.0,0.0,2333.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,long_gap,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,long_gap,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,1_before,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,1_before,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,2_before,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,2_before,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,3_before,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,3_before,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,1_after,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,1_after,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,2_after,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,2_after,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,3_after,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,3_after,46.0,0.0,2384.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,long_gap,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,long_gap,70.77272727272727,0.0,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,1_before,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,1_before,75.5,4.727272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,2_before,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,2_before,76.0,5.227272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,3_before,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,3_before,76.25,5.477272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,1_after,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,1_after,82.2,11.427272727272737,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,2_after,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,2_after,84.0,13.227272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,3_after,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,3_after,83.25,12.477272727272734,2210.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,long_gap,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,long_gap,47.0,0.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,1_before,73.55,-1.4500000000000028,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,1_before,47.3,0.2999999999999971,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,2_before,66.0,-9.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,2_before,50.95,3.950000000000003,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,3_before,66.0,-9.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,3_before,51.0,4.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,1_after,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,1_after,47.0,0.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,2_after,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,2_after,47.0,0.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,3_after,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,3_after,47.0,0.0,3821.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,long_gap,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,long_gap,60.88,0.0,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,1_before,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,1_before,62.15,1.269999999999996,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,2_before,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,2_before,45.35,-15.53,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,3_before,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,3_before,38.0,-22.880000000000003,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,1_after,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,1_after,56.0,-4.880000000000003,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,2_after,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,2_after,54.8,-6.080000000000005,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,3_after,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,3_after,53.0,-7.880000000000003,2561.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,long_gap,79.37096774193549,0.0,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,long_gap,27.24590163934426,0.0,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,1_before,67.0,-12.370967741935488,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,1_before,58.0,30.75409836065574,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,2_before,67.0,-12.370967741935488,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,2_before,59.45,32.20409836065574,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,3_before,67.0,-12.370967741935488,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,3_before,58.6,31.35409836065574,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,1_after,27.45,-51.920967741935485,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,1_after,4.0,-23.24590163934426,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,2_after,31.0,-48.37096774193549,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,2_after,4.0,-23.24590163934426,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,3_after,31.0,-48.37096774193549,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,3_after,4.0,-23.24590163934426,6127.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,long_gap,21.115384615384617,0.0,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,long_gap,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,1_before,24.4,3.284615384615382,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,1_before,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,2_before,28.0,6.884615384615383,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,2_before,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,3_before,28.0,6.884615384615383,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,3_before,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,1_after,19.0,-2.1153846153846168,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,1_after,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,2_after,19.0,-2.1153846153846168,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,2_after,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,3_after,19.6,-1.515384615384615,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,3_after,17.0,0.0,2589.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,long_gap,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,long_gap,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,1_before,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,1_before,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,2_before,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,2_before,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,3_before,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,3_before,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,1_after,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,1_after,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,2_after,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,2_after,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,3_after,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,3_after,17.0,0.0,2328.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,long_gap,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,long_gap,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,1_before,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,1_before,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,2_before,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,2_before,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,3_before,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,3_before,80.7,0.7000000000000028,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,1_after,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,1_after,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,2_after,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,2_after,79.8,-0.2000000000000028,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,3_after,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,3_after,77.05,-2.950000000000003,6060.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,long_gap,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,long_gap,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,1_before,79.1,0.0999999999999943,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,1_before,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,2_before,80.85,1.8499999999999943,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,2_before,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,3_before,81.0,2.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,3_before,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,1_after,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,1_after,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,2_after,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,2_after,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,3_after,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,3_after,19.0,0.0,2444.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,long_gap,13.80952380952381,0.0,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,long_gap,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,1_before,15.45,1.640476190476189,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,1_before,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,2_before,16.0,2.19047619047619,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,2_before,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,3_before,16.0,2.19047619047619,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,3_before,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,1_after,15.0,1.1904761904761898,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,1_after,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,2_after,15.0,1.1904761904761898,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,2_after,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,3_after,15.0,1.1904761904761898,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,3_after,38.0,0.0,2040.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,long_gap,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,long_gap,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,1_before,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,1_before,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,2_before,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,2_before,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,3_before,22.2,-2.8000000000000007,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,3_before,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,1_after,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,1_after,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,2_after,22.0,-3.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,2_after,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,3_after,21.0,-4.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,3_after,38.0,0.0,2139.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,long_gap,9.80952380952381,0.0,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,long_gap,35.0,0.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,1_before,10.0,0.1904761904761898,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,1_before,35.85,0.8500000000000014,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,2_before,12.5,2.69047619047619,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,2_before,37.0,2.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,3_before,41.9,32.09047619047619,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,3_before,37.0,2.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,1_after,5.7,-4.10952380952381,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,1_after,35.0,0.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,2_after,11.95,2.140476190476189,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,2_after,35.0,0.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,3_after,34.55,24.740476190476187,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,3_after,35.0,0.0,4170.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,long_gap,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,long_gap,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,1_before,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,1_before,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,2_before,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,2_before,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,3_before,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,3_before,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,1_after,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,1_after,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,2_after,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,2_after,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,3_after,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,3_after,35.0,0.0,2063.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,long_gap,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,long_gap,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,1_before,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,1_before,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,2_before,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,2_before,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,3_before,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,3_before,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,1_after,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,1_after,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,2_after,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,2_after,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,3_after,30.7,2.6999999999999997,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,3_after,38.0,0.0,3471.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,long_gap,37.34615384615385,0.0,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,long_gap,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,1_before,34.0,-3.3461538461538467,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,1_before,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,2_before,34.0,-3.3461538461538467,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,2_before,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,3_before,34.0,-3.3461538461538467,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,3_before,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,1_after,40.0,2.6538461538461533,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,1_after,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,2_after,40.0,2.6538461538461533,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,2_after,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,3_after,40.0,2.6538461538461533,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,3_after,38.0,0.0,2584.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,long_gap,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,long_gap,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,1_before,33.85,-1.1499999999999986,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,1_before,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,2_before,31.0,-4.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,2_before,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,3_before,31.0,-4.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,3_before,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,1_after,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,1_after,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,2_after,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,2_after,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,3_after,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,3_after,38.0,0.0,2104.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,long_gap,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,long_gap,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,1_before,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,1_before,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,2_before,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,2_before,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,3_before,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,3_before,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,1_after,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,1_after,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,2_after,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,2_after,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,3_after,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,3_after,50.0,0.0,2010.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,long_gap,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,long_gap,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,1_before,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,1_before,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,2_before,32.4,2.3999999999999986,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,2_before,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,3_before,34.0,4.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,3_before,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,1_after,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,1_after,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,2_after,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,2_after,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,3_after,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,3_after,65.0,0.0,3147.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,long_gap,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,long_gap,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,1_before,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,1_before,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,2_before,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,2_before,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,3_before,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,3_before,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,1_after,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,1_after,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,2_after,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,2_after,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,3_after,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,3_after,79.0,0.0,2996.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,long_gap,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,long_gap,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,1_before,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,1_before,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,2_before,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,2_before,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,3_before,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,3_before,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,1_after,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,1_after,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,2_after,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,2_after,50.25,4.25,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,3_after,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,3_after,58.0,12.0,2123.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,long_gap,50.794117647058826,0.0,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,long_gap,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,1_before,67.0,16.205882352941174,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,1_before,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,2_before,67.0,16.205882352941174,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,2_before,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,3_before,67.0,16.205882352941174,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,3_before,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,1_after,41.0,-9.794117647058826,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,1_after,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,2_after,41.1,-9.694117647058825,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,2_after,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,3_after,48.35,-2.4441176470588246,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,3_after,100.0,0.0,3335.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,long_gap,50.66666666666666,0.0,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,long_gap,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,1_before,70.45,19.78333333333334,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,1_before,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,2_before,50.0,-0.6666666666666643,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,2_before,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,3_before,60.85,10.183333333333335,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,3_before,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,1_after,55.85,5.183333333333337,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,1_after,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,2_after,59.0,8.333333333333336,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,2_after,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,3_after,59.0,8.333333333333336,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,3_after,100.0,0.0,2101.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,long_gap,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,long_gap,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,1_before,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,1_before,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,2_before,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,2_before,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,3_before,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,3_before,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,1_after,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,1_after,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,2_after,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,2_after,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,3_after,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,3_after,83.0,0.0,2196.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,long_gap,75.29166666666667,0.0,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,long_gap,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,1_before,78.3,3.0083333333333258,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,1_before,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,2_before,88.0,12.708333333333329,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,2_before,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,3_before,99.9,24.60833333333333,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,3_before,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,1_after,74.55,-0.7416666666666742,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,1_after,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,2_after,73.8,-1.4916666666666742,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,2_after,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,3_after,72.0,-3.2916666666666714,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,3_after,100.0,0.0,2400.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,long_gap,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,long_gap,41.0,0.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,1_before,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,1_before,42.95,1.9500000000000028,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,2_before,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,2_before,45.55,4.549999999999997,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,3_before,9.25,0.25,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,3_before,46.0,5.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,1_after,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,1_after,41.0,0.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,2_after,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,2_after,41.0,0.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,3_after,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,3_after,35.3,-5.700000000000003,3017.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,long_gap,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,long_gap,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,1_before,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,1_before,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,2_before,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,2_before,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,3_before,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,3_before,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,1_after,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,1_after,22.3,0.3000000000000007,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,2_after,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,2_after,23.0,1.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,3_after,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,3_after,23.0,1.0,2960.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,long_gap,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,long_gap,33.851851851851855,0.0,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,1_before,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,1_before,32.0,-1.8518518518518547,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,2_before,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,2_before,32.8,-1.0518518518518576,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,3_before,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,3_before,33.0,-0.8518518518518547,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,1_after,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,1_after,37.3,3.4481481481481424,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,2_after,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,2_after,38.0,4.148148148148145,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,3_after,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,3_after,37.25,3.3981481481481453,2700.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,long_gap,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,long_gap,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,1_before,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,1_before,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,2_before,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,2_before,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,3_before,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,3_before,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,1_after,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,1_after,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,2_after,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,2_after,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,3_after,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,3_after,46.0,0.0,3002.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,long_gap,40.09090909090909,0.0,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,long_gap,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,1_before,49.85,9.759090909090908,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,1_before,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,2_before,51.0,10.909090909090908,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,2_before,16.1,-3.899999999999999,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,3_before,51.0,10.909090909090908,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,3_before,0.0,-20.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,1_after,39.0,-1.0909090909090935,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,1_after,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,2_after,39.0,-1.0909090909090935,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,2_after,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,3_after,39.0,-1.0909090909090935,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,3_after,20.0,0.0,2202.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,long_gap,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,long_gap,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,1_before,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,1_before,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,2_before,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,2_before,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,3_before,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,3_before,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,1_after,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,1_after,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,2_after,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,2_after,35.9,-6.100000000000001,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,3_after,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,3_after,29.0,-13.0,4083.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,long_gap,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,long_gap,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,1_before,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,1_before,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,2_before,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,2_before,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,3_before,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,3_before,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,1_after,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,1_after,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,2_after,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,2_after,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,3_after,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,3_after,29.0,0.0,2076.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,long_gap,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,long_gap,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,1_before,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,1_before,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,2_before,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,2_before,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,3_before,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,3_before,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,1_after,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,1_after,45.8,-0.2000000000000028,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,2_after,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,2_after,45.0,-1.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,3_after,58.5,0.5,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,3_after,45.0,-1.0,2178.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,long_gap,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,long_gap,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,1_before,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,1_before,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,2_before,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,2_before,55.6,-0.3999999999999986,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,3_before,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,3_before,46.15,-9.85,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,1_after,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,1_after,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,2_after,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,2_after,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,3_after,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,3_after,56.0,0.0,3031.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,long_gap,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,long_gap,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,1_before,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,1_before,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,2_before,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,2_before,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,3_before,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,3_before,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,1_after,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,1_after,55.45,-0.5499999999999972,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,2_after,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,2_after,51.05,-4.950000000000003,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,3_after,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,3_after,50.0,-6.0,2537.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,long_gap,73.97058823529412,0.0,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,long_gap,70.41176470588235,0.0,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,1_before,74.0,0.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,1_before,71.0,0.5882352941176521,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,2_before,74.0,0.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,2_before,71.0,0.5882352941176521,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,3_before,74.0,0.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,3_before,71.0,0.5882352941176521,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,1_after,74.35,0.3794117647058783,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,1_after,72.45,2.038235294117655,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,2_after,75.0,1.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,2_after,84.75,14.338235294117652,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,3_after,75.0,1.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,3_after,87.0,16.588235294117652,3342.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,long_gap,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,long_gap,87.0,0.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,1_before,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,1_before,80.7,-6.299999999999997,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,2_before,74.05,-0.9500000000000028,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,2_before,72.0,-15.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,3_before,73.95,-1.0499999999999972,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,3_before,69.7,-17.299999999999997,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,1_after,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,1_after,87.0,0.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,2_after,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,2_after,87.0,0.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,3_after,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,3_after,87.0,0.0,4591.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,long_gap,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,long_gap,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,1_before,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,1_before,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,2_before,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,2_before,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,3_before,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,3_before,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,1_after,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,1_after,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,2_after,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,2_after,85.3,-1.7000000000000028,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,3_after,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,3_after,62.95,-24.05,2034.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,long_gap,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,long_gap,83.3061224489796,0.0,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,1_before,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,1_before,59.0,-24.306122448979597,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,2_before,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,2_before,58.65,-24.656122448979595,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,3_before,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,3_before,56.0,-27.306122448979597,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,1_after,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,1_after,85.0,1.6938775510204067,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,2_after,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,2_after,85.0,1.6938775510204067,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,3_after,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,3_after,85.0,1.6938775510204067,4942.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,long_gap,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,long_gap,43.85714285714285,0.0,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,1_before,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,1_before,43.0,-0.8571428571428541,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,2_before,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,2_before,43.0,-0.8571428571428541,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,3_before,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,3_before,41.15,-2.7071428571428555,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,1_after,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,1_after,44.0,0.1428571428571459,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,2_after,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,2_after,44.0,0.1428571428571459,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,3_after,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,3_after,44.0,0.1428571428571459,2082.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,long_gap,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,long_gap,63.90476190476191,0.0,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,1_before,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,1_before,60.85,-3.054761904761904,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,2_before,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,2_before,61.35,-2.554761904761904,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,3_before,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,3_before,72.95,9.045238095238098,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,1_after,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,1_after,65.3,1.395238095238092,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,2_after,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,2_after,66.0,2.095238095238095,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,3_after,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,3_after,66.0,2.095238095238095,4140.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,long_gap,71.0,0.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,long_gap,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,1_before,74.15,3.1500000000000057,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,1_before,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,2_before,75.0,4.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,2_before,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,3_before,75.0,4.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,3_before,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,1_after,67.8,-3.200000000000003,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,1_after,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,2_after,66.0,-5.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,2_after,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,3_after,66.0,-5.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,3_after,69.0,0.0,2141.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,long_gap,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,long_gap,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,1_before,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,1_before,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,2_before,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,2_before,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,3_before,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,3_before,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,1_after,55.1,4.100000000000001,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,1_after,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,2_after,57.9,6.899999999999999,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,2_after,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,3_after,58.0,7.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,3_after,69.0,0.0,2632.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,long_gap,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,long_gap,38.57142857142857,0.0,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,1_before,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,1_before,58.4,19.82857142857143,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,2_before,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,2_before,74.35,35.778571428571425,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,3_before,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,3_before,75.0,36.42857142857143,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,1_after,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,1_after,17.15,-21.42142857142857,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,2_after,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,2_after,15.0,-23.57142857142857,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,3_after,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,3_after,15.0,-23.57142857142857,2097.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,long_gap,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,long_gap,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,1_before,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,1_before,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,2_before,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,2_before,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,3_before,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,3_before,16.5,0.5,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,1_after,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,1_after,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,2_after,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,2_after,21.0,5.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,3_after,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,3_after,25.0,9.0,2640.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,long_gap,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,long_gap,56.64102564102564,0.0,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,1_before,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,1_before,57.45,0.8089743589743605,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,2_before,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,2_before,59.75,3.1089743589743577,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,3_before,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,3_before,59.65,3.0089743589743563,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,1_after,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,1_after,56.0,-0.6410256410256423,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,2_after,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,2_after,57.8,1.1589743589743549,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,3_after,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,3_after,56.05,-0.5910256410256451,3896.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,long_gap,6.115384615384615,0.0,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,long_gap,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,1_before,11.6,5.484615384615385,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,1_before,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,2_before,16.65,10.534615384615384,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,2_before,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,3_before,17.0,10.884615384615383,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,3_before,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,1_after,6.0,-0.1153846153846149,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,1_after,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,2_after,6.0,-0.1153846153846149,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,2_after,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,3_after,6.0,-0.1153846153846149,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,3_after,14.0,0.0,2610.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,long_gap,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,long_gap,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,1_before,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,1_before,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,2_before,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,2_before,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,3_before,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,3_before,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,1_after,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,1_after,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,2_after,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,2_after,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,3_after,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,3_after,29.0,0.0,4125.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,long_gap,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,long_gap,27.0,0.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,1_before,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,1_before,32.05,5.049999999999997,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,2_before,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,2_before,33.0,6.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,3_before,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,3_before,33.0,6.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,1_after,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,1_after,27.0,0.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,2_after,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,2_after,24.2,-2.8000000000000007,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,3_after,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,3_after,24.0,-3.0,2104.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,long_gap,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,long_gap,37.0,0.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,1_before,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,1_before,32.4,-4.600000000000001,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,2_before,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,2_before,26.0,-11.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,3_before,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,3_before,26.0,-11.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,1_after,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,1_after,37.0,0.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,2_after,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,2_after,37.0,0.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,3_after,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,3_after,37.0,0.0,3635.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,long_gap,27.170731707317078,0.0,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,long_gap,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,1_before,38.0,10.829268292682926,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,1_before,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,2_before,38.0,10.829268292682926,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,2_before,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,3_before,38.0,10.829268292682926,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,3_before,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,1_after,21.0,-6.170731707317074,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,1_after,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,2_after,21.0,-6.170731707317074,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,2_after,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,3_after,21.0,-6.170731707317074,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,3_after,61.0,0.0,4079.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,long_gap,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,long_gap,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,1_before,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,1_before,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,2_before,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,2_before,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,3_before,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,3_before,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,1_after,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,1_after,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,2_after,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,2_after,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,3_after,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,3_after,61.0,0.0,2382.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,long_gap,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,long_gap,68.0,0.0,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,1_before,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,1_before,68.0,0.0,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,2_before,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,2_before,68.4,0.4000000000000057,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,3_before,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,3_before,76.15,8.150000000000006,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,1_after,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,1_after,68.0,0.0,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,2_after,75.8,-3.200000000000003,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,2_after,68.4,0.4000000000000057,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,3_after,72.0,-7.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,3_after,67.7,-0.2999999999999971,2090.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,long_gap,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,long_gap,97.53846153846152,0.0,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,1_before,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,1_before,99.0,1.461538461538467,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,2_before,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,2_before,98.45,0.9115384615384698,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,3_before,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,3_before,98.0,0.461538461538467,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,1_after,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,1_after,96.8,-0.7384615384615358,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,2_after,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,2_after,98.0,0.461538461538467,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,3_after,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,3_after,95.6,-1.938461538461539,2591.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,long_gap,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,long_gap,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,1_before,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,1_before,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,2_before,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,2_before,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,3_before,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,3_before,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,1_after,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,1_after,28.0,-2.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,2_after,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,2_after,25.3,-4.699999999999999,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,3_after,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,3_after,24.0,-6.0,2309.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,long_gap,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,long_gap,24.51428571428572,0.0,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,1_before,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,1_before,27.35,2.8357142857142854,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,2_before,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,2_before,29.85,5.335714285714285,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,3_before,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,3_before,30.0,5.485714285714284,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,1_after,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,1_after,24.3,-0.2142857142857153,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,2_after,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,2_after,25.1,0.5857142857142854,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,3_after,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,3_after,26.55,2.0357142857142847,3576.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,long_gap,17.238095238095237,0.0,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,long_gap,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,1_before,15.4,-1.838095238095237,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,1_before,55.35,0.3500000000000014,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,2_before,17.0,-0.2380952380952372,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,2_before,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,3_before,17.0,-0.2380952380952372,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,3_before,54.6,-0.3999999999999986,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,1_after,19.0,1.7619047619047628,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,1_after,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,2_after,19.0,1.7619047619047628,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,2_after,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,3_after,19.0,1.7619047619047628,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,3_after,55.05,0.0499999999999971,2086.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,long_gap,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,long_gap,54.05555555555556,0.0,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,1_before,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,1_before,55.0,0.9444444444444428,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,2_before,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,2_before,55.0,0.9444444444444428,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,3_before,15.65,-3.35,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,3_before,55.0,0.9444444444444428,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,1_after,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,1_after,49.26315789473684,-4.792397660818715,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,2_after,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,2_after,45.73684210526316,-8.3187134502924,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,3_after,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,3_after,49.31578947368421,-4.739766081871345,3646.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,long_gap,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,long_gap,67.53333333333333,0.0,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,1_before,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,1_before,66.7,-0.8333333333333286,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,2_before,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,2_before,64.1,-3.433333333333337,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,3_before,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,3_before,63.9,-3.633333333333333,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,1_after,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,1_after,67.65,0.1166666666666742,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,2_after,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,2_after,62.0,-5.533333333333331,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,3_after,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,3_after,56.15,-11.383333333333333,2960.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,long_gap,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,long_gap,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,1_before,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,1_before,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,2_before,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,2_before,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,3_before,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,3_before,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,1_after,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,1_after,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,2_after,19.25,-0.75,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,2_after,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,3_after,21.5,1.5,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,3_after,41.0,0.0,3351.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,long_gap,52.8,0.0,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,long_gap,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,1_before,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,1_before,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,2_before,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,2_before,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,3_before,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,3_before,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,1_after,52.0,-0.7999999999999972,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,1_after,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,2_after,52.75,-0.0499999999999971,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,2_after,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,3_after,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,3_after,12.0,0.0,2565.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,long_gap,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,long_gap,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,1_before,52.15,-0.8500000000000014,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,1_before,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,2_before,52.0,-1.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,2_before,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,3_before,51.35,-1.6499999999999986,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,3_before,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,1_after,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,1_after,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,2_after,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,2_after,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,3_after,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,3_after,33.0,0.0,2914.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,long_gap,61.407407407407405,0.0,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,long_gap,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,1_before,61.0,-0.4074074074074048,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,1_before,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,2_before,61.0,-0.4074074074074048,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,2_before,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,3_before,61.0,-0.4074074074074048,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,3_before,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,1_after,63.35,1.9425925925925969,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,1_after,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,2_after,65.0,3.5925925925925952,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,2_after,68.1,14.099999999999994,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,3_after,65.0,3.5925925925925952,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,3_after,84.1,30.099999999999994,2666.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,long_gap,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,long_gap,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,1_before,82.9,-3.0999999999999943,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,1_before,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,2_before,71.0,-15.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,2_before,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,3_before,70.8,-15.200000000000005,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,3_before,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,1_after,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,1_after,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,2_after,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,2_after,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,3_after,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,3_after,100.0,0.0,3330.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,long_gap,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,long_gap,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,1_before,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,1_before,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,2_before,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,2_before,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,3_before,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,3_before,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,1_after,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,1_after,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,2_after,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,2_after,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,3_after,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,3_after,66.0,0.0,3420.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,long_gap,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,long_gap,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,1_before,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,1_before,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,2_before,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,2_before,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,3_before,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,3_before,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,1_after,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,1_after,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,2_after,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,2_after,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,3_after,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,3_after,96.6,-0.4000000000000057,2160.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,long_gap,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,long_gap,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,1_before,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,1_before,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,2_before,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,2_before,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,3_before,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,3_before,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,1_after,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,1_after,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,2_after,79.15,0.1500000000000056,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,2_after,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,3_after,80.0,1.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,3_after,58.05,1.0499999999999972,2090.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,long_gap,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,long_gap,33.0,0.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,1_before,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,1_before,34.0,1.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,2_before,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,2_before,34.0,1.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,3_before,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,3_before,34.0,1.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,1_after,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,1_after,33.0,0.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,2_after,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,2_after,33.0,0.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,3_after,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,3_after,33.0,0.0,2781.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,long_gap,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,long_gap,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,1_before,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,1_before,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,2_before,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,2_before,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,3_before,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,3_before,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,1_after,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,1_after,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,2_after,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,2_after,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,3_after,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,3_after,35.0,0.0,2065.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,long_gap,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,long_gap,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,1_before,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,1_before,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,2_before,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,2_before,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,3_before,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,3_before,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,1_after,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,1_after,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,2_after,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,2_after,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,3_after,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,3_after,38.0,0.0,2928.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,long_gap,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,long_gap,44.77272727272727,0.0,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,1_before,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,1_before,43.0,-1.7727272727272734,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,2_before,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,2_before,43.0,-1.7727272727272734,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,3_before,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,3_before,43.0,-1.7727272727272734,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,1_after,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,1_after,45.8,1.0272727272727238,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,2_after,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,2_after,38.95,-5.822727272727271,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,3_after,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,3_after,40.3,-4.472727272727276,2244.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,long_gap,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,long_gap,68.95,0.0,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,1_before,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,1_before,69.0,0.0499999999999971,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,2_before,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,2_before,69.0,0.0499999999999971,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,3_before,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,3_before,69.0,0.0499999999999971,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,1_after,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,1_after,69.95,1.0,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,2_after,55.1,1.1000000000000014,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,2_after,72.65,3.700000000000003,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,3_after,56.2,2.200000000000003,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,3_after,76.9,7.950000000000003,2033.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,long_gap,63.09090909090909,0.0,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,long_gap,66.0,0.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,1_before,60.7,-2.3909090909090907,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,1_before,66.0,0.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,2_before,58.05,-5.040909090909096,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,2_before,65.25,-0.75,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,3_before,57.05,-6.040909090909096,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,3_before,64.0,-2.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,1_after,64.0,0.9090909090909064,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,1_after,56.4,-9.6,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,2_after,63.95,0.8590909090909093,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,2_after,53.0,-13.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,3_after,61.75,-1.3409090909090935,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,3_after,54.15,-11.85,2197.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,long_gap,38.0,0.0,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,long_gap,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,1_before,38.0,0.0,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,1_before,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,2_before,38.4,0.3999999999999986,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,2_before,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,3_before,39.95,1.9500000000000028,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,3_before,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,1_after,38.0,0.0,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,1_after,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,2_after,37.2,-0.7999999999999972,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,2_after,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,3_after,36.2,-1.7999999999999972,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,3_after,80.0,0.0,2525.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,long_gap,43.333333333333336,0.0,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,long_gap,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,1_before,46.35,3.0166666666666657,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,1_before,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,2_before,47.0,3.666666666666664,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,2_before,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,3_before,46.75,3.4166666666666643,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,3_before,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,1_after,42.7,-0.6333333333333329,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,1_after,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,2_after,42.0,-1.3333333333333357,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,2_after,58.8,0.7999999999999972,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,3_after,42.0,-1.3333333333333357,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,3_after,59.0,1.0,2075.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,long_gap,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,long_gap,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,1_before,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,1_before,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,2_before,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,2_before,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,3_before,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,3_before,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,1_after,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,1_after,80.55,0.5499999999999972,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,2_after,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,2_after,82.0,2.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,3_after,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,3_after,82.0,2.0,2419.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,long_gap,46.03225806451613,0.0,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,long_gap,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,1_before,50.55,4.517741935483869,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,1_before,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,2_before,55.0,8.967741935483872,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,2_before,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,3_before,55.0,8.967741935483872,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,3_before,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,1_after,46.0,-0.0322580645161281,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,1_after,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,2_after,46.0,-0.0322580645161281,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,2_after,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,3_after,46.0,-0.0322580645161281,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,3_after,64.0,0.0,3120.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,long_gap,50.0,0.0,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,long_gap,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,1_before,48.25,-1.75,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,1_before,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,2_before,43.0,-7.0,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,2_before,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,3_before,43.0,-7.0,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,3_before,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,1_after,50.15,0.1499999999999985,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,1_after,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,2_after,54.65,4.649999999999999,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,2_after,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,3_after,60.25,10.25,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,3_after,9.6,2.6,2357.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,long_gap,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,long_gap,24.0,0.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,1_before,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,1_before,20.3,-3.6999999999999993,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,2_before,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,2_before,19.0,-5.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,3_before,39.75,-3.25,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,3_before,19.0,-5.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,1_after,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,1_after,24.0,0.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,2_after,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,2_after,24.0,0.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,3_after,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,3_after,24.0,0.0,2211.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,long_gap,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,long_gap,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,1_before,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,1_before,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,2_before,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,2_before,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,3_before,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,3_before,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,1_after,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,1_after,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,2_after,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,2_after,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,3_after,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,3_after,45.0,0.0,2356.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,long_gap,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,long_gap,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,1_before,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,1_before,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,2_before,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,2_before,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,3_before,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,3_before,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,1_after,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,1_after,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,2_after,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,2_after,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,3_after,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,3_after,53.0,0.0,2747.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,long_gap,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,long_gap,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,1_before,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,1_before,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,2_before,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,2_before,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,3_before,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,3_before,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,1_after,35.05,0.0499999999999971,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,1_after,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,2_after,40.55,5.549999999999997,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,2_after,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,3_after,44.0,9.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,3_after,66.0,0.0,2107.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,long_gap,45.0,0.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,long_gap,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,1_before,48.45,3.450000000000003,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,1_before,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,2_before,50.0,5.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,2_before,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,3_before,50.0,5.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,3_before,47.15,0.1499999999999985,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,1_after,47.0,2.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,1_after,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,2_after,47.0,2.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,2_after,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,3_after,47.0,2.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,3_after,48.9,1.8999999999999984,2580.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,long_gap,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,long_gap,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,1_before,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,1_before,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,2_before,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,2_before,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,3_before,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,3_before,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,1_after,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,1_after,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,2_after,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,2_after,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,3_after,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,3_after,4.0,0.0,2077.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,long_gap,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,long_gap,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,1_before,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,1_before,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,2_before,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,2_before,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,3_before,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,3_before,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,1_after,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,1_after,36.25,1.25,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,2_after,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,2_after,38.0,3.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,3_after,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,3_after,38.0,3.0,2110.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,long_gap,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,long_gap,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,1_before,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,1_before,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,2_before,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,2_before,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,3_before,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,3_before,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,1_after,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,1_after,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,2_after,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,2_after,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,3_after,67.9,-2.0999999999999943,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,3_after,38.0,0.0,3139.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,long_gap,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,long_gap,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,1_before,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,1_before,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,2_before,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,2_before,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,3_before,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,3_before,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,1_after,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,1_after,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,2_after,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,2_after,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,3_after,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,3_after,38.0,0.0,2247.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,long_gap,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,long_gap,53.48648648648648,0.0,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,1_before,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,1_before,76.4,22.91351351351352,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,2_before,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,2_before,79.0,25.51351351351352,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,3_before,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,3_before,79.0,25.51351351351352,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,1_after,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,1_after,47.05,-6.436486486486487,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,2_after,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,2_after,46.0,-7.486486486486484,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,3_after,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,3_after,46.0,-7.486486486486484,3781.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,long_gap,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,long_gap,19.0,0.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,1_before,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,1_before,12.2,-6.800000000000001,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,2_before,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,2_before,0.0,-19.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,3_before,39.4,-5.600000000000001,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,3_before,0.0,-19.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,1_after,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,1_after,19.0,0.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,2_after,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,2_after,19.0,0.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,3_after,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,3_after,19.0,0.0,2426.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,long_gap,66.0,0.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,long_gap,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,1_before,66.0,0.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,1_before,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,2_before,66.0,0.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,2_before,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,3_before,65.9,-0.0999999999999943,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,3_before,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,1_after,64.1,-1.9000000000000057,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,1_after,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,2_after,64.0,-2.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,2_after,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,3_after,64.0,-2.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,3_after,21.0,0.0,2085.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,long_gap,69.0,0.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,long_gap,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,1_before,69.0,0.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,1_before,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,2_before,70.4,1.4000000000000057,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,2_before,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,3_before,70.0,1.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,3_before,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,1_after,68.6,-0.4000000000000057,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,1_after,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,2_after,70.0,1.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,2_after,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,3_after,69.65,0.6500000000000057,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,3_after,44.0,0.0,3057.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,long_gap,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,long_gap,53.958333333333336,0.0,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,1_before,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,1_before,45.8,-8.158333333333339,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,2_before,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,2_before,44.0,-9.958333333333336,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,3_before,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,3_before,44.0,-9.958333333333336,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,1_after,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,1_after,56.0,2.0416666666666643,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,2_after,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,2_after,56.0,2.0416666666666643,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,3_after,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,3_after,56.0,2.0416666666666643,2393.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,long_gap,52.5,0.0,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,long_gap,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,1_before,54.85,2.3500000000000014,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,1_before,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,2_before,40.2,-12.299999999999995,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,2_before,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,3_before,24.0,-28.5,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,3_before,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,1_after,55.0,2.5,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,1_after,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,2_after,53.55,1.0499999999999972,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,2_after,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,3_after,52.0,-0.5,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,3_after,18.0,0.0,2741.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,long_gap,34.02564102564103,0.0,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,long_gap,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,1_before,53.0,18.97435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,1_before,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,2_before,52.1,18.07435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,2_before,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,3_before,54.95,20.924358974358974,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,3_before,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,1_after,22.65,-11.37564102564103,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,1_after,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,2_after,50.65,16.62435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,2_after,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,3_after,55.15,21.12435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,3_after,18.0,0.0,3937.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,long_gap,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,long_gap,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,1_before,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,1_before,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,2_before,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,2_before,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,3_before,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,3_before,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,1_after,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,1_after,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,2_after,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,2_after,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,3_after,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,3_after,53.0,0.0,2278.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,long_gap,42.84615384615385,0.0,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,long_gap,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,1_before,42.8,-0.0461538461538495,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,1_before,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,2_before,39.85,-2.9961538461538453,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,2_before,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,3_before,47.0,4.153846153846153,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,3_before,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,1_after,44.95,2.103846153846156,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,1_after,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,2_after,45.15,2.303846153846152,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,2_after,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,3_after,46.0,3.1538461538461533,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,3_after,83.0,0.0,2507.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,long_gap,58.60606060606061,0.0,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,long_gap,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,1_before,53.0,-5.606060606060609,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,1_before,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,2_before,42.0,-16.60606060606061,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,2_before,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,3_before,42.0,-16.60606060606061,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,3_before,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,1_after,58.0,-0.6060606060606091,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,1_after,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,2_after,58.0,-0.6060606060606091,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,2_after,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,3_after,57.9,-0.7060606060606105,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,3_after,49.0,0.0,3243.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,long_gap,53.95454545454545,0.0,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,long_gap,50.0,0.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,1_before,53.0,-0.9545454545454533,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,1_before,49.65,-0.3500000000000014,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,2_before,54.95,0.9954545454545496,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,2_before,49.0,-1.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,3_before,57.15,3.1954545454545453,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,3_before,49.0,-1.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,1_after,49.55,-4.404545454545456,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,1_after,50.0,0.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,2_after,48.0,-5.954545454545453,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,2_after,50.0,0.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,3_after,48.0,-5.954545454545453,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,3_after,50.0,0.0,2172.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,long_gap,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,long_gap,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,1_before,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,1_before,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,2_before,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,2_before,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,3_before,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,3_before,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,1_after,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,1_after,49.36,0.3599999999999994,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,2_after,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,2_after,50.0,1.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,3_after,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,3_after,50.0,1.0,2305.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,long_gap,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,long_gap,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,1_before,60.65,-0.3500000000000014,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,1_before,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,2_before,60.0,-1.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,2_before,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,3_before,60.0,-1.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,3_before,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,1_after,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,1_after,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,2_after,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,2_after,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,3_after,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,3_after,24.0,0.0,2246.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,long_gap,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,long_gap,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,1_before,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,1_before,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,2_before,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,2_before,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,3_before,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,3_before,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,1_after,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,1_after,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,2_after,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,2_after,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,3_after,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,3_after,38.785714285714285,-0.2142857142857153,2159.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,long_gap,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,long_gap,62.55882352941177,0.0,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,1_before,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,1_before,61.0,-1.558823529411768,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,2_before,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,2_before,61.45,-1.1088235294117652,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,3_before,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,3_before,61.5,-1.058823529411768,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,1_after,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,1_after,64.0,1.441176470588232,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,2_after,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,2_after,64.35,1.7911764705882265,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,3_after,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,3_after,63.25,0.691176470588232,3410.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,long_gap,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,long_gap,65.36363636363636,0.0,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,1_before,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,1_before,65.0,-0.3636363636363597,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,2_before,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,2_before,63.35,-2.0136363636363583,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,3_before,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,3_before,62.0,-3.36363636363636,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,1_after,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,1_after,66.0,0.6363636363636402,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,2_after,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,2_after,66.0,0.6363636363636402,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,3_after,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,3_after,66.0,0.6363636363636402,2172.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,long_gap,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,long_gap,70.0,0.0,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,1_before,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,1_before,70.55,0.5499999999999972,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,2_before,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,2_before,70.8,0.7999999999999972,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,3_before,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,3_before,71.0,1.0,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,1_after,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,1_after,70.0,0.0,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,2_after,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,2_after,63.3,-6.700000000000003,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,3_after,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,3_after,75.05,5.049999999999997,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,long_gap,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,long_gap,56.59090909090909,0.0,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,1_before,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,1_before,59.0,2.4090909090909065,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,2_before,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,2_before,40.8,-15.790909090909096,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,3_before,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,3_before,37.7,-18.89090909090909,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,1_after,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,1_after,66.3,9.709090909090904,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,2_after,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,2_after,65.25,8.659090909090907,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,3_after,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,3_after,67.55,10.959090909090904,2192.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,long_gap,62.59090909090909,0.0,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,long_gap,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,1_before,62.0,-0.5909090909090935,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,1_before,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,2_before,59.0,-3.590909090909093,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,2_before,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,3_before,59.0,-3.590909090909093,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,3_before,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,1_after,63.4,0.8090909090909051,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,1_after,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,2_after,63.0,0.4090909090909065,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,2_after,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,3_after,61.7,-0.8909090909090907,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,3_after,61.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,long_gap,34.04347826086956,0.0,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,long_gap,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,1_before,30.65,-3.393478260869564,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,1_before,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,2_before,26.0,-8.043478260869563,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,2_before,40.65,-0.3500000000000014,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,3_before,26.0,-8.043478260869563,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,3_before,39.6,-1.3999999999999986,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,1_after,34.0,-0.0434782608695627,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,1_after,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,2_after,41.9,7.856521739130436,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,2_after,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,3_after,51.0,16.956521739130437,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,3_after,41.0,0.0,2265.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,long_gap,60.85,0.0,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,long_gap,12.95,0.0,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,1_before,76.0,15.15,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,1_before,13.0,0.0500000000000007,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,2_before,76.0,15.15,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,2_before,13.0,0.0500000000000007,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,3_before,76.0,15.15,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,3_before,13.0,0.0500000000000007,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,1_after,52.45,-8.399999999999999,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,1_after,11.1,-1.85,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,2_after,64.4,3.5500000000000043,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,2_after,11.0,-1.9499999999999995,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,3_after,61.15,0.2999999999999971,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,3_after,11.0,-1.9499999999999995,3968.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,long_gap,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,long_gap,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,1_before,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,1_before,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,2_before,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,2_before,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,3_before,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,3_before,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,1_after,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,1_after,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,2_after,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,2_after,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,3_after,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,3_after,14.0,0.0,2115.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,long_gap,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,long_gap,59.0,0.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,1_before,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,1_before,57.95,-1.0499999999999972,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,2_before,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,2_before,58.0,-1.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,3_before,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,3_before,58.0,-1.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,1_after,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,1_after,59.0,0.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,2_after,83.85,0.8499999999999943,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,2_after,59.0,0.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,3_after,86.2,3.200000000000003,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,3_after,59.0,0.0,2149.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,long_gap,34.85,0.0,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,long_gap,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,1_before,37.0,2.1499999999999986,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,1_before,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,2_before,37.05,2.199999999999996,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,2_before,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,3_before,34.4,-0.4500000000000028,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,3_before,47.65,-1.3500000000000014,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,1_after,29.0,-5.850000000000001,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,1_after,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,2_after,29.0,-5.850000000000001,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,2_after,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,3_after,29.0,-5.850000000000001,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,3_after,46.5,-2.5,4034.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,long_gap,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,long_gap,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,1_before,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,1_before,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,2_before,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,2_before,55.45,-1.5499999999999972,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,3_before,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,3_before,47.3,-9.700000000000005,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,1_after,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,1_after,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,2_after,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,2_after,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,3_after,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,3_after,57.0,0.0,2008.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,long_gap,75.0,0.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,long_gap,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,1_before,75.0,0.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,1_before,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,2_before,75.45,0.4500000000000028,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,2_before,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,3_before,72.55,-2.450000000000003,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,3_before,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,1_after,75.0,0.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,1_after,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,2_after,73.6,-1.4000000000000057,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,2_after,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,3_after,73.0,-2.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,3_after,49.0,0.0,2009.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,long_gap,32.91428571428571,0.0,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,long_gap,86.85714285714286,0.0,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,1_before,74.75,41.83571428571429,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,1_before,88.0,1.1428571428571388,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,2_before,73.0,40.08571428571429,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,2_before,87.65,0.7928571428571445,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,3_before,72.6,39.68571428571428,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,3_before,88.0,1.1428571428571388,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,1_after,7.0,-25.91428571428571,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,1_after,81.35,-5.507142857142867,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,2_after,2.85,-30.06428571428571,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,2_after,60.5,-26.35714285714286,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,3_after,6.0,-26.91428571428571,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,3_after,63.25,-23.60714285714286,3476.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,long_gap,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,long_gap,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,1_before,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,1_before,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,2_before,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,2_before,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,3_before,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,3_before,73.85,-0.1500000000000056,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,1_after,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,1_after,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,2_after,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,2_after,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,3_after,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,3_after,72.15,-1.8499999999999943,4090.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,long_gap,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,long_gap,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,1_before,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,1_before,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,2_before,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,2_before,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,3_before,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,3_before,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,1_after,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,1_after,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,2_after,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,2_after,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,3_after,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,3_after,57.9,0.8999999999999986,2529.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,long_gap,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,long_gap,55.70967741935484,0.0,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,1_before,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,1_before,54.0,-1.70967741935484,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,2_before,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,2_before,54.0,-1.70967741935484,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,3_before,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,3_before,54.0,-1.70967741935484,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,1_after,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,1_after,65.25,9.54032258064516,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,2_after,8.85,8.85,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,2_after,65.0,9.29032258064516,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,3_after,13.0,13.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,3_after,65.0,9.29032258064516,3119.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,long_gap,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,long_gap,47.22727272727273,0.0,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,1_before,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,1_before,40.35,-6.877272727272725,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,2_before,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,2_before,40.0,-7.227272727272727,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,3_before,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,3_before,40.0,-7.227272727272727,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,1_after,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,1_after,51.0,3.772727272727274,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,2_after,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,2_after,51.0,3.772727272727274,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,3_after,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,3_after,51.0,3.772727272727274,2229.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,long_gap,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,long_gap,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,1_before,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,1_before,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,2_before,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,2_before,51.35,-1.6499999999999986,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,3_before,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,3_before,39.25,-13.75,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,1_after,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,1_after,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,2_after,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,2_after,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,3_after,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,3_after,53.0,0.0,2249.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,long_gap,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,long_gap,27.33333333333333,0.0,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,1_before,84.3,0.2999999999999971,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,1_before,38.3,10.966666666666663,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,2_before,67.7,-16.299999999999997,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,2_before,47.0,19.666666666666668,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,3_before,61.0,-23.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,3_before,47.0,19.666666666666668,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,1_after,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,1_after,27.0,-0.3333333333333321,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,2_after,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,2_after,27.0,-0.3333333333333321,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,3_after,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,3_after,34.25,6.916666666666668,4418.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,long_gap,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,long_gap,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,1_before,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,1_before,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,2_before,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,2_before,64.7,-0.2999999999999971,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,3_before,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,3_before,64.0,-1.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,1_after,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,1_after,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,2_after,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,2_after,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,3_after,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,3_after,65.0,0.0,2463.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,long_gap,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,long_gap,65.56521739130434,0.0,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,1_before,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,1_before,65.0,-0.5652173913043441,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,2_before,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,2_before,65.0,-0.5652173913043441,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,3_before,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,3_before,64.95,-0.6152173913043413,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,1_after,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,1_after,66.0,0.4347826086956559,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,2_after,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,2_after,64.3,-1.265217391304347,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,3_after,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,3_after,61.7,-3.865217391304341,2386.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,long_gap,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,long_gap,66.0,0.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,1_before,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,1_before,65.75,-0.25,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,2_before,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,2_before,65.0,-1.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,3_before,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,3_before,65.0,-1.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,1_after,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,1_after,62.2,-3.799999999999997,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,2_after,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,2_after,62.0,-4.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,3_after,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,3_after,62.0,-4.0,2706.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,long_gap,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,long_gap,61.84615384615385,0.0,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,1_before,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,1_before,72.0,10.153846153846152,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,2_before,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,2_before,72.0,10.153846153846152,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,3_before,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,3_before,72.0,10.153846153846152,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,1_after,49.4,-0.6000000000000014,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,1_after,52.05,-9.79615384615385,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,2_after,42.25,-7.75,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,2_after,56.2,-5.646153846153844,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,3_after,42.0,-8.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,3_after,57.0,-4.846153846153847,3882.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,long_gap,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,long_gap,60.60869565217392,0.0,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,1_before,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,1_before,59.0,-1.608695652173914,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,2_before,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,2_before,59.0,-1.608695652173914,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,3_before,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,3_before,59.0,-1.608695652173914,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,1_after,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,1_after,61.0,0.391304347826086,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,2_after,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,2_after,61.4,0.7913043478260846,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,3_after,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,3_after,63.95,3.341304347826089,2289.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,long_gap,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,long_gap,80.74193548387096,0.0,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,1_before,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,1_before,81.0,0.2580645161290391,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,2_before,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,2_before,81.0,0.2580645161290391,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,3_before,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,3_before,81.0,0.2580645161290391,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,1_after,45.6,3.6000000000000014,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,1_after,78.0,-2.741935483870961,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,2_after,51.0,9.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,2_after,78.0,-2.741935483870961,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,3_after,51.0,9.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,3_after,78.0,-2.741935483870961,3132.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,long_gap,42.45161290322581,0.0,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,long_gap,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,1_before,23.35,-19.101612903225806,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,1_before,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,2_before,22.0,-20.451612903225808,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,2_before,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,3_before,22.0,-20.451612903225808,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,3_before,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,1_after,45.0,2.548387096774192,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,1_after,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,2_after,45.05,2.5983870967741893,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,2_after,10.9,10.9,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,3_after,47.2,4.748387096774195,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,3_after,19.9,19.9,3170.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,long_gap,41.689655172413794,0.0,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,long_gap,22.0,0.0,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,1_before,45.5,3.8103448275862064,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,1_before,21.25,-0.75,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,2_before,47.35,5.660344827586208,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,2_before,20.5,-1.5,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,3_before,47.3,5.610344827586204,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,3_before,19.75,-2.25,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,1_after,42.35,0.6603448275862078,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,1_after,22.0,0.0,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,2_after,45.25,3.5603448275862064,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,2_after,22.0,0.0,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,3_after,46.0,4.310344827586206,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,3_after,25.55,3.5500000000000007,2863.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,long_gap,47.369565217391305,0.0,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,long_gap,33.17391304347826,0.0,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,1_before,46.3,-1.0695652173913075,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,1_before,34.65,1.4760869565217405,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,2_before,44.0,-3.3695652173913047,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,2_before,35.0,1.826086956521742,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,3_before,43.6,-3.7695652173913032,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,3_before,35.0,1.826086956521742,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,1_after,20.0,-27.369565217391305,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,1_after,33.0,-0.1739130434782581,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,2_after,13.0,-34.369565217391305,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,2_after,32.65,-0.5239130434782595,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,3_after,13.0,-34.369565217391305,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,3_after,33.0,-0.1739130434782581,9167.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,long_gap,26.36,0.0,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,long_gap,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,1_before,23.6,-2.759999999999998,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,1_before,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,2_before,23.0,-3.3599999999999994,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,2_before,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,3_before,21.3,-5.059999999999999,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,3_before,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,1_after,25.5,-0.8599999999999994,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,1_after,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,2_after,25.85,-0.509999999999998,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,2_after,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,3_after,25.0,-1.3599999999999994,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,3_after,33.0,0.0,2542.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,long_gap,26.98507462686567,0.0,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,long_gap,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,1_before,25.0,-1.9850746268656716,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,1_before,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,2_before,25.3,-1.6850746268656709,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,2_before,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,3_before,26.0,-0.9850746268656714,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,3_before,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,1_after,28.45,1.464925373134328,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,1_after,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,2_after,29.0,2.0149253731343286,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,2_after,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,3_after,29.0,2.0149253731343286,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,3_after,33.0,0.0,6628.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,long_gap,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,long_gap,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,1_before,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,1_before,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,2_before,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,2_before,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,3_before,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,3_before,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,1_after,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,1_after,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,2_after,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,2_after,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,3_after,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,3_after,53.0,0.0,3988.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,long_gap,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,long_gap,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,1_before,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,1_before,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,2_before,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,2_before,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,3_before,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,3_before,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,1_after,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,1_after,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,2_after,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,2_after,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,3_after,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,3_after,87.0,0.0,2816.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,long_gap,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,long_gap,45.0,0.0,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,1_before,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,1_before,45.0,0.0,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,2_before,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,2_before,45.0,0.0,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,3_before,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,3_before,39.65,-5.350000000000001,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,1_after,50.6,-6.399999999999999,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,1_after,47.15,2.1499999999999986,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,2_after,35.0,-22.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,2_after,50.2,5.200000000000003,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,3_after,35.0,-22.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,3_after,54.4,9.4,2372.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,long_gap,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,long_gap,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,1_before,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,1_before,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,2_before,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,2_before,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,3_before,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,3_before,71.3,1.2999999999999972,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,1_after,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,1_after,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,2_after,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,2_after,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,3_after,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,3_after,70.0,0.0,2726.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,long_gap,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,long_gap,69.0,0.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,1_before,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,1_before,69.6,0.5999999999999943,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,2_before,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,2_before,84.6,15.599999999999994,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,3_before,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,3_before,76.0,7.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,1_after,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,1_after,69.0,0.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,2_after,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,2_after,69.0,0.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,3_after,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,3_after,69.0,0.0,3078.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,long_gap,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,long_gap,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,1_before,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,1_before,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,2_before,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,2_before,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,3_before,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,3_before,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,1_after,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,1_after,70.6,1.5999999999999943,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,2_after,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,2_after,74.0,5.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,3_after,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,3_after,74.0,5.0,2068.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,long_gap,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,long_gap,31.45,0.0,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,1_before,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,1_before,68.1,36.64999999999999,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,2_before,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,2_before,74.0,42.55,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,3_before,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,3_before,74.0,42.55,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,1_after,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,1_after,25.75,-5.699999999999999,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,2_after,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,2_after,26.0,-5.449999999999999,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,3_after,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,3_after,26.0,-5.449999999999999,2030.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,long_gap,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,long_gap,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,1_before,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,1_before,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,2_before,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,2_before,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,3_before,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,3_before,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,1_after,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,1_after,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,2_after,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,2_after,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,3_after,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,3_after,25.0,0.0,2452.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,long_gap,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,long_gap,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,1_before,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,1_before,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,2_before,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,2_before,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,3_before,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,3_before,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,1_after,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,1_after,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,2_after,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,2_after,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,3_after,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,3_after,66.0,1.0,2393.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,long_gap,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,long_gap,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,1_before,38.85,0.8500000000000014,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,1_before,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,2_before,39.0,1.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,2_before,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,3_before,39.45,1.4500000000000028,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,3_before,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,1_after,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,1_after,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,2_after,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,2_after,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,3_after,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,3_after,8.0,0.0,3165.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,long_gap,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,long_gap,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,1_before,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,1_before,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,2_before,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,2_before,39.1,-2.8999999999999986,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,3_before,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,3_before,38.0,-4.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,1_after,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,1_after,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,2_after,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,2_after,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,3_after,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,3_after,42.0,0.0,3055.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,long_gap,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,long_gap,51.92857142857143,0.0,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,1_before,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,1_before,52.0,0.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,2_before,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,2_before,52.0,0.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,3_before,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,3_before,52.0,0.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,1_after,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,1_after,51.95,0.0214285714285722,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,2_after,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,2_after,53.0,1.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,3_after,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,3_after,30.55,-21.37857142857143,2825.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,long_gap,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,long_gap,14.0,0.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,1_before,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,1_before,22.75,8.75,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,2_before,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,2_before,53.0,39.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,3_before,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,3_before,52.35,38.35,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,1_after,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,1_after,14.0,0.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,2_after,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,2_after,14.0,0.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,3_after,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,3_after,14.0,0.0,5647.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,long_gap,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,long_gap,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,1_before,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,1_before,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,2_before,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,2_before,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,3_before,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,3_before,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,1_after,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,1_after,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,2_after,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,2_after,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,3_after,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,3_after,25.0,0.0,2279.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,long_gap,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,long_gap,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,1_before,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,1_before,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,2_before,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,2_before,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,3_before,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,3_before,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,1_after,25.15,-1.8500000000000012,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,1_after,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,2_after,22.2,-4.800000000000001,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,2_after,24.45,0.4499999999999993,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,3_after,22.0,-5.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,3_after,25.0,1.0,2064.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,long_gap,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,long_gap,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,1_before,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,1_before,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,2_before,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,2_before,40.3,-9.700000000000005,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,3_before,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,3_before,31.0,-19.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,1_after,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,1_after,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,2_after,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,2_after,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,3_after,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,3_after,50.0,0.0,4668.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,long_gap,32.833333333333336,0.0,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,long_gap,85.08,0.0,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,1_before,33.0,0.1666666666666643,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,1_before,85.5,0.4200000000000017,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,2_before,33.0,0.1666666666666643,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,2_before,85.4,0.3200000000000074,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,3_before,33.0,0.1666666666666643,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,3_before,53.75,-31.33,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,1_after,31.2,-1.6333333333333364,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,1_after,86.0,0.9200000000000016,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,2_after,36.45,3.616666666666667,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,2_after,84.8,-0.2800000000000011,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,3_after,38.0,5.166666666666664,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,3_after,84.0,-1.0799999999999983,2422.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,long_gap,45.92,0.0,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,long_gap,79.44,0.0,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,1_before,41.85,-4.07,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,1_before,81.0,1.5600000000000025,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,2_before,41.0,-4.920000000000002,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,2_before,81.85,2.4099999999999966,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,3_before,41.0,-4.920000000000002,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,3_before,83.9,4.460000000000008,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,1_after,46.0,0.0799999999999983,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,1_after,67.4,-12.039999999999992,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,2_after,46.0,0.0799999999999983,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,2_after,57.5,-21.94,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,3_after,46.0,0.0799999999999983,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,3_after,56.0,-23.44,2529.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,long_gap,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,long_gap,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,1_before,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,1_before,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,2_before,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,2_before,54.3,0.2999999999999971,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,3_before,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,3_before,43.0,-11.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,1_after,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,1_after,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,2_after,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,2_after,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,3_after,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,3_after,53.95,-0.0499999999999971,2337.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,long_gap,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,long_gap,44.5,0.0,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,1_before,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,1_before,43.85,-0.6499999999999986,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,2_before,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,2_before,40.0,-4.5,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,3_before,43.0,-2.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,3_before,25.0,-19.5,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,1_after,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,1_after,45.0,0.5,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,2_after,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,2_after,44.85,0.3500000000000014,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,3_after,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,3_after,45.55,1.0499999999999972,2653.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,long_gap,68.08,0.0,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,long_gap,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,1_before,70.0,1.9200000000000017,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,1_before,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,2_before,70.0,1.9200000000000017,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,2_before,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,3_before,70.0,1.9200000000000017,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,3_before,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,1_after,65.25,-2.8299999999999983,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,1_after,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,2_after,65.0,-3.0799999999999983,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,2_after,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,3_after,65.0,-3.0799999999999983,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,3_after,97.0,0.0,2516.0,self,strangers +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,long_gap,24.0,0.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,long_gap,53.0,0.0,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,1_before,29.35,5.350000000000001,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,1_before,53.0,0.0,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,2_before,31.0,7.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,2_before,52.9,-0.1000000000000014,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,3_before,31.85,7.850000000000001,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,3_before,51.7,-1.2999999999999972,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,1_after,22.9,-1.1000000000000014,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,1_after,53.0,0.0,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,2_after,20.0,-4.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,2_after,53.95,0.9500000000000028,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,3_after,20.0,-4.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,3_after,54.0,1.0,2169.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,long_gap,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,long_gap,60.7,0.0,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,1_before,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,1_before,60.8,0.0999999999999943,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,2_before,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,2_before,61.0,0.2999999999999971,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,3_before,88.85,0.8499999999999943,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,3_before,61.0,0.2999999999999971,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,1_after,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,1_after,62.0,1.2999999999999972,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,2_after,87.55,-0.4500000000000028,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,2_after,62.0,1.2999999999999972,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,3_after,85.0,-3.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,3_after,62.0,1.2999999999999972,2009.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,long_gap,90.55,0.0,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,long_gap,58.0,0.0,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,1_before,90.0,-0.5499999999999972,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,1_before,58.5,0.5,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,2_before,90.0,-0.5499999999999972,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,2_before,60.65,2.6499999999999986,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,3_before,90.0,-0.5499999999999972,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,3_before,64.85,6.849999999999994,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,1_after,91.0,0.4500000000000028,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,1_after,58.0,0.0,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,2_after,91.0,0.4500000000000028,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,2_after,57.55,-0.4500000000000028,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,3_after,91.0,0.4500000000000028,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,3_after,56.15,-1.8500000000000012,2026.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,long_gap,79.0,0.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,long_gap,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,1_before,78.9,-0.0999999999999943,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,1_before,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,2_before,75.05,-3.950000000000003,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,2_before,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,3_before,75.0,-4.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,3_before,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,1_after,79.0,0.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,1_after,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,2_after,90.25,11.25,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,2_after,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,3_after,90.0,11.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,3_after,91.0,0.0,3072.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,long_gap,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,long_gap,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,1_before,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,1_before,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,2_before,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,2_before,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,3_before,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,3_before,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,1_after,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,1_after,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,2_after,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,2_after,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,3_after,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,3_after,91.0,0.0,3485.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,long_gap,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,long_gap,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,1_before,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,1_before,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,2_before,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,2_before,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,3_before,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,3_before,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,1_after,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,1_after,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,2_after,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,2_after,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,3_after,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,3_after,96.0,0.0,2321.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,long_gap,80.0,0.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,long_gap,55.18181818181818,0.0,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,1_before,80.0,0.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,1_before,63.0,7.81818181818182,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,2_before,78.9,-1.0999999999999943,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,2_before,63.0,7.81818181818182,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,3_before,74.0,-6.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,3_before,63.0,7.81818181818182,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,1_after,80.5,0.5,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,1_after,54.0,-1.18181818181818,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,2_after,81.0,1.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,2_after,54.0,-1.18181818181818,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,3_after,81.0,1.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,3_after,54.0,-1.18181818181818,2128.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,long_gap,56.3,0.0,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,long_gap,74.35,0.0,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,1_before,56.95,0.6500000000000057,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,1_before,67.2,-7.149999999999992,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,2_before,60.4,4.100000000000001,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,2_before,65.9,-8.449999999999989,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,3_before,52.8,-3.5,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,3_before,64.05,-10.299999999999995,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,1_after,55.5,-0.7999999999999972,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,1_after,75.0,0.6500000000000057,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,2_after,56.0,-0.2999999999999971,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,2_after,75.0,0.6500000000000057,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,3_after,57.8,1.5,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,3_after,75.0,0.6500000000000057,2053.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,long_gap,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,long_gap,64.91666666666667,0.0,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,1_before,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,1_before,67.1,2.183333333333323,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,2_before,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,2_before,67.4,2.4833333333333343,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,3_before,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,3_before,67.0,2.0833333333333286,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,1_after,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,1_after,57.2,-7.716666666666669,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,2_after,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,2_after,50.7,-14.216666666666669,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,3_after,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,3_after,50.0,-14.916666666666671,2420.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,long_gap,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,long_gap,49.5,0.0,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,1_before,89.65,0.6500000000000057,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,1_before,39.45,-10.049999999999995,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,2_before,92.0,3.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,2_before,38.0,-11.5,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,3_before,92.0,3.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,3_before,38.0,-11.5,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,1_after,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,1_after,52.85,3.3500000000000014,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,2_after,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,2_after,53.0,3.5,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,3_after,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,3_after,53.0,3.5,2229.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,long_gap,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,long_gap,58.25,0.0,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,1_before,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,1_before,60.0,1.75,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,2_before,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,2_before,60.6,2.3500000000000014,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,3_before,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,3_before,62.55,4.299999999999997,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,1_after,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,1_after,55.0,-3.25,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,2_after,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,2_after,55.0,-3.25,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,3_after,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,3_after,60.05,1.7999999999999972,2408.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,long_gap,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,long_gap,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,1_before,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,1_before,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,2_before,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,2_before,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,3_before,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,3_before,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,1_after,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,1_after,26.75,0.75,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,2_after,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,2_after,27.0,1.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,3_after,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,3_after,27.0,1.0,2370.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,long_gap,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,long_gap,62.59090909090909,0.0,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,1_before,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,1_before,56.75,-5.8409090909090935,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,2_before,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,2_before,53.6,-8.990909090909092,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,3_before,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,3_before,49.05,-13.540909090909096,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,1_after,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,1_after,64.0,1.4090909090909065,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,2_after,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,2_after,64.0,1.4090909090909065,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,3_after,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,3_after,64.05,1.4590909090909037,2124.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,long_gap,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,long_gap,64.5,0.0,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,1_before,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,1_before,67.0,2.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,2_before,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,2_before,67.0,2.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,3_before,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,3_before,67.0,2.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,1_after,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,1_after,62.55,-1.9500000000000028,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,2_after,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,2_after,61.0,-3.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,3_after,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,3_after,61.0,-3.5,2067.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,long_gap,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,long_gap,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,1_before,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,1_before,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,2_before,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,2_before,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,3_before,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,3_before,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,1_after,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,1_after,60.45,-0.5499999999999972,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,2_after,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,2_after,60.0,-1.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,3_after,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,3_after,60.0,-1.0,2286.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,long_gap,72.58823529411765,0.0,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,long_gap,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,1_before,76.95,4.361764705882351,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,1_before,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,2_before,79.95,7.361764705882351,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,2_before,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,3_before,82.0,9.411764705882348,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,3_before,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,1_after,72.0,-0.5882352941176521,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,1_after,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,2_after,72.0,-0.5882352941176521,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,2_after,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,3_after,72.0,-0.5882352941176521,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,3_after,100.0,0.0,3372.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,long_gap,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,long_gap,98.28571428571428,0.0,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,1_before,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,1_before,100.0,1.7142857142857082,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,2_before,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,2_before,100.0,1.7142857142857082,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,3_before,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,3_before,100.0,1.7142857142857082,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,1_after,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,1_after,45.25,-53.03571428571429,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,2_after,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,2_after,47.65,-50.63571428571429,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,3_after,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,3_after,55.5,-42.78571428571429,2014.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,long_gap,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,long_gap,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,1_before,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,1_before,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,2_before,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,2_before,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,3_before,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,3_before,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,1_after,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,1_after,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,2_after,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,2_after,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,3_after,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,3_after,47.45,-0.5499999999999972,2538.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,long_gap,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,long_gap,53.0,0.0,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,1_before,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,1_before,58.9,5.899999999999999,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,2_before,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,2_before,61.0,8.0,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,3_before,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,3_before,60.8,7.799999999999997,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,1_after,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,1_after,53.0,0.0,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,2_after,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,2_after,54.85,1.8500000000000012,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,3_after,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,3_after,57.45,4.450000000000003,2449.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,long_gap,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,long_gap,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,1_before,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,1_before,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,2_before,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,2_before,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,3_before,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,3_before,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,1_after,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,1_after,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,2_after,60.9,0.8999999999999986,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,2_after,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,3_after,61.0,1.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,3_after,74.0,0.0,2548.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,long_gap,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,long_gap,69.78571428571429,0.0,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,1_before,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,1_before,68.8,-0.9857142857142946,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,2_before,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,2_before,64.0,-5.785714285714292,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,3_before,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,3_before,71.4,1.614285714285714,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,1_after,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,1_after,68.35,-1.4357142857142975,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,2_after,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,2_after,68.0,-1.7857142857142918,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,3_after,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,3_after,68.0,-1.7857142857142918,2747.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,long_gap,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,long_gap,50.75555555555555,0.0,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,1_before,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,1_before,59.0,8.244444444444447,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,2_before,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,2_before,59.0,8.244444444444447,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,3_before,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,3_before,59.0,8.244444444444447,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,1_after,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,1_after,25.9,-24.855555555555554,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,2_after,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,2_after,30.0,-20.755555555555556,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,3_after,38.25,-22.75,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,3_after,31.0,-19.755555555555556,4528.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,long_gap,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,long_gap,42.0,0.0,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,1_before,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,1_before,42.0,0.0,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,2_before,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,2_before,42.21052631578947,0.2105263157894725,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,3_before,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,3_before,44.0,2.0,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,1_after,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,1_after,42.4,0.3999999999999986,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,2_after,81.0,1.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,2_after,44.85,2.8500000000000014,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,3_after,81.0,1.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,3_after,44.75,2.75,3202.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,long_gap,80.14285714285714,0.0,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,long_gap,42.66666666666666,0.0,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,1_before,80.0,-0.1428571428571388,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,1_before,42.0,-0.6666666666666643,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,2_before,80.0,-0.1428571428571388,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,2_before,42.0,-0.6666666666666643,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,3_before,80.0,-0.1428571428571388,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,3_before,42.0,-0.6666666666666643,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,1_after,81.0,0.8571428571428612,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,1_after,45.0,2.333333333333336,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,2_after,81.0,0.8571428571428612,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,2_after,44.45,1.7833333333333383,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,3_after,81.0,0.8571428571428612,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,3_after,43.0,0.3333333333333357,2063.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,long_gap,81.2,0.0,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,long_gap,46.13333333333333,0.0,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,1_before,81.0,-0.2000000000000028,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,1_before,49.15,3.0166666666666657,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,2_before,81.0,-0.2000000000000028,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,2_before,48.45,2.31666666666667,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,3_before,81.0,-0.2000000000000028,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,3_before,46.5,0.3666666666666671,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,1_after,82.0,0.7999999999999972,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,1_after,43.0,-3.133333333333333,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,2_after,82.0,0.7999999999999972,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,2_after,42.8,-3.333333333333336,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,3_after,82.0,0.7999999999999972,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,3_after,39.05,-7.083333333333336,2965.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,long_gap,18.666666666666668,0.0,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,long_gap,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,1_before,23.8,5.133333333333333,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,1_before,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,2_before,26.0,7.333333333333332,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,2_before,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,3_before,26.0,7.333333333333332,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,3_before,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,1_after,18.0,-0.6666666666666679,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,1_after,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,2_after,18.8,0.1333333333333328,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,2_after,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,3_after,19.0,0.3333333333333321,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,3_after,58.0,0.0,2165.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,long_gap,74.73913043478261,0.0,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,long_gap,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,1_before,73.0,-1.739130434782609,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,1_before,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,2_before,73.0,-1.739130434782609,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,2_before,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,3_before,71.25,-3.4891304347826093,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,3_before,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,1_after,78.80952380952381,4.070393374741201,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,1_after,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,2_after,61.63157894736842,-13.107551487414192,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,2_after,64.1,0.0999999999999943,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,3_after,65.0,-9.73913043478261,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,3_after,69.8,5.799999999999997,2388.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,long_gap,39.76190476190476,0.0,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,long_gap,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,1_before,64.0,24.23809523809524,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,1_before,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,2_before,64.0,24.23809523809524,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,2_before,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,3_before,64.2,24.438095238095244,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,3_before,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,1_after,34.0,-5.761904761904759,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,1_after,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,2_after,34.0,-5.761904761904759,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,2_after,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,3_after,38.1,-1.6619047619047578,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,3_after,70.0,0.0,2065.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,long_gap,55.90909090909091,0.0,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,long_gap,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,1_before,53.25,-2.6590909090909065,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,1_before,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,2_before,30.7,-25.209090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,2_before,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,3_before,32.0,-23.909090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,3_before,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,1_after,47.3,-8.60909090909091,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,1_after,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,2_after,47.0,-8.909090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,2_after,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,3_after,29.25,-26.659090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,3_after,82.0,0.0,2215.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,long_gap,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,long_gap,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,1_before,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,1_before,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,2_before,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,2_before,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,3_before,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,3_before,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,1_after,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,1_after,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,2_after,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,2_after,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,3_after,87.35,-9.650000000000006,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,3_after,82.0,0.0,2400.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,long_gap,45.0,0.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,long_gap,61.28125,0.0,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,1_before,46.47368421052632,1.473684210526315,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,1_before,68.6842105263158,7.402960526315795,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,2_before,52.0,7.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,2_before,69.0,7.71875,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,3_before,52.0,7.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,3_before,66.65,5.368750000000006,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,1_after,52.578947368421055,7.578947368421055,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,1_after,65.0,3.71875,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,2_after,54.0,9.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,2_after,56.31578947368421,-4.965460526315788,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,3_after,54.0,9.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,3_after,60.8421052631579,-0.4391447368421026,3300.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,long_gap,88.90322580645162,0.0,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,long_gap,90.90322580645162,0.0,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,1_before,86.0,-2.903225806451616,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,1_before,92.0,1.0967741935483843,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,2_before,86.0,-2.903225806451616,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,2_before,92.4,1.49677419354839,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,3_before,86.0,-2.903225806451616,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,3_before,93.0,2.0967741935483843,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,1_after,90.0,1.0967741935483843,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,1_after,82.4,-8.50322580645161,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,2_after,90.0,1.0967741935483843,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,2_after,84.85,-6.053225806451621,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,3_after,89.1,0.1967741935483786,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,3_after,85.0,-5.903225806451616,3157.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,long_gap,35.6875,0.0,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,long_gap,60.85416666666666,0.0,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,1_before,33.0,-2.6875,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,1_before,60.0,-0.8541666666666643,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,2_before,33.0,-2.6875,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,2_before,59.6,-1.2541666666666629,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,3_before,33.0,-2.6875,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,3_before,51.1,-9.754166666666665,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,1_after,37.3,1.6124999999999972,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,1_after,61.0,0.1458333333333357,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,2_after,38.0,2.3125,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,2_after,61.0,0.1458333333333357,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,3_after,38.0,2.3125,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,3_after,61.0,0.1458333333333357,4852.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,long_gap,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,long_gap,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,1_before,37.6,-0.3999999999999986,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,1_before,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,2_before,36.0,-2.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,2_before,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,3_before,36.0,-2.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,3_before,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,1_after,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,1_after,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,2_after,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,2_after,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,3_after,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,3_after,61.15,0.1499999999999985,2360.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,long_gap,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,long_gap,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,1_before,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,1_before,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,2_before,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,2_before,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,3_before,39.8,-0.2000000000000028,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,3_before,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,1_after,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,1_after,61.45,0.4500000000000028,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,2_after,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,2_after,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,3_after,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,3_after,61.0,0.0,2106.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,long_gap,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,long_gap,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,1_before,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,1_before,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,2_before,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,2_before,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,3_before,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,3_before,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,1_after,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,1_after,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,2_after,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,2_after,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,3_after,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,3_after,73.0,0.0,2271.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,long_gap,65.15384615384616,0.0,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,long_gap,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,1_before,63.0,-2.1538461538461604,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,1_before,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,2_before,63.0,-2.1538461538461604,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,2_before,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,3_before,63.0,-2.1538461538461604,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,3_before,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,1_after,67.0,1.84615384615384,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,1_after,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,2_after,67.0,1.84615384615384,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,2_after,72.25,-0.75,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,3_after,67.0,1.84615384615384,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,3_after,74.9,1.9000000000000057,2567.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,long_gap,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,long_gap,74.25,0.0,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,1_before,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,1_before,75.0,0.75,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,2_before,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,2_before,72.95,-1.2999999999999972,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,3_before,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,3_before,73.0,-1.25,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,1_after,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,1_after,74.0,-0.25,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,2_after,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,2_after,74.0,-0.25,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,3_after,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,3_after,74.0,-0.25,2069.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,long_gap,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,long_gap,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,1_before,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,1_before,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,2_before,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,2_before,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,3_before,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,3_before,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,1_after,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,1_after,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,2_after,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,2_after,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,3_after,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,3_after,74.0,0.0,2315.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,long_gap,66.63829787234043,0.0,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,long_gap,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,1_before,67.0,0.3617021276595693,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,1_before,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,2_before,67.0,0.3617021276595693,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,2_before,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,3_before,67.0,0.3617021276595693,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,3_before,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,1_after,60.0,-6.638297872340431,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,1_after,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,2_after,60.0,-6.638297872340431,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,2_after,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,3_after,60.55,-6.0882978723404335,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,3_after,74.0,0.0,4780.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,long_gap,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,long_gap,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,1_before,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,1_before,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,2_before,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,2_before,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,3_before,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,3_before,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,1_after,71.7,0.7000000000000028,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,1_after,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,2_after,72.0,1.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,2_after,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,3_after,72.0,1.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,3_after,74.0,0.0,2605.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,long_gap,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,long_gap,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,1_before,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,1_before,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,2_before,73.35,-0.6500000000000057,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,2_before,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,3_before,73.0,-1.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,3_before,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,1_after,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,1_after,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,2_after,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,2_after,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,3_after,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,3_after,74.0,0.0,2050.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,long_gap,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,long_gap,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,1_before,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,1_before,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,2_before,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,2_before,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,3_before,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,3_before,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,1_after,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,1_after,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,2_after,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,2_after,74.8,0.7999999999999972,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,3_after,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,3_after,78.0,4.0,3620.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,long_gap,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,long_gap,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,1_before,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,1_before,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,2_before,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,2_before,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,3_before,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,3_before,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,1_after,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,1_after,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,2_after,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,2_after,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,3_after,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,3_after,78.0,0.0,2343.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,long_gap,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,long_gap,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,1_before,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,1_before,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,2_before,77.55,-5.450000000000003,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,2_before,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,3_before,76.0,-7.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,3_before,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,1_after,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,1_after,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,2_after,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,2_after,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,3_after,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,3_after,78.0,0.0,2239.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,long_gap,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,long_gap,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,1_before,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,1_before,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,2_before,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,2_before,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,3_before,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,3_before,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,1_after,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,1_after,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,2_after,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,2_after,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,3_after,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,3_after,78.0,0.0,2140.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,long_gap,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,long_gap,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,1_before,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,1_before,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,2_before,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,2_before,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,3_before,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,3_before,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,1_after,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,1_after,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,2_after,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,2_after,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,3_after,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,3_after,78.0,0.0,3005.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,long_gap,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,long_gap,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,1_before,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,1_before,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,2_before,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,2_before,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,3_before,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,3_before,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,1_after,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,1_after,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,2_after,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,2_after,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,3_after,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,3_after,78.0,0.0,4567.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,long_gap,95.0,0.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,long_gap,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,1_before,94.1,-0.9000000000000057,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,1_before,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,2_before,94.0,-1.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,2_before,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,3_before,93.6,-1.4000000000000057,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,3_before,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,1_after,95.0,0.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,1_after,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,2_after,95.0,0.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,2_after,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,3_after,95.9,0.9000000000000057,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,3_after,86.0,0.0,2035.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,long_gap,81.71428571428571,0.0,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,long_gap,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,1_before,87.25,5.535714285714292,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,1_before,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,2_before,88.0,6.285714285714292,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,2_before,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,3_before,88.65,6.9357142857142975,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,3_before,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,1_after,87.35,5.635714285714286,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,1_after,91.25,-3.75,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,2_after,85.85,4.135714285714286,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,2_after,79.0,-16.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,3_after,87.7,5.985714285714295,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,3_after,79.9,-15.099999999999994,7773.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,long_gap,86.4,0.0,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,long_gap,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,1_before,87.0,0.5999999999999943,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,1_before,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,2_before,90.75,4.349999999999994,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,2_before,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,3_before,91.05,4.649999999999992,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,3_before,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,1_after,89.1,2.6999999999999886,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,1_after,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,2_after,93.0,6.599999999999994,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,2_after,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,3_after,93.95,7.549999999999997,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,3_after,83.35,3.3499999999999943,2497.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,long_gap,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,long_gap,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,1_before,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,1_before,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,2_before,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,2_before,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,3_before,83.15,1.1500000000000057,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,3_before,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,1_after,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,1_after,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,2_after,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,2_after,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,3_after,81.35,-0.6500000000000057,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,3_after,79.0,0.0,2180.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,long_gap,89.25,0.0,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,long_gap,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,1_before,91.0,1.75,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,1_before,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,2_before,91.0,1.75,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,2_before,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,3_before,91.15,1.9000000000000057,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,3_before,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,1_after,89.0,-0.25,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,1_after,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,2_after,89.0,-0.25,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,2_after,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,3_after,89.0,-0.25,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,3_after,80.0,0.0,2354.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,long_gap,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,long_gap,77.42105263157895,0.0,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,1_before,41.85,-1.1499999999999986,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,1_before,79.0,1.5789473684210549,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,2_before,25.65,-17.35,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,2_before,78.0,0.5789473684210549,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,3_before,13.15,-29.85,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,3_before,76.0,-1.4210526315789451,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,1_after,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,1_after,76.2,-1.2210526315789425,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,2_after,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,2_after,77.0,-0.4210526315789451,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,3_after,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,3_after,77.0,-0.4210526315789451,3829.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,long_gap,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,long_gap,83.47619047619048,0.0,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,1_before,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,1_before,90.85,7.373809523809513,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,2_before,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,2_before,91.0,7.523809523809518,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,3_before,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,3_before,91.05,7.5738095238095156,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,1_after,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,1_after,81.0,-2.4761904761904816,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,2_after,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,2_after,81.0,-2.4761904761904816,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,3_after,70.0,1.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,3_after,81.0,-2.4761904761904816,2164.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,long_gap,70.0,0.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,long_gap,69.0,0.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,1_before,70.0,0.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,1_before,68.95,-0.0499999999999971,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,2_before,66.65,-3.3499999999999943,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,2_before,53.75,-15.25,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,3_before,66.0,-4.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,3_before,50.0,-19.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,1_after,72.55,2.549999999999997,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,1_after,69.0,0.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,2_after,73.0,3.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,2_after,69.0,0.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,3_after,73.0,3.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,3_after,69.0,0.0,2603.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,long_gap,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,long_gap,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,1_before,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,1_before,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,2_before,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,2_before,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,3_before,52.8,-4.200000000000003,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,3_before,75.45,-1.5499999999999972,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,1_after,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,1_after,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,2_after,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,2_after,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,3_after,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,3_after,77.0,0.0,2921.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,long_gap,60.69565217391305,0.0,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,long_gap,80.69565217391305,0.0,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,1_before,57.0,-3.695652173913047,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,1_before,81.0,0.3043478260869534,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,2_before,57.0,-3.695652173913047,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,2_before,80.05,-0.6456521739130494,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,3_before,57.0,-3.695652173913047,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,3_before,77.0,-3.695652173913047,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,1_after,68.0,7.304347826086953,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,1_after,80.0,-0.6956521739130466,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,2_after,68.0,7.304347826086953,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,2_after,80.0,-0.6956521739130466,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,3_after,68.0,7.304347826086953,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,3_after,80.0,-0.6956521739130466,2315.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,long_gap,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,long_gap,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,1_before,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,1_before,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,2_before,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,2_before,83.75,-1.25,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,3_before,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,3_before,83.0,-2.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,1_after,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,1_after,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,2_after,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,2_after,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,3_after,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,3_after,85.0,0.0,2414.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,long_gap,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,long_gap,84.92,0.0,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,1_before,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,1_before,72.95,-11.97,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,2_before,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,2_before,72.0,-12.920000000000002,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,3_before,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,3_before,72.0,-12.920000000000002,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,1_after,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,1_after,85.0,0.0799999999999983,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,2_after,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,2_after,85.0,0.0799999999999983,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,3_after,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,3_after,85.0,0.0799999999999983,2445.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,long_gap,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,long_gap,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,1_before,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,1_before,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,2_before,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,2_before,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,3_before,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,3_before,58.75,-14.25,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,1_after,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,1_after,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,2_after,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,2_after,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,3_after,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,3_after,73.0,0.0,2031.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,long_gap,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,long_gap,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,1_before,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,1_before,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,2_before,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,2_before,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,3_before,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,3_before,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,1_after,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,1_after,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,2_after,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,2_after,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,3_after,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,3_after,79.3,-0.7000000000000028,2956.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,long_gap,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,long_gap,80.8108108108108,0.0,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,1_before,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,1_before,81.0,0.189189189189193,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,2_before,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,2_before,81.0,0.189189189189193,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,3_before,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,3_before,81.0,0.189189189189193,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,1_after,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,1_after,80.95,0.1391891891891958,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,2_after,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,2_after,80.0,-0.810810810810807,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,3_after,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,3_after,80.0,-0.810810810810807,3706.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,long_gap,89.0,0.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,long_gap,54.5,0.0,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,1_before,86.15,-2.8499999999999943,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,1_before,58.9,4.399999999999999,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,2_before,85.0,-4.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,2_before,59.0,4.5,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,3_before,85.0,-4.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,3_before,59.0,4.5,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,1_after,89.0,0.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,1_after,50.05,-4.450000000000003,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,2_after,89.85,0.8499999999999943,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,2_after,37.9,-16.6,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,3_after,91.45,2.450000000000003,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,3_after,33.0,-21.5,2472.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,long_gap,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,long_gap,34.45454545454545,0.0,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,1_before,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,1_before,26.0,-8.454545454545453,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,2_before,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,2_before,26.0,-8.454545454545453,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,3_before,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,3_before,26.0,-8.454545454545453,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,1_after,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,1_after,71.45,36.99545454545455,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,2_after,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,2_after,73.0,38.54545454545455,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,3_after,97.2,1.2000000000000028,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,3_after,73.0,38.54545454545455,2248.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,long_gap,96.39473684210526,0.0,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,long_gap,73.0,0.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,1_before,96.0,-0.3947368421052601,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,1_before,70.85,-2.1500000000000057,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,2_before,96.0,-0.3947368421052601,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,2_before,33.55,-39.45,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,3_before,96.0,-0.3947368421052601,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,3_before,26.0,-47.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,1_after,99.0,2.60526315789474,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,1_after,73.0,0.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,2_after,99.0,2.60526315789474,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,2_after,73.0,0.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,3_after,99.0,2.60526315789474,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,3_after,73.0,0.0,3862.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,long_gap,93.0,0.0,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,long_gap,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,1_before,91.9,-1.0999999999999943,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,1_before,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,2_before,90.5,-2.5,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,2_before,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,3_before,89.75,-3.25,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,3_before,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,1_after,93.0,0.0,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,1_after,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,2_after,93.7,0.7000000000000028,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,2_after,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,3_after,95.2,2.200000000000003,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,3_after,54.0,0.0,2182.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,long_gap,97.90322580645162,0.0,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,long_gap,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,1_before,94.0,-3.903225806451616,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,1_before,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,2_before,94.0,-3.903225806451616,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,2_before,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,3_before,94.0,-3.903225806451616,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,3_before,68.5,-4.5,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,1_after,99.75,1.8467741935483843,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,1_after,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,2_after,99.0,1.0967741935483843,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,2_after,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,3_after,97.95,0.0467741935483871,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,3_after,73.0,0.0,3139.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,long_gap,90.3793103448276,0.0,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,long_gap,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,1_before,90.75,0.3706896551724128,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,1_before,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,2_before,89.0,-1.3793103448275872,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,2_before,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,3_before,89.6,-0.7793103448275929,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,3_before,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,1_after,93.2,2.820689655172416,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,1_after,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,2_after,98.55,8.17068965517241,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,2_after,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,3_after,99.5,9.120689655172413,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,3_after,100.0,0.0,2951.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,long_gap,61.31818181818182,0.0,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,long_gap,75.95454545454545,0.0,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,1_before,59.15,-2.168181818181821,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,1_before,67.45,-8.50454545454545,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,2_before,57.6,-3.7181818181818183,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,2_before,67.0,-8.954545454545453,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,3_before,52.5,-8.81818181818182,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,3_before,67.0,-8.954545454545453,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,1_after,75.15,13.831818181818186,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,1_after,76.0,0.0454545454545467,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,2_after,82.0,20.68181818181818,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,2_after,76.0,0.0454545454545467,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,3_after,82.85,21.53181818181817,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,3_after,76.0,0.0454545454545467,2159.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,long_gap,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,long_gap,95.64285714285714,0.0,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,1_before,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,1_before,94.6,-1.0428571428571445,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,2_before,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,2_before,92.4,-3.242857142857133,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,3_before,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,3_before,91.0,-4.642857142857139,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,1_after,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,1_after,89.05,-6.592857142857142,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,2_after,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,2_after,82.45,-13.192857142857136,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,3_after,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,3_after,75.1,-20.542857142857144,2858.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,long_gap,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,long_gap,93.45454545454544,0.0,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,1_before,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,1_before,96.3,2.845454545454544,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,2_before,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,2_before,99.6,6.145454545454541,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,3_before,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,3_before,99.0,5.545454545454547,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,1_after,67.85,4.849999999999994,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,1_after,91.0,-2.4545454545454533,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,2_after,69.0,6.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,2_after,95.65,2.1954545454545524,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,3_after,69.0,6.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,3_after,96.0,2.5454545454545467,2256.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,long_gap,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,long_gap,43.12,0.0,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,1_before,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,1_before,37.5,-5.619999999999997,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,2_before,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,2_before,31.8,-11.319999999999997,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,3_before,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,3_before,23.9,-19.22,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,1_after,56.35,0.3500000000000014,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,1_after,47.75,4.630000000000003,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,2_after,57.0,1.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,2_after,54.4,11.28,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,3_after,57.0,1.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,3_after,55.8,12.68,2462.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,long_gap,35.11538461538461,0.0,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,long_gap,48.42307692307692,0.0,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,1_before,37.5,2.384615384615387,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,1_before,50.35,1.9269230769230816,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,2_before,42.0,6.884615384615387,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,2_before,50.0,1.5769230769230802,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,3_before,27.25,-7.865384615384613,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,3_before,50.0,1.5769230769230802,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,1_after,42.6,7.484615384615388,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,1_after,48.0,-0.4230769230769198,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,2_after,41.75,6.634615384615387,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,2_after,48.0,-0.4230769230769198,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,3_after,35.0,-0.1153846153846132,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,3_after,48.65,0.2269230769230787,2533.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,long_gap,21.28,0.0,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,long_gap,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,1_before,35.0,13.72,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,1_before,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,2_before,35.0,13.72,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,2_before,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,3_before,35.0,13.72,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,3_before,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,1_after,28.05,6.77,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,1_after,51.65,0.6499999999999986,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,2_after,31.3,10.02,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,2_after,53.0,2.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,3_after,42.25,20.97,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,3_after,53.0,2.0,2516.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,long_gap,87.1086956521739,0.0,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,long_gap,61.19565217391305,0.0,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,1_before,58.1,-29.008695652173905,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,1_before,55.75,-5.445652173913047,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,2_before,67.3,-19.80869565217391,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,2_before,54.0,-7.195652173913047,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,3_before,71.0,-16.108695652173907,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,3_before,54.0,-7.195652173913047,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,1_after,51.1,-36.008695652173905,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,1_after,64.55,3.3543478260869506,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,2_after,18.05,-69.05869565217391,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,2_after,65.0,3.8043478260869534,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,3_after,33.15,-53.95869565217391,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,3_after,65.0,3.8043478260869534,4623.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,long_gap,79.76190476190476,0.0,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,long_gap,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,1_before,94.0,14.23809523809524,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,1_before,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,2_before,93.35,13.588095238095235,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,2_before,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,3_before,91.0,11.23809523809524,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,3_before,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,1_after,0.0,-79.76190476190476,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,1_after,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,2_after,27.5,-52.26190476190476,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,2_after,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,3_after,40.9,-38.86190476190476,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,3_after,98.0,0.0,2157.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,long_gap,85.77083333333333,0.0,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,long_gap,99.22916666666669,0.0,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,1_before,81.6,-4.170833333333334,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,1_before,99.0,-0.2291666666666714,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,2_before,71.9,-13.870833333333325,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,2_before,99.0,-0.2291666666666714,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,3_before,65.0,-20.77083333333333,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,3_before,99.0,-0.2291666666666714,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,1_after,89.2,3.4291666666666742,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,1_after,100.0,0.7708333333333286,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,2_after,90.0,4.229166666666671,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,2_after,100.0,0.7708333333333286,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,3_after,58.75,-27.02083333333333,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,3_after,100.0,0.7708333333333286,4758.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,long_gap,89.58620689655173,0.0,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,long_gap,100.0,0.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,1_before,90.21052631578948,0.6243194192377501,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,1_before,99.9,-0.0999999999999943,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,2_before,84.73684210526316,-4.849364791288565,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,2_before,99.0,-1.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,3_before,82.0,-7.5862068965517295,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,3_before,99.0,-1.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,1_after,59.8,-29.78620689655173,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,1_after,100.0,0.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,2_after,59.35,-30.236206896551728,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,2_after,99.25,-0.75,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,3_after,42.0,-47.58620689655173,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,3_after,99.0,-1.0,2919.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,long_gap,62.85,0.0,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,long_gap,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,1_before,66.0,3.1499999999999986,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,1_before,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,2_before,65.55,2.699999999999996,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,2_before,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,3_before,65.0,2.1499999999999986,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,3_before,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,1_after,54.4,-8.450000000000003,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,1_after,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,2_after,58.0,-4.850000000000001,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,2_after,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,3_after,58.0,-4.850000000000001,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,3_after,99.0,0.0,2036.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,long_gap,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,long_gap,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,1_before,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,1_before,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,2_before,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,2_before,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,3_before,83.8,0.7999999999999972,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,3_before,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,1_after,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,1_after,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,2_after,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,2_after,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,3_after,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,3_after,99.0,0.0,2171.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,long_gap,65.58064516129032,0.0,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,long_gap,63.45161290322581,0.0,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,1_before,65.0,-0.5806451612903203,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,1_before,61.75,-1.7016129032258078,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,2_before,65.0,-0.5806451612903203,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,2_before,51.4,-12.05161290322581,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,3_before,65.0,-0.5806451612903203,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,3_before,48.0,-15.451612903225808,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,1_after,66.0,0.4193548387096797,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,1_after,57.55,-5.901612903225811,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,2_after,66.0,0.4193548387096797,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,2_after,50.0,-13.451612903225808,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,3_after,66.0,0.4193548387096797,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,3_after,55.85,-7.601612903225806,3074.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,long_gap,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,long_gap,93.15,0.0,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,1_before,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,1_before,92.0,-1.1500000000000057,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,2_before,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,2_before,92.0,-1.1500000000000057,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,3_before,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,3_before,92.0,-1.1500000000000057,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,1_after,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,1_after,96.75,3.5999999999999943,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,2_after,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,2_after,97.0,3.8499999999999934,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,3_after,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,3_after,97.0,3.8499999999999934,3998.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,long_gap,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,long_gap,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,1_before,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,1_before,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,2_before,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,2_before,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,3_before,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,3_before,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,1_after,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,1_after,82.9,-14.099999999999994,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,2_after,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,2_after,74.15,-22.849999999999994,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,3_after,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,3_after,77.0,-20.0,2398.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,long_gap,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,long_gap,54.75,0.0,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,1_before,98.25,0.25,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,1_before,51.2,-3.549999999999997,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,2_before,97.2,-0.7999999999999972,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,2_before,51.0,-3.75,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,3_before,97.0,-1.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,3_before,51.0,-3.75,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,1_after,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,1_after,56.0,1.25,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,2_after,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,2_after,56.0,1.25,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,3_after,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,3_after,56.15,1.3999999999999986,2042.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,long_gap,90.0,0.0,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,long_gap,59.80952380952381,0.0,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,1_before,88.55,-1.4500000000000028,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,1_before,58.95,-0.8595238095238074,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,2_before,81.95,-8.049999999999997,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,2_before,55.35,-4.459523809523809,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,3_before,92.95,2.950000000000003,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,3_before,55.0,-4.80952380952381,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,1_after,90.0,0.0,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,1_after,60.55,0.740476190476187,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,2_after,90.6,0.5999999999999943,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,2_after,61.0,1.1904761904761898,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,3_after,94.75,4.75,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,3_after,61.25,1.4404761904761898,2035.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,long_gap,98.0,0.0,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,long_gap,82.5,0.0,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,1_before,97.8,-0.2000000000000028,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,1_before,93.65,11.150000000000006,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,2_before,98.55,0.5499999999999972,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,2_before,94.0,11.5,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,3_before,99.0,1.0,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,3_before,94.0,11.5,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,1_after,98.6,0.5999999999999943,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,1_after,65.1,-17.400000000000006,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,2_after,98.65,0.6500000000000057,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,2_after,65.0,-17.5,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,3_after,98.5,0.5,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,3_after,65.5,-17.0,3180.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,long_gap,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,long_gap,72.0,0.0,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,1_before,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,1_before,71.5,-0.5,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,2_before,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,2_before,71.5,-0.5,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,3_before,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,3_before,72.0,0.0,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,1_after,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,1_after,72.0,0.0,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,2_after,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,2_after,71.65,-0.3499999999999943,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,3_after,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,3_after,71.0,-1.0,2068.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,long_gap,88.48571428571428,0.0,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,long_gap,80.54285714285714,0.0,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,1_before,80.45,-8.035714285714278,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,1_before,76.75,-3.792857142857145,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,2_before,76.75,-11.73571428571428,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,2_before,75.35,-5.19285714285715,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,3_before,86.5,-1.9857142857142804,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,3_before,75.5,-5.0428571428571445,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,1_after,91.0,2.51428571428572,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,1_after,71.95,-8.592857142857142,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,2_after,91.85,3.364285714285714,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,2_after,76.0,-4.5428571428571445,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,3_after,93.0,4.51428571428572,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,3_after,74.2,-6.342857142857142,3526.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,long_gap,73.97435897435898,0.0,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,long_gap,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,1_before,91.2,17.225641025641025,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,1_before,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,2_before,95.25,21.275641025641026,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,2_before,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,3_before,95.0,21.025641025641026,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,3_before,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,1_after,55.7,-18.27435897435897,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,1_after,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,2_after,55.95,-18.02435897435897,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,2_after,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,3_after,58.3,-15.67435897435898,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,3_after,79.0,0.0,3904.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,long_gap,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,long_gap,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,1_before,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,1_before,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,2_before,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,2_before,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,3_before,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,3_before,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,1_after,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,1_after,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,2_after,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,2_after,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,3_after,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,3_after,75.0,0.0,4354.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,long_gap,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,long_gap,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,1_before,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,1_before,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,2_before,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,2_before,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,3_before,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,3_before,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,1_after,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,1_after,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,2_after,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,2_after,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,3_after,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,3_after,75.0,0.0,3798.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,long_gap,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,long_gap,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,1_before,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,1_before,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,2_before,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,2_before,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,3_before,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,3_before,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,1_after,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,1_after,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,2_after,50.55,0.5499999999999972,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,2_after,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,3_after,49.55,-0.4500000000000028,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,3_after,99.0,0.0,2118.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,long_gap,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,long_gap,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,1_before,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,1_before,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,2_before,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,2_before,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,3_before,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,3_before,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,1_after,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,1_after,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,2_after,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,2_after,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,3_after,48.65,-0.3500000000000014,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,3_after,99.0,0.0,3106.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,long_gap,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,long_gap,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,1_before,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,1_before,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,2_before,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,2_before,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,3_before,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,3_before,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,1_after,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,1_after,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,2_after,95.65,-4.349999999999994,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,2_after,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,3_after,92.4,-7.599999999999994,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,3_after,84.0,0.0,3017.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,long_gap,77.125,0.0,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,long_gap,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,1_before,74.0,-3.125,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,1_before,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,2_before,74.0,-3.125,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,2_before,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,3_before,74.0,-3.125,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,3_before,58.10526315789474,-1.89473684210526,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,1_after,65.7,-11.424999999999995,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,1_after,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,2_after,62.9,-14.225,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,2_after,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,3_after,66.45,-10.674999999999995,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,3_after,60.0,0.0,2379.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,long_gap,74.10526315789474,0.0,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,long_gap,63.0,0.0,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,1_before,60.36842105263158,-13.736842105263158,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,1_before,59.9,-3.1000000000000014,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,2_before,58.0,-16.10526315789474,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,2_before,42.05,-20.950000000000003,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,3_before,57.78947368421053,-16.315789473684212,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,3_before,45.7,-17.299999999999997,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,1_after,83.8,9.694736842105256,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,1_after,62.6,-0.3999999999999986,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,2_after,71.75,-2.35526315789474,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,2_after,51.3,-11.700000000000005,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,3_after,65.4,-8.705263157894734,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,3_after,51.0,-12.0,3889.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,long_gap,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,long_gap,69.0909090909091,0.0,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,1_before,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,1_before,69.0,-0.0909090909090934,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,2_before,99.85,-0.1500000000000056,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,2_before,68.0,-1.0909090909090935,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,3_before,98.45,-1.5499999999999972,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,3_before,72.6,3.509090909090901,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,1_after,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,1_after,75.3,6.209090909090904,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,2_after,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,2_after,76.0,6.9090909090909065,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,3_after,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,3_after,76.0,6.9090909090909065,2162.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,long_gap,85.9,0.0,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,long_gap,94.05,0.0,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,1_before,84.0,-1.9000000000000057,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,1_before,91.0,-3.049999999999997,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,2_before,84.2,-1.7000000000000028,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,2_before,91.0,-3.049999999999997,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,3_before,85.0,-0.9000000000000057,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,3_before,90.25,-3.799999999999997,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,1_after,91.95,6.049999999999997,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,1_after,100.0,5.950000000000003,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,2_after,97.4,11.5,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,2_after,100.0,5.950000000000003,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,3_after,97.0,11.099999999999994,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,3_after,100.0,5.950000000000003,2056.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,long_gap,85.95652173913044,0.0,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,long_gap,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,1_before,81.05,-4.90652173913044,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,1_before,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,2_before,81.0,-4.956521739130437,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,2_before,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,3_before,81.15,-4.806521739130432,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,3_before,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,1_after,87.0,1.0434782608695627,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,1_after,95.0,1.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,2_after,84.9,-1.0565217391304316,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,2_after,95.0,1.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,3_after,81.6,-4.356521739130443,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,3_after,95.0,1.0,2245.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,long_gap,90.88,0.0,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,long_gap,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,1_before,88.0,-2.8799999999999955,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,1_before,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,2_before,82.05,-8.829999999999998,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,2_before,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,3_before,81.75,-9.129999999999995,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,3_before,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,1_after,91.0,0.1200000000000045,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,1_after,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,2_after,91.0,0.1200000000000045,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,2_after,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,3_after,91.0,0.1200000000000045,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,3_after,95.0,0.0,2479.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,long_gap,94.0,0.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,long_gap,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,1_before,84.8,-9.200000000000005,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,1_before,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,2_before,91.3,-2.700000000000003,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,2_before,97.9,-0.0999999999999943,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,3_before,91.0,-3.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,3_before,95.1,-2.9000000000000057,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,1_after,93.45,-0.5499999999999972,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,1_after,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,2_after,93.0,-1.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,2_after,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,3_after,93.0,-1.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,3_after,98.0,0.0,2210.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,long_gap,87.88888888888889,0.0,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,long_gap,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,1_before,85.0,-2.8888888888888857,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,1_before,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,2_before,82.5,-5.388888888888886,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,2_before,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,3_before,81.0,-6.888888888888886,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,3_before,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,1_after,95.0,7.111111111111114,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,1_after,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,2_after,94.3,6.411111111111111,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,2_after,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,3_after,94.2,6.311111111111117,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,3_after,100.0,0.0,2711.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,long_gap,83.92857142857143,0.0,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,long_gap,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,1_before,78.0,-5.928571428571431,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,1_before,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,2_before,78.0,-5.928571428571431,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,2_before,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,3_before,78.0,-5.928571428571431,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,3_before,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,1_after,85.0,1.0714285714285694,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,1_after,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,2_after,85.0,1.0714285714285694,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,2_after,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,3_after,86.3,2.3714285714285666,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,3_after,100.0,0.0,2748.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,long_gap,95.38709677419357,0.0,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,long_gap,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,1_before,85.0,-10.387096774193552,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,1_before,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,2_before,85.0,-10.387096774193552,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,2_before,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,3_before,85.0,-10.387096774193552,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,3_before,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,1_after,100.0,4.612903225806448,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,1_after,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,2_after,100.0,4.612903225806448,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,2_after,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,3_after,100.0,4.612903225806448,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,3_after,100.0,0.0,3155.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,long_gap,70.0,0.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,long_gap,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,1_before,70.35,0.3499999999999943,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,1_before,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,2_before,71.45,1.4500000000000028,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,2_before,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,3_before,71.0,1.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,3_before,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,1_after,70.1,0.0999999999999943,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,1_after,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,2_after,71.0,1.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,2_after,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,3_after,71.0,1.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,3_after,89.0,0.0,2268.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,long_gap,62.9,0.0,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,long_gap,90.75,0.0,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,1_before,68.45,5.550000000000004,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,1_before,90.3,-0.4500000000000028,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,2_before,69.4,6.500000000000007,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,2_before,92.2,1.4500000000000028,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,3_before,69.0,6.100000000000001,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,3_before,91.3,0.5499999999999972,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,1_after,63.47368421052632,0.5736842105263165,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,1_after,83.15,-7.599999999999994,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,2_after,79.57142857142857,16.67142857142857,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,2_after,76.85,-13.900000000000006,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,3_after,79.0,16.1,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,3_after,71.7,-19.05,2007.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,long_gap,67.75,0.0,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,long_gap,58.130434782608695,0.0,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,1_before,64.0,-3.75,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,1_before,58.75,0.6195652173913047,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,2_before,64.0,-3.75,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,2_before,61.35,3.219565217391306,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,3_before,64.0,-3.75,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,3_before,64.1,5.969565217391299,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,1_after,69.0,1.25,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,1_after,43.0,-15.130434782608695,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,2_after,69.0,1.25,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,2_after,47.35,-10.780434782608694,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,3_after,69.0,1.25,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,3_after,55.0,-3.130434782608696,2299.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,long_gap,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,long_gap,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,1_before,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,1_before,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,2_before,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,2_before,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,3_before,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,3_before,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,1_after,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,1_after,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,2_after,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,2_after,99.7,-0.2999999999999971,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,3_after,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,3_after,98.8,-1.2000000000000028,3264.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,long_gap,74.22727272727273,0.0,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,long_gap,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,1_before,74.0,-0.2272727272727337,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,1_before,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,2_before,74.4,0.1727272727272719,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,2_before,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,3_before,76.95,2.722727272727269,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,3_before,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,1_after,76.7,2.472727272727269,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,1_after,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,2_after,77.0,2.7727272727272663,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,2_after,70.4,-11.599999999999994,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,3_after,79.0,4.772727272727266,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,3_after,69.8,-12.200000000000005,2179.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,long_gap,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,long_gap,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,1_before,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,1_before,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,2_before,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,2_before,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,3_before,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,3_before,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,1_after,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,1_after,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,2_after,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,2_after,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,3_after,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,3_after,95.0,0.0,2178.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,long_gap,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,long_gap,80.36,0.0,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,1_before,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,1_before,71.8,-8.560000000000002,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,2_before,49.15,0.1499999999999985,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,2_before,60.8,-19.56,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,3_before,57.2,8.200000000000003,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,3_before,68.7,-11.659999999999997,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,1_after,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,1_after,72.55,-7.810000000000002,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,2_after,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,2_after,71.0,-9.36,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,3_after,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,3_after,72.5,-7.859999999999999,2454.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,long_gap,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,long_gap,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,1_before,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,1_before,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,2_before,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,2_before,81.3,-3.700000000000003,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,3_before,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,3_before,79.0,-6.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,1_after,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,1_after,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,2_after,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,2_after,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,3_after,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,3_after,85.0,0.0,3616.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,long_gap,66.62962962962963,0.0,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,long_gap,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,1_before,76.9,10.270370370370372,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,1_before,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,2_before,79.0,12.370370370370368,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,2_before,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,3_before,79.0,12.370370370370368,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,3_before,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,1_after,51.3,-15.329629629629636,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,1_after,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,2_after,49.0,-17.629629629629633,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,2_after,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,3_after,49.0,-17.629629629629633,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,3_after,85.0,0.0,2687.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,long_gap,46.96153846153846,0.0,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,long_gap,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,1_before,49.0,2.03846153846154,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,1_before,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,2_before,49.0,2.03846153846154,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,2_before,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,3_before,49.15,2.1884615384615387,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,3_before,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,1_after,43.4,-3.5615384615384613,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,1_after,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,2_after,43.0,-3.96153846153846,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,2_after,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,3_after,43.95,-3.011538461538457,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,3_after,85.0,0.0,2630.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,long_gap,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,long_gap,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,1_before,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,1_before,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,2_before,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,2_before,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,3_before,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,3_before,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,1_after,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,1_after,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,2_after,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,2_after,87.1,2.0999999999999943,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,3_after,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,3_after,88.0,3.0,2311.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,long_gap,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,long_gap,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,1_before,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,1_before,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,2_before,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,2_before,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,3_before,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,3_before,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,1_after,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,1_after,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,2_after,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,2_after,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,3_after,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,3_after,88.0,0.0,4614.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,long_gap,81.03225806451613,0.0,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,long_gap,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,1_before,70.2,-10.832258064516123,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,1_before,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,2_before,65.0,-16.032258064516128,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,2_before,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,3_before,65.0,-16.032258064516128,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,3_before,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,1_after,81.0,-0.0322580645161281,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,1_after,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,2_after,81.0,-0.0322580645161281,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,2_after,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,3_after,81.0,-0.0322580645161281,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,3_after,88.0,0.0,6261.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,long_gap,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,long_gap,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,1_before,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,1_before,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,2_before,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,2_before,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,3_before,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,3_before,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,1_after,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,1_after,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,2_after,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,2_after,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,3_after,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,3_after,88.0,0.0,2094.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,long_gap,65.16666666666667,0.0,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,long_gap,62.92857142857143,0.0,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,1_before,63.0,-2.1666666666666714,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,1_before,52.55,-10.378571428571432,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,2_before,63.0,-2.1666666666666714,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,2_before,51.0,-11.92857142857143,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,3_before,62.15,-3.016666666666673,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,3_before,51.0,-11.92857142857143,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,1_after,66.95,1.7833333333333314,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,1_after,64.04545454545455,1.116883116883116,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,2_after,67.0,1.8333333333333288,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,2_after,65.0,2.0714285714285694,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,3_after,66.0,0.8333333333333286,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,3_after,66.15,3.221428571428575,2940.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,long_gap,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,long_gap,66.81818181818181,0.0,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,1_before,89.95,-1.0499999999999972,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,1_before,60.05,-6.768181818181816,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,2_before,90.0,-1.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,2_before,60.0,-6.818181818181813,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,3_before,90.0,-1.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,3_before,60.0,-6.818181818181813,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,1_after,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,1_after,71.0,4.181818181818187,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,2_after,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,2_after,71.0,4.181818181818187,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,3_after,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,3_after,71.0,4.181818181818187,2200.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,long_gap,89.95652173913044,0.0,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,long_gap,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,1_before,85.8,-4.15652173913044,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,1_before,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,2_before,85.0,-4.956521739130437,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,2_before,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,3_before,89.6,-0.3565217391304429,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,3_before,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,1_after,90.0,0.0434782608695627,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,1_after,94.65,-2.3499999999999943,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,2_after,90.6,0.6434782608695571,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,2_after,80.7,-16.299999999999997,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,3_after,97.3,7.34347826086956,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,3_after,79.0,-18.0,4627.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,long_gap,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,long_gap,39.42307692307692,0.0,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,1_before,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,1_before,33.4,-6.023076923076921,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,2_before,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,2_before,31.0,-8.42307692307692,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,3_before,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,3_before,31.0,-8.42307692307692,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,1_after,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,1_after,41.55,2.1269230769230774,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,2_after,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,2_after,42.0,2.57692307692308,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,3_after,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,3_after,42.2,2.776923076923083,2576.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,long_gap,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,long_gap,71.0,0.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,1_before,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,1_before,70.0,-1.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,2_before,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,2_before,70.0,-1.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,3_before,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,3_before,70.0,-1.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,1_after,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,1_after,71.0,0.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,2_after,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,2_after,71.0,0.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,3_after,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,3_after,71.0,0.0,2167.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,long_gap,67.91428571428571,0.0,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,long_gap,68.74285714285715,0.0,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,1_before,64.6,-3.3142857142857167,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,1_before,65.6,-3.142857142857153,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,2_before,63.15,-4.7642857142857125,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,2_before,64.0,-4.742857142857147,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,3_before,63.0,-4.914285714285711,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,3_before,64.0,-4.742857142857147,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,1_after,69.95,2.035714285714292,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,1_after,69.0,0.2571428571428527,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,2_after,72.9,4.985714285714295,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,2_after,74.85,6.107142857142847,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,3_after,71.2,3.285714285714292,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,3_after,77.45,8.707142857142856,3509.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,long_gap,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,long_gap,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,1_before,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,1_before,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,2_before,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,2_before,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,3_before,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,3_before,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,1_after,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,1_after,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,2_after,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,2_after,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,3_after,80.7,-0.2999999999999971,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,3_after,84.0,0.0,2410.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,long_gap,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,long_gap,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,1_before,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,1_before,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,2_before,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,2_before,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,3_before,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,3_before,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,1_after,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,1_after,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,2_after,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,2_after,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,3_after,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,3_after,84.2,1.2000000000000028,3235.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,long_gap,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,long_gap,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,1_before,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,1_before,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,2_before,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,2_before,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,3_before,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,3_before,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,1_after,65.2,0.2000000000000028,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,1_after,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,2_after,65.4,0.4000000000000057,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,2_after,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,3_after,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,3_after,81.0,0.0,2906.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,long_gap,99.1818181818182,0.0,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,long_gap,63.81818181818182,0.0,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,1_before,89.25,-9.931818181818189,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,1_before,56.5,-7.31818181818182,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,2_before,87.95,-11.231818181818184,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,2_before,56.0,-7.81818181818182,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,3_before,87.0,-12.181818181818189,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,3_before,60.35,-3.4681818181818187,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,1_after,98.7,-0.4818181818181841,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,1_after,64.0,0.1818181818181798,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,2_after,92.45,-6.731818181818184,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,2_after,64.0,0.1818181818181798,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,3_after,84.7,-14.481818181818184,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,3_after,64.0,0.1818181818181798,2149.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,long_gap,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,long_gap,100.0,0.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,1_before,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,1_before,99.0,-1.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,2_before,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,2_before,99.0,-1.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,3_before,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,3_before,98.55,-1.4500000000000028,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,1_after,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,1_after,100.0,0.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,2_after,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,2_after,100.0,0.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,3_after,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,3_after,100.0,0.0,2007.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,long_gap,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,long_gap,80.0,0.0,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,1_before,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,1_before,80.0,0.0,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,2_before,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,2_before,79.8,-0.2000000000000028,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,3_before,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,3_before,79.9,-0.0999999999999943,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,1_after,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,1_after,80.0,0.0,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,2_after,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,2_after,80.25,0.25,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,3_after,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,3_after,81.0,1.0,2556.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,long_gap,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,long_gap,70.0,0.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,1_before,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,1_before,70.0,0.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,2_before,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,2_before,70.0,0.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,3_before,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,3_before,69.35,-0.6500000000000057,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,1_after,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,1_after,71.0,1.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,2_after,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,2_after,71.0,1.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,3_after,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,3_after,71.0,1.0,2148.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,long_gap,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,long_gap,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,1_before,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,1_before,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,2_before,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,2_before,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,3_before,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,3_before,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,1_after,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,1_after,78.05,0.0499999999999971,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,2_after,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,2_after,79.75,1.75,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,3_after,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,3_after,80.2,2.200000000000003,2049.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,long_gap,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,long_gap,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,1_before,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,1_before,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,2_before,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,2_before,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,3_before,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,3_before,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,1_after,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,1_after,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,2_after,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,2_after,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,3_after,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,3_after,93.0,0.0,2072.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,long_gap,90.95454545454544,0.0,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,long_gap,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,1_before,93.0,2.0454545454545467,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,1_before,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,2_before,93.0,2.0454545454545467,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,2_before,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,3_before,93.3,2.345454545454544,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,3_before,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,1_after,88.35,-2.604545454545459,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,1_after,73.15,-2.8499999999999943,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,2_after,88.0,-2.9545454545454533,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,2_after,49.7,-26.3,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,3_after,87.25,-3.704545454545453,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,3_after,49.0,-27.0,2218.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,long_gap,97.3913043478261,0.0,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,long_gap,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,1_before,95.0,-2.391304347826093,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,1_before,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,2_before,95.0,-2.391304347826093,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,2_before,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,3_before,95.0,-2.391304347826093,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,3_before,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,1_after,100.0,2.608695652173907,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,1_after,55.35,1.3500000000000014,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,2_after,100.0,2.608695652173907,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,2_after,56.0,2.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,3_after,100.0,2.608695652173907,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,3_after,56.0,2.0,2241.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,long_gap,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,long_gap,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,1_before,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,1_before,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,2_before,95.15,0.1500000000000056,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,2_before,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,3_before,97.4,2.4000000000000057,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,3_before,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,1_after,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,1_after,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,2_after,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,2_after,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,3_after,98.2,3.200000000000003,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,3_after,58.0,0.0,2252.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,long_gap,93.0,0.0,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,long_gap,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,1_before,90.85,-2.1500000000000057,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,1_before,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,2_before,89.8,-3.200000000000003,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,2_before,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,3_before,90.0,-3.0,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,3_before,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,1_after,82.0,-11.0,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,1_after,83.2,1.2000000000000028,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,2_after,68.85,-24.15000000000001,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,2_after,82.15,0.1500000000000056,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,3_after,60.85,-32.15,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,3_after,82.0,0.0,2109.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,long_gap,78.0,0.0,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,long_gap,58.71428571428572,0.0,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,1_before,78.0,0.0,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,1_before,55.8,-2.914285714285718,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,2_before,75.7,-2.299999999999997,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,2_before,56.0,-2.7142857142857157,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,3_before,75.05,-2.950000000000003,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,3_before,53.95,-4.7642857142857125,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,1_after,78.8,0.7999999999999972,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,1_after,62.75,4.035714285714285,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,2_after,79.0,1.0,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,2_after,63.4,4.685714285714283,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,3_after,79.65,1.6500000000000057,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,3_after,62.95,4.235714285714288,2053.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,long_gap,96.8095238095238,0.0,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,long_gap,50.23809523809524,0.0,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,1_before,97.0,0.1904761904761898,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,1_before,63.8,13.561904761904756,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,2_before,96.05,-0.759523809523813,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,2_before,71.0,20.76190476190476,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,3_before,94.0,-2.80952380952381,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,3_before,71.0,20.76190476190476,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,1_after,96.0,-0.8095238095238102,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,1_after,44.0,-6.238095238095241,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,2_after,95.15,-1.6595238095238043,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,2_after,44.0,-6.238095238095241,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,3_after,94.35,-2.459523809523816,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,3_after,44.0,-6.238095238095241,2040.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,long_gap,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,long_gap,73.13636363636364,0.0,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,1_before,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,1_before,72.0,-1.1363636363636402,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,2_before,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,2_before,71.9,-1.2363636363636346,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,3_before,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,3_before,69.45,-3.686363636363637,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,1_after,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,1_after,73.4,0.2636363636363654,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,2_after,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,2_after,74.6,1.463636363636354,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,3_after,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,3_after,75.0,1.86363636363636,2228.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,long_gap,76.91304347826087,0.0,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,long_gap,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,1_before,75.15,-1.7630434782608688,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,1_before,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,2_before,75.0,-1.9130434782608745,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,2_before,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,3_before,75.0,-1.9130434782608745,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,3_before,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,1_after,77.0,0.0869565217391254,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,1_after,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,2_after,77.0,0.0869565217391254,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,2_after,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,3_after,77.0,0.0869565217391254,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,3_after,83.0,0.0,2218.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,long_gap,53.0625,0.0,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,long_gap,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,1_before,55.5,2.4375,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,1_before,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,2_before,59.25,6.1875,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,2_before,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,3_before,65.45,12.387500000000005,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,3_before,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,1_after,40.65,-12.4125,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,1_after,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,2_after,45.35,-7.712499999999999,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,2_after,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,3_after,59.05,5.987499999999997,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,3_after,82.0,0.0,3126.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,long_gap,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,long_gap,71.34,0.0,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,1_before,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,1_before,34.6,-36.74,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,2_before,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,2_before,68.95,-2.390000000000001,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,3_before,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,3_before,88.0,16.659999999999997,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,1_after,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,1_after,98.5,27.16,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,2_after,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,2_after,98.0,26.66,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,3_after,63.85,1.8500000000000012,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,3_after,98.0,26.66,4982.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,long_gap,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,long_gap,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,1_before,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,1_before,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,2_before,78.85,-1.1500000000000057,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,2_before,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,3_before,70.4,-9.599999999999994,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,3_before,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,1_after,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,1_after,66.0,-29.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,2_after,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,2_after,59.0,-36.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,3_after,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,3_after,53.35,-41.65,2487.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,long_gap,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,long_gap,65.48148148148148,0.0,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,1_before,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,1_before,69.0,3.518518518518519,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,2_before,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,2_before,69.0,3.518518518518519,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,3_before,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,3_before,61.3,-4.181481481481484,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,1_after,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,1_after,31.65,-33.83148148148148,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,2_after,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,2_after,26.55,-38.93148148148149,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,3_after,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,3_after,25.8,-39.68148148148149,2693.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,long_gap,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,long_gap,84.85714285714286,0.0,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,1_before,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,1_before,80.0,-4.857142857142861,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,2_before,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,2_before,80.0,-4.857142857142861,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,3_before,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,3_before,74.5,-10.35714285714286,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,1_after,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,1_after,85.0,0.1428571428571388,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,2_after,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,2_after,85.0,0.1428571428571388,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,3_after,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,3_after,77.5,-7.357142857142861,2052.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,long_gap,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,long_gap,96.3095238095238,0.0,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,1_before,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,1_before,93.0,-3.30952380952381,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,2_before,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,2_before,93.0,-3.30952380952381,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,3_before,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,3_before,93.0,-3.30952380952381,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,1_after,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,1_after,98.0,1.6904761904761898,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,2_after,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,2_after,98.0,1.6904761904761898,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,3_after,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,3_after,73.7,-22.609523809523807,4187.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,long_gap,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,long_gap,87.59615384615384,0.0,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,1_before,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,1_before,95.45,7.853846153846163,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,2_before,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,2_before,98.65,11.053846153846166,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,3_before,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,3_before,96.6842105263158,9.088056680161955,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,1_after,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,1_after,76.85,-10.746153846153844,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,2_after,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,2_after,74.0,-13.59615384615384,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,3_after,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,3_after,72.4,-15.196153846153834,5152.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,long_gap,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,long_gap,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,1_before,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,1_before,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,2_before,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,2_before,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,3_before,87.85,5.849999999999994,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,3_before,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,1_after,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,1_after,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,2_after,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,2_after,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,3_after,82.45,0.4500000000000028,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,3_after,80.0,0.0,2265.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,long_gap,70.0,0.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,long_gap,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,1_before,86.9,16.900000000000006,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,1_before,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,2_before,82.0,12.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,2_before,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,3_before,82.0,12.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,3_before,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,1_after,66.0,-4.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,1_after,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,2_after,66.0,-4.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,2_after,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,3_after,66.0,-4.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,3_after,80.0,0.0,2028.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,long_gap,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,long_gap,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,1_before,75.05,1.0499999999999972,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,1_before,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,2_before,86.3,12.299999999999995,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,2_before,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,3_before,89.0,15.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,3_before,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,1_after,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,1_after,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,2_after,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,2_after,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,3_after,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,3_after,97.0,0.0,3308.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,long_gap,67.42,0.0,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,long_gap,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,1_before,74.2,6.780000000000001,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,1_before,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,2_before,82.5,15.079999999999998,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,2_before,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,3_before,85.0,17.58,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,3_before,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,1_after,66.0,-1.4200000000000017,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,1_after,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,2_after,66.0,-1.4200000000000017,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,2_after,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,3_after,66.0,-1.4200000000000017,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,3_after,100.0,0.0,5027.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,long_gap,73.63636363636364,0.0,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,long_gap,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,1_before,68.85,-4.786363636363646,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,1_before,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,2_before,68.0,-5.63636363636364,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,2_before,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,3_before,68.0,-5.63636363636364,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,3_before,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,1_after,75.0,1.3636363636363598,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,1_after,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,2_after,75.0,1.3636363636363598,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,2_after,90.85,3.8499999999999934,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,3_after,75.0,1.3636363636363598,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,3_after,92.4,5.400000000000006,2248.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,long_gap,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,long_gap,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,1_before,74.05,-0.9500000000000028,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,1_before,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,2_before,69.7,-5.299999999999997,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,2_before,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,3_before,68.0,-7.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,3_before,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,1_after,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,1_after,93.78947368421052,6.78947368421052,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,2_after,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,2_after,85.73684210526316,-1.2631578947368354,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,3_after,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,3_after,70.52631578947368,-16.473684210526315,2298.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,long_gap,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,long_gap,90.0,0.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,1_before,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,1_before,89.4,-0.5999999999999943,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,2_before,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,2_before,82.25,-7.75,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,3_before,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,3_before,82.0,-8.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,1_after,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,1_after,90.0,0.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,2_after,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,2_after,90.0,0.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,3_after,87.15,9.150000000000006,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,3_after,90.0,0.0,2470.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,long_gap,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,long_gap,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,1_before,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,1_before,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,2_before,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,2_before,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,3_before,92.15,-1.8499999999999943,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,3_before,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,1_after,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,1_after,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,2_after,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,2_after,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,3_after,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,3_after,90.0,0.0,3268.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,long_gap,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,long_gap,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,1_before,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,1_before,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,2_before,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,2_before,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,3_before,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,3_before,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,1_after,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,1_after,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,2_after,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,2_after,92.9,2.9000000000000057,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,3_after,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,3_after,97.25,7.25,2067.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,long_gap,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,long_gap,73.0,0.0,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,1_before,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,1_before,72.1,-0.9000000000000057,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,2_before,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,2_before,60.5,-12.5,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,3_before,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,3_before,58.95,-14.049999999999995,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,1_after,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,1_after,77.5,4.5,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,2_after,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,2_after,84.9,11.900000000000006,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,3_after,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,3_after,85.05,12.049999999999995,2273.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,long_gap,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,long_gap,58.81818181818182,0.0,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,1_before,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,1_before,57.0,-1.81818181818182,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,2_before,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,2_before,57.0,-1.81818181818182,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,3_before,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,3_before,57.0,-1.81818181818182,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,1_after,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,1_after,64.0,5.18181818181818,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,2_after,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,2_after,65.0,6.18181818181818,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,3_after,90.75,2.75,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,3_after,65.0,6.18181818181818,2175.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,long_gap,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,long_gap,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,1_before,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,1_before,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,2_before,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,2_before,59.35,-1.6499999999999986,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,3_before,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,3_before,51.0,-10.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,1_after,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,1_after,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,2_after,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,2_after,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,3_after,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,3_after,61.0,0.0,3735.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,long_gap,92.70833333333331,0.0,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,long_gap,72.83333333333333,0.0,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,1_before,89.7,-3.0083333333333258,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,1_before,68.0,-4.833333333333329,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,2_before,89.9,-2.808333333333323,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,2_before,65.85,-6.983333333333334,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,3_before,88.95,-3.7583333333333258,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,3_before,65.0,-7.833333333333329,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,1_after,94.0,1.2916666666666714,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,1_after,75.7,2.8666666666666742,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,2_after,94.0,1.2916666666666714,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,2_after,75.0,2.1666666666666714,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,3_after,94.0,1.2916666666666714,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,3_after,75.0,2.1666666666666714,2396.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,long_gap,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,long_gap,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,1_before,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,1_before,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,2_before,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,2_before,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,3_before,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,3_before,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,1_after,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,1_after,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,2_after,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,2_after,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,3_after,93.25,-0.75,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,3_after,75.0,0.0,2594.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,long_gap,93.32,0.0,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,long_gap,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,1_before,94.0,0.6800000000000068,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,1_before,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,2_before,94.0,0.6800000000000068,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,2_before,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,3_before,94.0,0.6800000000000068,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,3_before,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,1_after,93.1,-0.2199999999999988,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,1_after,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,2_after,93.45,0.1300000000000096,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,2_after,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,3_after,92.0,-1.3199999999999932,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,3_after,75.0,0.0,2516.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,long_gap,65.07317073170732,0.0,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,long_gap,99.8780487804878,0.0,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,1_before,57.3,-7.773170731707324,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,1_before,100.0,0.1219512195121979,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,2_before,59.45,-5.623170731707319,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,2_before,100.0,0.1219512195121979,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,3_before,64.6,-0.4731707317073272,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,3_before,100.0,0.1219512195121979,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,1_after,76.3,11.226829268292676,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,1_after,99.0,-0.8780487804878021,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,2_after,79.4,14.326829268292684,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,2_after,99.0,-0.8780487804878021,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,3_after,82.0,16.92682926829268,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,3_after,99.0,-0.8780487804878021,4145.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,long_gap,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,long_gap,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,1_before,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,1_before,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,2_before,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,2_before,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,3_before,97.4,0.4000000000000057,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,3_before,88.95,2.950000000000003,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,1_after,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,1_after,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,2_after,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,2_after,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,3_after,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,3_after,86.0,0.0,5813.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,long_gap,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,long_gap,23.84210526315789,0.0,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,1_before,62.95,-1.0499999999999972,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,1_before,25.0,1.1578947368421062,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,2_before,66.55,2.549999999999997,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,2_before,25.0,1.1578947368421062,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,3_before,67.0,3.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,3_before,25.0,1.1578947368421062,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,1_after,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,1_after,17.7,-6.142105263157895,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,2_after,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,2_after,46.65,22.807894736842105,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,3_after,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,3_after,55.0,31.157894736842103,3771.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,long_gap,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,long_gap,44.629629629629626,0.0,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,1_before,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,1_before,41.0,-3.629629629629626,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,2_before,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,2_before,41.0,-3.629629629629626,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,3_before,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,3_before,41.0,-3.629629629629626,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,1_after,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,1_after,59.0,14.370370370370374,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,2_after,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,2_after,59.0,14.370370370370374,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,3_after,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,3_after,59.0,14.370370370370374,2683.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,long_gap,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,long_gap,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,1_before,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,1_before,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,2_before,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,2_before,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,3_before,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,3_before,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,1_after,20.5,3.5,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,1_after,32.65,-2.3500000000000014,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,2_after,21.0,4.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,2_after,29.0,-6.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,3_after,21.0,4.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,3_after,29.0,-6.0,2405.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,long_gap,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,long_gap,61.94871794871795,0.0,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,1_before,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,1_before,52.0,-9.948717948717947,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,2_before,15.65,-4.35,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,2_before,52.0,-9.948717948717947,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,3_before,12.55,-7.449999999999999,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,3_before,52.0,-9.948717948717947,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,1_after,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,1_after,85.0,23.05128205128205,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,2_after,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,2_after,85.0,23.05128205128205,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,3_after,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,3_after,85.0,23.05128205128205,3914.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,long_gap,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,long_gap,67.0,0.0,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,1_before,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,1_before,49.4,-17.6,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,2_before,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,2_before,50.3,-16.700000000000003,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,3_before,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,3_before,80.3,13.299999999999995,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,1_after,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,1_after,97.05,30.05,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,2_after,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,2_after,99.0,32.0,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,3_after,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,3_after,99.0,32.0,3747.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,long_gap,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,long_gap,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,1_before,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,1_before,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,2_before,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,2_before,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,3_before,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,3_before,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,1_after,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,1_after,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,2_after,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,2_after,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,3_after,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,3_after,99.0,0.0,2391.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,long_gap,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,long_gap,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,1_before,25.85,-0.1499999999999985,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,1_before,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,2_before,20.0,-6.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,2_before,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,3_before,20.0,-6.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,3_before,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,1_after,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,1_after,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,2_after,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,2_after,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,3_after,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,3_after,99.0,0.0,4725.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,long_gap,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,long_gap,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,1_before,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,1_before,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,2_before,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,2_before,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,3_before,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,3_before,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,1_after,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,1_after,95.1,-3.9000000000000057,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,2_after,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,2_after,93.0,-6.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,3_after,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,3_after,94.8,-4.200000000000003,2600.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,long_gap,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,long_gap,99.05555555555556,0.0,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,1_before,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,1_before,99.0,-0.0555555555555571,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,2_before,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,2_before,99.0,-0.0555555555555571,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,3_before,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,3_before,99.0,-0.0555555555555571,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,1_after,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,1_after,100.0,0.9444444444444428,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,2_after,17.3,-2.6999999999999997,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,2_after,100.0,0.9444444444444428,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,3_after,17.0,-3.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,3_after,100.0,0.9444444444444428,5463.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,long_gap,46.61904761904762,0.0,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,long_gap,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,1_before,34.0,-12.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,1_before,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,2_before,12.0,-34.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,2_before,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,3_before,12.0,-34.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,3_before,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,1_after,17.0,-29.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,1_after,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,2_after,17.0,-29.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,2_after,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,3_after,17.0,-29.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,3_after,100.0,0.0,2065.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,long_gap,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,long_gap,41.0,0.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,1_before,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,1_before,41.0,0.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,2_before,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,2_before,38.1,-2.8999999999999986,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,3_before,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,3_before,33.0,-8.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,1_after,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,1_after,42.25,1.25,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,2_after,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,2_after,45.0,4.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,3_after,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,3_after,45.5,4.5,2403.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,long_gap,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,long_gap,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,1_before,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,1_before,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,2_before,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,2_before,49.8,-12.200000000000005,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,3_before,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,3_before,46.0,-16.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,1_after,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,1_after,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,2_after,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,2_after,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,3_after,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,3_after,62.0,0.0,3637.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,long_gap,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,long_gap,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,1_before,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,1_before,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,2_before,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,2_before,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,3_before,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,3_before,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,1_after,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,1_after,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,2_after,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,2_after,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,3_after,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,3_after,62.0,0.0,2017.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,long_gap,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,long_gap,66.0,0.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,1_before,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,1_before,65.75,-0.25,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,2_before,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,2_before,64.0,-2.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,3_before,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,3_before,64.0,-2.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,1_after,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,1_after,66.0,0.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,2_after,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,2_after,66.0,0.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,3_after,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,3_after,66.0,0.0,2807.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,long_gap,59.77142857142857,0.0,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,long_gap,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,1_before,59.0,-0.7714285714285722,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,1_before,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,2_before,59.0,-0.7714285714285722,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,2_before,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,3_before,59.0,-0.7714285714285722,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,3_before,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,1_after,66.5,6.728571428571428,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,1_after,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,2_after,67.0,7.228571428571428,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,2_after,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,3_after,67.0,7.228571428571428,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,3_after,84.0,0.0,3513.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,long_gap,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,long_gap,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,1_before,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,1_before,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,2_before,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,2_before,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,3_before,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,3_before,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,1_after,62.55,-0.4500000000000028,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,1_after,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,2_after,58.6,-4.399999999999999,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,2_after,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,3_after,59.0,-4.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,3_after,84.0,0.0,3002.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,long_gap,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,long_gap,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,1_before,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,1_before,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,2_before,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,2_before,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,3_before,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,3_before,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,1_after,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,1_after,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,2_after,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,2_after,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,3_after,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,3_after,84.0,0.0,3137.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,long_gap,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,long_gap,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,1_before,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,1_before,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,2_before,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,2_before,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,3_before,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,3_before,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,1_after,58.7,-4.299999999999997,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,1_after,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,2_after,55.0,-8.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,2_after,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,3_after,55.0,-8.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,3_after,84.0,0.0,4126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,long_gap,72.0,0.0,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,long_gap,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,1_before,71.85,-0.1500000000000056,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,1_before,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,2_before,71.7,-0.2999999999999971,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,2_before,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,3_before,69.1,-2.9000000000000057,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,3_before,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,1_after,72.0,0.0,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,1_after,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,2_after,72.2,0.2000000000000028,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,2_after,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,3_after,76.0,4.0,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,3_after,100.0,0.0,3126.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,long_gap,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,long_gap,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,1_before,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,1_before,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,2_before,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,2_before,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,3_before,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,3_before,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,1_after,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,1_after,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,2_after,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,2_after,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,3_after,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,3_after,69.6,-7.400000000000006,2648.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,long_gap,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,long_gap,65.75,0.0,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,1_before,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,1_before,60.0,-5.75,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,2_before,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,2_before,60.0,-5.75,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,3_before,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,3_before,60.0,-5.75,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,1_after,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,1_after,99.65,33.900000000000006,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,2_after,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,2_after,100.0,34.25,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,3_after,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,3_after,100.0,34.25,3632.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,long_gap,67.77777777777777,0.0,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,long_gap,72.33333333333333,0.0,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,1_before,73.0,5.2222222222222285,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,1_before,66.0,-6.333333333333329,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,2_before,73.0,5.2222222222222285,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,2_before,66.0,-6.333333333333329,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,3_before,73.0,5.2222222222222285,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,3_before,66.0,-6.333333333333329,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,1_after,65.0,-2.7777777777777715,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,1_after,75.0,2.6666666666666714,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,2_after,65.0,-2.7777777777777715,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,2_after,75.0,2.6666666666666714,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,3_after,65.0,-2.7777777777777715,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,3_after,75.0,2.6666666666666714,2689.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,long_gap,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,long_gap,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,1_before,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,1_before,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,2_before,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,2_before,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,3_before,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,3_before,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,1_after,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,1_after,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,2_after,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,2_after,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,3_after,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,3_after,76.0,0.0,2034.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,long_gap,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,long_gap,59.0,0.0,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,1_before,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,1_before,55.5,-3.5,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,2_before,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,2_before,55.8,-3.200000000000003,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,3_before,83.75,-0.25,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,3_before,58.0,-1.0,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,1_after,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,1_after,59.0,0.0,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,2_after,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,2_after,61.95,2.950000000000003,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,3_after,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,3_after,58.05,-0.9500000000000028,2141.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,long_gap,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,long_gap,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,1_before,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,1_before,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,2_before,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,2_before,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,3_before,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,3_before,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,1_after,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,1_after,78.3,-0.7000000000000028,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,2_after,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,2_after,78.0,-1.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,3_after,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,3_after,79.5,0.5,2990.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,long_gap,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,long_gap,73.23809523809524,0.0,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,1_before,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,1_before,69.7,-3.538095238095238,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,2_before,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,2_before,71.85,-1.3880952380952465,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,3_before,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,3_before,72.05,-1.1880952380952436,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,1_after,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,1_after,75.0,1.7619047619047592,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,2_after,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,2_after,75.0,1.7619047619047592,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,3_after,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,3_after,75.05,1.8119047619047564,2053.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,long_gap,83.84210526315789,0.0,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,long_gap,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,1_before,83.0,-0.8421052631578902,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,1_before,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,2_before,83.0,-0.8421052631578902,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,2_before,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,3_before,83.0,-0.8421052631578902,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,3_before,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,1_after,84.35,0.5078947368421041,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,1_after,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,2_after,83.95,0.1078947368421125,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,2_after,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,3_after,79.45,-4.392105263157887,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,3_after,68.0,0.0,3875.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,long_gap,89.76666666666667,0.0,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,long_gap,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,1_before,90.0,0.2333333333333342,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,1_before,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,2_before,90.0,0.2333333333333342,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,2_before,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,3_before,89.85,0.0833333333333286,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,3_before,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,1_after,89.0,-0.7666666666666657,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,1_after,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,2_after,88.6,-1.1666666666666714,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,2_after,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,3_after,89.15,-0.61666666666666,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,3_after,68.0,0.0,3048.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,long_gap,99.61904761904762,0.0,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,long_gap,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,1_before,98.15,-1.4690476190476147,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,1_before,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,2_before,95.75,-3.869047619047621,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,2_before,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,3_before,94.0,-5.61904761904762,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,3_before,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,1_after,98.9,-0.7190476190476147,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,1_after,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,2_after,99.05,-0.5690476190476232,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,2_after,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,3_after,97.0,-2.6190476190476204,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,3_after,79.0,0.0,2127.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,long_gap,94.84444444444443,0.0,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,long_gap,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,1_before,91.95,-2.8944444444444457,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,1_before,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,2_before,93.0,-1.8444444444444483,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,2_before,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,3_before,93.0,-1.8444444444444483,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,3_before,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,1_after,96.0,1.1555555555555517,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,1_after,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,2_after,96.9,2.055555555555557,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,2_after,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,3_after,97.65,2.805555555555557,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,3_after,86.05,3.049999999999997,4447.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,long_gap,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,long_gap,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,1_before,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,1_before,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,2_before,98.45,-1.5499999999999972,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,2_before,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,3_before,99.2,-0.7999999999999972,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,3_before,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,1_after,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,1_after,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,2_after,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,2_after,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,3_after,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,3_after,88.0,0.0,3643.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,long_gap,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,long_gap,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,1_before,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,1_before,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,2_before,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,2_before,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,3_before,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,3_before,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,1_after,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,1_after,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,2_after,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,2_after,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,3_after,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,3_after,98.0,0.0,2291.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,long_gap,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,long_gap,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,1_before,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,1_before,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,2_before,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,2_before,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,3_before,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,3_before,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,1_after,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,1_after,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,2_after,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,2_after,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,3_after,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,3_after,98.0,0.0,2172.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,long_gap,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,long_gap,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,1_before,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,1_before,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,2_before,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,2_before,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,3_before,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,3_before,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,1_after,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,1_after,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,2_after,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,2_after,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,3_after,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,3_after,92.0,0.0,2864.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,long_gap,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,long_gap,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,1_before,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,1_before,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,2_before,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,2_before,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,3_before,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,3_before,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,1_after,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,1_after,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,2_after,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,2_after,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,3_after,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,3_after,92.0,0.0,2311.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,long_gap,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,long_gap,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,1_before,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,1_before,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,2_before,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,2_before,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,3_before,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,3_before,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,1_after,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,1_after,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,2_after,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,2_after,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,3_after,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,3_after,92.0,0.0,2609.0,self,friends diff --git a/Analyses/long_gap_connection_all_long_format_laughter_friends.csv b/Analyses/long_gap_connection_all_long_format_laughter_friends.csv new file mode 100644 index 0000000..3ca1b8c --- /dev/null +++ b/Analyses/long_gap_connection_all_long_format_laughter_friends.csv @@ -0,0 +1,2829 @@ +clip,gap_length_x,condition_x,dyad,turn_num,turn_start_msec,video_num,laughter,Unnamed: 8,Unnamed: 9,subID,partnerID,timepoint,connection,connection_change,gap_length_y,speaker,condition_y +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,long_gap,24.0,0.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,long_gap,53.0,0.0,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,1_before,29.35,5.350000000000001,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,1_before,53.0,0.0,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,2_before,31.0,7.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,2_before,52.9,-0.1000000000000014,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,3_before,31.85,7.850000000000001,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,3_before,51.7,-1.2999999999999972,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,1_after,22.9,-1.1000000000000014,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,1_after,53.0,0.0,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,2_after,20.0,-4.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,2_after,53.95,0.9500000000000028,2169.0,self,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,8.0,235.0,3_after,20.0,-4.0,2169.0,other,friends +263_2169_8_235.mov,2169,friends,8_235,13,42532,263,yes,,,235.0,8.0,3_after,54.0,1.0,2169.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,long_gap,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,long_gap,60.7,0.0,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,1_before,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,1_before,60.8,0.0999999999999943,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,2_before,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,2_before,61.0,0.2999999999999971,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,3_before,88.85,0.8499999999999943,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,3_before,61.0,0.2999999999999971,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,1_after,88.0,0.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,1_after,62.0,1.2999999999999972,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,2_after,87.55,-0.4500000000000028,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,2_after,62.0,1.2999999999999972,2009.0,self,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,8.0,235.0,3_after,85.0,-3.0,2009.0,other,friends +264_2009_8_235.mov,2009,friends,8_235,45,186657,264,no,,,235.0,8.0,3_after,62.0,1.2999999999999972,2009.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,long_gap,90.55,0.0,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,long_gap,58.0,0.0,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,1_before,90.0,-0.5499999999999972,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,1_before,58.5,0.5,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,2_before,90.0,-0.5499999999999972,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,2_before,60.65,2.6499999999999986,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,3_before,90.0,-0.5499999999999972,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,3_before,64.85,6.849999999999994,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,1_after,91.0,0.4500000000000028,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,1_after,58.0,0.0,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,2_after,91.0,0.4500000000000028,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,2_after,57.55,-0.4500000000000028,2026.0,self,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,8.0,235.0,3_after,91.0,0.4500000000000028,2026.0,other,friends +265_2026_8_235.mov,2026,friends,8_235,53,207064,265,no,,,235.0,8.0,3_after,56.15,-1.8500000000000012,2026.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,long_gap,79.0,0.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,long_gap,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,1_before,78.9,-0.0999999999999943,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,1_before,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,2_before,75.05,-3.950000000000003,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,2_before,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,3_before,75.0,-4.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,3_before,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,1_after,79.0,0.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,1_after,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,2_after,90.25,11.25,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,2_after,91.0,0.0,3072.0,self,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,30.0,215.0,3_after,90.0,11.0,3072.0,other,friends +266_3072_30_215.mov,3072,friends,30_215,7,21246,266,no,,,215.0,30.0,3_after,91.0,0.0,3072.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,long_gap,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,long_gap,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,1_before,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,1_before,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,2_before,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,2_before,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,3_before,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,3_before,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,1_after,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,1_after,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,2_after,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,2_after,91.0,0.0,3485.0,self,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,30.0,215.0,3_after,90.0,0.0,3485.0,other,friends +267_3485_30_215.mov,3485,friends,30_215,15,37992,267,no,,,215.0,30.0,3_after,91.0,0.0,3485.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,long_gap,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,long_gap,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,1_before,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,1_before,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,2_before,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,2_before,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,3_before,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,3_before,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,1_after,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,1_after,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,2_after,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,2_after,96.0,0.0,2321.0,self,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,30.0,215.0,3_after,98.0,0.0,2321.0,other,friends +268_2321_30_215.mov,2321,friends,30_215,35,195420,268,no,,,215.0,30.0,3_after,96.0,0.0,2321.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,long_gap,80.0,0.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,long_gap,55.18181818181818,0.0,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,1_before,80.0,0.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,1_before,63.0,7.81818181818182,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,2_before,78.9,-1.0999999999999943,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,2_before,63.0,7.81818181818182,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,3_before,74.0,-6.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,3_before,63.0,7.81818181818182,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,1_after,80.5,0.5,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,1_after,54.0,-1.18181818181818,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,2_after,81.0,1.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,2_after,54.0,-1.18181818181818,2128.0,other,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,21.0,212.0,3_after,81.0,1.0,2128.0,self,friends +269_2128_21_212.mov,2128,friends,21_212,128,465918,269,no,,,212.0,21.0,3_after,54.0,-1.18181818181818,2128.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,long_gap,56.3,0.0,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,long_gap,74.35,0.0,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,1_before,56.95,0.6500000000000057,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,1_before,67.2,-7.149999999999992,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,2_before,60.4,4.100000000000001,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,2_before,65.9,-8.449999999999989,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,3_before,52.8,-3.5,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,3_before,64.05,-10.299999999999995,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,1_after,55.5,-0.7999999999999972,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,1_after,75.0,0.6500000000000057,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,2_after,56.0,-0.2999999999999971,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,2_after,75.0,0.6500000000000057,2053.0,self,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,11.0,241.0,3_after,57.8,1.5,2053.0,other,friends +270_2053_11_241.mov,2053,friends,11_241,5,14898,270,no,,,241.0,11.0,3_after,75.0,0.6500000000000057,2053.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,long_gap,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,long_gap,64.91666666666667,0.0,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,1_before,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,1_before,67.1,2.183333333333323,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,2_before,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,2_before,67.4,2.4833333333333343,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,3_before,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,3_before,67.0,2.0833333333333286,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,1_after,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,1_after,57.2,-7.716666666666669,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,2_after,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,2_after,50.7,-14.216666666666669,2420.0,self,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,11.0,241.0,3_after,89.0,0.0,2420.0,other,friends +271_2420_11_241.mov,2420,friends,11_241,27,90297,271,no,,,241.0,11.0,3_after,50.0,-14.916666666666671,2420.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,long_gap,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,long_gap,49.5,0.0,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,1_before,89.65,0.6500000000000057,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,1_before,39.45,-10.049999999999995,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,2_before,92.0,3.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,2_before,38.0,-11.5,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,3_before,92.0,3.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,3_before,38.0,-11.5,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,1_after,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,1_after,52.85,3.3500000000000014,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,2_after,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,2_after,53.0,3.5,2229.0,self,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,11.0,241.0,3_after,89.0,0.0,2229.0,other,friends +272_2229_11_241.mov,2229,friends,11_241,57,212272,272,no,,,241.0,11.0,3_after,53.0,3.5,2229.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,long_gap,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,long_gap,58.25,0.0,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,1_before,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,1_before,60.0,1.75,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,2_before,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,2_before,60.6,2.3500000000000014,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,3_before,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,3_before,62.55,4.299999999999997,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,1_after,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,1_after,55.0,-3.25,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,2_after,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,2_after,55.0,-3.25,2408.0,self,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,11.0,241.0,3_after,89.0,0.0,2408.0,other,friends +273_2408_11_241.mov,2408,friends,11_241,73,277894,273,no,,,241.0,11.0,3_after,60.05,1.7999999999999972,2408.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,long_gap,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,long_gap,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,1_before,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,1_before,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,2_before,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,2_before,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,3_before,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,3_before,26.0,0.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,1_after,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,1_after,26.75,0.75,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,2_after,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,2_after,27.0,1.0,2370.0,other,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,11.0,241.0,3_after,89.0,0.0,2370.0,self,friends +274_2370_11_241.mov,2370,friends,11_241,96,336007,274,no,,,241.0,11.0,3_after,27.0,1.0,2370.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,long_gap,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,long_gap,62.59090909090909,0.0,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,1_before,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,1_before,56.75,-5.8409090909090935,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,2_before,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,2_before,53.6,-8.990909090909092,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,3_before,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,3_before,49.05,-13.540909090909096,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,1_after,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,1_after,64.0,1.4090909090909065,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,2_after,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,2_after,64.0,1.4090909090909065,2124.0,self,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,11.0,241.0,3_after,93.0,0.0,2124.0,other,friends +275_2124_11_241.mov,2124,friends,11_241,107,375814,275,yes,,,241.0,11.0,3_after,64.05,1.4590909090909037,2124.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,long_gap,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,long_gap,64.5,0.0,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,1_before,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,1_before,67.0,2.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,2_before,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,2_before,67.0,2.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,3_before,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,3_before,67.0,2.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,1_after,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,1_after,62.55,-1.9500000000000028,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,2_after,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,2_after,61.0,-3.5,2067.0,other,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,11.0,241.0,3_after,91.0,0.0,2067.0,self,friends +276_2067_11_241.mov,2067,friends,11_241,134,491769,276,yes,,,241.0,11.0,3_after,61.0,-3.5,2067.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,long_gap,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,long_gap,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,1_before,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,1_before,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,2_before,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,2_before,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,3_before,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,3_before,61.0,0.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,1_after,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,1_after,60.45,-0.5499999999999972,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,2_after,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,2_after,60.0,-1.0,2286.0,self,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,11.0,241.0,3_after,91.0,0.0,2286.0,other,friends +277_2286_11_241.mov,2286,friends,11_241,137,503119,277,no,,,241.0,11.0,3_after,60.0,-1.0,2286.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,long_gap,72.58823529411765,0.0,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,long_gap,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,1_before,76.95,4.361764705882351,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,1_before,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,2_before,79.95,7.361764705882351,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,2_before,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,3_before,82.0,9.411764705882348,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,3_before,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,1_after,72.0,-0.5882352941176521,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,1_after,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,2_after,72.0,-0.5882352941176521,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,2_after,100.0,0.0,3372.0,other,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,86.0,251.0,3_after,72.0,-0.5882352941176521,3372.0,self,friends +278_3372_86_251.mov,3372,friends,86_251,110,420226,278,no,,,251.0,86.0,3_after,100.0,0.0,3372.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,long_gap,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,long_gap,98.28571428571428,0.0,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,1_before,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,1_before,100.0,1.7142857142857082,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,2_before,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,2_before,100.0,1.7142857142857082,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,3_before,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,3_before,100.0,1.7142857142857082,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,1_after,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,1_after,45.25,-53.03571428571429,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,2_after,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,2_after,47.65,-50.63571428571429,2014.0,other,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,86.0,251.0,3_after,90.0,0.0,2014.0,self,friends +279_2014_86_251.mov,2014,friends,86_251,162,593201,279,no,,,251.0,86.0,3_after,55.5,-42.78571428571429,2014.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,long_gap,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,long_gap,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,1_before,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,1_before,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,2_before,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,2_before,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,3_before,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,3_before,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,1_after,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,1_after,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,2_after,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,2_after,48.0,0.0,2538.0,self,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,44.0,252.0,3_after,53.0,0.0,2538.0,other,friends +280_2538_44_252.mov,2538,friends,44_252,35,110329,280,no,,,252.0,44.0,3_after,47.45,-0.5499999999999972,2538.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,long_gap,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,long_gap,53.0,0.0,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,1_before,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,1_before,58.9,5.899999999999999,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,2_before,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,2_before,61.0,8.0,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,3_before,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,3_before,60.8,7.799999999999997,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,1_after,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,1_after,53.0,0.0,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,2_after,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,2_after,54.85,1.8500000000000012,2449.0,self,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,44.0,252.0,3_after,55.0,0.0,2449.0,other,friends +281_2449_44_252.mov,2449,friends,44_252,43,145894,281,no,,,252.0,44.0,3_after,57.45,4.450000000000003,2449.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,long_gap,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,long_gap,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,1_before,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,1_before,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,2_before,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,2_before,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,3_before,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,3_before,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,1_after,60.0,0.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,1_after,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,2_after,60.9,0.8999999999999986,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,2_after,74.0,0.0,2548.0,self,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,44.0,252.0,3_after,61.0,1.0,2548.0,other,friends +282_2548_44_252.mov,2548,friends,44_252,75,271810,282,no,,,252.0,44.0,3_after,74.0,0.0,2548.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,long_gap,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,long_gap,69.78571428571429,0.0,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,1_before,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,1_before,68.8,-0.9857142857142946,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,2_before,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,2_before,64.0,-5.785714285714292,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,3_before,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,3_before,71.4,1.614285714285714,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,1_after,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,1_after,68.35,-1.4357142857142975,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,2_after,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,2_after,68.0,-1.7857142857142918,2747.0,self,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,44.0,252.0,3_after,61.0,0.0,2747.0,other,friends +283_2747_44_252.mov,2747,friends,44_252,77,293418,283,no,,,252.0,44.0,3_after,68.0,-1.7857142857142918,2747.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,long_gap,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,long_gap,50.75555555555555,0.0,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,1_before,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,1_before,59.0,8.244444444444447,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,2_before,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,2_before,59.0,8.244444444444447,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,3_before,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,3_before,59.0,8.244444444444447,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,1_after,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,1_after,25.9,-24.855555555555554,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,2_after,61.0,0.0,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,2_after,30.0,-20.755555555555556,4528.0,self,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,44.0,252.0,3_after,38.25,-22.75,4528.0,other,friends +284_4528_44_252.mov,4528,friends,44_252,101,388049,284,no,,,252.0,44.0,3_after,31.0,-19.755555555555556,4528.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,long_gap,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,long_gap,42.0,0.0,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,1_before,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,1_before,42.0,0.0,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,2_before,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,2_before,42.21052631578947,0.2105263157894725,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,3_before,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,3_before,44.0,2.0,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,1_after,80.0,0.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,1_after,42.4,0.3999999999999986,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,2_after,81.0,1.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,2_after,44.85,2.8500000000000014,3202.0,self,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,44.0,252.0,3_after,81.0,1.0,3202.0,other,friends +285_3202_44_252.mov,3202,friends,44_252,127,506902,285,no,,,252.0,44.0,3_after,44.75,2.75,3202.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,long_gap,80.14285714285714,0.0,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,long_gap,42.66666666666666,0.0,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,1_before,80.0,-0.1428571428571388,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,1_before,42.0,-0.6666666666666643,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,2_before,80.0,-0.1428571428571388,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,2_before,42.0,-0.6666666666666643,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,3_before,80.0,-0.1428571428571388,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,3_before,42.0,-0.6666666666666643,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,1_after,81.0,0.8571428571428612,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,1_after,45.0,2.333333333333336,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,2_after,81.0,0.8571428571428612,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,2_after,44.45,1.7833333333333383,2063.0,other,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,44.0,252.0,3_after,81.0,0.8571428571428612,2063.0,self,friends +286_2063_44_252.mov,2063,friends,44_252,128,509213,286,no,,,252.0,44.0,3_after,43.0,0.3333333333333357,2063.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,long_gap,81.2,0.0,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,long_gap,46.13333333333333,0.0,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,1_before,81.0,-0.2000000000000028,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,1_before,49.15,3.0166666666666657,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,2_before,81.0,-0.2000000000000028,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,2_before,48.45,2.31666666666667,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,3_before,81.0,-0.2000000000000028,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,3_before,46.5,0.3666666666666671,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,1_after,82.0,0.7999999999999972,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,1_after,43.0,-3.133333333333333,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,2_after,82.0,0.7999999999999972,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,2_after,42.8,-3.333333333333336,2965.0,self,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,44.0,252.0,3_after,82.0,0.7999999999999972,2965.0,other,friends +287_2965_44_252.mov,2965,friends,44_252,129,532862,287,no,,,252.0,44.0,3_after,39.05,-7.083333333333336,2965.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,long_gap,18.666666666666668,0.0,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,long_gap,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,1_before,23.8,5.133333333333333,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,1_before,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,2_before,26.0,7.333333333333332,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,2_before,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,3_before,26.0,7.333333333333332,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,3_before,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,1_after,18.0,-0.6666666666666679,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,1_after,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,2_after,18.8,0.1333333333333328,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,2_after,58.0,0.0,2165.0,self,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,246.0,44.0,3_after,19.0,0.3333333333333321,2165.0,other,friends +288_2165_44_246.mov,2165,friends,44_246,75,266970,288,no,,,44.0,246.0,3_after,58.0,0.0,2165.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,long_gap,74.73913043478261,0.0,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,long_gap,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,1_before,73.0,-1.739130434782609,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,1_before,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,2_before,73.0,-1.739130434782609,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,2_before,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,3_before,71.25,-3.4891304347826093,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,3_before,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,1_after,78.80952380952381,4.070393374741201,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,1_after,64.0,0.0,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,2_after,61.63157894736842,-13.107551487414192,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,2_after,64.1,0.0999999999999943,2388.0,self,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,246.0,44.0,3_after,65.0,-9.73913043478261,2388.0,other,friends +289_2388_44_246.mov,2388,friends,44_246,91,333402,289,no,,,44.0,246.0,3_after,69.8,5.799999999999997,2388.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,long_gap,39.76190476190476,0.0,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,long_gap,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,1_before,64.0,24.23809523809524,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,1_before,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,2_before,64.0,24.23809523809524,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,2_before,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,3_before,64.2,24.438095238095244,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,3_before,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,1_after,34.0,-5.761904761904759,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,1_after,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,2_after,34.0,-5.761904761904759,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,2_after,70.0,0.0,2065.0,self,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,246.0,44.0,3_after,38.1,-1.6619047619047578,2065.0,other,friends +290_2065_44_246.mov,2065,friends,44_246,125,455838,290,no,,,44.0,246.0,3_after,70.0,0.0,2065.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,long_gap,55.90909090909091,0.0,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,long_gap,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,1_before,53.25,-2.6590909090909065,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,1_before,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,2_before,30.7,-25.209090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,2_before,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,3_before,32.0,-23.909090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,3_before,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,1_after,47.3,-8.60909090909091,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,1_after,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,2_after,47.0,-8.909090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,2_after,82.0,0.0,2215.0,self,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,246.0,44.0,3_after,29.25,-26.659090909090907,2215.0,other,friends +291_2215_44_246.mov,2215,friends,44_246,131,490521,291,no,,,44.0,246.0,3_after,82.0,0.0,2215.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,long_gap,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,long_gap,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,1_before,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,1_before,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,2_before,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,2_before,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,3_before,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,3_before,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,1_after,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,1_after,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,2_after,97.0,0.0,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,2_after,82.0,0.0,2400.0,other,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,246.0,44.0,3_after,87.35,-9.650000000000006,2400.0,self,friends +292_2400_44_246.mov,2400,friends,44_246,158,590861,292,no,,,44.0,246.0,3_after,82.0,0.0,2400.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,long_gap,45.0,0.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,long_gap,61.28125,0.0,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,1_before,46.47368421052632,1.473684210526315,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,1_before,68.6842105263158,7.402960526315795,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,2_before,52.0,7.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,2_before,69.0,7.71875,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,3_before,52.0,7.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,3_before,66.65,5.368750000000006,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,1_after,52.578947368421055,7.578947368421055,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,1_after,65.0,3.71875,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,2_after,54.0,9.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,2_after,56.31578947368421,-4.965460526315788,3300.0,self,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,5.0,228.0,3_after,54.0,9.0,3300.0,other,friends +293_3300_5_228.mov,3300,friends,5_228,5,10700,293,yes,,,228.0,5.0,3_after,60.8421052631579,-0.4391447368421026,3300.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,long_gap,88.90322580645162,0.0,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,long_gap,90.90322580645162,0.0,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,1_before,86.0,-2.903225806451616,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,1_before,92.0,1.0967741935483843,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,2_before,86.0,-2.903225806451616,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,2_before,92.4,1.49677419354839,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,3_before,86.0,-2.903225806451616,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,3_before,93.0,2.0967741935483843,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,1_after,90.0,1.0967741935483843,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,1_after,82.4,-8.50322580645161,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,2_after,90.0,1.0967741935483843,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,2_after,84.85,-6.053225806451621,3157.0,self,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,5.0,228.0,3_after,89.1,0.1967741935483786,3157.0,other,friends +294_3157_5_228.mov,3157,friends,5_228,125,533365,294,yes,,,228.0,5.0,3_after,85.0,-5.903225806451616,3157.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,long_gap,35.6875,0.0,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,long_gap,60.85416666666666,0.0,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,1_before,33.0,-2.6875,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,1_before,60.0,-0.8541666666666643,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,2_before,33.0,-2.6875,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,2_before,59.6,-1.2541666666666629,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,3_before,33.0,-2.6875,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,3_before,51.1,-9.754166666666665,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,1_after,37.3,1.6124999999999972,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,1_after,61.0,0.1458333333333357,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,2_after,38.0,2.3125,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,2_after,61.0,0.1458333333333357,4852.0,self,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,44.0,253.0,3_after,38.0,2.3125,4852.0,other,friends +295_4852_44_253.mov,4852,friends,44_253,5,17864,295,no,,,253.0,44.0,3_after,61.0,0.1458333333333357,4852.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,long_gap,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,long_gap,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,1_before,37.6,-0.3999999999999986,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,1_before,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,2_before,36.0,-2.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,2_before,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,3_before,36.0,-2.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,3_before,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,1_after,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,1_after,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,2_after,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,2_after,61.0,0.0,2360.0,self,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,44.0,253.0,3_after,38.0,0.0,2360.0,other,friends +296_2360_44_253.mov,2360,friends,44_253,7,22549,296,no,,,253.0,44.0,3_after,61.15,0.1499999999999985,2360.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,long_gap,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,long_gap,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,1_before,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,1_before,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,2_before,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,2_before,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,3_before,39.8,-0.2000000000000028,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,3_before,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,1_after,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,1_after,61.45,0.4500000000000028,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,2_after,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,2_after,61.0,0.0,2106.0,other,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,44.0,253.0,3_after,40.0,0.0,2106.0,self,friends +297_2106_44_253.mov,2106,friends,44_253,10,39382,297,no,,,253.0,44.0,3_after,61.0,0.0,2106.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,long_gap,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,long_gap,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,1_before,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,1_before,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,2_before,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,2_before,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,3_before,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,3_before,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,1_after,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,1_after,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,2_after,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,2_after,73.0,0.0,2271.0,self,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,44.0,253.0,3_after,63.0,0.0,2271.0,other,friends +298_2271_44_253.mov,2271,friends,44_253,45,235290,298,no,,,253.0,44.0,3_after,73.0,0.0,2271.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,long_gap,65.15384615384616,0.0,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,long_gap,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,1_before,63.0,-2.1538461538461604,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,1_before,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,2_before,63.0,-2.1538461538461604,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,2_before,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,3_before,63.0,-2.1538461538461604,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,3_before,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,1_after,67.0,1.84615384615384,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,1_after,73.0,0.0,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,2_after,67.0,1.84615384615384,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,2_after,72.25,-0.75,2567.0,self,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,44.0,253.0,3_after,67.0,1.84615384615384,2567.0,other,friends +299_2567_44_253.mov,2567,friends,44_253,57,288431,299,no,,,253.0,44.0,3_after,74.9,1.9000000000000057,2567.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,long_gap,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,long_gap,74.25,0.0,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,1_before,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,1_before,75.0,0.75,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,2_before,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,2_before,72.95,-1.2999999999999972,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,3_before,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,3_before,73.0,-1.25,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,1_after,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,1_after,74.0,-0.25,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,2_after,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,2_after,74.0,-0.25,2069.0,self,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,44.0,253.0,3_after,67.0,0.0,2069.0,other,friends +300_2069_44_253.mov,2069,friends,44_253,59,297277,300,no,,,253.0,44.0,3_after,74.0,-0.25,2069.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,long_gap,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,long_gap,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,1_before,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,1_before,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,2_before,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,2_before,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,3_before,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,3_before,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,1_after,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,1_after,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,2_after,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,2_after,74.0,0.0,2315.0,self,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,44.0,253.0,3_after,67.0,0.0,2315.0,other,friends +301_2315_44_253.mov,2315,friends,44_253,61,318622,301,no,,,253.0,44.0,3_after,74.0,0.0,2315.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,long_gap,66.63829787234043,0.0,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,long_gap,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,1_before,67.0,0.3617021276595693,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,1_before,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,2_before,67.0,0.3617021276595693,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,2_before,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,3_before,67.0,0.3617021276595693,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,3_before,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,1_after,60.0,-6.638297872340431,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,1_after,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,2_after,60.0,-6.638297872340431,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,2_after,74.0,0.0,4780.0,other,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,44.0,253.0,3_after,60.55,-6.0882978723404335,4780.0,self,friends +302_4780_44_253.mov,4780,friends,44_253,70,351996,302,no,,,253.0,44.0,3_after,74.0,0.0,4780.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,long_gap,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,long_gap,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,1_before,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,1_before,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,2_before,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,2_before,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,3_before,71.0,0.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,3_before,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,1_after,71.7,0.7000000000000028,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,1_after,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,2_after,72.0,1.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,2_after,74.0,0.0,2605.0,self,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,44.0,253.0,3_after,72.0,1.0,2605.0,other,friends +303_2605_44_253.mov,2605,friends,44_253,73,367551,303,no,,,253.0,44.0,3_after,74.0,0.0,2605.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,long_gap,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,long_gap,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,1_before,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,1_before,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,2_before,73.35,-0.6500000000000057,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,2_before,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,3_before,73.0,-1.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,3_before,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,1_after,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,1_after,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,2_after,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,2_after,74.0,0.0,2050.0,self,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,44.0,253.0,3_after,74.0,0.0,2050.0,other,friends +304_2050_44_253.mov,2050,friends,44_253,79,398700,304,no,,,253.0,44.0,3_after,74.0,0.0,2050.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,long_gap,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,long_gap,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,1_before,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,1_before,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,2_before,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,2_before,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,3_before,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,3_before,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,1_after,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,1_after,74.0,0.0,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,2_after,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,2_after,74.8,0.7999999999999972,3620.0,self,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,44.0,253.0,3_after,74.0,0.0,3620.0,other,friends +305_3620_44_253.mov,3620,friends,44_253,91,441573,305,yes,,,253.0,44.0,3_after,78.0,4.0,3620.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,long_gap,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,long_gap,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,1_before,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,1_before,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,2_before,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,2_before,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,3_before,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,3_before,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,1_after,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,1_after,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,2_after,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,2_after,78.0,0.0,2343.0,self,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,44.0,253.0,3_after,76.0,0.0,2343.0,other,friends +306_2343_44_253.mov,2343,friends,44_253,103,497509,306,no,,,253.0,44.0,3_after,78.0,0.0,2343.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,long_gap,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,long_gap,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,1_before,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,1_before,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,2_before,77.55,-5.450000000000003,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,2_before,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,3_before,76.0,-7.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,3_before,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,1_after,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,1_after,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,2_after,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,2_after,78.0,0.0,2239.0,other,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,44.0,253.0,3_after,83.0,0.0,2239.0,self,friends +307_2239_44_253.mov,2239,friends,44_253,112,521056,307,no,,,253.0,44.0,3_after,78.0,0.0,2239.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,long_gap,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,long_gap,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,1_before,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,1_before,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,2_before,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,2_before,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,3_before,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,3_before,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,1_after,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,1_after,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,2_after,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,2_after,78.0,0.0,2140.0,other,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,44.0,253.0,3_after,83.0,0.0,2140.0,self,friends +308_2140_44_253.mov,2140,friends,44_253,118,552138,308,no,,,253.0,44.0,3_after,78.0,0.0,2140.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,long_gap,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,long_gap,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,1_before,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,1_before,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,2_before,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,2_before,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,3_before,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,3_before,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,1_after,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,1_after,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,2_after,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,2_after,78.0,0.0,3005.0,other,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,44.0,253.0,3_after,83.0,0.0,3005.0,self,friends +309_3005_44_253.mov,3005,friends,44_253,126,582056,309,no,,,253.0,44.0,3_after,78.0,0.0,3005.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,long_gap,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,long_gap,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,1_before,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,1_before,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,2_before,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,2_before,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,3_before,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,3_before,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,1_after,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,1_after,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,2_after,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,2_after,78.0,0.0,4567.0,self,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,44.0,253.0,3_after,83.0,0.0,4567.0,other,friends +310_4567_44_253.mov,4567,friends,44_253,127,588711,310,yes,,,253.0,44.0,3_after,78.0,0.0,4567.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,long_gap,95.0,0.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,long_gap,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,1_before,94.1,-0.9000000000000057,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,1_before,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,2_before,94.0,-1.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,2_before,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,3_before,93.6,-1.4000000000000057,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,3_before,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,1_after,95.0,0.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,1_after,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,2_after,95.0,0.0,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,2_after,86.0,0.0,2035.0,self,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,240.0,11.0,3_after,95.9,0.9000000000000057,2035.0,other,friends +311_2035_11_240.mov,2035,friends,11_240,41,123050,311,no,,,11.0,240.0,3_after,86.0,0.0,2035.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,long_gap,81.71428571428571,0.0,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,long_gap,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,1_before,87.25,5.535714285714292,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,1_before,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,2_before,88.0,6.285714285714292,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,2_before,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,3_before,88.65,6.9357142857142975,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,3_before,95.0,0.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,1_after,87.35,5.635714285714286,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,1_after,91.25,-3.75,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,2_after,85.85,4.135714285714286,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,2_after,79.0,-16.0,7773.0,other,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,240.0,11.0,3_after,87.7,5.985714285714295,7773.0,self,friends +312_7773_11_240.mov,7773,friends,11_240,128,375993,312,no,,,11.0,240.0,3_after,79.9,-15.099999999999994,7773.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,long_gap,86.4,0.0,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,long_gap,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,1_before,87.0,0.5999999999999943,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,1_before,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,2_before,90.75,4.349999999999994,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,2_before,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,3_before,91.05,4.649999999999992,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,3_before,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,1_after,89.1,2.6999999999999886,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,1_after,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,2_after,93.0,6.599999999999994,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,2_after,80.0,0.0,2497.0,other,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,240.0,11.0,3_after,93.95,7.549999999999997,2497.0,self,friends +313_2497_11_240.mov,2497,friends,11_240,150,453951,313,no,,,11.0,240.0,3_after,83.35,3.3499999999999943,2497.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,long_gap,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,long_gap,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,1_before,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,1_before,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,2_before,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,2_before,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,3_before,83.15,1.1500000000000057,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,3_before,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,1_after,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,1_after,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,2_after,82.0,0.0,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,2_after,79.0,0.0,2180.0,other,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,240.0,11.0,3_after,81.35,-0.6500000000000057,2180.0,self,friends +314_2180_11_240.mov,2180,friends,11_240,158,491963,314,no,,,11.0,240.0,3_after,79.0,0.0,2180.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,long_gap,89.25,0.0,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,long_gap,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,1_before,91.0,1.75,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,1_before,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,2_before,91.0,1.75,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,2_before,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,3_before,91.15,1.9000000000000057,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,3_before,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,1_after,89.0,-0.25,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,1_after,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,2_after,89.0,-0.25,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,2_after,80.0,0.0,2354.0,other,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,240.0,11.0,3_after,89.0,-0.25,2354.0,self,friends +315_2354_11_240.mov,2354,friends,11_240,170,569731,315,no,,,11.0,240.0,3_after,80.0,0.0,2354.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,long_gap,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,long_gap,77.42105263157895,0.0,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,1_before,41.85,-1.1499999999999986,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,1_before,79.0,1.5789473684210549,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,2_before,25.65,-17.35,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,2_before,78.0,0.5789473684210549,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,3_before,13.15,-29.85,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,3_before,76.0,-1.4210526315789451,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,1_after,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,1_after,76.2,-1.2210526315789425,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,2_after,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,2_after,77.0,-0.4210526315789451,3829.0,other,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,204.0,101.0,3_after,43.0,0.0,3829.0,self,friends +316_3829_101_204.mov,3829,friends,101_204,4,14780,316,no,,,101.0,204.0,3_after,77.0,-0.4210526315789451,3829.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,long_gap,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,long_gap,83.47619047619048,0.0,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,1_before,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,1_before,90.85,7.373809523809513,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,2_before,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,2_before,91.0,7.523809523809518,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,3_before,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,3_before,91.05,7.5738095238095156,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,1_after,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,1_after,81.0,-2.4761904761904816,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,2_after,69.0,0.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,2_after,81.0,-2.4761904761904816,2164.0,self,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,204.0,101.0,3_after,70.0,1.0,2164.0,other,friends +317_2164_101_204.mov,2164,friends,101_204,53,199390,317,no,,,101.0,204.0,3_after,81.0,-2.4761904761904816,2164.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,long_gap,70.0,0.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,long_gap,69.0,0.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,1_before,70.0,0.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,1_before,68.95,-0.0499999999999971,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,2_before,66.65,-3.3499999999999943,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,2_before,53.75,-15.25,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,3_before,66.0,-4.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,3_before,50.0,-19.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,1_after,72.55,2.549999999999997,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,1_after,69.0,0.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,2_after,73.0,3.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,2_after,69.0,0.0,2603.0,other,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,21.0,213.0,3_after,73.0,3.0,2603.0,self,friends +318_2603_21_213.mov,2603,friends,21_213,24,114522,318,yes,,,213.0,21.0,3_after,69.0,0.0,2603.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,long_gap,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,long_gap,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,1_before,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,1_before,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,2_before,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,2_before,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,3_before,52.8,-4.200000000000003,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,3_before,75.45,-1.5499999999999972,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,1_after,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,1_after,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,2_after,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,2_after,77.0,0.0,2921.0,other,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,238.0,29.0,3_after,57.0,0.0,2921.0,self,friends +319_2921_29_238.mov,2921,friends,29_238,6,17120,319,no,,,29.0,238.0,3_after,77.0,0.0,2921.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,long_gap,60.69565217391305,0.0,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,long_gap,80.69565217391305,0.0,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,1_before,57.0,-3.695652173913047,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,1_before,81.0,0.3043478260869534,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,2_before,57.0,-3.695652173913047,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,2_before,80.05,-0.6456521739130494,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,3_before,57.0,-3.695652173913047,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,3_before,77.0,-3.695652173913047,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,1_after,68.0,7.304347826086953,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,1_after,80.0,-0.6956521739130466,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,2_after,68.0,7.304347826086953,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,2_after,80.0,-0.6956521739130466,2315.0,self,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,238.0,29.0,3_after,68.0,7.304347826086953,2315.0,other,friends +320_2315_29_238.mov,2315,friends,29_238,9,34640,320,no,,,29.0,238.0,3_after,80.0,-0.6956521739130466,2315.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,long_gap,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,long_gap,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,1_before,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,1_before,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,2_before,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,2_before,83.75,-1.25,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,3_before,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,3_before,83.0,-2.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,1_after,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,1_after,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,2_after,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,2_after,85.0,0.0,2414.0,self,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,238.0,29.0,3_after,80.0,0.0,2414.0,other,friends +321_2414_29_238.mov,2414,friends,29_238,86,445244,321,yes,,,29.0,238.0,3_after,85.0,0.0,2414.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,long_gap,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,long_gap,84.92,0.0,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,1_before,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,1_before,72.95,-11.97,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,2_before,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,2_before,72.0,-12.920000000000002,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,3_before,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,3_before,72.0,-12.920000000000002,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,1_after,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,1_after,85.0,0.0799999999999983,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,2_after,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,2_after,85.0,0.0799999999999983,2445.0,other,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,208.0,25.0,3_after,84.0,0.0,2445.0,self,friends +322_2445_25_208.mov,2445,friends,25_208,24,147843,322,no,,,25.0,208.0,3_after,85.0,0.0799999999999983,2445.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,long_gap,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,long_gap,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,1_before,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,1_before,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,2_before,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,2_before,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,3_before,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,3_before,58.75,-14.25,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,1_after,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,1_after,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,2_after,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,2_after,73.0,0.0,2031.0,self,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,208.0,25.0,3_after,79.0,0.0,2031.0,other,friends +323_2031_25_208.mov,2031,friends,25_208,43,296539,323,yes,,,25.0,208.0,3_after,73.0,0.0,2031.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,long_gap,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,long_gap,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,1_before,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,1_before,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,2_before,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,2_before,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,3_before,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,3_before,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,1_after,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,1_after,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,2_after,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,2_after,80.0,0.0,2956.0,self,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,25.0,209.0,3_after,90.0,0.0,2956.0,other,friends +324_2956_25_209.mov,2956,friends,25_209,47,303204,324,yes,,,209.0,25.0,3_after,79.3,-0.7000000000000028,2956.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,long_gap,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,long_gap,80.8108108108108,0.0,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,1_before,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,1_before,81.0,0.189189189189193,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,2_before,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,2_before,81.0,0.189189189189193,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,3_before,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,3_before,81.0,0.189189189189193,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,1_after,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,1_after,80.95,0.1391891891891958,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,2_after,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,2_after,80.0,-0.810810810810807,3706.0,other,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,25.0,209.0,3_after,90.0,0.0,3706.0,self,friends +325_3706_25_209.mov,3706,friends,25_209,64,357340,325,yes,,,209.0,25.0,3_after,80.0,-0.810810810810807,3706.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,long_gap,89.0,0.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,long_gap,54.5,0.0,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,1_before,86.15,-2.8499999999999943,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,1_before,58.9,4.399999999999999,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,2_before,85.0,-4.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,2_before,59.0,4.5,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,3_before,85.0,-4.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,3_before,59.0,4.5,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,1_after,89.0,0.0,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,1_after,50.05,-4.450000000000003,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,2_after,89.85,0.8499999999999943,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,2_after,37.9,-16.6,2472.0,other,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,107.0,234.0,3_after,91.45,2.450000000000003,2472.0,self,friends +326_2472_107_234.mov,2472,friends,107_234,28,109673,326,yes,,,234.0,107.0,3_after,33.0,-21.5,2472.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,long_gap,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,long_gap,34.45454545454545,0.0,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,1_before,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,1_before,26.0,-8.454545454545453,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,2_before,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,2_before,26.0,-8.454545454545453,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,3_before,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,3_before,26.0,-8.454545454545453,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,1_after,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,1_after,71.45,36.99545454545455,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,2_after,96.0,0.0,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,2_after,73.0,38.54545454545455,2248.0,other,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,107.0,234.0,3_after,97.2,1.2000000000000028,2248.0,self,friends +327_2248_107_234.mov,2248,friends,107_234,40,146948,327,yes,,,234.0,107.0,3_after,73.0,38.54545454545455,2248.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,long_gap,96.39473684210526,0.0,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,long_gap,73.0,0.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,1_before,96.0,-0.3947368421052601,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,1_before,70.85,-2.1500000000000057,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,2_before,96.0,-0.3947368421052601,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,2_before,33.55,-39.45,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,3_before,96.0,-0.3947368421052601,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,3_before,26.0,-47.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,1_after,99.0,2.60526315789474,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,1_after,73.0,0.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,2_after,99.0,2.60526315789474,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,2_after,73.0,0.0,3862.0,other,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,107.0,234.0,3_after,99.0,2.60526315789474,3862.0,self,friends +328_3862_107_234.mov,3862,friends,107_234,41,152678,328,yes,,,234.0,107.0,3_after,73.0,0.0,3862.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,long_gap,93.0,0.0,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,long_gap,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,1_before,91.9,-1.0999999999999943,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,1_before,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,2_before,90.5,-2.5,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,2_before,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,3_before,89.75,-3.25,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,3_before,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,1_after,93.0,0.0,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,1_after,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,2_after,93.7,0.7000000000000028,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,2_after,54.0,0.0,2182.0,other,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,107.0,234.0,3_after,95.2,2.200000000000003,2182.0,self,friends +329_2182_107_234.mov,2182,friends,107_234,45,183164,329,no,,,234.0,107.0,3_after,54.0,0.0,2182.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,long_gap,97.90322580645162,0.0,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,long_gap,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,1_before,94.0,-3.903225806451616,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,1_before,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,2_before,94.0,-3.903225806451616,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,2_before,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,3_before,94.0,-3.903225806451616,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,3_before,68.5,-4.5,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,1_after,99.75,1.8467741935483843,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,1_after,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,2_after,99.0,1.0967741935483843,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,2_after,73.0,0.0,3139.0,self,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,107.0,234.0,3_after,97.95,0.0467741935483871,3139.0,other,friends +330_3139_107_234.mov,3139,friends,107_234,54,215729,330,yes,,,234.0,107.0,3_after,73.0,0.0,3139.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,long_gap,90.3793103448276,0.0,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,long_gap,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,1_before,90.75,0.3706896551724128,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,1_before,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,2_before,89.0,-1.3793103448275872,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,2_before,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,3_before,89.6,-0.7793103448275929,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,3_before,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,1_after,93.2,2.820689655172416,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,1_after,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,2_after,98.55,8.17068965517241,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,2_after,100.0,0.0,2951.0,other,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,107.0,234.0,3_after,99.5,9.120689655172413,2951.0,self,friends +331_2951_107_234.mov,2951,friends,107_234,104,466082,331,yes,,,234.0,107.0,3_after,100.0,0.0,2951.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,long_gap,61.31818181818182,0.0,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,long_gap,75.95454545454545,0.0,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,1_before,59.15,-2.168181818181821,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,1_before,67.45,-8.50454545454545,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,2_before,57.6,-3.7181818181818183,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,2_before,67.0,-8.954545454545453,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,3_before,52.5,-8.81818181818182,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,3_before,67.0,-8.954545454545453,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,1_after,75.15,13.831818181818186,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,1_after,76.0,0.0454545454545467,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,2_after,82.0,20.68181818181818,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,2_after,76.0,0.0454545454545467,2159.0,self,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,4.0,258.0,3_after,82.85,21.53181818181817,2159.0,other,friends +332_2159_4_258.mov,2159,friends,4_258,65,330846,332,yes,,,258.0,4.0,3_after,76.0,0.0454545454545467,2159.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,long_gap,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,long_gap,95.64285714285714,0.0,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,1_before,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,1_before,94.6,-1.0428571428571445,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,2_before,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,2_before,92.4,-3.242857142857133,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,3_before,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,3_before,91.0,-4.642857142857139,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,1_after,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,1_after,89.05,-6.592857142857142,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,2_after,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,2_after,82.45,-13.192857142857136,2858.0,self,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,265.0,4.0,3_after,61.0,0.0,2858.0,other,friends +333_2858_4_265.mov,2858,friends,4_265,51,317489,333,no,,,4.0,265.0,3_after,75.1,-20.542857142857144,2858.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,long_gap,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,long_gap,93.45454545454544,0.0,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,1_before,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,1_before,96.3,2.845454545454544,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,2_before,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,2_before,99.6,6.145454545454541,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,3_before,63.0,0.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,3_before,99.0,5.545454545454547,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,1_after,67.85,4.849999999999994,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,1_after,91.0,-2.4545454545454533,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,2_after,69.0,6.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,2_after,95.65,2.1954545454545524,2256.0,self,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,265.0,4.0,3_after,69.0,6.0,2256.0,other,friends +334_2256_4_265.mov,2256,friends,4_265,57,399764,334,no,,,4.0,265.0,3_after,96.0,2.5454545454545467,2256.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,long_gap,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,long_gap,43.12,0.0,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,1_before,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,1_before,37.5,-5.619999999999997,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,2_before,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,2_before,31.8,-11.319999999999997,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,3_before,56.0,0.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,3_before,23.9,-19.22,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,1_after,56.35,0.3500000000000014,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,1_after,47.75,4.630000000000003,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,2_after,57.0,1.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,2_after,54.4,11.28,2462.0,self,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,265.0,4.0,3_after,57.0,1.0,2462.0,other,friends +335_2462_4_265.mov,2462,friends,4_265,67,552012,335,no,,,4.0,265.0,3_after,55.8,12.68,2462.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,long_gap,35.11538461538461,0.0,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,long_gap,48.42307692307692,0.0,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,1_before,37.5,2.384615384615387,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,1_before,50.35,1.9269230769230816,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,2_before,42.0,6.884615384615387,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,2_before,50.0,1.5769230769230802,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,3_before,27.25,-7.865384615384613,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,3_before,50.0,1.5769230769230802,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,1_after,42.6,7.484615384615388,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,1_after,48.0,-0.4230769230769198,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,2_after,41.75,6.634615384615387,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,2_after,48.0,-0.4230769230769198,2533.0,other,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,8.0,243.0,3_after,35.0,-0.1153846153846132,2533.0,self,friends +336_2533_8_243.mov,2533,friends,8_243,8,16421,336,no,,,243.0,8.0,3_after,48.65,0.2269230769230787,2533.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,long_gap,21.28,0.0,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,long_gap,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,1_before,35.0,13.72,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,1_before,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,2_before,35.0,13.72,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,2_before,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,3_before,35.0,13.72,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,3_before,51.0,0.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,1_after,28.05,6.77,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,1_after,51.65,0.6499999999999986,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,2_after,31.3,10.02,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,2_after,53.0,2.0,2516.0,self,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,8.0,243.0,3_after,42.25,20.97,2516.0,other,friends +337_2516_8_243.mov,2516,friends,8_243,13,32821,337,yes,,,243.0,8.0,3_after,53.0,2.0,2516.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,long_gap,87.1086956521739,0.0,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,long_gap,61.19565217391305,0.0,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,1_before,58.1,-29.008695652173905,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,1_before,55.75,-5.445652173913047,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,2_before,67.3,-19.80869565217391,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,2_before,54.0,-7.195652173913047,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,3_before,71.0,-16.108695652173907,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,3_before,54.0,-7.195652173913047,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,1_after,51.1,-36.008695652173905,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,1_after,64.55,3.3543478260869506,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,2_after,18.05,-69.05869565217391,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,2_after,65.0,3.8043478260869534,4623.0,self,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,8.0,243.0,3_after,33.15,-53.95869565217391,4623.0,other,friends +338_4623_8_243.mov,4623,friends,8_243,23,54925,338,yes,,,243.0,8.0,3_after,65.0,3.8043478260869534,4623.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,long_gap,79.76190476190476,0.0,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,long_gap,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,1_before,94.0,14.23809523809524,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,1_before,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,2_before,93.35,13.588095238095235,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,2_before,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,3_before,91.0,11.23809523809524,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,3_before,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,1_after,0.0,-79.76190476190476,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,1_after,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,2_after,27.5,-52.26190476190476,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,2_after,98.0,0.0,2157.0,other,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,8.0,243.0,3_after,40.9,-38.86190476190476,2157.0,self,friends +339_2157_8_243.mov,2157,friends,8_243,104,384291,339,no,,,243.0,8.0,3_after,98.0,0.0,2157.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,long_gap,85.77083333333333,0.0,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,long_gap,99.22916666666669,0.0,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,1_before,81.6,-4.170833333333334,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,1_before,99.0,-0.2291666666666714,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,2_before,71.9,-13.870833333333325,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,2_before,99.0,-0.2291666666666714,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,3_before,65.0,-20.77083333333333,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,3_before,99.0,-0.2291666666666714,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,1_after,89.2,3.4291666666666742,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,1_after,100.0,0.7708333333333286,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,2_after,90.0,4.229166666666671,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,2_after,100.0,0.7708333333333286,4758.0,other,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,8.0,243.0,3_after,58.75,-27.02083333333333,4758.0,self,friends +340_4758_8_243.mov,4758,friends,8_243,108,409029,340,yes,,,243.0,8.0,3_after,100.0,0.7708333333333286,4758.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,long_gap,89.58620689655173,0.0,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,long_gap,100.0,0.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,1_before,90.21052631578948,0.6243194192377501,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,1_before,99.9,-0.0999999999999943,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,2_before,84.73684210526316,-4.849364791288565,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,2_before,99.0,-1.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,3_before,82.0,-7.5862068965517295,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,3_before,99.0,-1.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,1_after,59.8,-29.78620689655173,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,1_after,100.0,0.0,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,2_after,59.35,-30.236206896551728,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,2_after,99.25,-0.75,2919.0,self,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,8.0,243.0,3_after,42.0,-47.58620689655173,2919.0,other,friends +341_2919_8_243.mov,2919,friends,8_243,109,412629,341,no,,,243.0,8.0,3_after,99.0,-1.0,2919.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,long_gap,62.85,0.0,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,long_gap,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,1_before,66.0,3.1499999999999986,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,1_before,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,2_before,65.55,2.699999999999996,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,2_before,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,3_before,65.0,2.1499999999999986,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,3_before,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,1_after,54.4,-8.450000000000003,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,1_after,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,2_after,58.0,-4.850000000000001,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,2_after,99.0,0.0,2036.0,self,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,8.0,243.0,3_after,58.0,-4.850000000000001,2036.0,other,friends +342_2036_8_243.mov,2036,friends,8_243,131,490788,342,no,,,243.0,8.0,3_after,99.0,0.0,2036.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,long_gap,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,long_gap,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,1_before,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,1_before,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,2_before,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,2_before,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,3_before,83.8,0.7999999999999972,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,3_before,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,1_after,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,1_after,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,2_after,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,2_after,99.0,0.0,2171.0,other,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,8.0,243.0,3_after,83.0,0.0,2171.0,self,friends +343_2171_8_243.mov,2171,friends,8_243,142,511646,343,no,,,243.0,8.0,3_after,99.0,0.0,2171.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,long_gap,65.58064516129032,0.0,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,long_gap,63.45161290322581,0.0,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,1_before,65.0,-0.5806451612903203,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,1_before,61.75,-1.7016129032258078,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,2_before,65.0,-0.5806451612903203,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,2_before,51.4,-12.05161290322581,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,3_before,65.0,-0.5806451612903203,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,3_before,48.0,-15.451612903225808,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,1_after,66.0,0.4193548387096797,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,1_after,57.55,-5.901612903225811,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,2_after,66.0,0.4193548387096797,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,2_after,50.0,-13.451612903225808,3074.0,other,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,86.0,227.0,3_after,66.0,0.4193548387096797,3074.0,self,friends +344_3074_86_227.mov,3074,friends,86_227,39,184213,344,yes,,,227.0,86.0,3_after,55.85,-7.601612903225806,3074.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,long_gap,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,long_gap,93.15,0.0,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,1_before,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,1_before,92.0,-1.1500000000000057,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,2_before,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,2_before,92.0,-1.1500000000000057,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,3_before,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,3_before,92.0,-1.1500000000000057,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,1_after,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,1_after,96.75,3.5999999999999943,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,2_after,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,2_after,97.0,3.8499999999999934,3998.0,other,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,8.0,244.0,3_after,99.0,0.0,3998.0,self,friends +345_3998_8_244.mov,3998,friends,8_244,69,148896,345,yes,,,244.0,8.0,3_after,97.0,3.8499999999999934,3998.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,long_gap,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,long_gap,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,1_before,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,1_before,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,2_before,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,2_before,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,3_before,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,3_before,97.0,0.0,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,1_after,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,1_after,82.9,-14.099999999999994,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,2_after,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,2_after,74.15,-22.849999999999994,2398.0,self,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,8.0,244.0,3_after,99.0,0.0,2398.0,other,friends +346_2398_8_244.mov,2398,friends,8_244,72,162735,346,no,,,244.0,8.0,3_after,77.0,-20.0,2398.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,long_gap,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,long_gap,54.75,0.0,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,1_before,98.25,0.25,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,1_before,51.2,-3.549999999999997,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,2_before,97.2,-0.7999999999999972,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,2_before,51.0,-3.75,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,3_before,97.0,-1.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,3_before,51.0,-3.75,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,1_after,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,1_after,56.0,1.25,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,2_after,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,2_after,56.0,1.25,2042.0,self,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,85.0,256.0,3_after,98.0,0.0,2042.0,other,friends +347_2042_85_256.mov,2042,friends,85_256,33,133417,347,yes,,,256.0,85.0,3_after,56.15,1.3999999999999986,2042.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,long_gap,90.0,0.0,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,long_gap,59.80952380952381,0.0,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,1_before,88.55,-1.4500000000000028,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,1_before,58.95,-0.8595238095238074,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,2_before,81.95,-8.049999999999997,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,2_before,55.35,-4.459523809523809,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,3_before,92.95,2.950000000000003,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,3_before,55.0,-4.80952380952381,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,1_after,90.0,0.0,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,1_after,60.55,0.740476190476187,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,2_after,90.6,0.5999999999999943,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,2_after,61.0,1.1904761904761898,2035.0,other,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,85.0,256.0,3_after,94.75,4.75,2035.0,self,friends +348_2035_85_256.mov,2035,friends,85_256,42,175314,348,no,,,256.0,85.0,3_after,61.25,1.4404761904761898,2035.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,long_gap,98.0,0.0,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,long_gap,82.5,0.0,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,1_before,97.8,-0.2000000000000028,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,1_before,93.65,11.150000000000006,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,2_before,98.55,0.5499999999999972,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,2_before,94.0,11.5,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,3_before,99.0,1.0,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,3_before,94.0,11.5,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,1_after,98.6,0.5999999999999943,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,1_after,65.1,-17.400000000000006,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,2_after,98.65,0.6500000000000057,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,2_after,65.0,-17.5,3180.0,self,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,85.0,256.0,3_after,98.5,0.5,3180.0,other,friends +349_3180_85_256.mov,3180,friends,85_256,51,208731,349,yes,,,256.0,85.0,3_after,65.5,-17.0,3180.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,long_gap,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,long_gap,72.0,0.0,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,1_before,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,1_before,71.5,-0.5,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,2_before,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,2_before,71.5,-0.5,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,3_before,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,3_before,72.0,0.0,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,1_after,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,1_after,72.0,0.0,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,2_after,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,2_after,71.65,-0.3499999999999943,2068.0,self,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,85.0,256.0,3_after,100.0,0.0,2068.0,other,friends +350_2068_85_256.mov,2068,friends,85_256,77,316255,350,no,,,256.0,85.0,3_after,71.0,-1.0,2068.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,long_gap,88.48571428571428,0.0,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,long_gap,80.54285714285714,0.0,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,1_before,80.45,-8.035714285714278,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,1_before,76.75,-3.792857142857145,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,2_before,76.75,-11.73571428571428,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,2_before,75.35,-5.19285714285715,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,3_before,86.5,-1.9857142857142804,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,3_before,75.5,-5.0428571428571445,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,1_after,91.0,2.51428571428572,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,1_after,71.95,-8.592857142857142,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,2_after,91.85,3.364285714285714,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,2_after,76.0,-4.5428571428571445,3526.0,other,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,85.0,256.0,3_after,93.0,4.51428571428572,3526.0,self,friends +351_3526_85_256.mov,3526,friends,85_256,98,395371,351,yes,,,256.0,85.0,3_after,74.2,-6.342857142857142,3526.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,long_gap,73.97435897435898,0.0,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,long_gap,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,1_before,91.2,17.225641025641025,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,1_before,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,2_before,95.25,21.275641025641026,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,2_before,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,3_before,95.0,21.025641025641026,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,3_before,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,1_after,55.7,-18.27435897435897,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,1_after,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,2_after,55.95,-18.02435897435897,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,2_after,79.0,0.0,3904.0,self,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,85.0,256.0,3_after,58.3,-15.67435897435898,3904.0,other,friends +352_3904_85_256.mov,3904,friends,85_256,145,509752,352,yes,,,256.0,85.0,3_after,79.0,0.0,3904.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,long_gap,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,long_gap,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,1_before,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,1_before,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,2_before,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,2_before,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,3_before,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,3_before,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,1_after,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,1_after,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,2_after,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,2_after,75.0,0.0,4354.0,other,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,229.0,50.0,3_after,0.0,0.0,4354.0,self,friends +353_4354_50_229.mov,4354,friends,50_229,6,16980,353,yes,,,50.0,229.0,3_after,75.0,0.0,4354.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,long_gap,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,long_gap,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,1_before,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,1_before,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,2_before,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,2_before,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,3_before,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,3_before,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,1_after,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,1_after,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,2_after,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,2_after,75.0,0.0,3798.0,other,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,229.0,50.0,3_after,0.0,0.0,3798.0,self,friends +354_3798_50_229.mov,3798,friends,50_229,8,25155,354,no,,,50.0,229.0,3_after,75.0,0.0,3798.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,long_gap,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,long_gap,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,1_before,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,1_before,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,2_before,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,2_before,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,3_before,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,3_before,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,1_after,50.0,0.0,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,1_after,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,2_after,50.55,0.5499999999999972,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,2_after,99.0,0.0,2118.0,self,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,229.0,50.0,3_after,49.55,-0.4500000000000028,2118.0,other,friends +355_2118_50_229.mov,2118,friends,50_229,74,246702,355,no,,,50.0,229.0,3_after,99.0,0.0,2118.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,long_gap,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,long_gap,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,1_before,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,1_before,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,2_before,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,2_before,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,3_before,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,3_before,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,1_after,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,1_after,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,2_after,49.0,0.0,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,2_after,99.0,0.0,3106.0,self,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,229.0,50.0,3_after,48.65,-0.3500000000000014,3106.0,other,friends +356_3106_50_229.mov,3106,friends,50_229,86,301950,356,yes,,,50.0,229.0,3_after,99.0,0.0,3106.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,long_gap,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,long_gap,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,1_before,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,1_before,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,2_before,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,2_before,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,3_before,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,3_before,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,1_after,100.0,0.0,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,1_after,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,2_after,95.65,-4.349999999999994,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,2_after,84.0,0.0,3017.0,other,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,107.0,233.0,3_after,92.4,-7.599999999999994,3017.0,self,friends +357_3017_107_233.mov,3017,friends,107_233,120,533097,357,yes,,,233.0,107.0,3_after,84.0,0.0,3017.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,long_gap,77.125,0.0,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,long_gap,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,1_before,74.0,-3.125,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,1_before,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,2_before,74.0,-3.125,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,2_before,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,3_before,74.0,-3.125,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,3_before,58.10526315789474,-1.89473684210526,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,1_after,65.7,-11.424999999999995,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,1_after,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,2_after,62.9,-14.225,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,2_after,60.0,0.0,2379.0,other,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,67.0,210.0,3_after,66.45,-10.674999999999995,2379.0,self,friends +358_2379_67_210.mov,2379,friends,67_210,70,297769,358,yes,,,210.0,67.0,3_after,60.0,0.0,2379.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,long_gap,74.10526315789474,0.0,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,long_gap,63.0,0.0,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,1_before,60.36842105263158,-13.736842105263158,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,1_before,59.9,-3.1000000000000014,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,2_before,58.0,-16.10526315789474,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,2_before,42.05,-20.950000000000003,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,3_before,57.78947368421053,-16.315789473684212,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,3_before,45.7,-17.299999999999997,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,1_after,83.8,9.694736842105256,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,1_after,62.6,-0.3999999999999986,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,2_after,71.75,-2.35526315789474,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,2_after,51.3,-11.700000000000005,3889.0,self,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,67.0,210.0,3_after,65.4,-8.705263157894734,3889.0,other,friends +359_3889_67_210.mov,3889,friends,67_210,113,533279,359,yes,,,210.0,67.0,3_after,51.0,-12.0,3889.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,long_gap,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,long_gap,69.0909090909091,0.0,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,1_before,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,1_before,69.0,-0.0909090909090934,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,2_before,99.85,-0.1500000000000056,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,2_before,68.0,-1.0909090909090935,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,3_before,98.45,-1.5499999999999972,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,3_before,72.6,3.509090909090901,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,1_after,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,1_after,75.3,6.209090909090904,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,2_after,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,2_after,76.0,6.9090909090909065,2162.0,other,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,218.0,63.0,3_after,100.0,0.0,2162.0,self,friends +360_2162_63_218.mov,2162,friends,63_218,50,128512,360,no,,,63.0,218.0,3_after,76.0,6.9090909090909065,2162.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,long_gap,85.9,0.0,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,long_gap,94.05,0.0,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,1_before,84.0,-1.9000000000000057,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,1_before,91.0,-3.049999999999997,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,2_before,84.2,-1.7000000000000028,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,2_before,91.0,-3.049999999999997,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,3_before,85.0,-0.9000000000000057,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,3_before,90.25,-3.799999999999997,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,1_after,91.95,6.049999999999997,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,1_after,100.0,5.950000000000003,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,2_after,97.4,11.5,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,2_after,100.0,5.950000000000003,2056.0,self,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,63.0,224.0,3_after,97.0,11.099999999999994,2056.0,other,friends +361_2056_63_224.mov,2056,friends,63_224,36,117894,361,yes,,,224.0,63.0,3_after,100.0,5.950000000000003,2056.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,long_gap,85.95652173913044,0.0,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,long_gap,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,1_before,81.05,-4.90652173913044,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,1_before,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,2_before,81.0,-4.956521739130437,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,2_before,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,3_before,81.15,-4.806521739130432,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,3_before,94.0,0.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,1_after,87.0,1.0434782608695627,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,1_after,95.0,1.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,2_after,84.9,-1.0565217391304316,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,2_after,95.0,1.0,2245.0,self,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,63.0,224.0,3_after,81.6,-4.356521739130443,2245.0,other,friends +362_2245_63_224.mov,2245,friends,63_224,100,360429,362,yes,,,224.0,63.0,3_after,95.0,1.0,2245.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,long_gap,90.88,0.0,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,long_gap,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,1_before,88.0,-2.8799999999999955,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,1_before,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,2_before,82.05,-8.829999999999998,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,2_before,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,3_before,81.75,-9.129999999999995,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,3_before,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,1_after,91.0,0.1200000000000045,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,1_after,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,2_after,91.0,0.1200000000000045,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,2_after,95.0,0.0,2479.0,self,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,63.0,224.0,3_after,91.0,0.1200000000000045,2479.0,other,friends +363_2479_63_224.mov,2479,friends,63_224,109,372006,363,yes,,,224.0,63.0,3_after,95.0,0.0,2479.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,long_gap,94.0,0.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,long_gap,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,1_before,84.8,-9.200000000000005,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,1_before,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,2_before,91.3,-2.700000000000003,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,2_before,97.9,-0.0999999999999943,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,3_before,91.0,-3.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,3_before,95.1,-2.9000000000000057,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,1_after,93.45,-0.5499999999999972,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,1_after,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,2_after,93.0,-1.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,2_after,98.0,0.0,2210.0,self,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,63.0,224.0,3_after,93.0,-1.0,2210.0,other,friends +364_2210_63_224.mov,2210,friends,63_224,117,386060,364,no,,,224.0,63.0,3_after,98.0,0.0,2210.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,long_gap,87.88888888888889,0.0,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,long_gap,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,1_before,85.0,-2.8888888888888857,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,1_before,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,2_before,82.5,-5.388888888888886,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,2_before,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,3_before,81.0,-6.888888888888886,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,3_before,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,1_after,95.0,7.111111111111114,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,1_after,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,2_after,94.3,6.411111111111111,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,2_after,100.0,0.0,2711.0,self,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,63.0,224.0,3_after,94.2,6.311111111111117,2711.0,other,friends +365_2711_63_224.mov,2711,friends,63_224,132,483549,365,yes,,,224.0,63.0,3_after,100.0,0.0,2711.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,long_gap,83.92857142857143,0.0,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,long_gap,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,1_before,78.0,-5.928571428571431,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,1_before,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,2_before,78.0,-5.928571428571431,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,2_before,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,3_before,78.0,-5.928571428571431,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,3_before,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,1_after,85.0,1.0714285714285694,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,1_after,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,2_after,85.0,1.0714285714285694,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,2_after,100.0,0.0,2748.0,other,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,63.0,224.0,3_after,86.3,2.3714285714285666,2748.0,self,friends +366_2748_63_224.mov,2748,friends,63_224,145,538423,366,yes,,,224.0,63.0,3_after,100.0,0.0,2748.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,long_gap,95.38709677419357,0.0,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,long_gap,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,1_before,85.0,-10.387096774193552,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,1_before,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,2_before,85.0,-10.387096774193552,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,2_before,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,3_before,85.0,-10.387096774193552,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,3_before,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,1_after,100.0,4.612903225806448,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,1_after,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,2_after,100.0,4.612903225806448,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,2_after,100.0,0.0,3155.0,other,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,63.0,224.0,3_after,100.0,4.612903225806448,3155.0,self,friends +367_3155_63_224.mov,3155,friends,63_224,146,546598,367,yes,,,224.0,63.0,3_after,100.0,0.0,3155.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,long_gap,70.0,0.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,long_gap,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,1_before,70.35,0.3499999999999943,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,1_before,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,2_before,71.45,1.4500000000000028,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,2_before,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,3_before,71.0,1.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,3_before,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,1_after,70.1,0.0999999999999943,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,1_after,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,2_after,71.0,1.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,2_after,89.0,0.0,2268.0,other,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,255.0,85.0,3_after,71.0,1.0,2268.0,self,friends +368_2268_85_255.mov,2268,friends,85_255,30,96695,368,yes,,,85.0,255.0,3_after,89.0,0.0,2268.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,long_gap,62.9,0.0,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,long_gap,90.75,0.0,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,1_before,68.45,5.550000000000004,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,1_before,90.3,-0.4500000000000028,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,2_before,69.4,6.500000000000007,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,2_before,92.2,1.4500000000000028,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,3_before,69.0,6.100000000000001,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,3_before,91.3,0.5499999999999972,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,1_after,63.47368421052632,0.5736842105263165,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,1_after,83.15,-7.599999999999994,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,2_after,79.57142857142857,16.67142857142857,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,2_after,76.85,-13.900000000000006,2007.0,self,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,255.0,85.0,3_after,79.0,16.1,2007.0,other,friends +369_2007_85_255.mov,2007,friends,85_255,33,113738,369,no,,,85.0,255.0,3_after,71.7,-19.05,2007.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,long_gap,67.75,0.0,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,long_gap,58.130434782608695,0.0,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,1_before,64.0,-3.75,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,1_before,58.75,0.6195652173913047,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,2_before,64.0,-3.75,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,2_before,61.35,3.219565217391306,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,3_before,64.0,-3.75,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,3_before,64.1,5.969565217391299,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,1_after,69.0,1.25,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,1_after,43.0,-15.130434782608695,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,2_after,69.0,1.25,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,2_after,47.35,-10.780434782608694,2299.0,self,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,255.0,85.0,3_after,69.0,1.25,2299.0,other,friends +370_2299_85_255.mov,2299,friends,85_255,43,177296,370,no,,,85.0,255.0,3_after,55.0,-3.130434782608696,2299.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,long_gap,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,long_gap,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,1_before,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,1_before,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,2_before,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,2_before,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,3_before,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,3_before,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,1_after,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,1_after,100.0,0.0,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,2_after,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,2_after,99.7,-0.2999999999999971,3264.0,self,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,255.0,85.0,3_after,50.0,0.0,3264.0,other,friends +371_3264_85_255.mov,3264,friends,85_255,123,452376,371,no,,,85.0,255.0,3_after,98.8,-1.2000000000000028,3264.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,long_gap,74.22727272727273,0.0,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,long_gap,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,1_before,74.0,-0.2272727272727337,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,1_before,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,2_before,74.4,0.1727272727272719,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,2_before,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,3_before,76.95,2.722727272727269,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,3_before,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,1_after,76.7,2.472727272727269,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,1_after,82.0,0.0,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,2_after,77.0,2.7727272727272663,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,2_after,70.4,-11.599999999999994,2179.0,other,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,255.0,85.0,3_after,79.0,4.772727272727266,2179.0,self,friends +372_2179_85_255.mov,2179,friends,85_255,158,564264,372,no,,,85.0,255.0,3_after,69.8,-12.200000000000005,2179.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,long_gap,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,long_gap,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,1_before,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,1_before,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,2_before,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,2_before,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,3_before,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,3_before,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,1_after,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,1_after,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,2_after,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,2_after,95.0,0.0,2178.0,self,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,205.0,67.0,3_after,64.0,0.0,2178.0,other,friends +373_2178_67_205.mov,2178,friends,67_205,99,309745,373,no,,,67.0,205.0,3_after,95.0,0.0,2178.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,long_gap,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,long_gap,80.36,0.0,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,1_before,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,1_before,71.8,-8.560000000000002,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,2_before,49.15,0.1499999999999985,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,2_before,60.8,-19.56,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,3_before,57.2,8.200000000000003,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,3_before,68.7,-11.659999999999997,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,1_after,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,1_after,72.55,-7.810000000000002,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,2_after,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,2_after,71.0,-9.36,2454.0,other,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,205.0,67.0,3_after,49.0,0.0,2454.0,self,friends +374_2454_67_205.mov,2454,friends,67_205,144,462184,374,yes,,,67.0,205.0,3_after,72.5,-7.859999999999999,2454.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,long_gap,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,long_gap,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,1_before,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,1_before,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,2_before,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,2_before,81.3,-3.700000000000003,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,3_before,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,3_before,79.0,-6.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,1_after,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,1_after,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,2_after,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,2_after,85.0,0.0,3616.0,self,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,86.0,239.0,3_after,66.0,0.0,3616.0,other,friends +375_3616_86_239.mov,3616,friends,86_239,15,99298,375,no,,,239.0,86.0,3_after,85.0,0.0,3616.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,long_gap,66.62962962962963,0.0,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,long_gap,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,1_before,76.9,10.270370370370372,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,1_before,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,2_before,79.0,12.370370370370368,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,2_before,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,3_before,79.0,12.370370370370368,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,3_before,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,1_after,51.3,-15.329629629629636,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,1_after,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,2_after,49.0,-17.629629629629633,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,2_after,85.0,0.0,2687.0,self,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,86.0,239.0,3_after,49.0,-17.629629629629633,2687.0,other,friends +376_2687_86_239.mov,2687,friends,86_239,27,205757,376,no,,,239.0,86.0,3_after,85.0,0.0,2687.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,long_gap,46.96153846153846,0.0,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,long_gap,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,1_before,49.0,2.03846153846154,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,1_before,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,2_before,49.0,2.03846153846154,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,2_before,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,3_before,49.15,2.1884615384615387,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,3_before,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,1_after,43.4,-3.5615384615384613,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,1_after,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,2_after,43.0,-3.96153846153846,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,2_after,85.0,0.0,2630.0,other,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,86.0,239.0,3_after,43.95,-3.011538461538457,2630.0,self,friends +377_2630_86_239.mov,2630,friends,86_239,30,215766,377,no,,,239.0,86.0,3_after,85.0,0.0,2630.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,long_gap,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,long_gap,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,1_before,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,1_before,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,2_before,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,2_before,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,3_before,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,3_before,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,1_after,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,1_after,85.0,0.0,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,2_after,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,2_after,87.1,2.0999999999999943,2311.0,other,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,86.0,239.0,3_after,56.0,0.0,2311.0,self,friends +378_2311_86_239.mov,2311,friends,86_239,40,292510,378,yes,,,239.0,86.0,3_after,88.0,3.0,2311.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,long_gap,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,long_gap,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,1_before,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,1_before,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,2_before,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,2_before,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,3_before,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,3_before,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,1_after,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,1_after,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,2_after,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,2_after,88.0,0.0,4614.0,self,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,86.0,239.0,3_after,65.0,0.0,4614.0,other,friends +379_4614_86_239.mov,4614,friends,86_239,49,325389,379,no,,,239.0,86.0,3_after,88.0,0.0,4614.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,long_gap,81.03225806451613,0.0,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,long_gap,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,1_before,70.2,-10.832258064516123,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,1_before,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,2_before,65.0,-16.032258064516128,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,2_before,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,3_before,65.0,-16.032258064516128,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,3_before,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,1_after,81.0,-0.0322580645161281,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,1_after,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,2_after,81.0,-0.0322580645161281,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,2_after,88.0,0.0,6261.0,self,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,86.0,239.0,3_after,81.0,-0.0322580645161281,6261.0,other,friends +380_6261_86_239.mov,6261,friends,86_239,53,394992,380,yes,,,239.0,86.0,3_after,88.0,0.0,6261.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,long_gap,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,long_gap,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,1_before,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,1_before,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,2_before,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,2_before,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,3_before,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,3_before,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,1_after,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,1_after,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,2_after,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,2_after,88.0,0.0,2094.0,self,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,86.0,239.0,3_after,81.0,0.0,2094.0,other,friends +381_2094_86_239.mov,2094,friends,86_239,55,413607,381,no,,,239.0,86.0,3_after,88.0,0.0,2094.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,long_gap,65.16666666666667,0.0,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,long_gap,62.92857142857143,0.0,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,1_before,63.0,-2.1666666666666714,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,1_before,52.55,-10.378571428571432,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,2_before,63.0,-2.1666666666666714,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,2_before,51.0,-11.92857142857143,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,3_before,62.15,-3.016666666666673,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,3_before,51.0,-11.92857142857143,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,1_after,66.95,1.7833333333333314,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,1_after,64.04545454545455,1.116883116883116,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,2_after,67.0,1.8333333333333288,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,2_after,65.0,2.0714285714285694,2940.0,other,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,49.0,225.0,3_after,66.0,0.8333333333333286,2940.0,self,friends +382_2940_49_225.mov,2940,friends,49_225,8,11034,382,no,,,225.0,49.0,3_after,66.15,3.221428571428575,2940.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,long_gap,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,long_gap,66.81818181818181,0.0,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,1_before,89.95,-1.0499999999999972,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,1_before,60.05,-6.768181818181816,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,2_before,90.0,-1.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,2_before,60.0,-6.818181818181813,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,3_before,90.0,-1.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,3_before,60.0,-6.818181818181813,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,1_after,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,1_after,71.0,4.181818181818187,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,2_after,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,2_after,71.0,4.181818181818187,2200.0,other,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,49.0,225.0,3_after,91.0,0.0,2200.0,self,friends +383_2200_49_225.mov,2200,friends,49_225,55,339661,383,no,,,225.0,49.0,3_after,71.0,4.181818181818187,2200.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,long_gap,89.95652173913044,0.0,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,long_gap,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,1_before,85.8,-4.15652173913044,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,1_before,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,2_before,85.0,-4.956521739130437,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,2_before,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,3_before,89.6,-0.3565217391304429,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,3_before,97.0,0.0,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,1_after,90.0,0.0434782608695627,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,1_after,94.65,-2.3499999999999943,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,2_after,90.6,0.6434782608695571,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,2_after,80.7,-16.299999999999997,4627.0,other,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,49.0,225.0,3_after,97.3,7.34347826086956,4627.0,self,friends +384_4627_49_225.mov,4627,friends,49_225,103,522231,384,yes,,,225.0,49.0,3_after,79.0,-18.0,4627.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,long_gap,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,long_gap,39.42307692307692,0.0,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,1_before,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,1_before,33.4,-6.023076923076921,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,2_before,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,2_before,31.0,-8.42307692307692,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,3_before,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,3_before,31.0,-8.42307692307692,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,1_after,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,1_after,41.55,2.1269230769230774,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,2_after,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,2_after,42.0,2.57692307692308,2576.0,other,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,50.0,230.0,3_after,99.0,0.0,2576.0,self,friends +385_2576_50_230.mov,2576,friends,50_230,4,22140,385,no,,,230.0,50.0,3_after,42.2,2.776923076923083,2576.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,long_gap,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,long_gap,71.0,0.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,1_before,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,1_before,70.0,-1.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,2_before,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,2_before,70.0,-1.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,3_before,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,3_before,70.0,-1.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,1_after,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,1_after,71.0,0.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,2_after,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,2_after,71.0,0.0,2167.0,other,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,50.0,230.0,3_after,99.0,0.0,2167.0,self,friends +386_2167_50_230.mov,2167,friends,50_230,70,498251,386,no,,,230.0,50.0,3_after,71.0,0.0,2167.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,long_gap,67.91428571428571,0.0,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,long_gap,68.74285714285715,0.0,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,1_before,64.6,-3.3142857142857167,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,1_before,65.6,-3.142857142857153,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,2_before,63.15,-4.7642857142857125,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,2_before,64.0,-4.742857142857147,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,3_before,63.0,-4.914285714285711,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,3_before,64.0,-4.742857142857147,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,1_after,69.95,2.035714285714292,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,1_after,69.0,0.2571428571428527,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,2_after,72.9,4.985714285714295,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,2_after,74.85,6.107142857142847,3509.0,self,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,242.0,29.0,3_after,71.2,3.285714285714292,3509.0,other,friends +387_3509_29_242.mov,3509,friends,29_242,3,13780,387,no,,,29.0,242.0,3_after,77.45,8.707142857142856,3509.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,long_gap,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,long_gap,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,1_before,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,1_before,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,2_before,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,2_before,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,3_before,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,3_before,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,1_after,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,1_after,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,2_after,81.0,0.0,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,2_after,84.0,0.0,2410.0,self,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,242.0,29.0,3_after,80.7,-0.2999999999999971,2410.0,other,friends +388_2410_29_242.mov,2410,friends,29_242,25,122767,388,no,,,29.0,242.0,3_after,84.0,0.0,2410.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,long_gap,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,long_gap,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,1_before,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,1_before,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,2_before,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,2_before,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,3_before,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,3_before,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,1_after,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,1_after,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,2_after,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,2_after,83.0,0.0,3235.0,other,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,242.0,29.0,3_after,69.0,0.0,3235.0,self,friends +389_3235_29_242.mov,3235,friends,29_242,42,238477,389,no,,,29.0,242.0,3_after,84.2,1.2000000000000028,3235.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,long_gap,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,long_gap,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,1_before,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,1_before,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,2_before,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,2_before,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,3_before,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,3_before,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,1_after,65.2,0.2000000000000028,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,1_after,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,2_after,65.4,0.4000000000000057,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,2_after,81.0,0.0,2906.0,other,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,242.0,29.0,3_after,65.0,0.0,2906.0,self,friends +390_2906_29_242.mov,2906,friends,29_242,86,538437,390,no,,,29.0,242.0,3_after,81.0,0.0,2906.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,long_gap,99.1818181818182,0.0,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,long_gap,63.81818181818182,0.0,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,1_before,89.25,-9.931818181818189,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,1_before,56.5,-7.31818181818182,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,2_before,87.95,-11.231818181818184,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,2_before,56.0,-7.81818181818182,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,3_before,87.0,-12.181818181818189,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,3_before,60.35,-3.4681818181818187,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,1_after,98.7,-0.4818181818181841,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,1_after,64.0,0.1818181818181798,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,2_after,92.45,-6.731818181818184,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,2_after,64.0,0.1818181818181798,2149.0,self,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,63.0,217.0,3_after,84.7,-14.481818181818184,2149.0,other,friends +391_2149_63_217.mov,2149,friends,63_217,37,166118,391,yes,,,217.0,63.0,3_after,64.0,0.1818181818181798,2149.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,long_gap,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,long_gap,100.0,0.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,1_before,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,1_before,99.0,-1.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,2_before,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,2_before,99.0,-1.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,3_before,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,3_before,98.55,-1.4500000000000028,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,1_after,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,1_after,100.0,0.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,2_after,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,2_after,100.0,0.0,2007.0,self,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,48.0,236.0,3_after,100.0,0.0,2007.0,other,friends +392_2007_48_236.mov,2007,friends,48_236,89,425473,392,yes,,,236.0,48.0,3_after,100.0,0.0,2007.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,long_gap,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,long_gap,80.0,0.0,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,1_before,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,1_before,80.0,0.0,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,2_before,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,2_before,79.8,-0.2000000000000028,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,3_before,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,3_before,79.9,-0.0999999999999943,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,1_after,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,1_after,80.0,0.0,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,2_after,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,2_after,80.25,0.25,2556.0,self,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,226.0,49.0,3_after,94.0,0.0,2556.0,other,friends +393_2556_49_226.mov,2556,friends,49_226,102,555323,393,no,,,49.0,226.0,3_after,81.0,1.0,2556.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,long_gap,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,long_gap,70.0,0.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,1_before,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,1_before,70.0,0.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,2_before,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,2_before,70.0,0.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,3_before,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,3_before,69.35,-0.6500000000000057,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,1_after,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,1_after,71.0,1.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,2_after,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,2_after,71.0,1.0,2148.0,other,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,50.0,231.0,3_after,100.0,0.0,2148.0,self,friends +394_2148_50_231.mov,2148,friends,50_231,21,49533,394,yes,,,231.0,50.0,3_after,71.0,1.0,2148.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,long_gap,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,long_gap,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,1_before,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,1_before,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,2_before,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,2_before,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,3_before,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,3_before,78.0,0.0,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,1_after,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,1_after,78.05,0.0499999999999971,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,2_after,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,2_after,79.75,1.75,2049.0,other,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,50.0,231.0,3_after,100.0,0.0,2049.0,self,friends +395_2049_50_231.mov,2049,friends,50_231,35,112360,395,yes,,,231.0,50.0,3_after,80.2,2.200000000000003,2049.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,long_gap,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,long_gap,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,1_before,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,1_before,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,2_before,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,2_before,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,3_before,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,3_before,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,1_after,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,1_after,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,2_after,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,2_after,93.0,0.0,2072.0,other,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,50.0,231.0,3_after,100.0,0.0,2072.0,self,friends +396_2072_50_231.mov,2072,friends,50_231,97,468916,396,no,,,231.0,50.0,3_after,93.0,0.0,2072.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,long_gap,90.95454545454544,0.0,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,long_gap,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,1_before,93.0,2.0454545454545467,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,1_before,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,2_before,93.0,2.0454545454545467,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,2_before,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,3_before,93.3,2.345454545454544,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,3_before,76.0,0.0,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,1_after,88.35,-2.604545454545459,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,1_after,73.15,-2.8499999999999943,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,2_after,88.0,-2.9545454545454533,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,2_after,49.7,-26.3,2218.0,other,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,48.0,232.0,3_after,87.25,-3.704545454545453,2218.0,self,friends +397_2218_48_232.mov,2218,friends,48_232,68,219689,397,no,,,232.0,48.0,3_after,49.0,-27.0,2218.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,long_gap,97.3913043478261,0.0,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,long_gap,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,1_before,95.0,-2.391304347826093,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,1_before,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,2_before,95.0,-2.391304347826093,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,2_before,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,3_before,95.0,-2.391304347826093,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,3_before,54.0,0.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,1_after,100.0,2.608695652173907,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,1_after,55.35,1.3500000000000014,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,2_after,100.0,2.608695652173907,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,2_after,56.0,2.0,2241.0,self,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,48.0,232.0,3_after,100.0,2.608695652173907,2241.0,other,friends +398_2241_48_232.mov,2241,friends,48_232,123,402414,398,no,,,232.0,48.0,3_after,56.0,2.0,2241.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,long_gap,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,long_gap,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,1_before,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,1_before,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,2_before,95.15,0.1500000000000056,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,2_before,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,3_before,97.4,2.4000000000000057,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,3_before,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,1_after,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,1_after,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,2_after,95.0,0.0,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,2_after,58.0,0.0,2252.0,other,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,48.0,232.0,3_after,98.2,3.200000000000003,2252.0,self,friends +399_2252_48_232.mov,2252,friends,48_232,130,424760,399,no,,,232.0,48.0,3_after,58.0,0.0,2252.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,long_gap,93.0,0.0,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,long_gap,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,1_before,90.85,-2.1500000000000057,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,1_before,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,2_before,89.8,-3.200000000000003,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,2_before,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,3_before,90.0,-3.0,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,3_before,82.0,0.0,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,1_after,82.0,-11.0,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,1_after,83.2,1.2000000000000028,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,2_after,68.85,-24.15000000000001,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,2_after,82.15,0.1500000000000056,2109.0,self,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,85.0,261.0,3_after,60.85,-32.15,2109.0,other,friends +400_2109_85_261.mov,2109,friends,85_261,97,334296,400,no,,,261.0,85.0,3_after,82.0,0.0,2109.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,long_gap,78.0,0.0,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,long_gap,58.71428571428572,0.0,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,1_before,78.0,0.0,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,1_before,55.8,-2.914285714285718,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,2_before,75.7,-2.299999999999997,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,2_before,56.0,-2.7142857142857157,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,3_before,75.05,-2.950000000000003,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,3_before,53.95,-4.7642857142857125,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,1_after,78.8,0.7999999999999972,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,1_after,62.75,4.035714285714285,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,2_after,79.0,1.0,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,2_after,63.4,4.685714285714283,2053.0,other,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,85.0,261.0,3_after,79.65,1.6500000000000057,2053.0,self,friends +401_2053_85_261.mov,2053,friends,85_261,116,409225,401,no,,,261.0,85.0,3_after,62.95,4.235714285714288,2053.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,long_gap,96.8095238095238,0.0,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,long_gap,50.23809523809524,0.0,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,1_before,97.0,0.1904761904761898,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,1_before,63.8,13.561904761904756,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,2_before,96.05,-0.759523809523813,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,2_before,71.0,20.76190476190476,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,3_before,94.0,-2.80952380952381,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,3_before,71.0,20.76190476190476,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,1_after,96.0,-0.8095238095238102,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,1_after,44.0,-6.238095238095241,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,2_after,95.15,-1.6595238095238043,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,2_after,44.0,-6.238095238095241,2040.0,self,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,85.0,261.0,3_after,94.35,-2.459523809523816,2040.0,other,friends +402_2040_85_261.mov,2040,friends,85_261,151,532625,402,no,,,261.0,85.0,3_after,44.0,-6.238095238095241,2040.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,long_gap,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,long_gap,73.13636363636364,0.0,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,1_before,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,1_before,72.0,-1.1363636363636402,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,2_before,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,2_before,71.9,-1.2363636363636346,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,3_before,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,3_before,69.45,-3.686363636363637,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,1_after,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,1_after,73.4,0.2636363636363654,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,2_after,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,2_after,74.6,1.463636363636354,2228.0,other,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,68.0,262.0,3_after,57.0,0.0,2228.0,self,friends +403_2228_68_262.mov,2228,friends,68_262,31,179051,403,no,,,262.0,68.0,3_after,75.0,1.86363636363636,2228.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,long_gap,76.91304347826087,0.0,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,long_gap,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,1_before,75.15,-1.7630434782608688,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,1_before,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,2_before,75.0,-1.9130434782608745,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,2_before,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,3_before,75.0,-1.9130434782608745,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,3_before,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,1_after,77.0,0.0869565217391254,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,1_after,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,2_after,77.0,0.0869565217391254,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,2_after,83.0,0.0,2218.0,self,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,5.0,223.0,3_after,77.0,0.0869565217391254,2218.0,other,friends +404_2218_5_223.mov,2218,friends,5_223,63,390195,404,yes,,,223.0,5.0,3_after,83.0,0.0,2218.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,long_gap,53.0625,0.0,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,long_gap,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,1_before,55.5,2.4375,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,1_before,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,2_before,59.25,6.1875,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,2_before,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,3_before,65.45,12.387500000000005,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,3_before,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,1_after,40.65,-12.4125,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,1_after,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,2_after,45.35,-7.712499999999999,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,2_after,82.0,0.0,3126.0,other,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,237.0,29.0,3_after,59.05,5.987499999999997,3126.0,self,friends +405_3126_29_237.mov,3126,friends,29_237,104,459507,405,yes,,,29.0,237.0,3_after,82.0,0.0,3126.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,long_gap,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,long_gap,71.34,0.0,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,1_before,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,1_before,34.6,-36.74,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,2_before,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,2_before,68.95,-2.390000000000001,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,3_before,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,3_before,88.0,16.659999999999997,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,1_after,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,1_after,98.5,27.16,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,2_after,62.0,0.0,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,2_after,98.0,26.66,4982.0,other,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,23.0,254.0,3_after,63.85,1.8500000000000012,4982.0,self,friends +406_4982_23_254.mov,4982,friends,23_254,14,51722,406,yes,,,254.0,23.0,3_after,98.0,26.66,4982.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,long_gap,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,long_gap,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,1_before,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,1_before,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,2_before,78.85,-1.1500000000000057,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,2_before,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,3_before,70.4,-9.599999999999994,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,3_before,95.0,0.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,1_after,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,1_after,66.0,-29.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,2_after,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,2_after,59.0,-36.0,2487.0,other,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,23.0,254.0,3_after,80.0,0.0,2487.0,self,friends +407_2487_23_254.mov,2487,friends,23_254,38,124040,407,yes,,,254.0,23.0,3_after,53.35,-41.65,2487.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,long_gap,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,long_gap,65.48148148148148,0.0,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,1_before,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,1_before,69.0,3.518518518518519,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,2_before,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,2_before,69.0,3.518518518518519,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,3_before,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,3_before,61.3,-4.181481481481484,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,1_after,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,1_after,31.65,-33.83148148148148,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,2_after,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,2_after,26.55,-38.93148148148149,2693.0,self,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,23.0,254.0,3_after,84.0,0.0,2693.0,other,friends +408_2693_23_254.mov,2693,friends,23_254,66,222123,408,yes,,,254.0,23.0,3_after,25.8,-39.68148148148149,2693.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,long_gap,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,long_gap,84.85714285714286,0.0,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,1_before,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,1_before,80.0,-4.857142857142861,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,2_before,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,2_before,80.0,-4.857142857142861,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,3_before,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,3_before,74.5,-10.35714285714286,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,1_after,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,1_after,85.0,0.1428571428571388,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,2_after,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,2_after,85.0,0.1428571428571388,2052.0,self,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,23.0,254.0,3_after,100.0,0.0,2052.0,other,friends +409_2052_23_254.mov,2052,friends,23_254,115,401529,409,yes,,,254.0,23.0,3_after,77.5,-7.357142857142861,2052.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,long_gap,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,long_gap,96.3095238095238,0.0,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,1_before,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,1_before,93.0,-3.30952380952381,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,2_before,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,2_before,93.0,-3.30952380952381,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,3_before,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,3_before,93.0,-3.30952380952381,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,1_after,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,1_after,98.0,1.6904761904761898,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,2_after,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,2_after,98.0,1.6904761904761898,4187.0,other,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,23.0,254.0,3_after,100.0,0.0,4187.0,self,friends +410_4187_23_254.mov,4187,friends,23_254,129,451273,410,yes,,,254.0,23.0,3_after,73.7,-22.609523809523807,4187.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,long_gap,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,long_gap,87.59615384615384,0.0,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,1_before,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,1_before,95.45,7.853846153846163,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,2_before,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,2_before,98.65,11.053846153846166,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,3_before,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,3_before,96.6842105263158,9.088056680161955,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,1_after,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,1_after,76.85,-10.746153846153844,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,2_after,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,2_after,74.0,-13.59615384615384,5152.0,self,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,23.0,254.0,3_after,100.0,0.0,5152.0,other,friends +411_5152_23_254.mov,5152,friends,23_254,142,508556,411,yes,,,254.0,23.0,3_after,72.4,-15.196153846153834,5152.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,long_gap,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,long_gap,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,1_before,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,1_before,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,2_before,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,2_before,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,3_before,87.85,5.849999999999994,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,3_before,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,1_after,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,1_after,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,2_after,82.0,0.0,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,2_after,80.0,0.0,2265.0,self,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,5.0,222.0,3_after,82.45,0.4500000000000028,2265.0,other,friends +412_2265_5_222.mov,2265,friends,5_222,47,194240,412,no,,,222.0,5.0,3_after,80.0,0.0,2265.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,long_gap,70.0,0.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,long_gap,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,1_before,86.9,16.900000000000006,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,1_before,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,2_before,82.0,12.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,2_before,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,3_before,82.0,12.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,3_before,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,1_after,66.0,-4.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,1_after,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,2_after,66.0,-4.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,2_after,80.0,0.0,2028.0,self,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,5.0,222.0,3_after,66.0,-4.0,2028.0,other,friends +413_2028_5_222.mov,2028,friends,5_222,51,203696,413,no,,,222.0,5.0,3_after,80.0,0.0,2028.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,long_gap,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,long_gap,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,1_before,75.05,1.0499999999999972,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,1_before,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,2_before,86.3,12.299999999999995,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,2_before,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,3_before,89.0,15.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,3_before,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,1_after,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,1_after,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,2_after,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,2_after,97.0,0.0,3308.0,other,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,5.0,222.0,3_after,74.0,0.0,3308.0,self,friends +414_3308_5_222.mov,3308,friends,5_222,62,300283,414,no,,,222.0,5.0,3_after,97.0,0.0,3308.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,long_gap,67.42,0.0,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,long_gap,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,1_before,74.2,6.780000000000001,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,1_before,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,2_before,82.5,15.079999999999998,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,2_before,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,3_before,85.0,17.58,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,3_before,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,1_after,66.0,-1.4200000000000017,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,1_after,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,2_after,66.0,-1.4200000000000017,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,2_after,100.0,0.0,5027.0,self,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,5.0,222.0,3_after,66.0,-1.4200000000000017,5027.0,other,friends +415_5027_5_222.mov,5027,friends,5_222,85,413161,415,yes,,,222.0,5.0,3_after,100.0,0.0,5027.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,long_gap,73.63636363636364,0.0,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,long_gap,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,1_before,68.85,-4.786363636363646,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,1_before,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,2_before,68.0,-5.63636363636364,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,2_before,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,3_before,68.0,-5.63636363636364,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,3_before,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,1_after,75.0,1.3636363636363598,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,1_after,87.0,0.0,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,2_after,75.0,1.3636363636363598,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,2_after,90.85,3.8499999999999934,2248.0,other,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,5.0,222.0,3_after,75.0,1.3636363636363598,2248.0,self,friends +416_2248_5_222.mov,2248,friends,5_222,97,455573,416,yes,,,222.0,5.0,3_after,92.4,5.400000000000006,2248.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,long_gap,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,long_gap,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,1_before,74.05,-0.9500000000000028,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,1_before,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,2_before,69.7,-5.299999999999997,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,2_before,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,3_before,68.0,-7.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,3_before,87.0,0.0,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,1_after,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,1_after,93.78947368421052,6.78947368421052,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,2_after,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,2_after,85.73684210526316,-1.2631578947368354,2298.0,self,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,5.0,222.0,3_after,75.0,0.0,2298.0,other,friends +417_2298_5_222.mov,2298,friends,5_222,98,458010,417,no,,,222.0,5.0,3_after,70.52631578947368,-16.473684210526315,2298.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,long_gap,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,long_gap,90.0,0.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,1_before,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,1_before,89.4,-0.5999999999999943,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,2_before,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,2_before,82.25,-7.75,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,3_before,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,3_before,82.0,-8.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,1_after,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,1_after,90.0,0.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,2_after,78.0,0.0,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,2_after,90.0,0.0,2470.0,self,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,5.0,222.0,3_after,87.15,9.150000000000006,2470.0,other,friends +418_2470_5_222.mov,2470,friends,5_222,100,476491,418,no,,,222.0,5.0,3_after,90.0,0.0,2470.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,long_gap,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,long_gap,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,1_before,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,1_before,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,2_before,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,2_before,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,3_before,92.15,-1.8499999999999943,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,3_before,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,1_after,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,1_after,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,2_after,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,2_after,90.0,0.0,3268.0,self,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,5.0,222.0,3_after,94.0,0.0,3268.0,other,friends +419_3268_5_222.mov,3268,friends,5_222,102,490414,419,no,,,222.0,5.0,3_after,90.0,0.0,3268.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,long_gap,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,long_gap,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,1_before,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,1_before,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,2_before,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,2_before,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,3_before,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,3_before,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,1_after,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,1_after,90.0,0.0,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,2_after,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,2_after,92.9,2.9000000000000057,2067.0,other,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,5.0,222.0,3_after,94.0,0.0,2067.0,self,friends +420_2067_5_222.mov,2067,friends,5_222,103,499111,420,yes,,,222.0,5.0,3_after,97.25,7.25,2067.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,long_gap,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,long_gap,73.0,0.0,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,1_before,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,1_before,72.1,-0.9000000000000057,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,2_before,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,2_before,60.5,-12.5,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,3_before,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,3_before,58.95,-14.049999999999995,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,1_after,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,1_after,77.5,4.5,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,2_after,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,2_after,84.9,11.900000000000006,2273.0,other,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,203.0,101.0,3_after,76.0,0.0,2273.0,self,friends +421_2273_203_101.mov,2273,friends,203_101,140,524805,421,no,,,101.0,203.0,3_after,85.05,12.049999999999995,2273.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,long_gap,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,long_gap,58.81818181818182,0.0,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,1_before,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,1_before,57.0,-1.81818181818182,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,2_before,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,2_before,57.0,-1.81818181818182,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,3_before,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,3_before,57.0,-1.81818181818182,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,1_after,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,1_after,64.0,5.18181818181818,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,2_after,88.0,0.0,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,2_after,65.0,6.18181818181818,2175.0,self,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,247.0,66.0,3_after,90.75,2.75,2175.0,other,friends +422_2175_66_247.mov,2175,friends,66_247,23,65365,422,no,,,66.0,247.0,3_after,65.0,6.18181818181818,2175.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,long_gap,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,long_gap,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,1_before,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,1_before,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,2_before,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,2_before,59.35,-1.6499999999999986,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,3_before,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,3_before,51.0,-10.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,1_after,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,1_after,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,2_after,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,2_after,61.0,0.0,3735.0,self,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,247.0,66.0,3_after,80.0,0.0,3735.0,other,friends +423_3735_66_247.mov,3735,friends,66_247,53,247529,423,no,,,66.0,247.0,3_after,61.0,0.0,3735.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,long_gap,92.70833333333331,0.0,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,long_gap,72.83333333333333,0.0,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,1_before,89.7,-3.0083333333333258,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,1_before,68.0,-4.833333333333329,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,2_before,89.9,-2.808333333333323,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,2_before,65.85,-6.983333333333334,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,3_before,88.95,-3.7583333333333258,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,3_before,65.0,-7.833333333333329,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,1_after,94.0,1.2916666666666714,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,1_after,75.7,2.8666666666666742,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,2_after,94.0,1.2916666666666714,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,2_after,75.0,2.1666666666666714,2396.0,self,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,247.0,66.0,3_after,94.0,1.2916666666666714,2396.0,other,friends +424_2396_66_247.mov,2396,friends,66_247,93,342019,424,yes,,,66.0,247.0,3_after,75.0,2.1666666666666714,2396.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,long_gap,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,long_gap,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,1_before,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,1_before,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,2_before,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,2_before,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,3_before,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,3_before,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,1_after,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,1_after,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,2_after,94.0,0.0,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,2_after,75.0,0.0,2594.0,self,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,247.0,66.0,3_after,93.25,-0.75,2594.0,other,friends +425_2594_66_247.mov,2594,friends,66_247,99,355644,425,no,,,66.0,247.0,3_after,75.0,0.0,2594.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,long_gap,93.32,0.0,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,long_gap,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,1_before,94.0,0.6800000000000068,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,1_before,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,2_before,94.0,0.6800000000000068,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,2_before,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,3_before,94.0,0.6800000000000068,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,3_before,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,1_after,93.1,-0.2199999999999988,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,1_after,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,2_after,93.45,0.1300000000000096,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,2_after,75.0,0.0,2516.0,self,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,247.0,66.0,3_after,92.0,-1.3199999999999932,2516.0,other,friends +426_2516_66_247.mov,2516,friends,66_247,101,361797,426,no,,,66.0,247.0,3_after,75.0,0.0,2516.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,long_gap,65.07317073170732,0.0,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,long_gap,99.8780487804878,0.0,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,1_before,57.3,-7.773170731707324,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,1_before,100.0,0.1219512195121979,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,2_before,59.45,-5.623170731707319,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,2_before,100.0,0.1219512195121979,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,3_before,64.6,-0.4731707317073272,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,3_before,100.0,0.1219512195121979,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,1_after,76.3,11.226829268292676,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,1_after,99.0,-0.8780487804878021,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,2_after,79.4,14.326829268292684,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,2_after,99.0,-0.8780487804878021,4145.0,self,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,247.0,66.0,3_after,82.0,16.92682926829268,4145.0,other,friends +427_4145_66_247.mov,4145,friends,66_247,137,460270,427,no,,,66.0,247.0,3_after,99.0,-0.8780487804878021,4145.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,long_gap,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,long_gap,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,1_before,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,1_before,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,2_before,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,2_before,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,3_before,97.4,0.4000000000000057,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,3_before,88.95,2.950000000000003,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,1_after,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,1_after,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,2_after,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,2_after,86.0,0.0,5813.0,other,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,247.0,66.0,3_after,97.0,0.0,5813.0,self,friends +428_5813_66_247.mov,5813,friends,66_247,178,579308,428,no,,,66.0,247.0,3_after,86.0,0.0,5813.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,long_gap,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,long_gap,23.84210526315789,0.0,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,1_before,62.95,-1.0499999999999972,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,1_before,25.0,1.1578947368421062,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,2_before,66.55,2.549999999999997,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,2_before,25.0,1.1578947368421062,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,3_before,67.0,3.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,3_before,25.0,1.1578947368421062,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,1_after,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,1_after,17.7,-6.142105263157895,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,2_after,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,2_after,46.65,22.807894736842105,3771.0,other,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,11.0,248.0,3_after,64.0,0.0,3771.0,self,friends +429_3771_11_248.mov,3771,friends,11_248,30,78554,429,yes,,,248.0,11.0,3_after,55.0,31.157894736842103,3771.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,long_gap,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,long_gap,44.629629629629626,0.0,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,1_before,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,1_before,41.0,-3.629629629629626,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,2_before,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,2_before,41.0,-3.629629629629626,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,3_before,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,3_before,41.0,-3.629629629629626,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,1_after,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,1_after,59.0,14.370370370370374,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,2_after,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,2_after,59.0,14.370370370370374,2683.0,other,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,11.0,248.0,3_after,58.0,0.0,2683.0,self,friends +430_2683_11_248.mov,2683,friends,11_248,74,244339,430,no,,,248.0,11.0,3_after,59.0,14.370370370370374,2683.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,long_gap,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,long_gap,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,1_before,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,1_before,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,2_before,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,2_before,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,3_before,17.0,0.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,3_before,35.0,0.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,1_after,20.5,3.5,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,1_after,32.65,-2.3500000000000014,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,2_after,21.0,4.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,2_after,29.0,-6.0,2405.0,other,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,110.0,221.0,3_after,21.0,4.0,2405.0,self,friends +431_2405_110_221.mov,2405,friends,110_221,24,55027,431,no,,,221.0,110.0,3_after,29.0,-6.0,2405.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,long_gap,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,long_gap,61.94871794871795,0.0,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,1_before,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,1_before,52.0,-9.948717948717947,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,2_before,15.65,-4.35,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,2_before,52.0,-9.948717948717947,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,3_before,12.55,-7.449999999999999,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,3_before,52.0,-9.948717948717947,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,1_after,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,1_after,85.0,23.05128205128205,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,2_after,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,2_after,85.0,23.05128205128205,3914.0,other,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,110.0,221.0,3_after,20.0,0.0,3914.0,self,friends +432_3914_110_221.mov,3914,friends,110_221,101,260492,432,yes,,,221.0,110.0,3_after,85.0,23.05128205128205,3914.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,long_gap,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,long_gap,67.0,0.0,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,1_before,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,1_before,49.4,-17.6,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,2_before,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,2_before,50.3,-16.700000000000003,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,3_before,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,3_before,80.3,13.299999999999995,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,1_after,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,1_after,97.05,30.05,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,2_after,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,2_after,99.0,32.0,3747.0,other,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,110.0,221.0,3_after,20.0,0.0,3747.0,self,friends +433_3747_110_221.mov,3747,friends,110_221,125,314461,433,yes,,,221.0,110.0,3_after,99.0,32.0,3747.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,long_gap,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,long_gap,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,1_before,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,1_before,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,2_before,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,2_before,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,3_before,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,3_before,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,1_after,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,1_after,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,2_after,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,2_after,99.0,0.0,2391.0,other,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,110.0,221.0,3_after,20.0,0.0,2391.0,self,friends +434_2391_110_221.mov,2391,friends,110_221,131,334419,434,no,,,221.0,110.0,3_after,99.0,0.0,2391.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,long_gap,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,long_gap,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,1_before,25.85,-0.1499999999999985,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,1_before,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,2_before,20.0,-6.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,2_before,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,3_before,20.0,-6.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,3_before,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,1_after,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,1_after,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,2_after,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,2_after,99.0,0.0,4725.0,self,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,110.0,221.0,3_after,26.0,0.0,4725.0,other,friends +435_4725_110_221.mov,4725,friends,110_221,149,379255,435,yes,,,221.0,110.0,3_after,99.0,0.0,4725.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,long_gap,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,long_gap,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,1_before,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,1_before,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,2_before,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,2_before,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,3_before,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,3_before,99.0,0.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,1_after,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,1_after,95.1,-3.9000000000000057,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,2_after,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,2_after,93.0,-6.0,2600.0,self,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,110.0,221.0,3_after,18.0,0.0,2600.0,other,friends +436_2600_110_221.mov,2600,friends,110_221,178,439230,436,yes,,,221.0,110.0,3_after,94.8,-4.200000000000003,2600.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,long_gap,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,long_gap,99.05555555555556,0.0,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,1_before,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,1_before,99.0,-0.0555555555555571,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,2_before,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,2_before,99.0,-0.0555555555555571,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,3_before,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,3_before,99.0,-0.0555555555555571,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,1_after,20.0,0.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,1_after,100.0,0.9444444444444428,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,2_after,17.3,-2.6999999999999997,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,2_after,100.0,0.9444444444444428,5463.0,self,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,110.0,221.0,3_after,17.0,-3.0,5463.0,other,friends +437_5463_110_221.mov,5463,friends,110_221,190,474067,437,yes,,,221.0,110.0,3_after,100.0,0.9444444444444428,5463.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,long_gap,46.61904761904762,0.0,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,long_gap,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,1_before,34.0,-12.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,1_before,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,2_before,12.0,-34.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,2_before,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,3_before,12.0,-34.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,3_before,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,1_after,17.0,-29.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,1_after,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,2_after,17.0,-29.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,2_after,100.0,0.0,2065.0,self,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,110.0,221.0,3_after,17.0,-29.61904761904762,2065.0,other,friends +438_2065_110_221.mov,2065,friends,110_221,216,525462,438,no,,,221.0,110.0,3_after,100.0,0.0,2065.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,long_gap,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,long_gap,41.0,0.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,1_before,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,1_before,41.0,0.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,2_before,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,2_before,38.1,-2.8999999999999986,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,3_before,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,3_before,33.0,-8.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,1_after,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,1_after,42.25,1.25,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,2_after,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,2_after,45.0,4.0,2403.0,self,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,257.0,23.0,3_after,44.0,0.0,2403.0,other,friends +439_2403_23_257.mov,2403,friends,23_257,11,28514,439,no,,,23.0,257.0,3_after,45.5,4.5,2403.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,long_gap,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,long_gap,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,1_before,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,1_before,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,2_before,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,2_before,49.8,-12.200000000000005,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,3_before,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,3_before,46.0,-16.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,1_after,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,1_after,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,2_after,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,2_after,62.0,0.0,3637.0,other,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,257.0,23.0,3_after,44.0,0.0,3637.0,self,friends +440_3637_23_257.mov,3637,friends,23_257,19,43404,440,yes,,,23.0,257.0,3_after,62.0,0.0,3637.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,long_gap,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,long_gap,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,1_before,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,1_before,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,2_before,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,2_before,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,3_before,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,3_before,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,1_after,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,1_after,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,2_after,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,2_after,62.0,0.0,2017.0,other,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,257.0,23.0,3_after,44.0,0.0,2017.0,self,friends +441_2017_23_257.mov,2017,friends,23_257,21,46657,441,no,,,23.0,257.0,3_after,62.0,0.0,2017.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,long_gap,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,long_gap,66.0,0.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,1_before,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,1_before,65.75,-0.25,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,2_before,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,2_before,64.0,-2.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,3_before,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,3_before,64.0,-2.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,1_after,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,1_after,66.0,0.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,2_after,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,2_after,66.0,0.0,2807.0,self,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,257.0,23.0,3_after,48.0,0.0,2807.0,other,friends +442_2807_23_257.mov,2807,friends,23_257,38,78439,442,no,,,23.0,257.0,3_after,66.0,0.0,2807.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,long_gap,59.77142857142857,0.0,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,long_gap,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,1_before,59.0,-0.7714285714285722,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,1_before,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,2_before,59.0,-0.7714285714285722,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,2_before,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,3_before,59.0,-0.7714285714285722,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,3_before,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,1_after,66.5,6.728571428571428,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,1_after,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,2_after,67.0,7.228571428571428,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,2_after,84.0,0.0,3513.0,self,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,257.0,23.0,3_after,67.0,7.228571428571428,3513.0,other,friends +443_3513_23_257.mov,3513,friends,23_257,100,254566,443,no,,,23.0,257.0,3_after,84.0,0.0,3513.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,long_gap,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,long_gap,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,1_before,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,1_before,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,2_before,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,2_before,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,3_before,63.0,0.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,3_before,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,1_after,62.55,-0.4500000000000028,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,1_after,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,2_after,58.6,-4.399999999999999,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,2_after,84.0,0.0,3002.0,self,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,257.0,23.0,3_after,59.0,-4.0,3002.0,other,friends +444_3002_23_257.mov,3002,friends,23_257,108,294845,444,no,,,23.0,257.0,3_after,84.0,0.0,3002.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,long_gap,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,long_gap,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,1_before,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,1_before,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,2_before,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,2_before,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,3_before,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,3_before,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,1_after,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,1_after,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,2_after,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,2_after,84.0,0.0,3137.0,self,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,257.0,23.0,3_after,63.0,0.0,3137.0,other,friends +445_3137_23_257.mov,3137,friends,23_257,114,321295,445,no,,,23.0,257.0,3_after,84.0,0.0,3137.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,long_gap,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,long_gap,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,1_before,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,1_before,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,2_before,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,2_before,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,3_before,63.0,0.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,3_before,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,1_after,58.7,-4.299999999999997,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,1_after,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,2_after,55.0,-8.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,2_after,84.0,0.0,4126.0,other,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,257.0,23.0,3_after,55.0,-8.0,4126.0,self,friends +446_4126_23_257.mov,4126,friends,23_257,117,335628,446,yes,,,23.0,257.0,3_after,84.0,0.0,4126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,long_gap,72.0,0.0,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,long_gap,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,1_before,71.85,-0.1500000000000056,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,1_before,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,2_before,71.7,-0.2999999999999971,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,2_before,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,3_before,69.1,-2.9000000000000057,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,3_before,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,1_after,72.0,0.0,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,1_after,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,2_after,72.2,0.2000000000000028,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,2_after,100.0,0.0,3126.0,self,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,257.0,23.0,3_after,76.0,4.0,3126.0,other,friends +447_3126_23_257.mov,3126,friends,23_257,168,527377,447,no,,,23.0,257.0,3_after,100.0,0.0,3126.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,long_gap,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,long_gap,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,1_before,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,1_before,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,2_before,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,2_before,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,3_before,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,3_before,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,1_after,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,1_after,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,2_after,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,2_after,77.0,0.0,2648.0,self,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,110.0,220.0,3_after,80.0,0.0,2648.0,other,friends +448_2648_110_220.mov,2648,friends,110_220,171,429374,448,yes,,,220.0,110.0,3_after,69.6,-7.400000000000006,2648.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,long_gap,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,long_gap,65.75,0.0,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,1_before,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,1_before,60.0,-5.75,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,2_before,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,2_before,60.0,-5.75,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,3_before,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,3_before,60.0,-5.75,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,1_after,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,1_after,99.65,33.900000000000006,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,2_after,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,2_after,100.0,34.25,3632.0,other,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,250.0,66.0,3_after,71.0,0.0,3632.0,self,friends +449_3632_66_250.mov,3632,friends,66_250,28,92542,449,yes,,,66.0,250.0,3_after,100.0,34.25,3632.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,long_gap,67.77777777777777,0.0,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,long_gap,72.33333333333333,0.0,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,1_before,73.0,5.2222222222222285,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,1_before,66.0,-6.333333333333329,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,2_before,73.0,5.2222222222222285,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,2_before,66.0,-6.333333333333329,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,3_before,73.0,5.2222222222222285,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,3_before,66.0,-6.333333333333329,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,1_after,65.0,-2.7777777777777715,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,1_after,75.0,2.6666666666666714,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,2_after,65.0,-2.7777777777777715,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,2_after,75.0,2.6666666666666714,2689.0,self,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,250.0,66.0,3_after,65.0,-2.7777777777777715,2689.0,other,friends +450_2689_66_250.mov,2689,friends,66_250,40,138837,450,yes,,,66.0,250.0,3_after,75.0,2.6666666666666714,2689.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,long_gap,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,long_gap,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,1_before,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,1_before,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,2_before,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,2_before,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,3_before,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,3_before,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,1_after,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,1_after,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,2_after,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,2_after,76.0,0.0,2034.0,other,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,250.0,66.0,3_after,84.0,0.0,2034.0,self,friends +451_2034_66_250.mov,2034,friends,66_250,76,313764,451,no,,,66.0,250.0,3_after,76.0,0.0,2034.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,long_gap,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,long_gap,59.0,0.0,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,1_before,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,1_before,55.5,-3.5,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,2_before,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,2_before,55.8,-3.200000000000003,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,3_before,83.75,-0.25,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,3_before,58.0,-1.0,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,1_after,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,1_after,59.0,0.0,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,2_after,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,2_after,61.95,2.950000000000003,2141.0,self,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,110.0,219.0,3_after,84.0,0.0,2141.0,other,friends +452_2141_110_219.mov,2141,friends,110_219,93,261168,452,yes,,,219.0,110.0,3_after,58.05,-0.9500000000000028,2141.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,long_gap,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,long_gap,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,1_before,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,1_before,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,2_before,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,2_before,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,3_before,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,3_before,79.0,0.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,1_after,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,1_after,78.3,-0.7000000000000028,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,2_after,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,2_after,78.0,-1.0,2990.0,self,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,110.0,219.0,3_after,84.0,0.0,2990.0,other,friends +453_2990_110_219.mov,2990,friends,110_219,109,299828,453,yes,,,219.0,110.0,3_after,79.5,0.5,2990.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,long_gap,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,long_gap,73.23809523809524,0.0,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,1_before,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,1_before,69.7,-3.538095238095238,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,2_before,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,2_before,71.85,-1.3880952380952465,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,3_before,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,3_before,72.05,-1.1880952380952436,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,1_after,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,1_after,75.0,1.7619047619047592,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,2_after,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,2_after,75.0,1.7619047619047592,2053.0,other,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,110.0,219.0,3_after,81.0,0.0,2053.0,self,friends +454_2053_110_219.mov,2053,friends,110_219,158,431234,454,yes,,,219.0,110.0,3_after,75.05,1.8119047619047564,2053.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,long_gap,83.84210526315789,0.0,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,long_gap,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,1_before,83.0,-0.8421052631578902,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,1_before,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,2_before,83.0,-0.8421052631578902,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,2_before,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,3_before,83.0,-0.8421052631578902,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,3_before,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,1_after,84.35,0.5078947368421041,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,1_after,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,2_after,83.95,0.1078947368421125,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,2_after,68.0,0.0,3875.0,other,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,107.0,259.0,3_after,79.45,-4.392105263157887,3875.0,self,friends +455_3875_107_259.mov,3875,friends,107_259,16,56083,455,yes,,,259.0,107.0,3_after,68.0,0.0,3875.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,long_gap,89.76666666666667,0.0,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,long_gap,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,1_before,90.0,0.2333333333333342,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,1_before,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,2_before,90.0,0.2333333333333342,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,2_before,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,3_before,89.85,0.0833333333333286,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,3_before,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,1_after,89.0,-0.7666666666666657,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,1_after,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,2_after,88.6,-1.1666666666666714,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,2_after,68.0,0.0,3048.0,other,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,107.0,259.0,3_after,89.15,-0.61666666666666,3048.0,self,friends +456_3048_107_259.mov,3048,friends,107_259,29,97956,456,no,,,259.0,107.0,3_after,68.0,0.0,3048.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,long_gap,99.61904761904762,0.0,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,long_gap,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,1_before,98.15,-1.4690476190476147,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,1_before,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,2_before,95.75,-3.869047619047621,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,2_before,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,3_before,94.0,-5.61904761904762,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,3_before,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,1_after,98.9,-0.7190476190476147,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,1_after,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,2_after,99.05,-0.5690476190476232,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,2_after,79.0,0.0,2127.0,other,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,107.0,259.0,3_after,97.0,-2.6190476190476204,2127.0,self,friends +457_2127_107_259.mov,2127,friends,107_259,33,160531,457,yes,,,259.0,107.0,3_after,79.0,0.0,2127.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,long_gap,94.84444444444443,0.0,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,long_gap,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,1_before,91.95,-2.8944444444444457,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,1_before,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,2_before,93.0,-1.8444444444444483,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,2_before,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,3_before,93.0,-1.8444444444444483,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,3_before,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,1_after,96.0,1.1555555555555517,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,1_after,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,2_after,96.9,2.055555555555557,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,2_after,83.0,0.0,4447.0,other,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,107.0,259.0,3_after,97.65,2.805555555555557,4447.0,self,friends +458_4447_107_259.mov,4447,friends,107_259,44,225431,458,yes,,,259.0,107.0,3_after,86.05,3.049999999999997,4447.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,long_gap,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,long_gap,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,1_before,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,1_before,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,2_before,98.45,-1.5499999999999972,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,2_before,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,3_before,99.2,-0.7999999999999972,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,3_before,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,1_after,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,1_after,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,2_after,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,2_after,88.0,0.0,3643.0,self,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,107.0,259.0,3_after,100.0,0.0,3643.0,other,friends +459_3643_107_259.mov,3643,friends,107_259,59,276702,459,yes,,,259.0,107.0,3_after,88.0,0.0,3643.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,long_gap,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,long_gap,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,1_before,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,1_before,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,2_before,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,2_before,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,3_before,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,3_before,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,1_after,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,1_after,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,2_after,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,2_after,98.0,0.0,2291.0,self,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,107.0,259.0,3_after,100.0,0.0,2291.0,other,friends +460_2291_107_259.mov,2291,friends,107_259,112,448228,460,yes,,,259.0,107.0,3_after,98.0,0.0,2291.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,long_gap,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,long_gap,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,1_before,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,1_before,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,2_before,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,2_before,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,3_before,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,3_before,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,1_after,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,1_after,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,2_after,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,2_after,98.0,0.0,2172.0,other,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,107.0,259.0,3_after,100.0,0.0,2172.0,self,friends +461_2172_107_259.mov,2172,friends,107_259,123,513287,461,yes,,,259.0,107.0,3_after,98.0,0.0,2172.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,long_gap,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,long_gap,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,1_before,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,1_before,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,2_before,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,2_before,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,3_before,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,3_before,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,1_after,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,1_after,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,2_after,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,2_after,92.0,0.0,2864.0,self,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,107.0,259.0,3_after,100.0,0.0,2864.0,other,friends +462_2864_107_259.mov,2864,friends,107_259,130,543388,462,yes,,,259.0,107.0,3_after,92.0,0.0,2864.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,long_gap,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,long_gap,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,1_before,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,1_before,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,2_before,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,2_before,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,3_before,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,3_before,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,1_after,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,1_after,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,2_after,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,2_after,92.0,0.0,2311.0,self,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,107.0,259.0,3_after,100.0,0.0,2311.0,other,friends +463_2311_107_259.mov,2311,friends,107_259,131,546987,463,yes,,,259.0,107.0,3_after,92.0,0.0,2311.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,long_gap,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,long_gap,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,1_before,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,1_before,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,2_before,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,2_before,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,3_before,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,3_before,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,1_after,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,1_after,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,2_after,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,2_after,92.0,0.0,2609.0,self,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,107.0,259.0,3_after,100.0,0.0,2609.0,other,friends +464_2609_107_259.mov,2609,friends,107_259,142,588189,464,yes,,,259.0,107.0,3_after,92.0,0.0,2609.0,self,friends diff --git a/Analyses/long_gap_connection_all_long_format_laughter_strangers.csv b/Analyses/long_gap_connection_all_long_format_laughter_strangers.csv new file mode 100644 index 0000000..c409b06 --- /dev/null +++ b/Analyses/long_gap_connection_all_long_format_laughter_strangers.csv @@ -0,0 +1,3669 @@ +clip,gap_length_x,condition_x,dyad,turn_num,turn_start_msec,video_num,laughter,Unnamed: 8,Unnamed: 9,subID,partnerID,timepoint,connection,connection_change,gap_length_y,speaker,condition_y +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,long_gap,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,long_gap,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,1_before,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,1_before,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,2_before,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,2_before,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,3_before,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,3_before,71.0,0.0,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,1_after,63.0,0.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,1_after,70.7,-0.2999999999999971,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,2_after,63.95,0.9500000000000028,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,2_after,46.05,-24.950000000000003,2388.0,other,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,103.0,101.0,3_after,65.0,2.0,2388.0,self,strangers +1_2388_103_101.mov,2388,strangers,103_101,56,131574,1,no,,,101.0,103.0,3_after,43.0,-28.0,2388.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,long_gap,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,long_gap,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,1_before,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,1_before,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,2_before,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,2_before,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,3_before,74.35,-4.650000000000006,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,3_before,76.0,0.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,1_after,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,1_after,75.35,-0.6500000000000057,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,2_after,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,2_after,75.0,-1.0,2183.0,other,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,103.0,101.0,3_after,79.0,0.0,2183.0,self,strangers +2_2183_103_101.mov,2183,strangers,103_101,97,317519,2,no,,,101.0,103.0,3_after,75.0,-1.0,2183.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,long_gap,82.15384615384616,0.0,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,long_gap,68.42307692307692,0.0,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,1_before,82.0,-0.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,1_before,76.0,7.57692307692308,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,2_before,82.0,-0.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,2_before,76.0,7.57692307692308,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,3_before,82.0,-0.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,3_before,77.85,9.426923076923076,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,1_after,80.0,-2.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,1_after,51.9,-16.52307692307692,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,2_after,80.0,-2.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,2_after,42.0,-26.42307692307692,2612.0,other,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,103.0,101.0,3_after,80.0,-2.1538461538461604,2612.0,self,strangers +3_2612_103_101.mov,2612,strangers,103_101,113,388341,3,no,,,101.0,103.0,3_after,42.0,-26.42307692307692,2612.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,long_gap,73.14814814814815,0.0,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,long_gap,51.0,0.0,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,1_before,68.0,-5.148148148148152,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,1_before,51.0,0.0,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,2_before,68.0,-5.148148148148152,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,2_before,51.0,0.0,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,3_before,68.0,-5.148148148148152,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,3_before,49.95,-1.0499999999999972,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,1_after,79.6,6.451851851851842,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,1_after,51.85,0.8500000000000014,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,2_after,81.35,8.201851851851842,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,2_after,70.1,19.099999999999994,2747.0,self,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,103.0,101.0,3_after,80.7,7.55185185185185,2747.0,other,strangers +4_2747_103_101.mov,2747,strangers,103_101,140,482268,4,no,,,101.0,103.0,3_after,75.0,24.0,2747.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,long_gap,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,long_gap,11.74074074074074,0.0,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,1_before,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,1_before,10.6,-1.1407407407407408,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,2_before,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,2_before,9.0,-2.7407407407407405,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,3_before,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,3_before,9.0,-2.7407407407407405,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,1_after,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,1_after,12.0,0.2592592592592595,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,2_after,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,2_after,12.0,0.2592592592592595,2693.0,other,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,110.0,102.0,3_after,16.0,0.0,2693.0,self,strangers +5_2693_102_110.mov,2693,strangers,102_110,10,16875,5,no,,,102.0,110.0,3_after,21.45,9.70925925925926,2693.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,long_gap,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,long_gap,35.63636363636363,0.0,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,1_before,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,1_before,35.0,-0.6363636363636331,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,2_before,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,2_before,37.4,1.7636363636363654,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,3_before,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,3_before,38.0,2.363636363636367,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,1_after,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,1_after,36.0,0.3636363636363668,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,2_after,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,2_after,36.0,0.3636363636363668,2205.0,self,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,110.0,102.0,3_after,16.0,0.0,2205.0,other,strangers +6_2205_102_110.mov,2205,strangers,102_110,25,41912,6,no,,,102.0,110.0,3_after,37.9,2.263636363636365,2205.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,long_gap,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,long_gap,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,1_before,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,1_before,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,2_before,75.5,0.5,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,2_before,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,3_before,75.65,0.6500000000000057,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,3_before,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,1_after,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,1_after,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,2_after,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,2_after,47.0,0.0,2217.0,self,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,88.0,85.0,3_after,75.0,0.0,2217.0,other,strangers +7_2217_88_85.mov,2217,strangers,88_85,27,100176,7,no,,,85.0,88.0,3_after,44.9,-2.1000000000000014,2217.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,long_gap,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,long_gap,47.4,0.0,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,1_before,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,1_before,61.95,14.550000000000004,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,2_before,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,2_before,63.0,15.6,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,3_before,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,3_before,63.0,15.6,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,1_after,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,1_after,28.45,-18.95,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,2_after,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,2_after,31.7,-15.7,5539.0,self,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,88.0,85.0,3_after,68.0,0.0,5539.0,other,strangers +8_5539_88_85.mov,5539,strangers,88_85,91,434191,8,no,,,85.0,88.0,3_after,32.4,-15.0,5539.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,long_gap,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,long_gap,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,1_before,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,1_before,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,2_before,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,2_before,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,3_before,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,3_before,74.35,-0.6500000000000057,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,1_after,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,1_after,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,2_after,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,2_after,75.0,0.0,2153.0,other,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,88.0,85.0,3_after,68.0,0.0,2153.0,self,strangers +9_2153_88_85.mov,2153,strangers,88_85,101,494513,9,yes,,,85.0,88.0,3_after,75.0,0.0,2153.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,long_gap,58.0,0.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,long_gap,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,1_before,58.0,0.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,1_before,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,2_before,57.85,-0.1499999999999985,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,2_before,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,3_before,57.0,-1.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,3_before,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,1_after,58.0,0.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,1_after,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,2_after,58.7,0.7000000000000028,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,2_after,0.0,0.0,2796.0,other,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,88.0,81.0,3_after,60.0,2.0,2796.0,self,strangers +10_2796_81_88.mov,2796,strangers,81_88,36,78191,10,no,,,81.0,88.0,3_after,0.0,0.0,2796.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,long_gap,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,long_gap,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,1_before,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,1_before,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,2_before,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,2_before,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,3_before,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,3_before,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,1_after,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,1_after,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,2_after,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,2_after,20.0,0.0,2130.0,other,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,6.0,7.0,3_after,0.0,0.0,2130.0,self,strangers +11_2130_6_7.mov,2130,strangers,6_7,30,56177,11,no,,,7.0,6.0,3_after,20.0,0.0,2130.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,long_gap,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,long_gap,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,1_before,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,1_before,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,2_before,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,2_before,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,3_before,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,3_before,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,1_after,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,1_after,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,2_after,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,2_after,22.0,0.0,2972.0,other,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,6.0,7.0,3_after,16.0,0.0,2972.0,self,strangers +12_2972_6_7.mov,2972,strangers,6_7,48,107242,12,no,,,7.0,6.0,3_after,22.0,0.0,2972.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,long_gap,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,long_gap,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,1_before,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,1_before,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,2_before,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,2_before,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,3_before,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,3_before,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,1_after,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,1_after,22.0,0.0,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,2_after,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,2_after,25.75,3.75,2365.0,self,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,6.0,7.0,3_after,16.0,0.0,2365.0,other,strangers +13_2365_6_7.mov,2365,strangers,6_7,53,118200,13,no,,,7.0,6.0,3_after,31.0,9.0,2365.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,long_gap,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,long_gap,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,1_before,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,1_before,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,2_before,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,2_before,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,3_before,17.15,-7.850000000000001,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,3_before,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,1_after,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,1_after,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,2_after,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,2_after,31.0,0.0,2399.0,other,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,6.0,7.0,3_after,25.0,0.0,2399.0,self,strangers +14_2399_6_7.mov,2399,strangers,6_7,76,177828,14,no,,,7.0,6.0,3_after,31.0,0.0,2399.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,long_gap,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,long_gap,56.8,0.0,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,1_before,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,1_before,51.3,-5.5,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,2_before,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,2_before,51.0,-5.799999999999997,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,3_before,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,3_before,51.0,-5.799999999999997,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,1_after,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,1_after,57.0,0.2000000000000028,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,2_after,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,2_after,57.0,0.2000000000000028,3003.0,other,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,6.0,7.0,3_after,40.0,0.0,3003.0,self,strangers +15_3003_6_7.mov,3003,strangers,6_7,96,268135,15,no,,,7.0,6.0,3_after,57.0,0.2000000000000028,3003.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,long_gap,30.77777777777778,0.0,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,long_gap,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,1_before,48.0,17.22222222222222,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,1_before,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,2_before,48.0,17.22222222222222,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,2_before,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,3_before,48.0,17.22222222222222,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,3_before,57.0,0.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,1_after,30.0,-0.7777777777777786,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,1_after,60.9,3.899999999999999,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,2_after,30.0,-0.7777777777777786,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,2_after,62.0,5.0,4523.0,other,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,6.0,7.0,3_after,30.0,-0.7777777777777786,4523.0,self,strangers +16_4523_6_7.mov,4523,strangers,6_7,112,360285,16,no,,,7.0,6.0,3_after,62.0,5.0,4523.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,long_gap,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,long_gap,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,1_before,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,1_before,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,2_before,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,2_before,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,3_before,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,3_before,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,1_after,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,1_after,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,2_after,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,2_after,62.0,0.0,2388.0,other,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,6.0,7.0,3_after,30.0,0.0,2388.0,self,strangers +17_2388_6_7.mov,2388,strangers,6_7,122,384885,17,no,,,7.0,6.0,3_after,62.0,0.0,2388.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,long_gap,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,long_gap,48.0,0.0,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,1_before,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,1_before,47.65,-0.3500000000000014,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,2_before,49.15,-0.8500000000000014,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,2_before,46.0,-2.0,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,3_before,46.55,-3.450000000000003,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,3_before,46.0,-2.0,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,1_after,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,1_after,40.15,-7.850000000000001,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,2_after,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,2_after,39.05,-8.950000000000003,2066.0,self,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,62.0,60.0,3_after,50.0,0.0,2066.0,other,strangers +18_2066_60_62.mov,2066,strangers,60_62,51,159745,18,no,,,60.0,62.0,3_after,39.0,-9.0,2066.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,long_gap,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,long_gap,38.96774193548387,0.0,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,1_before,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,1_before,40.0,1.032258064516128,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,2_before,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,2_before,40.0,1.032258064516128,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,3_before,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,3_before,40.0,1.032258064516128,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,1_after,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,1_after,35.0,-3.967741935483872,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,2_after,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,2_after,35.0,-3.967741935483872,3082.0,self,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,62.0,60.0,3_after,50.0,0.0,3082.0,other,strangers +19_3082_60_62.mov,3082,strangers,60_62,63,225904,19,no,,,60.0,62.0,3_after,35.0,-3.967741935483872,3082.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,long_gap,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,long_gap,81.0,0.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,1_before,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,1_before,81.05,0.0499999999999971,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,2_before,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,2_before,82.0,1.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,3_before,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,3_before,82.0,1.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,1_after,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,1_after,81.0,0.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,2_after,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,2_after,81.0,0.0,2133.0,other,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,62.0,60.0,3_after,57.0,0.0,2133.0,self,strangers +20_2133_60_62.mov,2133,strangers,60_62,106,438590,20,no,,,60.0,62.0,3_after,81.0,0.0,2133.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,long_gap,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,long_gap,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,1_before,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,1_before,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,2_before,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,2_before,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,3_before,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,3_before,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,1_after,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,1_after,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,2_after,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,2_after,89.0,0.0,2415.0,other,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,87.0,88.0,3_after,90.0,0.0,2415.0,self,strangers +21_2415_88_87.mov,2415,strangers,88_87,46,206014,21,no,,,88.0,87.0,3_after,89.0,0.0,2415.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,long_gap,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,long_gap,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,1_before,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,1_before,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,2_before,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,2_before,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,3_before,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,3_before,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,1_after,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,1_after,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,2_after,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,2_after,93.0,0.0,2459.0,other,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,87.0,88.0,3_after,90.0,0.0,2459.0,self,strangers +22_2459_88_87.mov,2459,strangers,88_87,78,375151,22,yes,,,88.0,87.0,3_after,93.0,0.0,2459.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,long_gap,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,long_gap,37.04761904761905,0.0,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,1_before,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,1_before,37.0,-0.047619047619051,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,2_before,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,2_before,37.0,-0.047619047619051,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,3_before,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,3_before,37.0,-0.047619047619051,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,1_after,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,1_after,38.0,0.952380952380949,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,2_after,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,2_after,38.0,0.952380952380949,2057.0,other,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,41.0,46.0,3_after,23.0,0.0,2057.0,self,strangers +23_2057_41_46.mov,2057,strangers,41_46,94,372813,23,no,,,46.0,41.0,3_after,38.0,0.952380952380949,2057.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,long_gap,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,long_gap,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,1_before,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,1_before,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,2_before,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,2_before,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,3_before,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,3_before,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,1_after,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,1_after,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,2_after,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,2_after,35.0,0.0,3849.0,self,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,41.0,46.0,3_after,27.0,0.0,3849.0,other,strangers +24_3849_41_46.mov,3849,strangers,41_46,119,528791,24,no,,,46.0,41.0,3_after,35.0,0.0,3849.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,long_gap,92.1818181818182,0.0,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,long_gap,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,1_before,94.0,1.818181818181813,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,1_before,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,2_before,94.0,1.818181818181813,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,2_before,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,3_before,93.0,0.818181818181813,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,3_before,68.25,-0.75,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,1_after,86.7,-5.481818181818184,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,1_after,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,2_after,85.0,-7.181818181818187,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,2_after,69.0,0.0,2127.0,other,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,60.0,63.0,3_after,85.0,-7.181818181818187,2127.0,self,strangers +25_2127_60_63.mov,2127,strangers,60_63,36,203091,25,no,,,63.0,60.0,3_after,69.0,0.0,2127.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,long_gap,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,long_gap,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,1_before,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,1_before,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,2_before,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,2_before,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,3_before,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,3_before,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,1_after,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,1_after,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,2_after,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,2_after,50.0,0.0,2555.0,other,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,102.0,106.0,3_after,61.0,0.0,2555.0,self,strangers +26_2555_102_106.mov,2555,strangers,102_106,50,144955,26,no,,,106.0,102.0,3_after,50.0,0.0,2555.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,long_gap,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,long_gap,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,1_before,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,1_before,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,2_before,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,2_before,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,3_before,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,3_before,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,1_after,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,1_after,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,2_after,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,2_after,49.0,0.0,2174.0,self,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,102.0,106.0,3_after,70.0,0.0,2174.0,other,strangers +27_2174_102_106.mov,2174,strangers,102_106,67,182651,27,no,,,106.0,102.0,3_after,49.0,0.0,2174.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,long_gap,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,long_gap,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,1_before,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,1_before,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,2_before,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,2_before,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,3_before,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,3_before,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,1_after,70.0,0.0,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,1_after,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,2_after,69.8,-0.2000000000000028,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,2_after,49.0,0.0,2303.0,self,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,102.0,106.0,3_after,67.6,-2.4000000000000057,2303.0,other,strangers +28_2303_102_106.mov,2303,strangers,102_106,77,218151,28,no,,,106.0,102.0,3_after,49.0,0.0,2303.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,long_gap,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,long_gap,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,1_before,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,1_before,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,2_before,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,2_before,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,3_before,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,3_before,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,1_after,64.0,0.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,1_after,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,2_after,63.65,-0.3500000000000014,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,2_after,41.0,0.0,2094.0,self,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,102.0,106.0,3_after,61.0,-3.0,2094.0,other,strangers +29_2094_102_106.mov,2094,strangers,102_106,127,385553,29,no,,,106.0,102.0,3_after,41.0,0.0,2094.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,long_gap,63.91428571428571,0.0,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,long_gap,42.62857142857143,0.0,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,1_before,63.0,-0.914285714285711,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,1_before,41.0,-1.6285714285714263,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,2_before,61.8,-2.114285714285714,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,2_before,41.0,-1.6285714285714263,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,3_before,62.0,-1.914285714285711,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,3_before,41.0,-1.6285714285714263,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,1_after,64.0,0.0857142857142889,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,1_after,44.0,1.3714285714285737,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,2_after,64.0,0.0857142857142889,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,2_after,44.0,1.3714285714285737,3457.0,self,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,102.0,106.0,3_after,64.0,0.0857142857142889,3457.0,other,strangers +30_3457_102_106.mov,3457,strangers,102_106,129,398110,30,no,,,106.0,102.0,3_after,44.0,1.3714285714285737,3457.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,long_gap,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,long_gap,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,1_before,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,1_before,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,2_before,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,2_before,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,3_before,63.8,-0.2000000000000028,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,3_before,41.45,-2.549999999999997,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,1_after,64.0,0.0,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,1_after,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,2_after,63.6,-0.3999999999999986,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,2_after,44.0,0.0,2390.0,other,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,102.0,106.0,3_after,56.4,-7.600000000000001,2390.0,self,strangers +31_2390_102_106.mov,2390,strangers,102_106,130,402965,31,no,,,106.0,102.0,3_after,44.0,0.0,2390.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,long_gap,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,long_gap,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,1_before,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,1_before,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,2_before,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,2_before,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,3_before,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,3_before,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,1_after,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,1_after,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,2_after,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,2_after,44.0,0.0,2122.0,self,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,102.0,106.0,3_after,56.0,0.0,2122.0,other,strangers +32_2122_102_106.mov,2122,strangers,102_106,139,419467,32,no,,,106.0,102.0,3_after,44.0,0.0,2122.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,long_gap,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,long_gap,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,1_before,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,1_before,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,2_before,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,2_before,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,3_before,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,3_before,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,1_after,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,1_after,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,2_after,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,2_after,46.0,0.0,3199.0,other,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,102.0,106.0,3_after,59.0,0.0,3199.0,self,strangers +33_3199_102_106.mov,3199,strangers,102_106,168,530141,33,no,,,106.0,102.0,3_after,46.0,0.0,3199.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,long_gap,82.76190476190476,0.0,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,long_gap,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,1_before,82.3,-0.461904761904762,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,1_before,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,2_before,82.7,-0.0619047619047563,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,2_before,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,3_before,83.95,1.1880952380952436,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,3_before,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,1_after,81.4,-1.3619047619047535,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,1_after,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,2_after,76.05,-6.711904761904762,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,2_after,0.0,0.0,2066.0,self,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,24.0,23.0,3_after,82.9,0.1380952380952465,2066.0,other,strangers +34_2066_24_23.mov,2066,strangers,24_23,107,540859,34,no,,,23.0,24.0,3_after,0.0,0.0,2066.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,long_gap,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,long_gap,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,1_before,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,1_before,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,2_before,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,2_before,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,3_before,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,3_before,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,1_after,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,1_after,23.0,0.0,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,2_after,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,2_after,23.25,0.25,2343.0,self,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,70.0,62.0,3_after,6.0,0.0,2343.0,other,strangers +35_2343_70_62.mov,2343,strangers,70_62,19,38078,35,no,,,62.0,70.0,3_after,27.35,4.350000000000001,2343.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,long_gap,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,long_gap,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,1_before,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,1_before,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,2_before,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,2_before,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,3_before,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,3_before,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,1_after,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,1_after,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,2_after,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,2_after,51.0,0.0,2029.0,other,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,70.0,62.0,3_after,88.0,0.0,2029.0,self,strangers +36_2029_70_62.mov,2029,strangers,70_62,36,129189,36,no,,,62.0,70.0,3_after,51.0,0.0,2029.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,long_gap,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,long_gap,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,1_before,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,1_before,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,2_before,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,2_before,32.3,9.299999999999995,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,3_before,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,3_before,53.5,30.5,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,1_after,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,1_after,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,2_after,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,2_after,23.0,0.0,2805.0,self,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,70.0,62.0,3_after,91.0,0.0,2805.0,other,strangers +37_2805_70_62.mov,2805,strangers,70_62,101,497661,37,no,,,62.0,70.0,3_after,23.0,0.0,2805.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,long_gap,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,long_gap,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,1_before,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,1_before,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,2_before,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,2_before,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,3_before,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,3_before,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,1_after,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,1_after,58.0,0.0,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,2_after,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,2_after,60.85,2.8500000000000014,2266.0,other,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,27.0,26.0,3_after,26.0,0.0,2266.0,self,strangers +38_2266_26_27.mov,2266,strangers,26_27,20,34454,38,no,,,26.0,27.0,3_after,64.15,6.150000000000006,2266.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,long_gap,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,long_gap,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,1_before,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,1_before,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,2_before,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,2_before,99.75,-0.25,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,3_before,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,3_before,94.05,-5.950000000000003,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,1_after,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,1_after,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,2_after,60.0,0.0,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,2_after,100.0,0.0,2170.0,self,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,27.0,26.0,3_after,60.7,0.7000000000000028,2170.0,other,strangers +39_2170_26_27.mov,2170,strangers,26_27,53,158080,39,no,,,26.0,27.0,3_after,100.0,0.0,2170.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,long_gap,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,long_gap,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,1_before,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,1_before,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,2_before,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,2_before,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,3_before,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,3_before,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,1_after,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,1_after,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,2_after,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,2_after,77.0,0.0,3310.0,other,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,27.0,26.0,3_after,67.0,0.0,3310.0,self,strangers +40_3310_26_27.mov,3310,strangers,26_27,62,194771,40,no,,,26.0,27.0,3_after,77.0,0.0,3310.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,long_gap,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,long_gap,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,1_before,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,1_before,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,2_before,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,2_before,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,3_before,82.0,0.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,3_before,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,1_after,82.05,0.0499999999999971,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,1_after,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,2_after,83.0,1.0,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,2_after,100.0,0.0,2074.0,self,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,27.0,26.0,3_after,83.3,1.2999999999999972,2074.0,other,strangers +41_2074_26_27.mov,2074,strangers,26_27,95,327408,41,no,,,26.0,27.0,3_after,100.0,0.0,2074.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,long_gap,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,long_gap,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,1_before,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,1_before,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,2_before,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,2_before,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,3_before,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,3_before,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,1_after,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,1_after,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,2_after,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,2_after,52.0,0.0,2077.0,other,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,27.0,26.0,3_after,89.0,0.0,2077.0,self,strangers +42_2077_26_27.mov,2077,strangers,26_27,154,574467,42,no,,,26.0,27.0,3_after,52.0,0.0,2077.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,long_gap,56.0,0.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,long_gap,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,1_before,55.1,-0.8999999999999986,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,1_before,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,2_before,55.0,-1.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,2_before,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,3_before,55.0,-1.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,3_before,75.9,-0.0999999999999943,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,1_after,56.0,0.0,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,1_after,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,2_after,64.55,8.549999999999997,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,2_after,76.0,0.0,2706.0,other,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,28.0,22.0,3_after,78.2,22.200000000000003,2706.0,self,strangers +43_2706_28_22.mov,2706,strangers,28_22,16,63787,43,no,,,22.0,28.0,3_after,76.0,0.0,2706.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,long_gap,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,long_gap,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,1_before,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,1_before,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,2_before,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,2_before,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,3_before,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,3_before,55.9,0.8999999999999986,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,1_after,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,1_after,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,2_after,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,2_after,55.0,0.0,2837.0,other,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,43.0,46.0,3_after,11.0,0.0,2837.0,self,strangers +44_2837_43_46.mov,2837,strangers,43_46,66,180134,44,no,,,46.0,43.0,3_after,55.0,0.0,2837.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,long_gap,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,long_gap,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,1_before,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,1_before,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,2_before,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,2_before,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,3_before,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,3_before,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,1_after,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,1_after,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,2_after,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,2_after,43.0,0.0,2028.0,other,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,43.0,46.0,3_after,14.0,0.0,2028.0,self,strangers +45_2028_43_46.mov,2028,strangers,43_46,118,396855,45,no,,,46.0,43.0,3_after,43.0,0.0,2028.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,long_gap,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,long_gap,70.14285714285714,0.0,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,1_before,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,1_before,73.0,2.857142857142861,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,2_before,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,2_before,73.0,2.857142857142861,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,3_before,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,3_before,73.0,2.857142857142861,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,1_after,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,1_after,69.25,-0.8928571428571388,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,2_after,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,2_after,58.75,-11.39285714285714,2093.0,other,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,102.0,103.0,3_after,39.0,0.0,2093.0,self,strangers +46_2093_102_103.mov,2093,strangers,102_103,28,76932,46,no,,,103.0,102.0,3_after,58.0,-12.14285714285714,2093.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,long_gap,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,long_gap,89.13793103448276,0.0,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,1_before,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,1_before,89.0,-0.1379310344827615,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,2_before,58.9,-3.1000000000000014,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,2_before,89.0,-0.1379310344827615,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,3_before,56.0,-6.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,3_before,89.35,0.2120689655172327,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,1_after,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,1_after,88.15,-0.987931034482756,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,2_after,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,2_after,87.0,-2.1379310344827616,2917.0,self,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,102.0,103.0,3_after,62.0,0.0,2917.0,other,strangers +47_2917_102_103.mov,2917,strangers,102_103,105,366571,47,no,,,103.0,102.0,3_after,87.0,-2.1379310344827616,2917.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,long_gap,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,long_gap,63.0,0.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,1_before,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,1_before,65.9,2.9000000000000057,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,2_before,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,2_before,74.0,11.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,3_before,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,3_before,74.0,11.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,1_after,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,1_after,63.0,0.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,2_after,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,2_after,63.0,0.0,2719.0,self,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,102.0,103.0,3_after,67.0,0.0,2719.0,other,strangers +48_2719_102_103.mov,2719,strangers,102_103,153,515415,48,no,,,103.0,102.0,3_after,63.0,0.0,2719.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,long_gap,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,long_gap,35.9,0.0,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,1_before,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,1_before,72.35,36.45,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,2_before,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,2_before,86.25,50.35,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,3_before,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,3_before,85.0,49.1,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,1_after,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,1_after,20.55,-15.349999999999998,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,2_after,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,2_after,10.55,-25.35,2016.0,self,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,102.0,103.0,3_after,67.0,0.0,2016.0,other,strangers +49_2016_102_103.mov,2016,strangers,102_103,165,551150,49,no,,,103.0,102.0,3_after,22.4,-13.5,2016.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,long_gap,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,long_gap,41.0,0.0,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,1_before,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,1_before,41.73684210526316,0.7368421052631575,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,2_before,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,2_before,56.05263157894737,15.05263157894737,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,3_before,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,3_before,70.89473684210526,29.89473684210526,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,1_after,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,1_after,41.5,0.5,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,2_after,36.0,0.0,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,2_after,49.55,8.549999999999997,2352.0,self,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,6.0,3.0,3_after,36.4,0.3999999999999986,2352.0,other,strangers +50_2352_6_3.mov,2352,strangers,6_3,23,107852,50,no,,,3.0,6.0,3_after,48.25,7.25,2352.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,long_gap,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,long_gap,43.125,0.0,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,1_before,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,1_before,39.9,-3.2250000000000014,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,2_before,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,2_before,62.8,19.675,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,3_before,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,3_before,100.0,56.875,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,1_after,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,1_after,49.0,5.875,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,2_after,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,2_after,59.0,15.875,2440.0,other,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,6.0,3.0,3_after,48.0,0.0,2440.0,self,strangers +51_2440_6_3.mov,2440,strangers,6_3,44,244506,51,no,,,3.0,6.0,3_after,23.95,-19.175,2440.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,long_gap,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,long_gap,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,1_before,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,1_before,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,2_before,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,2_before,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,3_before,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,3_before,58.6,-23.4,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,1_after,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,1_after,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,2_after,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,2_after,82.0,0.0,2886.0,self,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,6.0,3.0,3_after,59.0,0.0,2886.0,other,strangers +52_2886_6_3.mov,2886,strangers,6_3,97,433262,52,no,,,3.0,6.0,3_after,82.0,0.0,2886.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,long_gap,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,long_gap,22.0,0.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,1_before,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,1_before,22.15,0.1499999999999985,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,2_before,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,2_before,23.0,1.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,3_before,15.0,0.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,3_before,23.0,1.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,1_after,14.5,-0.5,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,1_after,22.0,0.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,2_after,13.0,-2.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,2_after,22.0,0.0,2057.0,other,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,110.0,103.0,3_after,13.0,-2.0,2057.0,self,strangers +53_2057_103_110.mov,2057,strangers,103_110,42,95937,53,yes,,,103.0,110.0,3_after,22.0,0.0,2057.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,long_gap,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,long_gap,72.0,0.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,1_before,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,1_before,72.0,0.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,2_before,14.05,1.0500000000000007,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,2_before,57.1,-14.9,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,3_before,15.0,2.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,3_before,20.0,-52.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,1_after,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,1_after,72.0,0.0,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,2_after,13.0,0.0,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,2_after,72.35,0.3499999999999943,2588.0,self,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,110.0,103.0,3_after,12.05,-0.9499999999999992,2588.0,other,strangers +54_2588_103_110.mov,2588,strangers,103_110,52,129569,54,yes,,,103.0,110.0,3_after,75.75,3.75,2588.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,long_gap,15.0,0.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,long_gap,69.33333333333333,0.0,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,1_before,15.0,0.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,1_before,71.0,1.6666666666666714,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,2_before,15.2,0.1999999999999993,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,2_before,71.0,1.6666666666666714,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,3_before,16.0,1.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,3_before,71.0,1.6666666666666714,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,1_after,15.0,0.0,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,1_after,68.15,-1.183333333333323,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,2_after,10.55,-4.449999999999999,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,2_after,69.0,-0.3333333333333286,2033.0,other,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,110.0,103.0,3_after,12.45,-2.5500000000000007,2033.0,self,strangers +55_2033_103_110.mov,2033,strangers,103_110,69,189019,55,no,,,103.0,110.0,3_after,75.4,6.066666666666677,2033.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,long_gap,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,long_gap,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,1_before,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,1_before,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,2_before,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,2_before,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,3_before,11.05,0.0500000000000007,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,3_before,63.3,-6.700000000000003,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,1_after,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,1_after,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,2_after,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,2_after,70.0,0.0,2370.0,self,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,110.0,103.0,3_after,11.0,0.0,2370.0,other,strangers +56_2370_103_110.mov,2370,strangers,103_110,146,352739,56,no,,,103.0,110.0,3_after,70.0,0.0,2370.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,long_gap,8.0,0.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,long_gap,65.61904761904762,0.0,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,1_before,8.0,0.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,1_before,85.5,19.88095238095238,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,2_before,8.0,0.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,2_before,85.95,20.330952380952382,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,3_before,6.55,-1.4500000000000002,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,3_before,84.0,18.38095238095238,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,1_after,9.2,1.1999999999999993,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,1_after,65.0,-0.6190476190476204,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,2_after,10.0,2.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,2_after,65.0,-0.6190476190476204,2139.0,other,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,110.0,103.0,3_after,10.0,2.0,2139.0,self,strangers +57_2139_103_110.mov,2139,strangers,103_110,253,574087,57,no,,,103.0,110.0,3_after,64.95,-0.6690476190476176,2139.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,long_gap,43.67857142857143,0.0,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,long_gap,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,1_before,48.9,5.221428571428568,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,1_before,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,2_before,49.0,5.321428571428569,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,2_before,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,3_before,49.0,5.321428571428569,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,3_before,0.0,0.0,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,1_after,44.0,0.3214285714285694,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,1_after,3.1,3.1,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,2_after,44.0,0.3214285714285694,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,2_after,24.35,24.35,2781.0,self,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,68.0,65.0,3_after,44.05,0.3714285714285665,2781.0,other,strangers +58_2781_65_68.mov,2781,strangers,65_68,13,35029,58,yes,,,65.0,68.0,3_after,24.0,24.0,2781.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,long_gap,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,long_gap,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,1_before,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,1_before,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,2_before,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,2_before,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,3_before,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,3_before,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,1_after,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,1_after,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,2_after,50.0,0.0,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,2_after,37.0,0.0,2820.0,other,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,68.0,65.0,3_after,49.45,-0.5499999999999972,2820.0,self,strangers +59_2820_65_68.mov,2820,strangers,65_68,92,517879,59,no,,,65.0,68.0,3_after,37.0,0.0,2820.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,long_gap,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,long_gap,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,1_before,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,1_before,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,2_before,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,2_before,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,3_before,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,3_before,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,1_after,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,1_after,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,2_after,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,2_after,71.0,0.0,2304.0,other,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,64.0,60.0,3_after,61.0,0.0,2304.0,self,strangers +60_2304_60_64.mov,2304,strangers,60_64,98,480037,60,no,,,60.0,64.0,3_after,71.0,0.0,2304.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,long_gap,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,long_gap,68.0,0.0,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,1_before,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,1_before,68.6,0.5999999999999943,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,2_before,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,2_before,70.7,2.700000000000003,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,3_before,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,3_before,70.3,2.299999999999997,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,1_after,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,1_after,68.0,0.0,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,2_after,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,2_after,68.0,0.0,2269.0,self,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,90.0,86.0,3_after,16.0,0.0,2269.0,other,strangers +61_2269_86_90.mov,2269,strangers,86_90,165,497308,61,yes,,,86.0,90.0,3_after,68.0,0.0,2269.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,long_gap,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,long_gap,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,1_before,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,1_before,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,2_before,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,2_before,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,3_before,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,3_before,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,1_after,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,1_after,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,2_after,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,2_after,39.0,0.0,2224.0,other,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,26.0,30.0,3_after,56.0,0.0,2224.0,self,strangers +62_2224_26_30.mov,2224,strangers,26_30,10,12788,62,no,,,30.0,26.0,3_after,39.0,0.0,2224.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,long_gap,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,long_gap,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,1_before,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,1_before,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,2_before,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,2_before,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,3_before,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,3_before,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,1_after,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,1_after,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,2_after,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,2_after,24.0,0.0,5029.0,other,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,26.0,30.0,3_after,58.0,0.0,5029.0,self,strangers +63_5029_26_30.mov,5029,strangers,26_30,28,55998,63,yes,,,30.0,26.0,3_after,24.0,0.0,5029.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,long_gap,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,long_gap,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,1_before,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,1_before,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,2_before,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,2_before,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,3_before,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,3_before,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,1_after,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,1_after,0.0,0.0,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,2_after,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,2_after,1.05,1.05,2027.0,other,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,60.0,65.0,3_after,0.0,0.0,2027.0,self,strangers +64_2027_60_65.mov,2027,strangers,60_65,10,11129,64,no,,,65.0,60.0,3_after,4.0,4.0,2027.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,long_gap,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,long_gap,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,1_before,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,1_before,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,2_before,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,2_before,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,3_before,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,3_before,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,1_after,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,1_after,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,2_after,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,2_after,57.0,0.0,2628.0,self,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,60.0,65.0,3_after,84.0,0.0,2628.0,other,strangers +65_2628_60_65.mov,2628,strangers,60_65,103,360256,65,no,,<- check the timing of this one in the transcript.,65.0,60.0,3_after,57.0,0.0,2628.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,long_gap,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,long_gap,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,1_before,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,1_before,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,2_before,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,2_before,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,3_before,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,3_before,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,1_after,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,1_after,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,2_after,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,2_after,57.0,0.0,2128.0,other,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,60.0,65.0,3_after,84.0,0.0,2128.0,self,strangers +66_2128_60_65.mov,2128,strangers,60_65,106,387654,66,yes,,,65.0,60.0,3_after,57.0,0.0,2128.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,long_gap,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,long_gap,57.81818181818182,0.0,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,1_before,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,1_before,57.0,-0.8181818181818201,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,2_before,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,2_before,57.0,-0.8181818181818201,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,3_before,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,3_before,57.0,-0.8181818181818201,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,1_after,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,1_after,69.0,11.18181818181818,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,2_after,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,2_after,70.0,12.18181818181818,2199.0,self,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,60.0,65.0,3_after,84.0,0.0,2199.0,other,strangers +67_2199_60_65.mov,2199,strangers,60_65,123,440751,67,no,,,65.0,60.0,3_after,70.0,12.18181818181818,2199.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,long_gap,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,long_gap,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,1_before,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,1_before,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,2_before,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,2_before,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,3_before,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,3_before,57.0,0.0,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,1_after,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,1_after,59.35,2.3500000000000014,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,2_after,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,2_after,63.35,6.350000000000001,2508.0,other,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,65.0,69.0,3_after,39.0,0.0,2508.0,self,strangers +68_2508_65_69.mov,2508,strangers,65_69,36,126979,68,yes,,,69.0,65.0,3_after,63.0,6.0,2508.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,long_gap,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,long_gap,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,1_before,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,1_before,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,2_before,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,2_before,68.1,-1.9000000000000057,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,3_before,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,3_before,63.0,-7.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,1_after,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,1_after,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,2_after,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,2_after,70.0,0.0,2026.0,self,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,65.0,69.0,3_after,39.0,0.0,2026.0,other,strangers +69_2026_65_69.mov,2026,strangers,65_69,47,195806,69,yes,,,69.0,65.0,3_after,70.0,0.0,2026.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,long_gap,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,long_gap,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,1_before,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,1_before,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,2_before,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,2_before,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,3_before,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,3_before,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,1_after,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,1_after,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,2_after,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,2_after,91.0,0.0,2100.0,self,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,65.0,69.0,3_after,59.0,0.0,2100.0,other,strangers +70_2100_65_69.mov,2100,strangers,65_69,87,408468,70,yes,,,69.0,65.0,3_after,91.0,0.0,2100.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,long_gap,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,long_gap,90.0,0.0,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,1_before,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,1_before,87.1,-2.9000000000000057,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,2_before,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,2_before,85.0,-5.0,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,3_before,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,3_before,85.0,-5.0,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,1_after,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,1_after,89.85,-0.1500000000000056,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,2_after,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,2_after,78.05,-11.950000000000005,3540.0,self,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,65.0,69.0,3_after,70.0,0.0,3540.0,other,strangers +71_3540_65_69.mov,3540,strangers,65_69,97,455139,71,yes,,,69.0,65.0,3_after,68.35,-21.65000000000001,3540.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,long_gap,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,long_gap,88.6,0.0,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,1_before,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,1_before,90.0,1.4000000000000057,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,2_before,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,2_before,90.0,1.4000000000000057,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,3_before,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,3_before,86.6,-2.0,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,1_after,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,1_after,72.3,-16.299999999999997,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,2_after,70.0,0.0,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,2_after,68.0,-20.599999999999994,2498.0,other,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,65.0,69.0,3_after,70.1,0.0999999999999943,2498.0,self,strangers +72_2498_65_69.mov,2498,strangers,65_69,98,457895,72,no,,,69.0,65.0,3_after,68.05,-20.55,2498.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,long_gap,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,long_gap,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,1_before,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,1_before,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,2_before,10.9,-1.0999999999999996,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,2_before,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,3_before,9.0,-3.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,3_before,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,1_after,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,1_after,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,2_after,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,2_after,67.0,0.0,2220.0,self,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,84.0,81.0,3_after,12.0,0.0,2220.0,other,strangers +73_2220_84_81.mov,2220,strangers,84_81,99,304087,73,no,,,81.0,84.0,3_after,67.0,0.0,2220.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,long_gap,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,long_gap,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,1_before,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,1_before,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,2_before,15.65,0.6500000000000004,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,2_before,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,3_before,16.0,1.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,3_before,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,1_after,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,1_after,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,2_after,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,2_after,67.0,0.0,3392.0,self,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,84.0,81.0,3_after,15.0,0.0,3392.0,other,strangers +74_3392_84_81.mov,3392,strangers,84_81,123,408854,74,no,,,81.0,84.0,3_after,67.0,0.0,3392.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,long_gap,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,long_gap,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,1_before,79.8,-2.200000000000003,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,1_before,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,2_before,80.0,-2.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,2_before,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,3_before,80.0,-2.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,3_before,70.8,1.7999999999999972,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,1_after,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,1_after,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,2_after,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,2_after,69.0,0.0,2322.0,self,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,22.0,20.0,3_after,82.0,0.0,2322.0,other,strangers +75_2322_20_22.mov,2322,strangers,20_22,79,198071,75,no,,,20.0,22.0,3_after,69.0,0.0,2322.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,long_gap,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,long_gap,51.130434782608695,0.0,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,1_before,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,1_before,49.25,-1.8804347826086956,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,2_before,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,2_before,49.0,-2.130434782608696,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,3_before,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,3_before,49.0,-2.130434782608696,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,1_after,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,1_after,52.0,0.8695652173913047,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,2_after,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,2_after,52.0,0.8695652173913047,2281.0,self,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,63.0,66.0,3_after,61.0,0.0,2281.0,other,strangers +76_2281_66_63.mov,2281,strangers,66_63,47,297309,76,yes,,,66.0,63.0,3_after,52.0,0.8695652173913047,2281.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,long_gap,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,long_gap,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,1_before,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,1_before,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,2_before,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,2_before,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,3_before,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,3_before,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,1_after,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,1_after,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,2_after,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,2_after,79.0,0.0,2133.0,self,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,110.0,108.0,3_after,7.0,0.0,2133.0,other,strangers +77_2133_108_110.mov,2133,strangers,108_110,175,505958,77,yes,,,108.0,110.0,3_after,79.0,0.0,2133.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,long_gap,35.77272727272727,0.0,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,long_gap,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,1_before,39.0,3.227272727272726,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,1_before,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,2_before,39.0,3.227272727272726,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,2_before,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,3_before,39.0,3.227272727272726,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,3_before,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,1_after,33.0,-2.7727272727272734,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,1_after,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,2_after,33.0,-2.7727272727272734,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,2_after,68.0,0.0,2248.0,self,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,22.0,27.0,3_after,33.0,-2.7727272727272734,2248.0,other,strangers +78_2248_22_27.mov,2248,strangers,22_27,77,205452,78,yes,,,27.0,22.0,3_after,68.0,0.0,2248.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,long_gap,12.545454545454543,0.0,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,long_gap,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,1_before,9.85,-2.6954545454545453,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,1_before,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,2_before,5.0,-7.545454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,2_before,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,3_before,5.0,-7.545454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,3_before,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,1_after,5.15,-7.395454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,1_after,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,2_after,0.4,-12.145454545454545,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,2_after,72.0,0.0,2256.0,other,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,22.0,27.0,3_after,0.0,-12.545454545454543,2256.0,self,strangers +79_2256_22_27.mov,2256,strangers,22_27,140,392876,79,yes,,,27.0,22.0,3_after,72.0,0.0,2256.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,long_gap,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,long_gap,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,1_before,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,1_before,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,2_before,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,2_before,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,3_before,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,3_before,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,1_after,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,1_after,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,2_after,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,2_after,72.0,0.0,2250.0,self,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,22.0,27.0,3_after,44.0,0.0,2250.0,other,strangers +80_2250_22_27.mov,2250,strangers,22_27,191,592510,80,yes,,,27.0,22.0,3_after,72.0,0.0,2250.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,long_gap,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,long_gap,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,1_before,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,1_before,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,2_before,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,2_before,69.65,-2.3499999999999943,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,3_before,87.0,0.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,3_before,67.0,-5.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,1_after,87.65,0.6500000000000057,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,1_after,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,2_after,89.0,2.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,2_after,72.0,0.0,2007.0,other,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,11.0,6.0,3_after,89.0,2.0,2007.0,self,strangers +81_2007_6_11.mov,2007,strangers,6_11,94,414557,81,no,,,6.0,11.0,3_after,72.0,0.0,2007.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,long_gap,45.90909090909091,0.0,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,long_gap,29.0,0.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,1_before,59.75,13.840909090909092,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,1_before,33.45,4.450000000000003,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,2_before,67.85,21.940909090909088,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,2_before,36.0,7.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,3_before,68.05,22.14090909090909,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,3_before,36.0,7.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,1_after,48.2,2.2909090909090963,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,1_after,29.0,0.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,2_after,48.85,2.940909090909095,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,2_after,29.0,0.0,2169.0,self,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,103.0,100.0,3_after,48.0,2.090909090909093,2169.0,other,strangers +82_2169_100_103.mov,2169,strangers,100_103,55,98322,82,no,,,100.0,103.0,3_after,29.0,0.0,2169.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,long_gap,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,long_gap,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,1_before,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,1_before,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,2_before,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,2_before,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,3_before,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,3_before,80.0,0.0,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,1_after,35.0,0.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,1_after,60.65,-19.35,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,2_after,46.8,11.799999999999995,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,2_after,48.8,-31.200000000000003,2011.0,other,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,103.0,100.0,3_after,47.0,12.0,2011.0,self,strangers +83_2011_100_103.mov,2011,strangers,100_103,208,535738,83,no,,,100.0,103.0,3_after,69.55,-10.450000000000005,2011.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,long_gap,44.833333333333336,0.0,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,long_gap,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,1_before,51.0,6.166666666666664,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,1_before,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,2_before,51.0,6.166666666666664,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,2_before,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,3_before,51.0,6.166666666666664,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,3_before,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,1_after,46.85,2.0166666666666657,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,1_after,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,2_after,54.9,10.066666666666665,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,2_after,52.0,0.0,2408.0,other,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,22.0,26.0,3_after,60.45,15.616666666666667,2408.0,self,strangers +84_2408_22_26.mov,2408,strangers,22_26,46,180336,84,yes,,,26.0,22.0,3_after,52.0,0.0,2408.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,long_gap,45.121212121212125,0.0,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,long_gap,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,1_before,53.75,8.628787878787875,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,1_before,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,2_before,54.0,8.878787878787875,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,2_before,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,3_before,54.0,8.878787878787875,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,3_before,100.0,0.0,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,1_after,40.2,-4.921212121212122,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,1_after,83.55,-16.450000000000003,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,2_after,40.0,-5.121212121212125,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,2_after,70.85,-29.15000000000001,3275.0,other,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,22.0,26.0,3_after,39.2,-5.921212121212122,3275.0,self,strangers +85_3275_22_26.mov,3275,strangers,22_26,138,536221,85,no,,,26.0,22.0,3_after,70.0,-30.0,3275.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,long_gap,61.34782608695652,0.0,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,long_gap,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,1_before,59.4,-1.9478260869565247,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,1_before,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,2_before,53.5,-7.847826086956523,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,2_before,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,3_before,49.9,-11.447826086956525,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,3_before,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,1_after,61.55,0.2021739130434738,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,1_after,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,2_after,60.05,-1.297826086956526,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,2_after,77.0,0.0,2285.0,self,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,86.0,82.0,3_after,61.2,-0.1478260869565204,2285.0,other,strangers +86_2285_82_86.mov,2285,strangers,82_86,200,557164,86,no,,,82.0,86.0,3_after,77.0,0.0,2285.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,long_gap,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,long_gap,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,1_before,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,1_before,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,2_before,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,2_before,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,3_before,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,3_before,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,1_after,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,1_after,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,2_after,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,2_after,23.0,0.0,2113.0,self,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,108.0,105.0,3_after,73.0,0.0,2113.0,other,strangers +87_2113_108_105.mov,2113,strangers,108_105,57,260827,87,no,,,105.0,108.0,3_after,23.0,0.0,2113.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,long_gap,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,long_gap,15.0,0.0,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,1_before,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,1_before,15.0,0.0,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,2_before,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,2_before,11.95,-3.0500000000000007,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,3_before,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,3_before,0.55,-14.45,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,1_after,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,1_after,15.6,0.5999999999999996,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,2_after,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,2_after,16.0,1.0,3152.0,self,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,108.0,105.0,3_after,92.0,0.0,3152.0,other,strangers +88_3152_108_105.mov,3152,strangers,108_105,103,504569,88,no,,,105.0,108.0,3_after,17.35,2.3500000000000014,3152.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,long_gap,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,long_gap,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,1_before,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,1_before,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,2_before,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,2_before,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,3_before,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,3_before,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,1_after,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,1_after,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,2_after,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,2_after,72.0,0.0,2470.0,self,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,100.0,101.0,3_after,73.0,0.0,2470.0,other,strangers +89_2470_100_101.mov,2470,strangers,100_101,103,348097,89,no,,,101.0,100.0,3_after,72.0,0.0,2470.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,long_gap,39.74285714285714,0.0,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,long_gap,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,1_before,41.2,1.4571428571428626,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,1_before,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,2_before,41.95,2.207142857142862,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,2_before,53.05,0.0499999999999971,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,3_before,42.0,2.25714285714286,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,3_before,54.6,1.6000000000000014,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,1_after,39.15,-0.5928571428571416,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,1_after,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,2_after,39.8,0.0571428571428569,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,2_after,53.0,0.0,3425.0,self,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,48.0,42.0,3_after,25.2,-14.54285714285714,3425.0,other,strangers +90_3425_42_48.mov,3425,strangers,42_48,54,483522,90,no,,,42.0,48.0,3_after,53.0,0.0,3425.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,long_gap,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,long_gap,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,1_before,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,1_before,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,2_before,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,2_before,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,3_before,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,3_before,35.0,0.0,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,1_after,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,1_after,41.55,6.549999999999997,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,2_after,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,2_after,48.95,13.950000000000005,3519.0,self,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,105.0,101.0,3_after,55.0,0.0,3519.0,other,strangers +91_3519_101_105.mov,3519,strangers,101_105,41,64086,91,no,,,101.0,105.0,3_after,49.0,14.0,3519.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,long_gap,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,long_gap,63.96296296296296,0.0,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,1_before,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,1_before,53.6,-10.36296296296296,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,2_before,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,2_before,46.0,-17.962962962962962,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,3_before,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,3_before,46.0,-17.962962962962962,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,1_after,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,1_after,64.0,0.037037037037038,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,2_after,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,2_after,64.0,0.037037037037038,2656.0,self,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,105.0,101.0,3_after,32.0,0.0,2656.0,other,strangers +92_2656_101_105.mov,2656,strangers,101_105,67,127711,92,no,,,101.0,105.0,3_after,64.0,0.037037037037038,2656.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,long_gap,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,long_gap,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,1_before,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,1_before,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,2_before,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,2_before,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,3_before,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,3_before,64.75,0.75,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,1_after,32.0,0.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,1_after,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,2_after,40.0,8.0,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,2_after,64.0,0.0,3549.0,self,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,105.0,101.0,3_after,57.15,25.15,3549.0,other,strangers +93_3549_101_105.mov,3549,strangers,101_105,71,136147,93,no,,,101.0,105.0,3_after,64.0,0.0,3549.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,long_gap,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,long_gap,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,1_before,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,1_before,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,2_before,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,2_before,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,3_before,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,3_before,44.6,-0.3999999999999986,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,1_after,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,1_after,45.0,0.0,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,2_after,21.0,0.0,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,2_after,47.25,2.25,2499.0,other,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,105.0,101.0,3_after,25.2,4.199999999999999,2499.0,self,strangers +94_2499_101_105.mov,2499,strangers,101_105,90,178036,94,no,,,101.0,105.0,3_after,49.0,4.0,2499.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,long_gap,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,long_gap,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,1_before,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,1_before,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,2_before,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,2_before,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,3_before,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,3_before,71.0,0.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,1_after,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,1_after,50.25,-20.75,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,2_after,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,2_after,50.0,-21.0,2456.0,other,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,105.0,101.0,3_after,29.0,0.0,2456.0,self,strangers +95_2456_101_105.mov,2456,strangers,101_105,148,376835,95,no,,,101.0,105.0,3_after,50.0,-21.0,2456.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,long_gap,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,long_gap,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,1_before,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,1_before,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,2_before,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,2_before,88.45,-0.5499999999999972,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,3_before,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,3_before,82.4,-6.599999999999994,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,1_after,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,1_after,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,2_after,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,2_after,89.0,0.0,2542.0,other,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,105.0,101.0,3_after,60.0,0.0,2542.0,self,strangers +96_2542_101_105.mov,2542,strangers,101_105,172,460749,96,no,<-- check transcript,,101.0,105.0,3_after,89.0,0.0,2542.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,long_gap,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,long_gap,88.0,0.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,1_before,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,1_before,87.55,-0.4500000000000028,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,2_before,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,2_before,85.95,-2.049999999999997,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,3_before,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,3_before,87.0,-1.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,1_after,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,1_after,88.0,0.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,2_after,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,2_after,88.0,0.0,2192.0,other,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,105.0,101.0,3_after,30.0,0.0,2192.0,self,strangers +97_2192_101_105.mov,2192,strangers,101_105,194,554332,97,no,,,101.0,105.0,3_after,88.0,0.0,2192.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,long_gap,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,long_gap,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,1_before,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,1_before,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,2_before,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,2_before,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,3_before,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,3_before,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,1_after,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,1_after,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,2_after,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,2_after,93.0,0.0,2464.0,self,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,7.0,5.0,3_after,100.0,0.0,2464.0,other,strangers +98_2464_5_7.mov,2464,strangers,5_7,117,474810,98,yes,,,5.0,7.0,3_after,93.0,0.0,2464.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,long_gap,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,long_gap,57.81818181818182,0.0,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,1_before,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,1_before,63.0,5.18181818181818,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,2_before,58.65,0.6499999999999986,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,2_before,63.0,5.18181818181818,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,3_before,60.9,2.8999999999999986,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,3_before,63.0,5.18181818181818,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,1_after,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,1_after,45.1,-12.71818181818182,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,2_after,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,2_after,39.35,-18.46818181818182,2193.0,other,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,82.0,85.0,3_after,58.0,0.0,2193.0,self,strangers +99_2193_82_85.mov,2193,strangers,82_85,60,176270,99,no,,,85.0,82.0,3_after,40.45,-17.368181818181817,2193.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,long_gap,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,long_gap,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,1_before,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,1_before,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,2_before,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,2_before,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,3_before,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,3_before,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,1_after,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,1_after,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,2_after,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,2_after,32.0,0.0,2125.0,self,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,106.0,108.0,3_after,46.0,0.0,2125.0,other,strangers +100_2125_108_106.mov,2125,strangers,108_106,11,21615,100,no,,,108.0,106.0,3_after,32.0,0.0,2125.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,long_gap,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,long_gap,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,1_before,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,1_before,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,2_before,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,2_before,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,3_before,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,3_before,42.7,-0.2999999999999971,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,1_after,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,1_after,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,2_after,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,2_after,43.0,0.0,2495.0,other,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,106.0,108.0,3_after,39.0,0.0,2495.0,self,strangers +101_2495_108_106.mov,2495,strangers,108_106,28,77894,101,no,,,108.0,106.0,3_after,54.25,11.25,2495.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,long_gap,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,long_gap,70.0,0.0,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,1_before,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,1_before,65.15,-4.849999999999994,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,2_before,72.2,-0.7999999999999972,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,2_before,57.65,-12.35,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,3_before,63.65,-9.35,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,3_before,49.3,-20.700000000000003,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,1_after,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,1_after,69.95,-0.0499999999999971,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,2_after,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,2_after,33.15,-36.85,2421.0,other,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,109.0,105.0,3_after,73.0,0.0,2421.0,self,strangers +102_2421_105_109.mov,2421,strangers,105_109,12,32917,102,no,,,105.0,109.0,3_after,7.0,-63.0,2421.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,long_gap,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,long_gap,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,1_before,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,1_before,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,2_before,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,2_before,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,3_before,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,3_before,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,1_after,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,1_after,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,2_after,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,2_after,25.0,0.0,2105.0,self,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,109.0,105.0,3_after,73.0,0.0,2105.0,other,strangers +103_2105_105_109.mov,2105,strangers,105_109,13,74776,103,no,,,105.0,109.0,3_after,25.0,0.0,2105.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,long_gap,44.0,0.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,long_gap,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,1_before,45.6,1.6000000000000014,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,1_before,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,2_before,49.4,5.399999999999999,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,2_before,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,3_before,61.0,17.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,3_before,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,1_after,44.0,0.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,1_after,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,2_after,44.0,0.0,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,2_after,40.0,0.0,2148.0,self,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,109.0,105.0,3_after,41.2,-2.799999999999997,2148.0,other,strangers +104_2148_105_109.mov,2148,strangers,105_109,35,296153,104,no,,,105.0,109.0,3_after,40.0,0.0,2148.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,long_gap,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,long_gap,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,1_before,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,1_before,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,2_before,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,2_before,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,3_before,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,3_before,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,1_after,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,1_after,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,2_after,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,2_after,19.0,0.0,2207.0,self,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,109.0,105.0,3_after,6.0,0.0,2207.0,other,strangers +105_2207_105_109.mov,2207,strangers,105_109,63,538641,105,no,,,105.0,109.0,3_after,16.75,-2.25,2207.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,long_gap,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,long_gap,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,1_before,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,1_before,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,2_before,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,2_before,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,3_before,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,3_before,18.8,-0.1999999999999993,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,1_after,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,1_after,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,2_after,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,2_after,19.0,0.0,2434.0,self,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,80.0,84.0,3_after,74.0,0.0,2434.0,other,strangers +106_2434_80_84.mov,2434,strangers,80_84,113,340553,106,no,,,84.0,80.0,3_after,19.0,0.0,2434.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,long_gap,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,long_gap,49.869565217391305,0.0,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,1_before,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,1_before,51.0,1.1304347826086951,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,2_before,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,2_before,49.6,-0.2695652173913032,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,3_before,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,3_before,47.8,-2.0695652173913075,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,1_after,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,1_after,47.15,-2.719565217391306,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,2_after,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,2_after,45.0,-4.869565217391305,2320.0,other,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,81.0,82.0,3_after,7.0,0.0,2320.0,self,strangers +107_2320_82_81.mov,2320,strangers,82_81,36,73446,107,no,,,82.0,81.0,3_after,43.3,-6.569565217391308,2320.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,long_gap,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,long_gap,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,1_before,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,1_before,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,2_before,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,2_before,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,3_before,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,3_before,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,1_after,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,1_after,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,2_after,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,2_after,51.0,0.0,2472.0,self,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,81.0,82.0,3_after,62.0,0.0,2472.0,other,strangers +108_2472_82_81.mov,2472,strangers,82_81,155,479225,108,no,,,82.0,81.0,3_after,51.0,0.0,2472.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,long_gap,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,long_gap,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,1_before,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,1_before,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,2_before,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,2_before,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,3_before,57.0,0.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,3_before,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,1_after,52.45,-4.549999999999997,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,1_after,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,2_after,42.0,-15.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,2_after,43.0,0.0,2169.0,self,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,100.0,104.0,3_after,42.0,-15.0,2169.0,other,strangers +109_2169_100_104.mov,2169,strangers,100_104,99,237682,109,yes,,,104.0,100.0,3_after,43.0,0.0,2169.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,long_gap,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,long_gap,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,1_before,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,1_before,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,2_before,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,2_before,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,3_before,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,3_before,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,1_after,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,1_after,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,2_after,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,2_after,26.0,0.0,2226.0,other,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,104.0,110.0,3_after,60.0,0.0,2226.0,self,strangers +110_2226_110_104.mov,2226,strangers,110_104,128,269166,110,yes,,,110.0,104.0,3_after,26.0,0.0,2226.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,long_gap,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,long_gap,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,1_before,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,1_before,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,2_before,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,2_before,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,3_before,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,3_before,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,1_after,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,1_after,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,2_after,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,2_after,54.0,0.0,2333.0,other,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,45.0,44.0,3_after,84.0,0.0,2333.0,self,strangers +111_2333_45_44.mov,2333,strangers,45_44,54,164292,111,no,,,44.0,45.0,3_after,54.0,0.0,2333.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,long_gap,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,long_gap,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,1_before,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,1_before,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,2_before,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,2_before,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,3_before,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,3_before,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,1_after,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,1_after,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,2_after,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,2_after,46.0,0.0,2384.0,other,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,100.0,105.0,3_after,31.0,0.0,2384.0,self,strangers +112_2384_100_105.mov,2384,strangers,100_105,94,333999,112,no,,,105.0,100.0,3_after,46.0,0.0,2384.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,long_gap,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,long_gap,70.77272727272727,0.0,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,1_before,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,1_before,75.5,4.727272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,2_before,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,2_before,76.0,5.227272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,3_before,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,3_before,76.25,5.477272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,1_after,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,1_after,82.2,11.427272727272737,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,2_after,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,2_after,84.0,13.227272727272734,2210.0,self,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,80.0,82.0,3_after,75.0,0.0,2210.0,other,strangers +113_2210_82_80.mov,2210,strangers,82_80,158,481422,113,no,,,82.0,80.0,3_after,83.25,12.477272727272734,2210.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,long_gap,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,long_gap,47.0,0.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,1_before,73.55,-1.4500000000000028,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,1_before,47.3,0.2999999999999971,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,2_before,66.0,-9.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,2_before,50.95,3.950000000000003,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,3_before,66.0,-9.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,3_before,51.0,4.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,1_after,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,1_after,47.0,0.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,2_after,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,2_after,47.0,0.0,3821.0,self,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,108.0,103.0,3_after,75.0,0.0,3821.0,other,strangers +114_3821_108_103.mov,3821,strangers,108_103,36,98510,114,no,,,103.0,108.0,3_after,47.0,0.0,3821.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,long_gap,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,long_gap,60.88,0.0,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,1_before,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,1_before,62.15,1.269999999999996,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,2_before,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,2_before,45.35,-15.53,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,3_before,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,3_before,38.0,-22.880000000000003,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,1_after,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,1_after,56.0,-4.880000000000003,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,2_after,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,2_after,54.8,-6.080000000000005,2561.0,self,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,108.0,103.0,3_after,65.0,0.0,2561.0,other,strangers +115_2561_108_103.mov,2561,strangers,108_103,48,137485,115,no,,,103.0,108.0,3_after,53.0,-7.880000000000003,2561.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,long_gap,79.37096774193549,0.0,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,long_gap,27.24590163934426,0.0,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,1_before,67.0,-12.370967741935488,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,1_before,58.0,30.75409836065574,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,2_before,67.0,-12.370967741935488,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,2_before,59.45,32.20409836065574,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,3_before,67.0,-12.370967741935488,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,3_before,58.6,31.35409836065574,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,1_after,27.45,-51.920967741935485,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,1_after,4.0,-23.24590163934426,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,2_after,31.0,-48.37096774193549,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,2_after,4.0,-23.24590163934426,6127.0,self,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,108.0,103.0,3_after,31.0,-48.37096774193549,6127.0,other,strangers +116_6127_108_103.mov,6127,strangers,108_103,180,584333,116,yes,,,103.0,108.0,3_after,4.0,-23.24590163934426,6127.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,long_gap,21.115384615384617,0.0,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,long_gap,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,1_before,24.4,3.284615384615382,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,1_before,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,2_before,28.0,6.884615384615383,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,2_before,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,3_before,28.0,6.884615384615383,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,3_before,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,1_after,19.0,-2.1153846153846168,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,1_after,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,2_after,19.0,-2.1153846153846168,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,2_after,17.0,0.0,2589.0,self,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,47.0,43.0,3_after,19.6,-1.515384615384615,2589.0,other,strangers +117_2589_47_43.mov,2589,strangers,47_43,33,216738,117,no,,,43.0,47.0,3_after,17.0,0.0,2589.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,long_gap,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,long_gap,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,1_before,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,1_before,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,2_before,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,2_before,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,3_before,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,3_before,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,1_after,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,1_after,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,2_after,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,2_after,17.0,0.0,2328.0,self,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,47.0,43.0,3_after,48.0,0.0,2328.0,other,strangers +118_2328_47_43.mov,2328,strangers,47_43,57,496105,118,no,,,43.0,47.0,3_after,17.0,0.0,2328.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,long_gap,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,long_gap,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,1_before,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,1_before,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,2_before,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,2_before,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,3_before,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,3_before,80.7,0.7000000000000028,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,1_after,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,1_after,80.0,0.0,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,2_after,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,2_after,79.8,-0.2000000000000028,6060.0,other,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,109.0,110.0,3_after,96.0,0.0,6060.0,self,strangers +119_6060_109_110.mov,6060,strangers,109_110,114,412468,119,yes,,,110.0,109.0,3_after,77.05,-2.950000000000003,6060.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,long_gap,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,long_gap,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,1_before,79.1,0.0999999999999943,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,1_before,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,2_before,80.85,1.8499999999999943,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,2_before,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,3_before,81.0,2.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,3_before,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,1_after,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,1_after,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,2_after,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,2_after,19.0,0.0,2444.0,self,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,107.0,110.0,3_after,79.0,0.0,2444.0,other,strangers +120_2444_110_107.mov,2444,strangers,110_107,132,466711,120,yes,,,110.0,107.0,3_after,19.0,0.0,2444.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,long_gap,13.80952380952381,0.0,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,long_gap,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,1_before,15.45,1.640476190476189,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,1_before,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,2_before,16.0,2.19047619047619,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,2_before,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,3_before,16.0,2.19047619047619,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,3_before,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,1_after,15.0,1.1904761904761898,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,1_after,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,2_after,15.0,1.1904761904761898,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,2_after,38.0,0.0,2040.0,self,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,100.0,106.0,3_after,15.0,1.1904761904761898,2040.0,other,strangers +121_2040_100_106.mov,2040,strangers,100_106,15,38346,121,no,,,106.0,100.0,3_after,38.0,0.0,2040.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,long_gap,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,long_gap,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,1_before,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,1_before,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,2_before,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,2_before,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,3_before,22.2,-2.8000000000000007,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,3_before,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,1_after,25.0,0.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,1_after,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,2_after,22.0,-3.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,2_after,38.0,0.0,2139.0,other,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,100.0,106.0,3_after,21.0,-4.0,2139.0,self,strangers +122_2139_100_106.mov,2139,strangers,100_106,76,296966,122,no,,,106.0,100.0,3_after,38.0,0.0,2139.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,long_gap,9.80952380952381,0.0,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,long_gap,35.0,0.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,1_before,10.0,0.1904761904761898,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,1_before,35.85,0.8500000000000014,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,2_before,12.5,2.69047619047619,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,2_before,37.0,2.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,3_before,41.9,32.09047619047619,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,3_before,37.0,2.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,1_after,5.7,-4.10952380952381,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,1_after,35.0,0.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,2_after,11.95,2.140476190476189,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,2_after,35.0,0.0,4170.0,self,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,100.0,106.0,3_after,34.55,24.740476190476187,4170.0,other,strangers +123_4170_100_106.mov,4170,strangers,100_106,101,484830,123,no,,,106.0,100.0,3_after,35.0,0.0,4170.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,long_gap,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,long_gap,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,1_before,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,1_before,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,2_before,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,2_before,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,3_before,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,3_before,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,1_after,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,1_after,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,2_after,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,2_after,35.0,0.0,2063.0,self,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,100.0,106.0,3_after,27.0,0.0,2063.0,other,strangers +124_2063_100_106.mov,2063,strangers,100_106,109,511904,124,no,,,106.0,100.0,3_after,35.0,0.0,2063.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,long_gap,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,long_gap,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,1_before,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,1_before,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,2_before,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,2_before,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,3_before,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,3_before,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,1_after,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,1_after,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,2_after,28.0,0.0,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,2_after,38.0,0.0,3471.0,other,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,110.0,106.0,3_after,30.7,2.6999999999999997,3471.0,self,strangers +125_3471_110_106.mov,3471,strangers,110_106,4,32500,125,no,,,106.0,110.0,3_after,38.0,0.0,3471.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,long_gap,37.34615384615385,0.0,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,long_gap,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,1_before,34.0,-3.3461538461538467,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,1_before,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,2_before,34.0,-3.3461538461538467,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,2_before,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,3_before,34.0,-3.3461538461538467,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,3_before,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,1_after,40.0,2.6538461538461533,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,1_after,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,2_after,40.0,2.6538461538461533,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,2_after,38.0,0.0,2584.0,self,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,110.0,106.0,3_after,40.0,2.6538461538461533,2584.0,other,strangers +126_2584_110_106.mov,2584,strangers,110_106,13,69600,126,no,,,106.0,110.0,3_after,38.0,0.0,2584.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,long_gap,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,long_gap,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,1_before,33.85,-1.1499999999999986,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,1_before,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,2_before,31.0,-4.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,2_before,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,3_before,31.0,-4.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,3_before,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,1_after,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,1_after,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,2_after,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,2_after,38.0,0.0,2104.0,self,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,110.0,106.0,3_after,35.0,0.0,2104.0,other,strangers +127_2104_110_106.mov,2104,strangers,110_106,39,134274,127,no,,,106.0,110.0,3_after,38.0,0.0,2104.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,long_gap,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,long_gap,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,1_before,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,1_before,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,2_before,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,2_before,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,3_before,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,3_before,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,1_after,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,1_after,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,2_after,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,2_after,50.0,0.0,2010.0,other,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,110.0,106.0,3_after,35.0,0.0,2010.0,self,strangers +128_2010_110_106.mov,2010,strangers,110_106,69,226979,128,yes,,,106.0,110.0,3_after,50.0,0.0,2010.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,long_gap,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,long_gap,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,1_before,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,1_before,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,2_before,32.4,2.3999999999999986,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,2_before,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,3_before,34.0,4.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,3_before,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,1_after,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,1_after,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,2_after,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,2_after,65.0,0.0,3147.0,self,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,110.0,106.0,3_after,30.0,0.0,3147.0,other,strangers +129_3147_110_106.mov,3147,strangers,110_106,122,409572,129,no,,,106.0,110.0,3_after,65.0,0.0,3147.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,long_gap,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,long_gap,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,1_before,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,1_before,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,2_before,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,2_before,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,3_before,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,3_before,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,1_after,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,1_after,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,2_after,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,2_after,79.0,0.0,2996.0,other,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,110.0,106.0,3_after,34.0,0.0,2996.0,self,strangers +130_2996_110_106.mov,2996,strangers,110_106,141,531913,130,no,,,106.0,110.0,3_after,79.0,0.0,2996.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,long_gap,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,long_gap,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,1_before,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,1_before,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,2_before,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,2_before,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,3_before,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,3_before,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,1_after,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,1_after,46.0,0.0,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,2_after,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,2_after,50.25,4.25,2123.0,self,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,22.0,23.0,3_after,88.0,0.0,2123.0,other,strangers +131_2123_22_23.mov,2123,strangers,22_23,76,239515,131,no,,,23.0,22.0,3_after,58.0,12.0,2123.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,long_gap,50.794117647058826,0.0,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,long_gap,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,1_before,67.0,16.205882352941174,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,1_before,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,2_before,67.0,16.205882352941174,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,2_before,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,3_before,67.0,16.205882352941174,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,3_before,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,1_after,41.0,-9.794117647058826,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,1_after,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,2_after,41.1,-9.694117647058825,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,2_after,100.0,0.0,3335.0,self,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,3.0,7.0,3_after,48.35,-2.4441176470588246,3335.0,other,strangers +132_3335_3_7.mov,3335,strangers,3_7,101,373825,132,yes,,,7.0,3.0,3_after,100.0,0.0,3335.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,long_gap,50.66666666666666,0.0,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,long_gap,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,1_before,70.45,19.78333333333334,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,1_before,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,2_before,50.0,-0.6666666666666643,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,2_before,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,3_before,60.85,10.183333333333335,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,3_before,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,1_after,55.85,5.183333333333337,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,1_after,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,2_after,59.0,8.333333333333336,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,2_after,100.0,0.0,2101.0,self,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,3.0,7.0,3_after,59.0,8.333333333333336,2101.0,other,strangers +133_2101_3_7.mov,2101,strangers,3_7,135,501441,133,yes,,,7.0,3.0,3_after,100.0,0.0,2101.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,long_gap,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,long_gap,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,1_before,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,1_before,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,2_before,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,2_before,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,3_before,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,3_before,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,1_after,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,1_after,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,2_after,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,2_after,83.0,0.0,2196.0,other,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,86.0,80.0,3_after,66.0,0.0,2196.0,self,strangers +134_2196_80_86.mov,2196,strangers,80_86,76,292104,134,no,,,80.0,86.0,3_after,83.0,0.0,2196.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,long_gap,75.29166666666667,0.0,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,long_gap,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,1_before,78.3,3.0083333333333258,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,1_before,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,2_before,88.0,12.708333333333329,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,2_before,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,3_before,99.9,24.60833333333333,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,3_before,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,1_after,74.55,-0.7416666666666742,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,1_after,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,2_after,73.8,-1.4916666666666742,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,2_after,100.0,0.0,2400.0,other,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,1.0,8.0,3_after,72.0,-3.2916666666666714,2400.0,self,strangers +135_2400_1_8.mov,2400,strangers,1_8,213,570242,135,yes,,,8.0,1.0,3_after,100.0,0.0,2400.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,long_gap,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,long_gap,41.0,0.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,1_before,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,1_before,42.95,1.9500000000000028,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,2_before,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,2_before,45.55,4.549999999999997,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,3_before,9.25,0.25,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,3_before,46.0,5.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,1_after,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,1_after,41.0,0.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,2_after,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,2_after,41.0,0.0,3017.0,self,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,40.0,46.0,3_after,9.0,0.0,3017.0,other,strangers +136_3017_40_46.mov,3017,strangers,40_46,29,82896,136,no,,,46.0,40.0,3_after,35.3,-5.700000000000003,3017.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,long_gap,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,long_gap,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,1_before,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,1_before,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,2_before,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,2_before,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,3_before,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,3_before,22.0,0.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,1_after,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,1_after,22.3,0.3000000000000007,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,2_after,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,2_after,23.0,1.0,2960.0,other,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,40.0,46.0,3_after,9.0,0.0,2960.0,self,strangers +137_2960_40_46.mov,2960,strangers,40_46,86,317183,137,no,,,46.0,40.0,3_after,23.0,1.0,2960.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,long_gap,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,long_gap,33.851851851851855,0.0,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,1_before,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,1_before,32.0,-1.8518518518518547,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,2_before,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,2_before,32.8,-1.0518518518518576,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,3_before,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,3_before,33.0,-0.8518518518518547,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,1_after,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,1_after,37.3,3.4481481481481424,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,2_after,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,2_after,38.0,4.148148148148145,2700.0,self,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,40.0,46.0,3_after,9.0,0.0,2700.0,other,strangers +138_2700_40_46.mov,2700,strangers,40_46,91,367607,138,yes,,,46.0,40.0,3_after,37.25,3.3981481481481453,2700.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,long_gap,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,long_gap,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,1_before,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,1_before,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,2_before,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,2_before,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,3_before,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,3_before,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,1_after,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,1_after,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,2_after,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,2_after,46.0,0.0,3002.0,self,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,40.0,46.0,3_after,9.0,0.0,3002.0,other,strangers +139_3002_40_46.mov,3002,strangers,40_46,109,416518,139,no,,,46.0,40.0,3_after,46.0,0.0,3002.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,long_gap,40.09090909090909,0.0,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,long_gap,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,1_before,49.85,9.759090909090908,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,1_before,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,2_before,51.0,10.909090909090908,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,2_before,16.1,-3.899999999999999,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,3_before,51.0,10.909090909090908,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,3_before,0.0,-20.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,1_after,39.0,-1.0909090909090935,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,1_after,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,2_after,39.0,-1.0909090909090935,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,2_after,20.0,0.0,2202.0,other,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,106.0,105.0,3_after,39.0,-1.0909090909090935,2202.0,self,strangers +140_2202_105_106.mov,2202,strangers,105_106,14,20959,140,no,,,105.0,106.0,3_after,20.0,0.0,2202.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,long_gap,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,long_gap,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,1_before,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,1_before,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,2_before,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,2_before,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,3_before,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,3_before,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,1_after,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,1_after,42.0,0.0,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,2_after,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,2_after,35.9,-6.100000000000001,4083.0,self,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,106.0,105.0,3_after,37.0,0.0,4083.0,other,strangers +141_4083_105_106.mov,4083,strangers,105_106,45,119981,141,no,,,105.0,106.0,3_after,29.0,-13.0,4083.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,long_gap,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,long_gap,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,1_before,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,1_before,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,2_before,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,2_before,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,3_before,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,3_before,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,1_after,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,1_after,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,2_after,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,2_after,29.0,0.0,2076.0,other,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,106.0,105.0,3_after,36.0,0.0,2076.0,self,strangers +142_2076_105_106.mov,2076,strangers,105_106,54,143048,142,no,,,105.0,106.0,3_after,29.0,0.0,2076.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,long_gap,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,long_gap,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,1_before,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,1_before,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,2_before,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,2_before,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,3_before,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,3_before,46.0,0.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,1_after,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,1_after,45.8,-0.2000000000000028,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,2_after,58.0,0.0,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,2_after,45.0,-1.0,2178.0,other,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,106.0,105.0,3_after,58.5,0.5,2178.0,self,strangers +143_2178_105_106.mov,2178,strangers,105_106,110,286294,143,yes,,,105.0,106.0,3_after,45.0,-1.0,2178.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,long_gap,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,long_gap,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,1_before,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,1_before,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,2_before,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,2_before,55.6,-0.3999999999999986,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,3_before,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,3_before,46.15,-9.85,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,1_after,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,1_after,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,2_after,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,2_after,56.0,0.0,3031.0,self,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,106.0,105.0,3_after,60.0,0.0,3031.0,other,strangers +144_3031_105_106.mov,3031,strangers,105_106,115,301152,144,no,,,105.0,106.0,3_after,56.0,0.0,3031.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,long_gap,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,long_gap,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,1_before,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,1_before,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,2_before,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,2_before,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,3_before,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,3_before,56.0,0.0,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,1_after,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,1_after,55.45,-0.5499999999999972,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,2_after,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,2_after,51.05,-4.950000000000003,2537.0,other,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,106.0,105.0,3_after,60.0,0.0,2537.0,self,strangers +145_2537_105_106.mov,2537,strangers,105_106,118,309261,145,no,,,105.0,106.0,3_after,50.0,-6.0,2537.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,long_gap,73.97058823529412,0.0,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,long_gap,70.41176470588235,0.0,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,1_before,74.0,0.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,1_before,71.0,0.5882352941176521,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,2_before,74.0,0.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,2_before,71.0,0.5882352941176521,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,3_before,74.0,0.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,3_before,71.0,0.5882352941176521,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,1_after,74.35,0.3794117647058783,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,1_after,72.45,2.038235294117655,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,2_after,75.0,1.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,2_after,84.75,14.338235294117652,3342.0,other,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,106.0,105.0,3_after,75.0,1.029411764705884,3342.0,self,strangers +146_3342_105_106.mov,3342,strangers,105_106,142,367017,146,yes,,,105.0,106.0,3_after,87.0,16.588235294117652,3342.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,long_gap,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,long_gap,87.0,0.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,1_before,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,1_before,80.7,-6.299999999999997,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,2_before,74.05,-0.9500000000000028,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,2_before,72.0,-15.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,3_before,73.95,-1.0499999999999972,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,3_before,69.7,-17.299999999999997,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,1_after,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,1_after,87.0,0.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,2_after,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,2_after,87.0,0.0,4591.0,self,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,106.0,105.0,3_after,75.0,0.0,4591.0,other,strangers +147_4591_105_106.mov,4591,strangers,105_106,145,375038,147,yes,,,105.0,106.0,3_after,87.0,0.0,4591.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,long_gap,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,long_gap,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,1_before,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,1_before,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,2_before,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,2_before,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,3_before,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,3_before,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,1_after,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,1_after,87.0,0.0,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,2_after,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,2_after,85.3,-1.7000000000000028,2034.0,other,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,106.0,105.0,3_after,75.0,0.0,2034.0,self,strangers +148_2034_105_106.mov,2034,strangers,105_106,146,380576,148,no,,,105.0,106.0,3_after,62.95,-24.05,2034.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,long_gap,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,long_gap,83.3061224489796,0.0,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,1_before,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,1_before,59.0,-24.306122448979597,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,2_before,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,2_before,58.65,-24.656122448979595,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,3_before,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,3_before,56.0,-27.306122448979597,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,1_after,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,1_after,85.0,1.6938775510204067,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,2_after,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,2_after,85.0,1.6938775510204067,4942.0,self,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,106.0,105.0,3_after,75.0,0.0,4942.0,other,strangers +149_4942_105_106.mov,4942,strangers,105_106,163,451349,149,yes,,,105.0,106.0,3_after,85.0,1.6938775510204067,4942.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,long_gap,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,long_gap,43.85714285714285,0.0,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,1_before,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,1_before,43.0,-0.8571428571428541,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,2_before,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,2_before,43.0,-0.8571428571428541,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,3_before,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,3_before,41.15,-2.7071428571428555,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,1_after,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,1_after,44.0,0.1428571428571459,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,2_after,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,2_after,44.0,0.1428571428571459,2082.0,other,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,106.0,105.0,3_after,75.0,0.0,2082.0,self,strangers +150_2082_105_106.mov,2082,strangers,105_106,176,492617,150,yes,,,105.0,106.0,3_after,44.0,0.1428571428571459,2082.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,long_gap,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,long_gap,63.90476190476191,0.0,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,1_before,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,1_before,60.85,-3.054761904761904,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,2_before,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,2_before,61.35,-2.554761904761904,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,3_before,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,3_before,72.95,9.045238095238098,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,1_after,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,1_after,65.3,1.395238095238092,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,2_after,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,2_after,66.0,2.095238095238095,4140.0,self,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,106.0,105.0,3_after,75.0,0.0,4140.0,other,strangers +151_4140_105_106.mov,4140,strangers,105_106,203,589118,151,yes,,,105.0,106.0,3_after,66.0,2.095238095238095,4140.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,long_gap,71.0,0.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,long_gap,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,1_before,74.15,3.1500000000000057,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,1_before,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,2_before,75.0,4.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,2_before,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,3_before,75.0,4.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,3_before,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,1_after,67.8,-3.200000000000003,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,1_after,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,2_after,66.0,-5.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,2_after,69.0,0.0,2141.0,other,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,89.0,87.0,3_after,66.0,-5.0,2141.0,self,strangers +152_2141_89_87.mov,2141,strangers,89_87,58,232637,152,no,,,87.0,89.0,3_after,69.0,0.0,2141.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,long_gap,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,long_gap,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,1_before,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,1_before,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,2_before,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,2_before,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,3_before,51.0,0.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,3_before,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,1_after,55.1,4.100000000000001,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,1_after,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,2_after,57.9,6.899999999999999,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,2_after,69.0,0.0,2632.0,other,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,89.0,87.0,3_after,58.0,7.0,2632.0,self,strangers +153_2632_89_87.mov,2632,strangers,89_87,68,288320,153,no,,,87.0,89.0,3_after,69.0,0.0,2632.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,long_gap,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,long_gap,38.57142857142857,0.0,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,1_before,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,1_before,58.4,19.82857142857143,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,2_before,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,2_before,74.35,35.778571428571425,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,3_before,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,3_before,75.0,36.42857142857143,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,1_after,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,1_after,17.15,-21.42142857142857,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,2_after,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,2_after,15.0,-23.57142857142857,2097.0,other,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,104.0,103.0,3_after,24.0,0.0,2097.0,self,strangers +154_2097_104_103.mov,2097,strangers,104_103,42,173851,154,no,,,103.0,104.0,3_after,15.0,-23.57142857142857,2097.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,long_gap,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,long_gap,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,1_before,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,1_before,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,2_before,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,2_before,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,3_before,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,3_before,16.5,0.5,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,1_after,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,1_after,16.0,0.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,2_after,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,2_after,21.0,5.0,2640.0,other,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,104.0,103.0,3_after,60.0,0.0,2640.0,self,strangers +155_2640_104_103.mov,2640,strangers,104_103,126,483111,155,no,,,103.0,104.0,3_after,25.0,9.0,2640.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,long_gap,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,long_gap,56.64102564102564,0.0,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,1_before,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,1_before,57.45,0.8089743589743605,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,2_before,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,2_before,59.75,3.1089743589743577,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,3_before,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,3_before,59.65,3.0089743589743563,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,1_after,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,1_after,56.0,-0.6410256410256423,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,2_after,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,2_after,57.8,1.1589743589743549,3896.0,self,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,87.0,85.0,3_after,38.0,0.0,3896.0,other,strangers +156_3896_85_87.mov,3896,strangers,85_87,39,139010,156,yes,,,85.0,87.0,3_after,56.05,-0.5910256410256451,3896.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,long_gap,6.115384615384615,0.0,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,long_gap,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,1_before,11.6,5.484615384615385,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,1_before,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,2_before,16.65,10.534615384615384,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,2_before,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,3_before,17.0,10.884615384615383,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,3_before,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,1_after,6.0,-0.1153846153846149,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,1_after,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,2_after,6.0,-0.1153846153846149,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,2_after,14.0,0.0,2610.0,other,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,40.0,47.0,3_after,6.0,-0.1153846153846149,2610.0,self,strangers +157_2610_40_47.mov,2610,strangers,40_47,16,94069,157,yes,,,47.0,40.0,3_after,14.0,0.0,2610.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,long_gap,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,long_gap,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,1_before,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,1_before,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,2_before,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,2_before,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,3_before,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,3_before,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,1_after,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,1_after,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,2_after,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,2_after,29.0,0.0,4125.0,self,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,40.0,47.0,3_after,6.0,0.0,4125.0,other,strangers +158_4125_40_47.mov,4125,strangers,40_47,39,347084,158,no,,,47.0,40.0,3_after,29.0,0.0,4125.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,long_gap,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,long_gap,27.0,0.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,1_before,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,1_before,32.05,5.049999999999997,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,2_before,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,2_before,33.0,6.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,3_before,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,3_before,33.0,6.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,1_after,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,1_after,27.0,0.0,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,2_after,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,2_after,24.2,-2.8000000000000007,2104.0,other,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,40.0,47.0,3_after,13.0,0.0,2104.0,self,strangers +159_2104_40_47.mov,2104,strangers,40_47,46,451902,159,no,,,47.0,40.0,3_after,24.0,-3.0,2104.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,long_gap,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,long_gap,37.0,0.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,1_before,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,1_before,32.4,-4.600000000000001,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,2_before,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,2_before,26.0,-11.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,3_before,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,3_before,26.0,-11.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,1_after,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,1_after,37.0,0.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,2_after,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,2_after,37.0,0.0,3635.0,self,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,40.0,47.0,3_after,9.0,0.0,3635.0,other,strangers +160_3635_40_47.mov,3635,strangers,40_47,51,518965,160,no,,,47.0,40.0,3_after,37.0,0.0,3635.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,long_gap,27.170731707317078,0.0,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,long_gap,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,1_before,38.0,10.829268292682926,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,1_before,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,2_before,38.0,10.829268292682926,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,2_before,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,3_before,38.0,10.829268292682926,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,3_before,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,1_after,21.0,-6.170731707317074,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,1_after,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,2_after,21.0,-6.170731707317074,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,2_after,61.0,0.0,4079.0,other,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,61.0,62.0,3_after,21.0,-6.170731707317074,4079.0,self,strangers +161_4079_61_62.mov,4079,strangers,61_62,62,287614,161,no,,,62.0,61.0,3_after,61.0,0.0,4079.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,long_gap,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,long_gap,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,1_before,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,1_before,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,2_before,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,2_before,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,3_before,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,3_before,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,1_after,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,1_after,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,2_after,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,2_after,61.0,0.0,2382.0,self,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,61.0,62.0,3_after,21.0,0.0,2382.0,other,strangers +162_2382_61_62.mov,2382,strangers,61_62,65,300769,162,no,,,62.0,61.0,3_after,61.0,0.0,2382.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,long_gap,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,long_gap,68.0,0.0,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,1_before,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,1_before,68.0,0.0,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,2_before,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,2_before,68.4,0.4000000000000057,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,3_before,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,3_before,76.15,8.150000000000006,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,1_after,79.0,0.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,1_after,68.0,0.0,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,2_after,75.8,-3.200000000000003,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,2_after,68.4,0.4000000000000057,2090.0,other,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,28.0,20.0,3_after,72.0,-7.0,2090.0,self,strangers +163_2090_20_28.mov,2090,strangers,20_28,28,104968,163,no,,,20.0,28.0,3_after,67.7,-0.2999999999999971,2090.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,long_gap,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,long_gap,97.53846153846152,0.0,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,1_before,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,1_before,99.0,1.461538461538467,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,2_before,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,2_before,98.45,0.9115384615384698,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,3_before,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,3_before,98.0,0.461538461538467,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,1_after,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,1_after,96.8,-0.7384615384615358,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,2_after,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,2_after,98.0,0.461538461538467,2591.0,self,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,90.0,85.0,3_after,7.0,0.0,2591.0,other,strangers +164_2591_85_90.mov,2591,strangers,85_90,164,536741,164,yes,,,85.0,90.0,3_after,95.6,-1.938461538461539,2591.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,long_gap,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,long_gap,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,1_before,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,1_before,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,2_before,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,2_before,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,3_before,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,3_before,30.0,0.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,1_after,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,1_after,28.0,-2.0,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,2_after,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,2_after,25.3,-4.699999999999999,2309.0,self,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,84.0,85.0,3_after,10.0,0.0,2309.0,other,strangers +165_2309_85_84.mov,2309,strangers,85_84,30,89326,165,yes,,,85.0,84.0,3_after,24.0,-6.0,2309.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,long_gap,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,long_gap,24.51428571428572,0.0,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,1_before,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,1_before,27.35,2.8357142857142854,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,2_before,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,2_before,29.85,5.335714285714285,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,3_before,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,3_before,30.0,5.485714285714284,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,1_after,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,1_after,24.3,-0.2142857142857153,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,2_after,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,2_after,25.1,0.5857142857142854,3576.0,other,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,84.0,85.0,3_after,10.0,0.0,3576.0,self,strangers +166_3576_85_84.mov,3576,strangers,85_84,32,95274,166,yes,,,85.0,84.0,3_after,26.55,2.0357142857142847,3576.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,long_gap,17.238095238095237,0.0,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,long_gap,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,1_before,15.4,-1.838095238095237,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,1_before,55.35,0.3500000000000014,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,2_before,17.0,-0.2380952380952372,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,2_before,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,3_before,17.0,-0.2380952380952372,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,3_before,54.6,-0.3999999999999986,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,1_after,19.0,1.7619047619047628,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,1_after,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,2_after,19.0,1.7619047619047628,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,2_after,55.0,0.0,2086.0,self,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,84.0,85.0,3_after,19.0,1.7619047619047628,2086.0,other,strangers +167_2086_85_84.mov,2086,strangers,85_84,79,273282,167,yes,,,85.0,84.0,3_after,55.05,0.0499999999999971,2086.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,long_gap,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,long_gap,54.05555555555556,0.0,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,1_before,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,1_before,55.0,0.9444444444444428,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,2_before,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,2_before,55.0,0.9444444444444428,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,3_before,15.65,-3.35,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,3_before,55.0,0.9444444444444428,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,1_after,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,1_after,49.26315789473684,-4.792397660818715,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,2_after,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,2_after,45.73684210526316,-8.3187134502924,3646.0,self,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,84.0,85.0,3_after,19.0,0.0,3646.0,other,strangers +168_3646_85_84.mov,3646,strangers,85_84,81,280290,168,yes,,,85.0,84.0,3_after,49.31578947368421,-4.739766081871345,3646.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,long_gap,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,long_gap,67.53333333333333,0.0,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,1_before,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,1_before,66.7,-0.8333333333333286,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,2_before,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,2_before,64.1,-3.433333333333337,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,3_before,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,3_before,63.9,-3.633333333333333,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,1_after,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,1_after,67.65,0.1166666666666742,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,2_after,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,2_after,62.0,-5.533333333333331,2960.0,self,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,84.0,85.0,3_after,21.0,0.0,2960.0,other,strangers +169_2960_85_84.mov,2960,strangers,85_84,113,420207,169,yes,,,85.0,84.0,3_after,56.15,-11.383333333333333,2960.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,long_gap,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,long_gap,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,1_before,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,1_before,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,2_before,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,2_before,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,3_before,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,3_before,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,1_after,20.0,0.0,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,1_after,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,2_after,19.25,-0.75,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,2_after,41.0,0.0,3351.0,self,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,84.0,85.0,3_after,21.5,1.5,3351.0,other,strangers +170_3351_85_84.mov,3351,strangers,85_84,119,490496,170,no,,,85.0,84.0,3_after,41.0,0.0,3351.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,long_gap,52.8,0.0,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,long_gap,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,1_before,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,1_before,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,2_before,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,2_before,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,3_before,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,3_before,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,1_after,52.0,-0.7999999999999972,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,1_after,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,2_after,52.75,-0.0499999999999971,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,2_after,12.0,0.0,2565.0,self,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,42.0,41.0,3_after,53.0,0.2000000000000028,2565.0,other,strangers +171_2565_42_41.mov,2565,strangers,42_41,53,165795,171,no,,,41.0,42.0,3_after,12.0,0.0,2565.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,long_gap,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,long_gap,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,1_before,52.15,-0.8500000000000014,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,1_before,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,2_before,52.0,-1.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,2_before,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,3_before,51.35,-1.6499999999999986,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,3_before,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,1_after,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,1_after,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,2_after,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,2_after,33.0,0.0,2914.0,other,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,42.0,41.0,3_after,53.0,0.0,2914.0,self,strangers +172_2914_42_41.mov,2914,strangers,42_41,116,320297,172,yes,,,41.0,42.0,3_after,33.0,0.0,2914.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,long_gap,61.407407407407405,0.0,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,long_gap,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,1_before,61.0,-0.4074074074074048,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,1_before,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,2_before,61.0,-0.4074074074074048,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,2_before,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,3_before,61.0,-0.4074074074074048,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,3_before,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,1_after,63.35,1.9425925925925969,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,1_after,54.0,0.0,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,2_after,65.0,3.5925925925925952,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,2_after,68.1,14.099999999999994,2666.0,other,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,11.0,5.0,3_after,65.0,3.5925925925925952,2666.0,self,strangers +173_2666_11_5.mov,2666,strangers,11_5,12,30154,173,yes,,,5.0,11.0,3_after,84.1,30.099999999999994,2666.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,long_gap,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,long_gap,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,1_before,82.9,-3.0999999999999943,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,1_before,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,2_before,71.0,-15.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,2_before,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,3_before,70.8,-15.200000000000005,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,3_before,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,1_after,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,1_after,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,2_after,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,2_after,100.0,0.0,3330.0,other,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,89.0,85.0,3_after,86.0,0.0,3330.0,self,strangers +174_3330_89_85.mov,3330,strangers,89_85,145,549771,174,yes,,,85.0,89.0,3_after,100.0,0.0,3330.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,long_gap,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,long_gap,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,1_before,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,1_before,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,2_before,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,2_before,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,3_before,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,3_before,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,1_after,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,1_after,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,2_after,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,2_after,66.0,0.0,3420.0,self,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,110.0,105.0,3_after,3.0,0.0,3420.0,other,strangers +175_3420_105_110.mov,3420,strangers,105_110,163,524791,175,yes,,,105.0,110.0,3_after,66.0,0.0,3420.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,long_gap,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,long_gap,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,1_before,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,1_before,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,2_before,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,2_before,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,3_before,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,3_before,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,1_after,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,1_after,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,2_after,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,2_after,97.0,0.0,2160.0,self,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,27.0,24.0,3_after,65.0,0.0,2160.0,other,strangers +176_2160_27_24.mov,2160,strangers,27_24,65,206728,176,no,,,24.0,27.0,3_after,96.6,-0.4000000000000057,2160.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,long_gap,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,long_gap,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,1_before,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,1_before,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,2_before,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,2_before,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,3_before,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,3_before,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,1_after,79.0,0.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,1_after,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,2_after,79.15,0.1500000000000056,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,2_after,57.0,0.0,2090.0,other,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,49.0,47.0,3_after,80.0,1.0,2090.0,self,strangers +177_2090_47_49.mov,2090,strangers,47_49,41,361482,177,no,,,47.0,49.0,3_after,58.05,1.0499999999999972,2090.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,long_gap,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,long_gap,33.0,0.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,1_before,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,1_before,34.0,1.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,2_before,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,2_before,34.0,1.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,3_before,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,3_before,34.0,1.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,1_after,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,1_after,33.0,0.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,2_after,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,2_after,33.0,0.0,2781.0,other,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,81.0,89.0,3_after,57.0,0.0,2781.0,self,strangers +178_2781_89_81.mov,2781,strangers,89_81,114,315425,178,no,,,89.0,81.0,3_after,33.0,0.0,2781.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,long_gap,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,long_gap,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,1_before,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,1_before,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,2_before,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,2_before,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,3_before,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,3_before,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,1_after,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,1_after,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,2_after,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,2_after,35.0,0.0,2065.0,other,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,104.0,105.0,3_after,15.0,0.0,2065.0,self,strangers +179_2065_104_105.mov,2065,strangers,104_105,42,115512,179,no,,,105.0,104.0,3_after,35.0,0.0,2065.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,long_gap,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,long_gap,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,1_before,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,1_before,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,2_before,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,2_before,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,3_before,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,3_before,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,1_after,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,1_after,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,2_after,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,2_after,38.0,0.0,2928.0,self,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,104.0,105.0,3_after,19.0,0.0,2928.0,other,strangers +180_2928_104_105.mov,2928,strangers,104_105,79,269806,180,no,,,105.0,104.0,3_after,38.0,0.0,2928.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,long_gap,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,long_gap,44.77272727272727,0.0,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,1_before,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,1_before,43.0,-1.7727272727272734,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,2_before,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,2_before,43.0,-1.7727272727272734,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,3_before,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,3_before,43.0,-1.7727272727272734,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,1_after,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,1_after,45.8,1.0272727272727238,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,2_after,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,2_after,38.95,-5.822727272727271,2244.0,self,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,108.0,101.0,3_after,72.0,0.0,2244.0,other,strangers +181_2244_101_108.mov,2244,strangers,101_108,51,147809,181,yes,,,101.0,108.0,3_after,40.3,-4.472727272727276,2244.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,long_gap,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,long_gap,68.95,0.0,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,1_before,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,1_before,69.0,0.0499999999999971,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,2_before,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,2_before,69.0,0.0499999999999971,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,3_before,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,3_before,69.0,0.0499999999999971,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,1_after,54.0,0.0,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,1_after,69.95,1.0,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,2_after,55.1,1.1000000000000014,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,2_after,72.65,3.700000000000003,2033.0,self,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,64.0,69.0,3_after,56.2,2.200000000000003,2033.0,other,strangers +182_2033_64_69.mov,2033,strangers,64_69,105,395581,182,no,,,69.0,64.0,3_after,76.9,7.950000000000003,2033.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,long_gap,63.09090909090909,0.0,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,long_gap,66.0,0.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,1_before,60.7,-2.3909090909090907,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,1_before,66.0,0.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,2_before,58.05,-5.040909090909096,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,2_before,65.25,-0.75,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,3_before,57.05,-6.040909090909096,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,3_before,64.0,-2.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,1_after,64.0,0.9090909090909064,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,1_after,56.4,-9.6,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,2_after,63.95,0.8590909090909093,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,2_after,53.0,-13.0,2197.0,self,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,42.0,45.0,3_after,61.75,-1.3409090909090935,2197.0,other,strangers +183_2197_42_45.mov,2197,strangers,42_45,31,178147,183,no,,,45.0,42.0,3_after,54.15,-11.85,2197.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,long_gap,38.0,0.0,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,long_gap,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,1_before,38.0,0.0,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,1_before,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,2_before,38.4,0.3999999999999986,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,2_before,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,3_before,39.95,1.9500000000000028,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,3_before,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,1_after,38.0,0.0,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,1_after,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,2_after,37.2,-0.7999999999999972,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,2_after,80.0,0.0,2525.0,self,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,42.0,45.0,3_after,36.2,-1.7999999999999972,2525.0,other,strangers +184_2525_42_45.mov,2525,strangers,42_45,83,471584,184,no,,,45.0,42.0,3_after,80.0,0.0,2525.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,long_gap,43.333333333333336,0.0,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,long_gap,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,1_before,46.35,3.0166666666666657,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,1_before,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,2_before,47.0,3.666666666666664,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,2_before,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,3_before,46.75,3.4166666666666643,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,3_before,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,1_after,42.7,-0.6333333333333329,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,1_after,58.0,0.0,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,2_after,42.0,-1.3333333333333357,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,2_after,58.8,0.7999999999999972,2075.0,other,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,42.0,44.0,3_after,42.0,-1.3333333333333357,2075.0,self,strangers +185_2075_42_44.mov,2075,strangers,42_44,42,152065,185,no,,,44.0,42.0,3_after,59.0,1.0,2075.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,long_gap,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,long_gap,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,1_before,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,1_before,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,2_before,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,2_before,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,3_before,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,3_before,80.0,0.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,1_after,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,1_after,80.55,0.5499999999999972,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,2_after,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,2_after,82.0,2.0,2419.0,other,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,9.0,6.0,3_after,32.0,0.0,2419.0,self,strangers +186_2419_9_6.mov,2419,strangers,9_6,74,311137,186,yes,,,6.0,9.0,3_after,82.0,2.0,2419.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,long_gap,46.03225806451613,0.0,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,long_gap,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,1_before,50.55,4.517741935483869,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,1_before,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,2_before,55.0,8.967741935483872,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,2_before,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,3_before,55.0,8.967741935483872,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,3_before,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,1_after,46.0,-0.0322580645161281,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,1_after,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,2_after,46.0,-0.0322580645161281,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,2_after,64.0,0.0,3120.0,other,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,9.0,6.0,3_after,46.0,-0.0322580645161281,3120.0,self,strangers +187_3120_9_6.mov,3120,strangers,9_6,88,464789,187,no,,,6.0,9.0,3_after,64.0,0.0,3120.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,long_gap,50.0,0.0,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,long_gap,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,1_before,48.25,-1.75,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,1_before,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,2_before,43.0,-7.0,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,2_before,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,3_before,43.0,-7.0,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,3_before,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,1_after,50.15,0.1499999999999985,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,1_after,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,2_after,54.65,4.649999999999999,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,2_after,7.0,0.0,2357.0,self,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,61.0,63.0,3_after,60.25,10.25,2357.0,other,strangers +188_2357_63_61.mov,2357,strangers,63_61,56,248961,188,no,,,63.0,61.0,3_after,9.6,2.6,2357.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,long_gap,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,long_gap,24.0,0.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,1_before,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,1_before,20.3,-3.6999999999999993,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,2_before,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,2_before,19.0,-5.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,3_before,39.75,-3.25,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,3_before,19.0,-5.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,1_after,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,1_after,24.0,0.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,2_after,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,2_after,24.0,0.0,2211.0,self,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,9.0,7.0,3_after,43.0,0.0,2211.0,other,strangers +189_2211_7_9.mov,2211,strangers,7_9,21,60762,189,yes,,,7.0,9.0,3_after,24.0,0.0,2211.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,long_gap,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,long_gap,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,1_before,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,1_before,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,2_before,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,2_before,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,3_before,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,3_before,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,1_after,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,1_after,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,2_after,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,2_after,45.0,0.0,2356.0,self,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,9.0,7.0,3_after,46.0,0.0,2356.0,other,strangers +190_2356_7_9.mov,2356,strangers,7_9,63,180375,190,yes,,,7.0,9.0,3_after,45.0,0.0,2356.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,long_gap,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,long_gap,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,1_before,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,1_before,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,2_before,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,2_before,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,3_before,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,3_before,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,1_after,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,1_after,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,2_after,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,2_after,53.0,0.0,2747.0,other,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,9.0,7.0,3_after,36.0,0.0,2747.0,self,strangers +191_2747_7_9.mov,2747,strangers,7_9,76,242676,191,yes,,,7.0,9.0,3_after,53.0,0.0,2747.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,long_gap,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,long_gap,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,1_before,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,1_before,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,2_before,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,2_before,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,3_before,35.0,0.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,3_before,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,1_after,35.05,0.0499999999999971,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,1_after,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,2_after,40.55,5.549999999999997,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,2_after,66.0,0.0,2107.0,other,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,89.0,80.0,3_after,44.0,9.0,2107.0,self,strangers +192_2107_89_80.mov,2107,strangers,89_80,42,150996,192,no,,,80.0,89.0,3_after,66.0,0.0,2107.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,long_gap,45.0,0.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,long_gap,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,1_before,48.45,3.450000000000003,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,1_before,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,2_before,50.0,5.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,2_before,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,3_before,50.0,5.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,3_before,47.15,0.1499999999999985,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,1_after,47.0,2.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,1_after,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,2_after,47.0,2.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,2_after,47.0,0.0,2580.0,other,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,89.0,80.0,3_after,47.0,2.0,2580.0,self,strangers +193_2580_89_80.mov,2580,strangers,89_80,94,373646,193,no,,,80.0,89.0,3_after,48.9,1.8999999999999984,2580.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,long_gap,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,long_gap,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,1_before,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,1_before,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,2_before,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,2_before,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,3_before,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,3_before,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,1_after,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,1_after,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,2_after,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,2_after,4.0,0.0,2077.0,self,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,40.0,41.0,3_after,46.0,0.0,2077.0,other,strangers +194_2077_40_41.mov,2077,strangers,40_41,11,17975,194,no,,,41.0,40.0,3_after,4.0,0.0,2077.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,long_gap,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,long_gap,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,1_before,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,1_before,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,2_before,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,2_before,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,3_before,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,3_before,35.0,0.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,1_after,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,1_after,36.25,1.25,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,2_after,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,2_after,38.0,3.0,2110.0,other,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,40.0,41.0,3_after,67.0,0.0,2110.0,self,strangers +195_2110_40_41.mov,2110,strangers,40_41,92,363497,195,no,,,41.0,40.0,3_after,38.0,3.0,2110.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,long_gap,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,long_gap,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,1_before,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,1_before,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,2_before,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,2_before,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,3_before,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,3_before,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,1_after,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,1_after,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,2_after,70.0,0.0,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,2_after,38.0,0.0,3139.0,self,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,40.0,41.0,3_after,67.9,-2.0999999999999943,3139.0,other,strangers +196_3139_40_41.mov,3139,strangers,40_41,104,412995,196,no,,,41.0,40.0,3_after,38.0,0.0,3139.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,long_gap,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,long_gap,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,1_before,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,1_before,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,2_before,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,2_before,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,3_before,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,3_before,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,1_after,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,1_after,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,2_after,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,2_after,38.0,0.0,2247.0,other,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,40.0,41.0,3_after,32.0,0.0,2247.0,self,strangers +197_2247_40_41.mov,2247,strangers,40_41,115,446057,197,no,,,41.0,40.0,3_after,38.0,0.0,2247.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,long_gap,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,long_gap,53.48648648648648,0.0,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,1_before,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,1_before,76.4,22.91351351351352,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,2_before,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,2_before,79.0,25.51351351351352,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,3_before,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,3_before,79.0,25.51351351351352,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,1_after,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,1_after,47.05,-6.436486486486487,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,2_after,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,2_after,46.0,-7.486486486486484,3781.0,other,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,70.0,61.0,3_after,75.0,0.0,3781.0,self,strangers +198_3781_61_70.mov,3781,strangers,61_70,68,352690,198,no,,,61.0,70.0,3_after,46.0,-7.486486486486484,3781.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,long_gap,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,long_gap,19.0,0.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,1_before,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,1_before,12.2,-6.800000000000001,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,2_before,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,2_before,0.0,-19.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,3_before,39.4,-5.600000000000001,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,3_before,0.0,-19.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,1_after,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,1_after,19.0,0.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,2_after,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,2_after,19.0,0.0,2426.0,other,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,103.0,105.0,3_after,45.0,0.0,2426.0,self,strangers +199_2426_105_103.mov,2426,strangers,105_103,6,15220,199,yes,,,105.0,103.0,3_after,19.0,0.0,2426.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,long_gap,66.0,0.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,long_gap,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,1_before,66.0,0.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,1_before,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,2_before,66.0,0.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,2_before,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,3_before,65.9,-0.0999999999999943,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,3_before,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,1_after,64.1,-1.9000000000000057,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,1_after,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,2_after,64.0,-2.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,2_after,21.0,0.0,2085.0,other,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,103.0,105.0,3_after,64.0,-2.0,2085.0,self,strangers +200_2085_105_103.mov,2085,strangers,105_103,92,301298,200,yes,,,105.0,103.0,3_after,21.0,0.0,2085.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,long_gap,69.0,0.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,long_gap,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,1_before,69.0,0.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,1_before,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,2_before,70.4,1.4000000000000057,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,2_before,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,3_before,70.0,1.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,3_before,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,1_after,68.6,-0.4000000000000057,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,1_after,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,2_after,70.0,1.0,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,2_after,44.0,0.0,3057.0,self,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,103.0,105.0,3_after,69.65,0.6500000000000057,3057.0,other,strangers +201_3057_105_103.mov,3057,strangers,105_103,113,367335,201,no,,,105.0,103.0,3_after,44.0,0.0,3057.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,long_gap,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,long_gap,53.958333333333336,0.0,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,1_before,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,1_before,45.8,-8.158333333333339,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,2_before,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,2_before,44.0,-9.958333333333336,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,3_before,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,3_before,44.0,-9.958333333333336,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,1_after,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,1_after,56.0,2.0416666666666643,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,2_after,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,2_after,56.0,2.0416666666666643,2393.0,other,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,103.0,105.0,3_after,23.0,0.0,2393.0,self,strangers +202_2393_105_103.mov,2393,strangers,105_103,119,394709,202,yes,,,105.0,103.0,3_after,56.0,2.0416666666666643,2393.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,long_gap,52.5,0.0,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,long_gap,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,1_before,54.85,2.3500000000000014,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,1_before,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,2_before,40.2,-12.299999999999995,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,2_before,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,3_before,24.0,-28.5,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,3_before,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,1_after,55.0,2.5,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,1_after,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,2_after,53.55,1.0499999999999972,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,2_after,18.0,0.0,2741.0,other,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,103.0,105.0,3_after,52.0,-0.5,2741.0,self,strangers +203_2741_105_103.mov,2741,strangers,105_103,137,485316,203,no,,,105.0,103.0,3_after,18.0,0.0,2741.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,long_gap,34.02564102564103,0.0,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,long_gap,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,1_before,53.0,18.97435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,1_before,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,2_before,52.1,18.07435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,2_before,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,3_before,54.95,20.924358974358974,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,3_before,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,1_after,22.65,-11.37564102564103,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,1_after,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,2_after,50.65,16.62435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,2_after,18.0,0.0,3937.0,self,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,103.0,105.0,3_after,55.15,21.12435897435897,3937.0,other,strangers +204_3937_105_103.mov,3937,strangers,105_103,140,496252,204,no,,,105.0,103.0,3_after,18.0,0.0,3937.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,long_gap,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,long_gap,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,1_before,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,1_before,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,2_before,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,2_before,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,3_before,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,3_before,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,1_after,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,1_after,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,2_after,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,2_after,53.0,0.0,2278.0,other,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,63.0,62.0,3_after,61.0,0.0,2278.0,self,strangers +205_2278_63_62.mov,2278,strangers,63_62,94,490668,205,no,,,62.0,63.0,3_after,53.0,0.0,2278.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,long_gap,42.84615384615385,0.0,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,long_gap,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,1_before,42.8,-0.0461538461538495,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,1_before,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,2_before,39.85,-2.9961538461538453,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,2_before,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,3_before,47.0,4.153846153846153,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,3_before,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,1_after,44.95,2.103846153846156,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,1_after,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,2_after,45.15,2.303846153846152,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,2_after,83.0,0.0,2507.0,other,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,3.0,11.0,3_after,46.0,3.1538461538461533,2507.0,self,strangers +206_2507_11_3.mov,2507,strangers,11_3,164,523793,206,no,,,11.0,3.0,3_after,83.0,0.0,2507.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,long_gap,58.60606060606061,0.0,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,long_gap,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,1_before,53.0,-5.606060606060609,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,1_before,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,2_before,42.0,-16.60606060606061,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,2_before,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,3_before,42.0,-16.60606060606061,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,3_before,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,1_after,58.0,-0.6060606060606091,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,1_after,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,2_after,58.0,-0.6060606060606091,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,2_after,49.0,0.0,3243.0,other,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,3.0,10.0,3_after,57.9,-0.7060606060606105,3243.0,self,strangers +207_3243_3_10.mov,3243,strangers,3_10,34,103819,207,no,,,10.0,3.0,3_after,49.0,0.0,3243.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,long_gap,53.95454545454545,0.0,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,long_gap,50.0,0.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,1_before,53.0,-0.9545454545454533,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,1_before,49.65,-0.3500000000000014,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,2_before,54.95,0.9954545454545496,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,2_before,49.0,-1.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,3_before,57.15,3.1954545454545453,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,3_before,49.0,-1.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,1_after,49.55,-4.404545454545456,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,1_after,50.0,0.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,2_after,48.0,-5.954545454545453,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,2_after,50.0,0.0,2172.0,other,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,3.0,10.0,3_after,48.0,-5.954545454545453,2172.0,self,strangers +208_2172_3_10.mov,2172,strangers,3_10,36,117121,208,yes,,,10.0,3.0,3_after,50.0,0.0,2172.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,long_gap,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,long_gap,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,1_before,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,1_before,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,2_before,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,2_before,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,3_before,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,3_before,49.0,0.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,1_after,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,1_after,49.36,0.3599999999999994,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,2_after,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,2_after,50.0,1.0,2305.0,other,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,102.0,105.0,3_after,63.0,0.0,2305.0,self,strangers +209_2305_105_102.mov,2305,strangers,105_102,82,223290,209,no,,,105.0,102.0,3_after,50.0,1.0,2305.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,long_gap,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,long_gap,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,1_before,60.65,-0.3500000000000014,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,1_before,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,2_before,60.0,-1.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,2_before,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,3_before,60.0,-1.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,3_before,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,1_after,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,1_after,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,2_after,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,2_after,24.0,0.0,2246.0,other,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,102.0,105.0,3_after,61.0,0.0,2246.0,self,strangers +210_2246_105_102.mov,2246,strangers,105_102,94,283426,210,no,,,105.0,102.0,3_after,24.0,0.0,2246.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,long_gap,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,long_gap,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,1_before,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,1_before,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,2_before,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,2_before,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,3_before,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,3_before,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,1_after,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,1_after,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,2_after,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,2_after,39.0,0.0,2159.0,other,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,102.0,105.0,3_after,61.0,0.0,2159.0,self,strangers +211_2159_105_102.mov,2159,strangers,105_102,106,313190,211,no,,,105.0,102.0,3_after,38.785714285714285,-0.2142857142857153,2159.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,long_gap,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,long_gap,62.55882352941177,0.0,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,1_before,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,1_before,61.0,-1.558823529411768,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,2_before,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,2_before,61.45,-1.1088235294117652,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,3_before,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,3_before,61.5,-1.058823529411768,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,1_after,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,1_after,64.0,1.441176470588232,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,2_after,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,2_after,64.35,1.7911764705882265,3410.0,self,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,65.0,64.0,3_after,11.0,0.0,3410.0,other,strangers +212_3410_65_64.mov,3410,strangers,65_64,17,26742,212,no,,,64.0,65.0,3_after,63.25,0.691176470588232,3410.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,long_gap,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,long_gap,65.36363636363636,0.0,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,1_before,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,1_before,65.0,-0.3636363636363597,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,2_before,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,2_before,63.35,-2.0136363636363583,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,3_before,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,3_before,62.0,-3.36363636363636,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,1_after,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,1_after,66.0,0.6363636363636402,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,2_after,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,2_after,66.0,0.6363636363636402,2172.0,self,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,65.0,64.0,3_after,27.0,0.0,2172.0,other,strangers +213_2172_65_64.mov,2172,strangers,65_64,43,140959,213,no,,,64.0,65.0,3_after,66.0,0.6363636363636402,2172.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,long_gap,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,long_gap,70.0,0.0,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,1_before,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,1_before,70.55,0.5499999999999972,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,2_before,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,2_before,70.8,0.7999999999999972,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,3_before,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,3_before,71.0,1.0,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,1_after,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,1_after,70.0,0.0,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,2_after,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,2_after,63.3,-6.700000000000003,2192.0,other,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,43.0,48.0,3_after,0.0,0.0,2192.0,self,strangers +214_2192_43_48.mov,2192,strangers,43_48,18,35252,214,no,,,48.0,43.0,3_after,75.05,5.049999999999997,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,long_gap,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,long_gap,56.59090909090909,0.0,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,1_before,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,1_before,59.0,2.4090909090909065,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,2_before,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,2_before,40.8,-15.790909090909096,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,3_before,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,3_before,37.7,-18.89090909090909,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,1_after,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,1_after,66.3,9.709090909090904,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,2_after,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,2_after,65.25,8.659090909090907,2192.0,other,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,43.0,48.0,3_after,78.0,0.0,2192.0,self,strangers +215_2192_43_48.mov,2192,strangers,43_48,114,528911,215,no,,,48.0,43.0,3_after,67.55,10.959090909090904,2192.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,long_gap,62.59090909090909,0.0,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,long_gap,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,1_before,62.0,-0.5909090909090935,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,1_before,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,2_before,59.0,-3.590909090909093,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,2_before,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,3_before,59.0,-3.590909090909093,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,3_before,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,1_after,63.4,0.8090909090909051,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,1_after,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,2_after,63.0,0.4090909090909065,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,2_after,61.0,0.0,2265.0,self,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,104.0,106.0,3_after,61.7,-0.8909090909090907,2265.0,other,strangers +216_2265_106_104.mov,2265,strangers,106_104,111,386386,216,yes,,,106.0,104.0,3_after,61.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,long_gap,34.04347826086956,0.0,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,long_gap,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,1_before,30.65,-3.393478260869564,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,1_before,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,2_before,26.0,-8.043478260869563,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,2_before,40.65,-0.3500000000000014,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,3_before,26.0,-8.043478260869563,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,3_before,39.6,-1.3999999999999986,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,1_after,34.0,-0.0434782608695627,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,1_after,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,2_after,41.9,7.856521739130436,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,2_after,41.0,0.0,2265.0,self,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,109.0,103.0,3_after,51.0,16.956521739130437,2265.0,other,strangers +217_2265_103_109.mov,2265,strangers,103_109,19,37758,217,no,,,103.0,109.0,3_after,41.0,0.0,2265.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,long_gap,60.85,0.0,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,long_gap,12.95,0.0,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,1_before,76.0,15.15,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,1_before,13.0,0.0500000000000007,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,2_before,76.0,15.15,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,2_before,13.0,0.0500000000000007,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,3_before,76.0,15.15,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,3_before,13.0,0.0500000000000007,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,1_after,52.45,-8.399999999999999,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,1_after,11.1,-1.85,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,2_after,64.4,3.5500000000000043,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,2_after,11.0,-1.9499999999999995,3968.0,self,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,10.0,7.0,3_after,61.15,0.2999999999999971,3968.0,other,strangers +218_3968_10_7.mov,3968,strangers,10_7,21,29024,218,yes,,,7.0,10.0,3_after,11.0,-1.9499999999999995,3968.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,long_gap,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,long_gap,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,1_before,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,1_before,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,2_before,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,2_before,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,3_before,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,3_before,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,1_after,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,1_after,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,2_after,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,2_after,14.0,0.0,2115.0,self,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,10.0,7.0,3_after,78.0,0.0,2115.0,other,strangers +219_2115_10_7.mov,2115,strangers,10_7,43,84191,219,no,,,7.0,10.0,3_after,14.0,0.0,2115.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,long_gap,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,long_gap,59.0,0.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,1_before,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,1_before,57.95,-1.0499999999999972,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,2_before,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,2_before,58.0,-1.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,3_before,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,3_before,58.0,-1.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,1_after,83.0,0.0,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,1_after,59.0,0.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,2_after,83.85,0.8499999999999943,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,2_after,59.0,0.0,2149.0,other,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,10.0,7.0,3_after,86.2,3.200000000000003,2149.0,self,strangers +220_2149_10_7.mov,2149,strangers,10_7,68,180488,220,yes,,,7.0,10.0,3_after,59.0,0.0,2149.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,long_gap,34.85,0.0,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,long_gap,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,1_before,37.0,2.1499999999999986,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,1_before,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,2_before,37.05,2.199999999999996,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,2_before,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,3_before,34.4,-0.4500000000000028,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,3_before,47.65,-1.3500000000000014,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,1_after,29.0,-5.850000000000001,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,1_after,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,2_after,29.0,-5.850000000000001,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,2_after,49.0,0.0,4034.0,other,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,47.0,46.0,3_after,29.0,-5.850000000000001,4034.0,self,strangers +221_4034_46_47.mov,4034,strangers,46_47,34,163971,221,no,,,46.0,47.0,3_after,46.5,-2.5,4034.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,long_gap,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,long_gap,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,1_before,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,1_before,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,2_before,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,2_before,55.45,-1.5499999999999972,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,3_before,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,3_before,47.3,-9.700000000000005,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,1_after,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,1_after,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,2_after,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,2_after,57.0,0.0,2008.0,other,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,47.0,46.0,3_after,41.0,0.0,2008.0,self,strangers +222_2008_46_47.mov,2008,strangers,46_47,49,536458,222,no,,,46.0,47.0,3_after,57.0,0.0,2008.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,long_gap,75.0,0.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,long_gap,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,1_before,75.0,0.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,1_before,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,2_before,75.45,0.4500000000000028,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,2_before,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,3_before,72.55,-2.450000000000003,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,3_before,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,1_after,75.0,0.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,1_after,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,2_after,73.6,-1.4000000000000057,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,2_after,49.0,0.0,2009.0,other,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,22.0,21.0,3_after,73.0,-2.0,2009.0,self,strangers +223_2009_21_22.mov,2009,strangers,21_22,18,47335,223,no,,,21.0,22.0,3_after,49.0,0.0,2009.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,long_gap,32.91428571428571,0.0,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,long_gap,86.85714285714286,0.0,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,1_before,74.75,41.83571428571429,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,1_before,88.0,1.1428571428571388,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,2_before,73.0,40.08571428571429,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,2_before,87.65,0.7928571428571445,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,3_before,72.6,39.68571428571428,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,3_before,88.0,1.1428571428571388,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,1_after,7.0,-25.91428571428571,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,1_after,81.35,-5.507142857142867,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,2_after,2.85,-30.06428571428571,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,2_after,60.5,-26.35714285714286,3476.0,other,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,22.0,21.0,3_after,6.0,-26.91428571428571,3476.0,self,strangers +224_3476_21_22.mov,3476,strangers,21_22,113,355639,224,yes,,,21.0,22.0,3_after,63.25,-23.60714285714286,3476.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,long_gap,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,long_gap,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,1_before,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,1_before,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,2_before,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,2_before,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,3_before,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,3_before,73.85,-0.1500000000000056,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,1_after,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,1_after,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,2_after,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,2_after,74.0,0.0,4090.0,self,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,65.0,66.0,3_after,12.0,0.0,4090.0,other,strangers +225_4090_65_66.mov,4090,strangers,65_66,109,477610,225,yes,,,66.0,65.0,3_after,72.15,-1.8499999999999943,4090.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,long_gap,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,long_gap,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,1_before,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,1_before,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,2_before,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,2_before,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,3_before,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,3_before,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,1_after,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,1_after,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,2_after,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,2_after,57.0,0.0,2529.0,self,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,23.0,27.0,3_after,28.0,0.0,2529.0,other,strangers +226_2529_23_27.mov,2529,strangers,23_27,59,320972,226,yes,,,27.0,23.0,3_after,57.9,0.8999999999999986,2529.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,long_gap,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,long_gap,55.70967741935484,0.0,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,1_before,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,1_before,54.0,-1.70967741935484,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,2_before,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,2_before,54.0,-1.70967741935484,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,3_before,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,3_before,54.0,-1.70967741935484,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,1_after,0.0,0.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,1_after,65.25,9.54032258064516,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,2_after,8.85,8.85,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,2_after,65.0,9.29032258064516,3119.0,other,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,65.0,62.0,3_after,13.0,13.0,3119.0,self,strangers +227_3119_65_62.mov,3119,strangers,65_62,28,75063,227,yes,,,62.0,65.0,3_after,65.0,9.29032258064516,3119.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,long_gap,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,long_gap,47.22727272727273,0.0,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,1_before,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,1_before,40.35,-6.877272727272725,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,2_before,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,2_before,40.0,-7.227272727272727,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,3_before,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,3_before,40.0,-7.227272727272727,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,1_after,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,1_after,51.0,3.772727272727274,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,2_after,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,2_after,51.0,3.772727272727274,2229.0,other,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,65.0,62.0,3_after,41.0,0.0,2229.0,self,strangers +228_2229_65_62.mov,2229,strangers,65_62,130,397389,228,no,,,62.0,65.0,3_after,51.0,3.772727272727274,2229.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,long_gap,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,long_gap,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,1_before,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,1_before,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,2_before,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,2_before,51.35,-1.6499999999999986,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,3_before,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,3_before,39.25,-13.75,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,1_after,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,1_after,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,2_after,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,2_after,53.0,0.0,2249.0,self,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,65.0,62.0,3_after,61.0,0.0,2249.0,other,strangers +229_2249_65_62.mov,2249,strangers,65_62,153,476513,229,yes,,,62.0,65.0,3_after,53.0,0.0,2249.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,long_gap,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,long_gap,27.33333333333333,0.0,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,1_before,84.3,0.2999999999999971,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,1_before,38.3,10.966666666666663,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,2_before,67.7,-16.299999999999997,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,2_before,47.0,19.666666666666668,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,3_before,61.0,-23.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,3_before,47.0,19.666666666666668,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,1_after,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,1_after,27.0,-0.3333333333333321,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,2_after,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,2_after,27.0,-0.3333333333333321,4418.0,other,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,65.0,62.0,3_after,84.0,0.0,4418.0,self,strangers +230_4418_65_62.mov,4418,strangers,65_62,174,543821,230,yes,,,62.0,65.0,3_after,34.25,6.916666666666668,4418.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,long_gap,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,long_gap,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,1_before,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,1_before,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,2_before,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,2_before,64.7,-0.2999999999999971,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,3_before,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,3_before,64.0,-1.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,1_after,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,1_after,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,2_after,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,2_after,65.0,0.0,2463.0,other,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,62.0,69.0,3_after,48.0,0.0,2463.0,self,strangers +231_2463_69_62.mov,2463,strangers,69_62,66,236241,231,no,,,69.0,62.0,3_after,65.0,0.0,2463.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,long_gap,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,long_gap,65.56521739130434,0.0,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,1_before,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,1_before,65.0,-0.5652173913043441,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,2_before,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,2_before,65.0,-0.5652173913043441,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,3_before,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,3_before,64.95,-0.6152173913043413,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,1_after,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,1_after,66.0,0.4347826086956559,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,2_after,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,2_after,64.3,-1.265217391304347,2386.0,self,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,62.0,69.0,3_after,50.0,0.0,2386.0,other,strangers +232_2386_69_62.mov,2386,strangers,69_62,91,328087,232,yes,,,69.0,62.0,3_after,61.7,-3.865217391304341,2386.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,long_gap,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,long_gap,66.0,0.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,1_before,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,1_before,65.75,-0.25,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,2_before,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,2_before,65.0,-1.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,3_before,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,3_before,65.0,-1.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,1_after,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,1_after,62.2,-3.799999999999997,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,2_after,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,2_after,62.0,-4.0,2706.0,other,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,62.0,69.0,3_after,50.0,0.0,2706.0,self,strangers +233_2706_69_62.mov,2706,strangers,69_62,92,330950,233,no,,,69.0,62.0,3_after,62.0,-4.0,2706.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,long_gap,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,long_gap,61.84615384615385,0.0,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,1_before,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,1_before,72.0,10.153846153846152,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,2_before,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,2_before,72.0,10.153846153846152,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,3_before,50.0,0.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,3_before,72.0,10.153846153846152,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,1_after,49.4,-0.6000000000000014,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,1_after,52.05,-9.79615384615385,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,2_after,42.25,-7.75,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,2_after,56.2,-5.646153846153844,3882.0,other,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,62.0,69.0,3_after,42.0,-8.0,3882.0,self,strangers +234_3882_69_62.mov,3882,strangers,69_62,102,362620,234,no,,,69.0,62.0,3_after,57.0,-4.846153846153847,3882.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,long_gap,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,long_gap,60.60869565217392,0.0,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,1_before,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,1_before,59.0,-1.608695652173914,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,2_before,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,2_before,59.0,-1.608695652173914,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,3_before,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,3_before,59.0,-1.608695652173914,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,1_after,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,1_after,61.0,0.391304347826086,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,2_after,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,2_after,61.4,0.7913043478260846,2289.0,other,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,62.0,69.0,3_after,42.0,0.0,2289.0,self,strangers +235_2289_69_62.mov,2289,strangers,69_62,106,383832,235,no,,,69.0,62.0,3_after,63.95,3.341304347826089,2289.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,long_gap,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,long_gap,80.74193548387096,0.0,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,1_before,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,1_before,81.0,0.2580645161290391,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,2_before,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,2_before,81.0,0.2580645161290391,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,3_before,42.0,0.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,3_before,81.0,0.2580645161290391,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,1_after,45.6,3.6000000000000014,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,1_after,78.0,-2.741935483870961,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,2_after,51.0,9.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,2_after,78.0,-2.741935483870961,3132.0,self,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,62.0,69.0,3_after,51.0,9.0,3132.0,other,strangers +236_3132_69_62.mov,3132,strangers,69_62,123,551080,236,yes,,,69.0,62.0,3_after,78.0,-2.741935483870961,3132.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,long_gap,42.45161290322581,0.0,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,long_gap,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,1_before,23.35,-19.101612903225806,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,1_before,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,2_before,22.0,-20.451612903225808,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,2_before,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,3_before,22.0,-20.451612903225808,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,3_before,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,1_after,45.0,2.548387096774192,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,1_after,0.0,0.0,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,2_after,45.05,2.5983870967741893,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,2_after,10.9,10.9,3170.0,other,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,42.0,46.0,3_after,47.2,4.748387096774195,3170.0,self,strangers +237_3170_46_42.mov,3170,strangers,46_42,8,12488,237,yes,,,46.0,42.0,3_after,19.9,19.9,3170.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,long_gap,41.689655172413794,0.0,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,long_gap,22.0,0.0,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,1_before,45.5,3.8103448275862064,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,1_before,21.25,-0.75,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,2_before,47.35,5.660344827586208,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,2_before,20.5,-1.5,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,3_before,47.3,5.610344827586204,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,3_before,19.75,-2.25,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,1_after,42.35,0.6603448275862078,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,1_after,22.0,0.0,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,2_after,45.25,3.5603448275862064,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,2_after,22.0,0.0,2863.0,self,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,42.0,46.0,3_after,46.0,4.310344827586206,2863.0,other,strangers +238_2863_46_42.mov,2863,strangers,46_42,9,25462,238,yes,,,46.0,42.0,3_after,25.55,3.5500000000000007,2863.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,long_gap,47.369565217391305,0.0,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,long_gap,33.17391304347826,0.0,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,1_before,46.3,-1.0695652173913075,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,1_before,34.65,1.4760869565217405,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,2_before,44.0,-3.3695652173913047,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,2_before,35.0,1.826086956521742,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,3_before,43.6,-3.7695652173913032,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,3_before,35.0,1.826086956521742,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,1_after,20.0,-27.369565217391305,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,1_after,33.0,-0.1739130434782581,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,2_after,13.0,-34.369565217391305,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,2_after,32.65,-0.5239130434782595,9167.0,other,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,42.0,46.0,3_after,13.0,-34.369565217391305,9167.0,self,strangers +239_9167_46_42.mov,9167,strangers,46_42,31,210553,239,no,,,46.0,42.0,3_after,33.0,-0.1739130434782581,9167.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,long_gap,26.36,0.0,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,long_gap,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,1_before,23.6,-2.759999999999998,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,1_before,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,2_before,23.0,-3.3599999999999994,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,2_before,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,3_before,21.3,-5.059999999999999,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,3_before,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,1_after,25.5,-0.8599999999999994,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,1_after,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,2_after,25.85,-0.509999999999998,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,2_after,33.0,0.0,2542.0,self,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,42.0,46.0,3_after,25.0,-1.3599999999999994,2542.0,other,strangers +240_2542_46_42.mov,2542,strangers,46_42,32,230559,240,yes,,,46.0,42.0,3_after,33.0,0.0,2542.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,long_gap,26.98507462686567,0.0,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,long_gap,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,1_before,25.0,-1.9850746268656716,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,1_before,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,2_before,25.3,-1.6850746268656709,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,2_before,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,3_before,26.0,-0.9850746268656714,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,3_before,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,1_after,28.45,1.464925373134328,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,1_after,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,2_after,29.0,2.0149253731343286,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,2_after,33.0,0.0,6628.0,self,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,42.0,46.0,3_after,29.0,2.0149253731343286,6628.0,other,strangers +241_6628_46_42.mov,6628,strangers,46_42,34,244305,241,no,,,46.0,42.0,3_after,33.0,0.0,6628.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,long_gap,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,long_gap,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,1_before,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,1_before,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,2_before,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,2_before,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,3_before,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,3_before,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,1_after,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,1_after,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,2_after,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,2_after,53.0,0.0,3988.0,self,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,42.0,46.0,3_after,42.0,0.0,3988.0,other,strangers +242_3988_46_42.mov,3988,strangers,46_42,52,415949,242,no,,,46.0,42.0,3_after,53.0,0.0,3988.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,long_gap,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,long_gap,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,1_before,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,1_before,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,2_before,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,2_before,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,3_before,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,3_before,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,1_after,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,1_after,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,2_after,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,2_after,87.0,0.0,2816.0,self,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,81.0,86.0,3_after,100.0,0.0,2816.0,other,strangers +243_2816_81_86.mov,2816,strangers,81_86,67,288222,243,no,,,86.0,81.0,3_after,87.0,0.0,2816.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,long_gap,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,long_gap,45.0,0.0,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,1_before,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,1_before,45.0,0.0,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,2_before,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,2_before,45.0,0.0,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,3_before,57.0,0.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,3_before,39.65,-5.350000000000001,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,1_after,50.6,-6.399999999999999,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,1_after,47.15,2.1499999999999986,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,2_after,35.0,-22.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,2_after,50.2,5.200000000000003,2372.0,other,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,106.0,103.0,3_after,35.0,-22.0,2372.0,self,strangers +244_2372_106_103.mov,2372,strangers,106_103,40,118547,244,no,,,103.0,106.0,3_after,54.4,9.4,2372.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,long_gap,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,long_gap,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,1_before,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,1_before,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,2_before,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,2_before,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,3_before,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,3_before,71.3,1.2999999999999972,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,1_after,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,1_after,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,2_after,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,2_after,70.0,0.0,2726.0,self,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,106.0,103.0,3_after,35.0,0.0,2726.0,other,strangers +245_2726_106_103.mov,2726,strangers,106_103,47,142059,245,yes,,,103.0,106.0,3_after,70.0,0.0,2726.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,long_gap,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,long_gap,69.0,0.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,1_before,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,1_before,69.6,0.5999999999999943,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,2_before,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,2_before,84.6,15.599999999999994,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,3_before,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,3_before,76.0,7.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,1_after,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,1_after,69.0,0.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,2_after,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,2_after,69.0,0.0,3078.0,self,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,106.0,103.0,3_after,42.0,0.0,3078.0,other,strangers +246_3078_106_103.mov,3078,strangers,106_103,114,468819,246,yes,,,103.0,106.0,3_after,69.0,0.0,3078.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,long_gap,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,long_gap,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,1_before,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,1_before,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,2_before,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,2_before,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,3_before,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,3_before,69.0,0.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,1_after,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,1_after,70.6,1.5999999999999943,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,2_after,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,2_after,74.0,5.0,2068.0,other,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,106.0,103.0,3_after,42.0,0.0,2068.0,self,strangers +247_2068_106_103.mov,2068,strangers,106_103,117,474397,247,no,,,103.0,106.0,3_after,74.0,5.0,2068.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,long_gap,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,long_gap,31.45,0.0,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,1_before,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,1_before,68.1,36.64999999999999,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,2_before,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,2_before,74.0,42.55,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,3_before,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,3_before,74.0,42.55,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,1_after,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,1_after,25.75,-5.699999999999999,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,2_after,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,2_after,26.0,-5.449999999999999,2030.0,other,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,106.0,103.0,3_after,42.0,0.0,2030.0,self,strangers +248_2030_106_103.mov,2030,strangers,106_103,119,487477,248,no,,,103.0,106.0,3_after,26.0,-5.449999999999999,2030.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,long_gap,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,long_gap,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,1_before,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,1_before,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,2_before,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,2_before,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,3_before,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,3_before,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,1_after,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,1_after,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,2_after,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,2_after,25.0,0.0,2452.0,other,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,48.0,46.0,3_after,14.0,0.0,2452.0,self,strangers +249_2452_48_46.mov,2452,strangers,48_46,104,559477,249,no,,,46.0,48.0,3_after,25.0,0.0,2452.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,long_gap,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,long_gap,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,1_before,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,1_before,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,2_before,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,2_before,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,3_before,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,3_before,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,1_after,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,1_after,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,2_after,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,2_after,65.0,0.0,2393.0,other,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,9.0,11.0,3_after,41.0,0.0,2393.0,self,strangers +250_2393_9_11.mov,2393,strangers,9_11,18,65080,250,no,,,11.0,9.0,3_after,66.0,1.0,2393.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,long_gap,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,long_gap,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,1_before,38.85,0.8500000000000014,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,1_before,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,2_before,39.0,1.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,2_before,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,3_before,39.45,1.4500000000000028,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,3_before,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,1_after,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,1_after,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,2_after,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,2_after,8.0,0.0,3165.0,self,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,44.0,46.0,3_after,38.0,0.0,3165.0,other,strangers +251_3165_44_46.mov,3165,strangers,44_46,35,125055,251,no,,,46.0,44.0,3_after,8.0,0.0,3165.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,long_gap,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,long_gap,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,1_before,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,1_before,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,2_before,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,2_before,39.1,-2.8999999999999986,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,3_before,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,3_before,38.0,-4.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,1_after,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,1_after,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,2_after,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,2_after,42.0,0.0,3055.0,self,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,44.0,46.0,3_after,42.0,0.0,3055.0,other,strangers +252_3055_44_46.mov,3055,strangers,44_46,37,186873,252,no,,,46.0,44.0,3_after,42.0,0.0,3055.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,long_gap,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,long_gap,51.92857142857143,0.0,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,1_before,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,1_before,52.0,0.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,2_before,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,2_before,52.0,0.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,3_before,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,3_before,52.0,0.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,1_after,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,1_after,51.95,0.0214285714285722,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,2_after,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,2_after,53.0,1.0714285714285694,2825.0,other,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,44.0,46.0,3_after,54.0,0.0,2825.0,self,strangers +253_2825_44_46.mov,2825,strangers,44_46,56,389839,253,no,,,46.0,44.0,3_after,30.55,-21.37857142857143,2825.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,long_gap,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,long_gap,14.0,0.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,1_before,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,1_before,22.75,8.75,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,2_before,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,2_before,53.0,39.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,3_before,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,3_before,52.35,38.35,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,1_after,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,1_after,14.0,0.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,2_after,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,2_after,14.0,0.0,5647.0,self,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,44.0,46.0,3_after,54.0,0.0,5647.0,other,strangers +254_5647_44_46.mov,5647,strangers,44_46,59,401946,254,no,,,46.0,44.0,3_after,14.0,0.0,5647.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,long_gap,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,long_gap,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,1_before,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,1_before,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,2_before,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,2_before,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,3_before,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,3_before,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,1_after,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,1_after,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,2_after,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,2_after,25.0,0.0,2279.0,other,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,44.0,46.0,3_after,30.0,0.0,2279.0,self,strangers +255_2279_44_46.mov,2279,strangers,44_46,66,480011,255,no,,,46.0,44.0,3_after,25.0,0.0,2279.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,long_gap,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,long_gap,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,1_before,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,1_before,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,2_before,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,2_before,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,3_before,27.0,0.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,3_before,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,1_after,25.15,-1.8500000000000012,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,1_after,24.0,0.0,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,2_after,22.2,-4.800000000000001,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,2_after,24.45,0.4499999999999993,2064.0,self,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,44.0,46.0,3_after,22.0,-5.0,2064.0,other,strangers +256_2064_44_46.mov,2064,strangers,44_46,77,540411,256,yes,,,46.0,44.0,3_after,25.0,1.0,2064.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,long_gap,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,long_gap,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,1_before,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,1_before,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,2_before,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,2_before,40.3,-9.700000000000005,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,3_before,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,3_before,31.0,-19.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,1_after,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,1_after,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,2_after,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,2_after,50.0,0.0,4668.0,other,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,106.0,101.0,3_after,49.0,0.0,4668.0,self,strangers +257_4668_106_101.mov,4668,strangers,106_101,34,144444,257,no,,,101.0,106.0,3_after,50.0,0.0,4668.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,long_gap,32.833333333333336,0.0,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,long_gap,85.08,0.0,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,1_before,33.0,0.1666666666666643,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,1_before,85.5,0.4200000000000017,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,2_before,33.0,0.1666666666666643,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,2_before,85.4,0.3200000000000074,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,3_before,33.0,0.1666666666666643,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,3_before,53.75,-31.33,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,1_after,31.2,-1.6333333333333364,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,1_after,86.0,0.9200000000000016,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,2_after,36.45,3.616666666666667,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,2_after,84.8,-0.2800000000000011,2422.0,self,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,106.0,101.0,3_after,38.0,5.166666666666664,2422.0,other,strangers +258_2422_106_101.mov,2422,strangers,106_101,47,198529,258,no,,,101.0,106.0,3_after,84.0,-1.0799999999999983,2422.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,long_gap,45.92,0.0,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,long_gap,79.44,0.0,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,1_before,41.85,-4.07,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,1_before,81.0,1.5600000000000025,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,2_before,41.0,-4.920000000000002,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,2_before,81.85,2.4099999999999966,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,3_before,41.0,-4.920000000000002,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,3_before,83.9,4.460000000000008,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,1_after,46.0,0.0799999999999983,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,1_after,67.4,-12.039999999999992,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,2_after,46.0,0.0799999999999983,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,2_after,57.5,-21.94,2529.0,other,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,106.0,101.0,3_after,46.0,0.0799999999999983,2529.0,self,strangers +259_2529_106_101.mov,2529,strangers,106_101,50,218951,259,no,,,101.0,106.0,3_after,56.0,-23.44,2529.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,long_gap,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,long_gap,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,1_before,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,1_before,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,2_before,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,2_before,54.3,0.2999999999999971,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,3_before,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,3_before,43.0,-11.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,1_after,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,1_after,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,2_after,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,2_after,54.0,0.0,2337.0,other,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,106.0,101.0,3_after,51.0,0.0,2337.0,self,strangers +260_2337_106_101.mov,2337,strangers,106_101,80,343148,260,no,,,101.0,106.0,3_after,53.95,-0.0499999999999971,2337.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,long_gap,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,long_gap,44.5,0.0,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,1_before,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,1_before,43.85,-0.6499999999999986,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,2_before,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,2_before,40.0,-4.5,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,3_before,43.0,-2.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,3_before,25.0,-19.5,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,1_after,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,1_after,45.0,0.5,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,2_after,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,2_after,44.85,0.3500000000000014,2653.0,other,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,106.0,101.0,3_after,45.0,0.0,2653.0,self,strangers +261_2653_106_101.mov,2653,strangers,106_101,118,534860,261,no,,,101.0,106.0,3_after,45.55,1.0499999999999972,2653.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,long_gap,68.08,0.0,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,long_gap,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,1_before,70.0,1.9200000000000017,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,1_before,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,2_before,70.0,1.9200000000000017,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,2_before,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,3_before,70.0,1.9200000000000017,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,3_before,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,1_after,65.25,-2.8299999999999983,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,1_after,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,2_after,65.0,-3.0799999999999983,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,2_after,97.0,0.0,2516.0,self,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,107.0,104.0,3_after,65.0,-3.0799999999999983,2516.0,other,strangers +262_2516_107_104.mov,2516,strangers,107_104,139,541450,262,yes,,,104.0,107.0,3_after,97.0,0.0,2516.0,self,strangers diff --git a/Analyses/long_gap_connection_all_long_format_strangers.csv b/Analyses/long_gap_connection_all_long_format_strangers.csv new file mode 100644 index 0000000..d60d16c --- /dev/null +++ b/Analyses/long_gap_connection_all_long_format_strangers.csv @@ -0,0 +1,3669 @@ +subID,partnerID,dyad,timepoint,connection,connection_change,gap_length,speaker,turn_num +103.0,101.0,103_101,long_gap,63.0,0.0,2388.0,self,56.0 +101.0,103.0,103_101,long_gap,71.0,0.0,2388.0,other,56.0 +103.0,101.0,103_101,1_before,63.0,0.0,2388.0,self,56.0 +101.0,103.0,103_101,1_before,71.0,0.0,2388.0,other,56.0 +103.0,101.0,103_101,2_before,63.0,0.0,2388.0,self,56.0 +101.0,103.0,103_101,2_before,71.0,0.0,2388.0,other,56.0 +103.0,101.0,103_101,3_before,63.0,0.0,2388.0,self,56.0 +101.0,103.0,103_101,3_before,71.0,0.0,2388.0,other,56.0 +103.0,101.0,103_101,1_after,63.0,0.0,2388.0,self,56.0 +101.0,103.0,103_101,1_after,70.7,-0.29999999999999716,2388.0,other,56.0 +103.0,101.0,103_101,2_after,63.95,0.9500000000000028,2388.0,self,56.0 +101.0,103.0,103_101,2_after,46.05,-24.950000000000003,2388.0,other,56.0 +103.0,101.0,103_101,3_after,65.0,2.0,2388.0,self,56.0 +101.0,103.0,103_101,3_after,43.0,-28.0,2388.0,other,56.0 +103.0,101.0,103_101,long_gap,79.0,0.0,2183.0,self,97.0 +101.0,103.0,103_101,long_gap,76.0,0.0,2183.0,other,97.0 +103.0,101.0,103_101,1_before,79.0,0.0,2183.0,self,97.0 +101.0,103.0,103_101,1_before,76.0,0.0,2183.0,other,97.0 +103.0,101.0,103_101,2_before,79.0,0.0,2183.0,self,97.0 +101.0,103.0,103_101,2_before,76.0,0.0,2183.0,other,97.0 +103.0,101.0,103_101,3_before,74.35,-4.650000000000006,2183.0,self,97.0 +101.0,103.0,103_101,3_before,76.0,0.0,2183.0,other,97.0 +103.0,101.0,103_101,1_after,79.0,0.0,2183.0,self,97.0 +101.0,103.0,103_101,1_after,75.35,-0.6500000000000057,2183.0,other,97.0 +103.0,101.0,103_101,2_after,79.0,0.0,2183.0,self,97.0 +101.0,103.0,103_101,2_after,75.0,-1.0,2183.0,other,97.0 +103.0,101.0,103_101,3_after,79.0,0.0,2183.0,self,97.0 +101.0,103.0,103_101,3_after,75.0,-1.0,2183.0,other,97.0 +103.0,101.0,103_101,long_gap,82.15384615384616,0.0,2612.0,self,113.0 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+100_103,60,after,yes,12,strangers,not_long +100_103,61,before,yes,12,strangers,not_long +100_103,61,after,no,26,strangers,not_long +100_103,62,before,no,26,strangers,not_long +100_103,62,after,no,1,strangers,not_long +100_103,63,before,no,1,strangers,not_long +100_103,63,after,no,10,strangers,not_long +100_103,64,before,no,10,strangers,not_long +100_103,64,after,no,5,strangers,not_long +100_103,65,before,no,5,strangers,not_long +100_103,65,after,no,16,strangers,not_long +100_103,66,before,no,16,strangers,not_long +100_103,66,after,yes,6,strangers,not_long +100_103,67,before,yes,6,strangers,not_long +100_103,67,after,no,13,strangers,not_long +100_103,68,before,no,13,strangers,not_long +100_103,68,after,no,1,strangers,not_long +100_103,69,before,no,1,strangers,not_long +100_103,69,after,no,35,strangers,not_long +100_103,70,before,no,35,strangers,not_long +100_103,70,after,no,16,strangers,not_long +100_103,71,before,no,16,strangers,not_long +100_103,71,after,yes,3,strangers,not_long 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+100_103,83,after,yes,15,strangers,not_long +100_103,84,before,yes,15,strangers,not_long +100_103,84,after,no,3,strangers,not_long +100_103,85,before,no,3,strangers,not_long +100_103,85,after,no,1,strangers,not_long +100_103,86,before,no,1,strangers,not_long +100_103,86,after,no,4,strangers,not_long +100_103,87,before,no,4,strangers,not_long +100_103,87,after,no,22,strangers,not_long +100_103,88,before,no,22,strangers,not_long +100_103,88,after,no,3,strangers,not_long +100_103,89,before,no,3,strangers,not_long +100_103,89,after,yes,12,strangers,not_long +100_103,90,before,yes,12,strangers,not_long +100_103,90,after,no,7,strangers,not_long +100_103,91,before,no,7,strangers,not_long +100_103,91,after,no,1,strangers,not_long +100_103,92,before,no,1,strangers,not_long +100_103,92,after,no,13,strangers,not_long +100_103,93,before,no,13,strangers,not_long +100_103,93,after,yes,4,strangers,not_long +100_103,94,before,yes,4,strangers,not_long +100_103,94,after,no,7,strangers,not_long 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+100_103,39,after,no,1,strangers,not_long +100_103,40,before,no,1,strangers,not_long +100_103,40,after,no,17,strangers,not_long +100_103,41,before,no,17,strangers,not_long +100_103,41,after,yes,6,strangers,not_long +100_103,42,before,yes,6,strangers,not_long +100_103,42,after,no,11,strangers,not_long +100_103,43,before,no,11,strangers,not_long +100_103,43,after,no,22,strangers,not_long +100_103,44,before,no,22,strangers,not_long +100_103,44,after,no,11,strangers,not_long +100_103,45,before,no,11,strangers,not_long +100_103,45,after,yes,8,strangers,not_long +100_103,46,before,yes,8,strangers,not_long +100_103,46,after,no,4,strangers,not_long +100_103,47,before,no,4,strangers,not_long +100_103,47,after,no,2,strangers,not_long +100_103,48,before,no,2,strangers,not_long +100_103,48,after,no,1,strangers,not_long +100_103,49,before,no,1,strangers,not_long +100_103,49,after,yes,3,strangers,not_long +100_103,50,before,yes,3,strangers,not_long +100_103,50,after,no,1,strangers,not_long 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+100_104,168,before,yes,3,strangers,not_long +100_104,168,after,no,4,strangers,not_long +100_104,169,before,no,4,strangers,not_long +100_104,169,after,yes,11,strangers,not_long +100_104,170,before,yes,11,strangers,not_long +100_104,170,after,no,23,strangers,not_long +100_104,171,before,no,23,strangers,not_long +100_104,171,after,yes,6,strangers,not_long +100_104,172,before,yes,6,strangers,not_long +100_104,172,after,no,3,strangers,not_long +100_104,173,before,no,3,strangers,not_long +100_104,173,after,yes,4,strangers,not_long +100_104,174,before,yes,4,strangers,not_long +100_104,174,after,no,3,strangers,not_long +100_104,175,before,no,3,strangers,not_long +100_104,175,after,yes,6,strangers,not_long +100_104,176,before,yes,6,strangers,not_long +100_104,176,after,no,43,strangers,not_long +100_104,177,before,no,43,strangers,not_long +100_104,177,after,no,16,strangers,not_long +100_104,178,before,no,16,strangers,not_long +100_104,178,after,no,25,strangers,not_long 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+110_104,8,after,yes,4,strangers,not_long +110_104,9,before,yes,4,strangers,not_long +110_104,9,after,yes,4,strangers,not_long +110_104,10,before,yes,4,strangers,not_long +110_104,10,after,no,1,strangers,not_long +110_104,11,before,no,1,strangers,not_long +110_104,11,after,yes,6,strangers,not_long +110_104,12,before,yes,6,strangers,not_long +110_104,12,after,no,7,strangers,not_long +110_104,13,before,no,7,strangers,not_long +110_104,13,after,yes,9,strangers,not_long +110_104,14,before,yes,9,strangers,not_long +110_104,14,after,no,5,strangers,not_long +110_104,15,before,no,5,strangers,not_long +110_104,15,after,yes,2,strangers,not_long +110_104,16,before,yes,2,strangers,not_long +110_104,16,after,no,1,strangers,not_long +110_104,17,before,no,1,strangers,not_long +110_104,17,after,no,1,strangers,not_long +110_104,18,before,no,1,strangers,not_long +110_104,18,after,no,4,strangers,not_long +110_104,19,before,no,4,strangers,not_long +110_104,19,after,no,1,strangers,not_long +110_104,20,before,no,1,strangers,not_long +110_104,20,after,yes,3,strangers,not_long +110_104,21,before,yes,3,strangers,not_long +110_104,21,after,no,12,strangers,not_long +110_104,22,before,no,12,strangers,not_long +110_104,22,after,yes,6,strangers,not_long +110_104,23,before,yes,6,strangers,not_long +110_104,23,after,no,2,strangers,not_long +110_104,24,before,no,2,strangers,not_long +110_104,24,after,yes,6,strangers,not_long +110_104,25,before,yes,6,strangers,not_long +110_104,25,after,no,9,strangers,not_long +110_104,26,before,no,9,strangers,not_long +110_104,26,after,no,11,strangers,not_long +110_104,27,before,no,11,strangers,not_long +110_104,27,after,no,1,strangers,not_long +110_104,28,before,no,1,strangers,not_long +110_104,28,after,no,26,strangers,not_long +110_104,29,before,no,26,strangers,not_long +110_104,29,after,yes,6,strangers,not_long +110_104,30,before,yes,6,strangers,not_long +110_104,30,after,no,5,strangers,not_long +110_104,31,before,no,5,strangers,not_long +110_104,31,after,yes,13,strangers,not_long +110_104,32,before,yes,13,strangers,not_long +110_104,32,after,no,3,strangers,not_long +110_104,33,before,no,3,strangers,not_long +110_104,33,after,no,15,strangers,not_long +110_104,34,before,no,15,strangers,not_long +110_104,34,after,no,1,strangers,not_long +110_104,35,before,no,1,strangers,not_long +110_104,35,after,yes,3,strangers,not_long +110_104,36,before,yes,3,strangers,not_long +110_104,36,after,no,1,strangers,not_long +110_104,37,before,no,1,strangers,not_long +110_104,37,after,no,2,strangers,not_long +110_104,38,before,no,2,strangers,not_long +110_104,38,after,yes,3,strangers,not_long +110_104,39,before,yes,3,strangers,not_long +110_104,39,after,yes,4,strangers,not_long +110_104,40,before,yes,4,strangers,not_long +110_104,40,after,no,5,strangers,not_long +110_104,41,before,no,5,strangers,not_long +110_104,41,after,no,1,strangers,not_long +110_104,42,before,no,1,strangers,not_long +110_104,42,after,no,2,strangers,not_long +110_104,43,before,no,2,strangers,not_long +110_104,43,after,yes,6,strangers,not_long +110_104,44,before,yes,6,strangers,not_long +110_104,44,after,no,33,strangers,not_long +110_104,45,before,no,33,strangers,not_long +110_104,45,after,no,2,strangers,not_long +110_104,46,before,no,2,strangers,not_long +110_104,46,after,no,5,strangers,not_long +110_104,47,before,no,5,strangers,not_long +110_104,47,after,yes,4,strangers,not_long +110_104,48,before,yes,4,strangers,not_long +110_104,48,after,no,24,strangers,not_long +110_104,49,before,no,24,strangers,not_long +110_104,49,after,yes,6,strangers,not_long +110_104,50,before,yes,6,strangers,not_long +110_104,50,after,no,18,strangers,not_long +110_104,51,before,no,18,strangers,not_long +110_104,51,after,yes,11,strangers,not_long +110_104,52,before,yes,11,strangers,not_long +110_104,52,after,no,10,strangers,not_long +110_104,53,before,no,10,strangers,not_long +110_104,53,after,no,1,strangers,not_long +110_104,54,before,no,1,strangers,not_long +110_104,54,after,no,20,strangers,not_long +110_104,55,before,no,20,strangers,not_long +110_104,55,after,yes,7,strangers,not_long +110_104,56,before,yes,7,strangers,not_long +110_104,56,after,no,1,strangers,not_long +110_104,57,before,no,1,strangers,not_long +110_104,57,after,no,4,strangers,not_long +110_104,58,before,no,4,strangers,not_long +110_104,58,after,no,4,strangers,not_long +110_104,59,before,no,4,strangers,not_long +110_104,59,after,yes,6,strangers,not_long +110_104,60,before,yes,6,strangers,not_long +110_104,60,after,no,2,strangers,not_long +110_104,61,before,no,2,strangers,not_long +110_104,61,after,no,3,strangers,not_long +110_104,62,before,no,3,strangers,not_long +110_104,62,after,yes,4,strangers,not_long +110_104,63,before,yes,4,strangers,not_long +110_104,63,after,no,2,strangers,not_long +110_104,64,before,no,2,strangers,not_long +110_104,64,after,yes,3,strangers,not_long +110_104,65,before,yes,3,strangers,not_long +110_104,65,after,no,3,strangers,not_long +110_104,66,before,no,3,strangers,not_long +110_104,66,after,no,3,strangers,not_long +110_104,67,before,no,3,strangers,not_long +110_104,67,after,no,1,strangers,not_long +110_104,68,before,no,1,strangers,not_long +110_104,68,after,no,6,strangers,not_long +110_104,69,before,no,6,strangers,not_long +110_104,69,after,no,1,strangers,not_long +110_104,70,before,no,1,strangers,not_long +110_104,70,after,no,5,strangers,not_long +110_104,71,before,no,5,strangers,not_long +110_104,71,after,yes,17,strangers,not_long +110_104,72,before,yes,17,strangers,not_long +110_104,72,after,no,1,strangers,not_long +110_104,73,before,no,1,strangers,not_long +110_104,73,after,no,14,strangers,not_long +110_104,74,before,no,14,strangers,not_long +110_104,74,after,yes,1,strangers,not_long +110_104,75,before,yes,1,strangers,not_long +110_104,75,after,no,13,strangers,not_long +110_104,76,before,no,13,strangers,not_long +110_104,76,after,yes,10,strangers,not_long +110_104,77,before,yes,10,strangers,not_long +110_104,77,after,no,6,strangers,not_long +110_104,78,before,no,6,strangers,not_long +110_104,78,after,no,9,strangers,not_long +110_104,79,before,no,9,strangers,not_long +110_104,79,after,yes,12,strangers,not_long +110_104,80,before,yes,12,strangers,not_long +110_104,80,after,no,15,strangers,not_long +110_104,81,before,no,15,strangers,not_long +110_104,81,after,yes,14,strangers,not_long +110_104,82,before,yes,14,strangers,not_long +110_104,82,after,no,24,strangers,not_long +110_104,83,before,no,24,strangers,not_long +110_104,83,after,no,8,strangers,not_long +110_104,84,before,no,8,strangers,not_long +110_104,84,after,no,6,strangers,not_long +110_104,85,before,no,6,strangers,not_long +110_104,85,after,yes,9,strangers,not_long +110_104,86,before,yes,9,strangers,not_long +110_104,86,after,no,9,strangers,not_long +110_104,87,before,no,9,strangers,not_long +110_104,87,after,no,1,strangers,not_long +110_104,88,before,no,1,strangers,not_long +110_104,88,after,no,6,strangers,not_long 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+110_104,100,after,no,1,strangers,not_long +110_104,101,before,no,1,strangers,not_long +110_104,101,after,yes,11,strangers,not_long +110_104,102,before,yes,11,strangers,not_long +110_104,102,after,no,1,strangers,not_long +110_104,103,before,no,1,strangers,not_long +110_104,103,after,no,8,strangers,not_long +110_104,104,before,no,8,strangers,not_long +110_104,104,after,no,20,strangers,not_long +110_104,105,before,no,20,strangers,not_long +110_104,105,after,no,7,strangers,not_long +110_104,106,before,no,7,strangers,not_long +110_104,106,after,yes,6,strangers,not_long +110_104,107,before,yes,6,strangers,not_long +110_104,107,after,no,8,strangers,not_long +110_104,108,before,no,8,strangers,not_long +110_104,108,after,yes,5,strangers,not_long +110_104,109,before,yes,5,strangers,not_long +110_104,109,after,no,14,strangers,not_long +110_104,110,before,no,14,strangers,not_long +110_104,110,after,no,3,strangers,not_long +110_104,111,before,no,3,strangers,not_long +110_104,111,after,no,6,strangers,not_long +110_104,112,before,no,6,strangers,not_long +110_104,112,after,yes,9,strangers,not_long +110_104,113,before,yes,9,strangers,not_long +110_104,113,after,no,13,strangers,not_long +110_104,114,before,no,13,strangers,not_long +110_104,114,after,no,2,strangers,not_long +110_104,115,before,no,2,strangers,not_long +110_104,115,after,no,8,strangers,not_long +110_104,116,before,no,8,strangers,not_long +110_104,116,after,yes,1,strangers,not_long +110_104,117,before,yes,1,strangers,not_long +110_104,117,after,no,26,strangers,not_long +110_104,118,before,no,26,strangers,not_long +110_104,118,after,no,16,strangers,not_long +110_104,119,before,no,16,strangers,not_long +110_104,119,after,no,1,strangers,not_long +110_104,120,before,no,1,strangers,not_long +110_104,120,after,no,2,strangers,not_long +110_104,121,before,no,2,strangers,not_long +110_104,121,after,no,8,strangers,not_long +110_104,122,before,no,8,strangers,not_long +110_104,122,after,no,10,strangers,not_long +110_104,123,before,no,10,strangers,not_long +110_104,123,after,no,1,strangers,not_long +110_104,124,before,no,1,strangers,not_long +110_104,124,after,no,5,strangers,not_long +110_104,125,before,no,5,strangers,not_long +110_104,125,after,yes,22,strangers,not_long +110_104,126,before,yes,22,strangers,not_long +110_104,126,after,no,18,strangers,not_long +110_104,127,before,no,18,strangers,not_long +110_104,127,after,no,2,strangers,not_long +110_104,129,before,yes,7,strangers,not_long +110_104,129,after,no,35,strangers,not_long +110_104,130,before,no,35,strangers,not_long +110_104,130,after,no,19,strangers,not_long +110_104,131,before,no,19,strangers,not_long +110_104,131,after,no,10,strangers,not_long +110_104,132,before,no,10,strangers,not_long +110_104,132,after,yes,1,strangers,not_long +110_104,133,before,yes,1,strangers,not_long +110_104,133,after,yes,10,strangers,not_long +110_104,134,before,yes,10,strangers,not_long +110_104,134,after,no,4,strangers,not_long +110_104,135,before,no,4,strangers,not_long +110_104,135,after,yes,1,strangers,not_long +110_104,136,before,yes,1,strangers,not_long +110_104,136,after,no,8,strangers,not_long +110_104,137,before,no,8,strangers,not_long +110_104,137,after,yes,10,strangers,not_long +110_104,138,before,yes,10,strangers,not_long +110_104,138,after,no,1,strangers,not_long +110_104,139,before,no,1,strangers,not_long +110_104,139,after,no,1,strangers,not_long +110_104,140,before,no,1,strangers,not_long +110_104,140,after,yes,8,strangers,not_long +110_104,141,before,yes,8,strangers,not_long +110_104,141,after,no,24,strangers,not_long +110_104,142,before,no,24,strangers,not_long +110_104,142,after,no,7,strangers,not_long +110_104,143,before,no,7,strangers,not_long +110_104,143,after,yes,29,strangers,not_long +110_104,144,before,yes,29,strangers,not_long +110_104,144,after,no,36,strangers,not_long +110_104,145,before,no,36,strangers,not_long +110_104,145,after,yes,6,strangers,not_long +110_104,146,before,yes,6,strangers,not_long +110_104,146,after,no,10,strangers,not_long +110_104,147,before,no,10,strangers,not_long +110_104,147,after,yes,3,strangers,not_long +110_104,148,before,yes,3,strangers,not_long +110_104,148,after,no,3,strangers,not_long +110_104,149,before,no,3,strangers,not_long +110_104,149,after,yes,4,strangers,not_long +110_104,150,before,yes,4,strangers,not_long +110_104,150,after,yes,41,strangers,not_long +110_104,151,before,yes,41,strangers,not_long +110_104,151,after,no,5,strangers,not_long +110_104,152,before,no,5,strangers,not_long +110_104,152,after,yes,6,strangers,not_long +110_104,153,before,yes,6,strangers,not_long +110_104,153,after,no,15,strangers,not_long +110_104,154,before,no,15,strangers,not_long +110_104,154,after,yes,1,strangers,not_long +110_104,155,before,yes,1,strangers,not_long +110_104,155,after,no,1,strangers,not_long +110_104,156,before,no,1,strangers,not_long +110_104,156,after,yes,3,strangers,not_long +110_104,157,before,yes,3,strangers,not_long +110_104,157,after,no,1,strangers,not_long +110_104,158,before,no,1,strangers,not_long +110_104,158,after,yes,5,strangers,not_long +110_104,159,before,yes,5,strangers,not_long +110_104,159,after,no,7,strangers,not_long +110_104,160,before,no,7,strangers,not_long +110_104,160,after,yes,2,strangers,not_long +110_104,161,before,yes,2,strangers,not_long +110_104,161,after,no,4,strangers,not_long +110_104,162,before,no,4,strangers,not_long +110_104,162,after,yes,7,strangers,not_long +110_104,163,before,yes,7,strangers,not_long +110_104,163,after,no,13,strangers,not_long +110_104,164,before,no,13,strangers,not_long +110_104,164,after,no,2,strangers,not_long +110_104,165,before,no,2,strangers,not_long +110_104,165,after,yes,8,strangers,not_long +110_104,166,before,yes,8,strangers,not_long +110_104,166,after,no,10,strangers,not_long +110_104,167,before,no,10,strangers,not_long +110_104,167,after,no,3,strangers,not_long +110_104,168,before,no,3,strangers,not_long +110_104,168,after,no,17,strangers,not_long +110_104,169,before,no,17,strangers,not_long +110_104,169,after,yes,1,strangers,not_long +110_104,170,before,yes,1,strangers,not_long +110_104,170,after,no,1,strangers,not_long +110_104,171,before,no,1,strangers,not_long +110_104,171,after,no,2,strangers,not_long +110_104,172,before,no,2,strangers,not_long +110_104,172,after,no,31,strangers,not_long +110_104,173,before,no,31,strangers,not_long +110_104,173,after,yes,9,strangers,not_long +110_104,174,before,yes,9,strangers,not_long +110_104,174,after,no,27,strangers,not_long +110_104,175,before,no,27,strangers,not_long +110_104,175,after,yes,9,strangers,not_long +110_104,176,before,yes,9,strangers,not_long +110_104,176,after,no,29,strangers,not_long +110_104,177,before,no,29,strangers,not_long +110_104,177,after,yes,6,strangers,not_long +110_104,178,before,yes,6,strangers,not_long +110_104,178,after,no,24,strangers,not_long +110_104,179,before,no,24,strangers,not_long +110_104,179,after,no,7,strangers,not_long +110_104,180,before,no,7,strangers,not_long +110_104,180,after,no,13,strangers,not_long +110_104,181,before,no,13,strangers,not_long +110_104,181,after,yes,9,strangers,not_long +110_104,182,before,yes,9,strangers,not_long +110_104,182,after,no,8,strangers,not_long +110_104,183,before,no,8,strangers,not_long +110_104,183,after,yes,2,strangers,not_long +110_104,184,before,yes,2,strangers,not_long +110_104,184,after,no,2,strangers,not_long +110_104,185,before,no,2,strangers,not_long +110_104,185,after,yes,3,strangers,not_long +110_104,186,before,yes,3,strangers,not_long +110_104,186,after,no,84,strangers,not_long +110_104,187,before,no,84,strangers,not_long +110_104,187,after,yes,13,strangers,not_long +110_104,188,before,yes,13,strangers,not_long +110_104,188,after,no,19,strangers,not_long +110_104,189,before,no,19,strangers,not_long 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+110_104,233,after,no,15,strangers,not_long +110_104,234,before,no,15,strangers,not_long +110_104,234,after,no,7,strangers,not_long +110_104,235,before,no,7,strangers,not_long +110_104,235,after,no,16,strangers,not_long +110_104,236,before,no,16,strangers,not_long +110_104,236,after,yes,3,strangers,not_long +110_104,237,before,yes,3,strangers,not_long +110_104,237,after,no,3,strangers,not_long +110_104,238,before,no,3,strangers,not_long +110_104,238,after,yes,17,strangers,not_long +110_104,239,before,yes,17,strangers,not_long +110_104,239,after,yes,2,strangers,not_long +110_104,2,before,yes,3,strangers,not_long +110_104,2,after,no,1,strangers,not_long +110_104,3,before,no,1,strangers,not_long +110_104,3,after,no,3,strangers,not_long +110_104,4,before,no,3,strangers,not_long +110_104,4,after,yes,5,strangers,not_long +110_104,5,before,yes,5,strangers,not_long +110_104,5,after,no,7,strangers,not_long +110_104,6,before,no,7,strangers,not_long +110_104,6,after,yes,6,strangers,not_long 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+108_103,28,after,no,19,strangers,not_long +108_103,29,before,no,19,strangers,not_long +108_103,29,after,no,8,strangers,not_long +108_103,30,before,no,8,strangers,not_long +108_103,30,after,yes,3,strangers,not_long +108_103,31,before,yes,3,strangers,not_long +108_103,31,after,no,19,strangers,not_long +108_103,32,before,no,19,strangers,not_long +108_103,32,after,no,6,strangers,not_long +108_103,33,before,no,6,strangers,not_long +108_103,33,after,no,7,strangers,not_long +108_103,34,before,no,7,strangers,not_long +108_103,34,after,no,2,strangers,not_long +108_103,35,before,no,2,strangers,not_long +108_103,35,after,no,5,strangers,not_long +108_103,37,before,yes,13,strangers,not_long +108_103,37,after,no,11,strangers,not_long +108_103,38,before,no,11,strangers,not_long +108_103,38,after,yes,8,strangers,not_long +108_103,39,before,yes,8,strangers,not_long +108_103,39,after,no,22,strangers,not_long +108_103,40,before,no,22,strangers,not_long +108_103,40,after,yes,9,strangers,not_long 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+108_103,154,after,no,18,strangers,not_long +108_103,155,before,no,18,strangers,not_long +108_103,155,after,no,23,strangers,not_long +108_103,156,before,no,23,strangers,not_long +108_103,156,after,yes,5,strangers,not_long +108_103,157,before,yes,5,strangers,not_long +108_103,157,after,no,23,strangers,not_long +108_103,158,before,no,23,strangers,not_long +108_103,158,after,no,6,strangers,not_long +108_103,159,before,no,6,strangers,not_long +108_103,159,after,no,36,strangers,not_long +108_103,160,before,no,36,strangers,not_long +108_103,160,after,no,2,strangers,not_long +108_103,161,before,no,2,strangers,not_long +108_103,161,after,no,11,strangers,not_long +108_103,162,before,no,11,strangers,not_long +108_103,162,after,no,1,strangers,not_long +108_103,163,before,no,1,strangers,not_long +108_103,163,after,yes,23,strangers,not_long +108_103,164,before,yes,23,strangers,not_long +108_103,164,after,no,11,strangers,not_long +108_103,165,before,no,11,strangers,not_long 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+108_103,176,after,no,7,strangers,not_long +108_103,177,before,no,7,strangers,not_long +108_103,177,after,no,4,strangers,not_long +108_103,178,before,no,4,strangers,not_long +108_103,178,after,no,2,strangers,not_long +108_103,179,before,no,2,strangers,not_long +108_103,179,after,no,5,strangers,not_long +108_103,181,before,no,16,strangers,not_long +108_103,181,after,no,5,strangers,not_long +108_103,182,before,no,5,strangers,not_long +108_103,182,after,yes,9,strangers,not_long +108_103,183,before,yes,9,strangers,not_long +108_103,183,after,no,6,strangers,not_long +108_103,184,before,no,6,strangers,not_long +108_103,184,after,no,1,strangers,not_long +108_103,185,before,no,1,strangers,not_long +108_103,185,after,no,10,strangers,not_long +108_103,186,before,no,10,strangers,not_long +108_103,186,after,yes,2,strangers,not_long +108_103,187,before,yes,2,strangers,not_long +108_103,187,after,no,9,strangers,not_long +108_103,2,before,no,2,strangers,not_long +108_103,2,after,yes,2,strangers,not_long +108_103,3,before,yes,2,strangers,not_long +108_103,3,after,no,4,strangers,not_long +108_103,4,before,no,4,strangers,not_long +108_103,4,after,no,4,strangers,not_long +108_103,5,before,no,4,strangers,not_long +108_103,5,after,yes,10,strangers,not_long +108_103,6,before,yes,10,strangers,not_long +108_103,6,after,no,2,strangers,not_long +108_103,7,before,no,2,strangers,not_long +108_103,7,after,yes,3,strangers,not_long +108_103,8,before,yes,3,strangers,not_long +108_103,8,after,yes,3,strangers,not_long +108_103,9,before,yes,3,strangers,not_long +108_103,9,after,yes,10,strangers,not_long +108_103,10,before,yes,10,strangers,not_long +108_103,10,after,no,5,strangers,not_long +108_103,11,before,no,5,strangers,not_long +108_103,11,after,yes,8,strangers,not_long +108_103,12,before,yes,8,strangers,not_long +108_103,12,after,no,3,strangers,not_long +108_103,13,before,no,3,strangers,not_long +108_103,13,after,no,2,strangers,not_long +108_103,14,before,no,2,strangers,not_long +108_103,14,after,yes,5,strangers,not_long +108_103,15,before,yes,5,strangers,not_long +108_103,15,after,no,3,strangers,not_long +108_103,16,before,no,3,strangers,not_long +108_103,16,after,no,3,strangers,not_long +108_103,17,before,no,3,strangers,not_long +108_103,17,after,no,1,strangers,not_long +108_103,18,before,no,1,strangers,not_long +108_103,18,after,no,14,strangers,not_long +108_103,19,before,no,14,strangers,not_long +108_103,19,after,yes,64,strangers,not_long +108_103,20,before,yes,64,strangers,not_long +108_103,20,after,no,3,strangers,not_long +108_103,21,before,no,3,strangers,not_long +108_103,21,after,no,3,strangers,not_long +108_103,22,before,no,3,strangers,not_long +108_103,22,after,yes,4,strangers,not_long +108_103,23,before,yes,4,strangers,not_long +108_103,23,after,no,16,strangers,not_long +108_103,24,before,no,16,strangers,not_long +108_103,24,after,no,1,strangers,not_long +108_103,25,before,no,1,strangers,not_long +108_103,25,after,yes,8,strangers,not_long +108_103,26,before,yes,8,strangers,not_long +108_103,26,after,yes,15,strangers,not_long +108_103,27,before,yes,15,strangers,not_long +108_103,27,after,yes,8,strangers,not_long +108_103,28,before,yes,8,strangers,not_long +108_103,28,after,no,19,strangers,not_long +108_103,29,before,no,19,strangers,not_long +108_103,29,after,no,8,strangers,not_long +108_103,30,before,no,8,strangers,not_long +108_103,30,after,yes,3,strangers,not_long +108_103,31,before,yes,3,strangers,not_long +108_103,31,after,no,19,strangers,not_long +108_103,32,before,no,19,strangers,not_long +108_103,32,after,no,6,strangers,not_long +108_103,33,before,no,6,strangers,not_long +108_103,33,after,no,7,strangers,not_long +108_103,34,before,no,7,strangers,not_long +108_103,34,after,no,2,strangers,not_long +108_103,35,before,no,2,strangers,not_long +108_103,35,after,no,5,strangers,not_long +108_103,37,before,yes,13,strangers,not_long +108_103,37,after,no,11,strangers,not_long +108_103,38,before,no,11,strangers,not_long +108_103,38,after,yes,8,strangers,not_long +108_103,39,before,yes,8,strangers,not_long +108_103,39,after,no,22,strangers,not_long +108_103,40,before,no,22,strangers,not_long +108_103,40,after,yes,9,strangers,not_long +108_103,41,before,yes,9,strangers,not_long +108_103,41,after,no,21,strangers,not_long +108_103,42,before,no,21,strangers,not_long +108_103,42,after,no,1,strangers,not_long +108_103,43,before,no,1,strangers,not_long +108_103,43,after,no,25,strangers,not_long +108_103,44,before,no,25,strangers,not_long +108_103,44,after,no,4,strangers,not_long +108_103,45,before,no,4,strangers,not_long +108_103,45,after,no,4,strangers,not_long +108_103,46,before,no,4,strangers,not_long +108_103,46,after,no,1,strangers,not_long +108_103,47,before,no,1,strangers,not_long +108_103,47,after,yes,14,strangers,not_long +108_103,49,before,no,15,strangers,not_long +108_103,49,after,no,18,strangers,not_long +108_103,50,before,no,18,strangers,not_long 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+108_103,22,after,yes,4,strangers,not_long +108_103,23,before,yes,4,strangers,not_long +108_103,23,after,no,16,strangers,not_long +108_103,24,before,no,16,strangers,not_long +108_103,24,after,no,1,strangers,not_long +108_103,25,before,no,1,strangers,not_long +108_103,25,after,yes,8,strangers,not_long +108_103,26,before,yes,8,strangers,not_long +108_103,26,after,yes,15,strangers,not_long +108_103,27,before,yes,15,strangers,not_long +108_103,27,after,yes,8,strangers,not_long +108_103,28,before,yes,8,strangers,not_long +108_103,28,after,no,19,strangers,not_long +108_103,29,before,no,19,strangers,not_long +108_103,29,after,no,8,strangers,not_long +108_103,30,before,no,8,strangers,not_long +108_103,30,after,yes,3,strangers,not_long +108_103,31,before,yes,3,strangers,not_long +108_103,31,after,no,19,strangers,not_long +108_103,32,before,no,19,strangers,not_long +108_103,32,after,no,6,strangers,not_long +108_103,33,before,no,6,strangers,not_long +108_103,33,after,no,7,strangers,not_long 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+108_103,46,after,no,1,strangers,not_long +108_103,47,before,no,1,strangers,not_long +108_103,47,after,yes,14,strangers,not_long +108_103,49,before,no,15,strangers,not_long +108_103,49,after,no,18,strangers,not_long +108_103,50,before,no,18,strangers,not_long +108_103,50,after,no,11,strangers,not_long +108_103,51,before,no,11,strangers,not_long +108_103,51,after,yes,8,strangers,not_long +108_103,52,before,yes,8,strangers,not_long +108_103,52,after,no,8,strangers,not_long +108_103,53,before,no,8,strangers,not_long +108_103,53,after,no,4,strangers,not_long +108_103,54,before,no,4,strangers,not_long +108_103,54,after,no,21,strangers,not_long +108_103,55,before,no,21,strangers,not_long +108_103,55,after,no,4,strangers,not_long +108_103,56,before,no,4,strangers,not_long +108_103,56,after,yes,5,strangers,not_long +108_103,57,before,yes,5,strangers,not_long +108_103,57,after,no,7,strangers,not_long +108_103,58,before,no,7,strangers,not_long +108_103,58,after,no,21,strangers,not_long 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+108_103,43,after,no,25,strangers,not_long +108_103,44,before,no,25,strangers,not_long +108_103,44,after,no,4,strangers,not_long +108_103,45,before,no,4,strangers,not_long +108_103,45,after,no,4,strangers,not_long +108_103,46,before,no,4,strangers,not_long +108_103,46,after,no,1,strangers,not_long +108_103,47,before,no,1,strangers,not_long +108_103,47,after,yes,14,strangers,not_long +108_103,49,before,no,15,strangers,not_long +108_103,49,after,no,18,strangers,not_long +108_103,50,before,no,18,strangers,not_long +108_103,50,after,no,11,strangers,not_long +108_103,51,before,no,11,strangers,not_long +108_103,51,after,yes,8,strangers,not_long +108_103,52,before,yes,8,strangers,not_long +108_103,52,after,no,8,strangers,not_long +108_103,53,before,no,8,strangers,not_long +108_103,53,after,no,4,strangers,not_long +108_103,54,before,no,4,strangers,not_long +108_103,54,after,no,21,strangers,not_long +108_103,55,before,no,21,strangers,not_long +108_103,55,after,no,4,strangers,not_long 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+100_106,8,after,yes,11,strangers,not_long +100_106,9,before,yes,11,strangers,not_long +100_106,9,after,no,14,strangers,not_long +100_106,10,before,no,14,strangers,not_long +100_106,10,after,no,1,strangers,not_long +100_106,11,before,no,1,strangers,not_long +100_106,11,after,no,4,strangers,not_long +100_106,12,before,no,4,strangers,not_long +100_106,12,after,no,23,strangers,not_long +100_106,13,before,no,23,strangers,not_long +100_106,13,after,no,1,strangers,not_long +100_106,14,before,no,1,strangers,not_long +100_106,14,after,yes,7,strangers,not_long +100_106,16,before,no,11,strangers,not_long +100_106,16,after,no,4,strangers,not_long +100_106,17,before,no,4,strangers,not_long +100_106,17,after,yes,3,strangers,not_long +100_106,18,before,yes,3,strangers,not_long +100_106,18,after,no,18,strangers,not_long +100_106,19,before,no,18,strangers,not_long +100_106,19,after,yes,5,strangers,not_long +100_106,20,before,yes,5,strangers,not_long +100_106,20,after,no,52,strangers,not_long +100_106,21,before,no,52,strangers,not_long +100_106,21,after,no,2,strangers,not_long +100_106,22,before,no,2,strangers,not_long +100_106,22,after,yes,8,strangers,not_long +100_106,23,before,yes,8,strangers,not_long +100_106,23,after,no,7,strangers,not_long +100_106,24,before,no,7,strangers,not_long +100_106,24,after,no,1,strangers,not_long +100_106,25,before,no,1,strangers,not_long +100_106,25,after,no,4,strangers,not_long +100_106,26,before,no,4,strangers,not_long +100_106,26,after,no,2,strangers,not_long +100_106,27,before,no,2,strangers,not_long +100_106,27,after,no,11,strangers,not_long +100_106,28,before,no,11,strangers,not_long +100_106,28,after,yes,5,strangers,not_long +100_106,29,before,yes,5,strangers,not_long +100_106,29,after,no,3,strangers,not_long +100_106,30,before,no,3,strangers,not_long +100_106,30,after,yes,6,strangers,not_long +100_106,31,before,yes,6,strangers,not_long +100_106,31,after,no,1,strangers,not_long +100_106,32,before,no,1,strangers,not_long +100_106,32,after,no,2,strangers,not_long +100_106,33,before,no,2,strangers,not_long +100_106,33,after,no,7,strangers,not_long +100_106,34,before,no,7,strangers,not_long +100_106,34,after,no,5,strangers,not_long +100_106,35,before,no,5,strangers,not_long +100_106,35,after,yes,2,strangers,not_long +100_106,36,before,yes,2,strangers,not_long +100_106,36,after,no,27,strangers,not_long +100_106,37,before,no,27,strangers,not_long +100_106,37,after,no,2,strangers,not_long +100_106,38,before,no,2,strangers,not_long +100_106,38,after,yes,12,strangers,not_long +100_106,39,before,yes,12,strangers,not_long +100_106,39,after,no,5,strangers,not_long +100_106,40,before,no,5,strangers,not_long +100_106,40,after,yes,12,strangers,not_long +100_106,41,before,yes,12,strangers,not_long +100_106,41,after,no,26,strangers,not_long +100_106,42,before,no,26,strangers,not_long +100_106,42,after,no,1,strangers,not_long +100_106,43,before,no,1,strangers,not_long +100_106,43,after,no,6,strangers,not_long 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+100_106,78,after,no,2,strangers,not_long +100_106,79,before,no,2,strangers,not_long +100_106,79,after,no,17,strangers,not_long +100_106,80,before,no,17,strangers,not_long +100_106,80,after,yes,33,strangers,not_long +100_106,81,before,yes,33,strangers,not_long +100_106,81,after,no,21,strangers,not_long +100_106,82,before,no,21,strangers,not_long +100_106,82,after,no,1,strangers,not_long +100_106,83,before,no,1,strangers,not_long +100_106,83,after,no,15,strangers,not_long +100_106,84,before,no,15,strangers,not_long +100_106,84,after,no,1,strangers,not_long +100_106,85,before,no,1,strangers,not_long +100_106,85,after,no,16,strangers,not_long +100_106,86,before,no,16,strangers,not_long +100_106,86,after,yes,23,strangers,not_long +100_106,87,before,yes,23,strangers,not_long +100_106,87,after,no,47,strangers,not_long +100_106,88,before,no,47,strangers,not_long +100_106,88,after,no,2,strangers,not_long +100_106,89,before,no,2,strangers,not_long +100_106,89,after,no,12,strangers,not_long 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+104_103,44,after,no,3,strangers,not_long +104_103,45,before,no,3,strangers,not_long +104_103,45,after,no,14,strangers,not_long +104_103,46,before,no,14,strangers,not_long +104_103,46,after,yes,3,strangers,not_long +104_103,47,before,yes,3,strangers,not_long +104_103,47,after,no,8,strangers,not_long +104_103,48,before,no,8,strangers,not_long +104_103,48,after,no,12,strangers,not_long +104_103,49,before,no,12,strangers,not_long +104_103,49,after,yes,12,strangers,not_long +104_103,50,before,yes,12,strangers,not_long +104_103,50,after,yes,13,strangers,not_long +104_103,51,before,yes,13,strangers,not_long +104_103,51,after,no,1,strangers,not_long +104_103,52,before,no,1,strangers,not_long +104_103,52,after,no,29,strangers,not_long +104_103,53,before,no,29,strangers,not_long +104_103,53,after,no,1,strangers,not_long +104_103,54,before,no,1,strangers,not_long +104_103,54,after,no,6,strangers,not_long +104_103,55,before,no,6,strangers,not_long +104_103,55,after,yes,19,strangers,not_long 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to Analyses/turn_taking/round_robin/8_6.csv diff --git a/Analyses/turn_taking/strangers/8_7.csv b/Analyses/turn_taking/round_robin/8_7.csv similarity index 100% rename from Analyses/turn_taking/strangers/8_7.csv rename to Analyses/turn_taking/round_robin/8_7.csv diff --git a/Analyses/turn_taking/strangers/8_9.csv b/Analyses/turn_taking/round_robin/8_9.csv similarity index 100% rename from Analyses/turn_taking/strangers/8_9.csv rename to Analyses/turn_taking/round_robin/8_9.csv diff --git a/Analyses/turn_taking/strangers/90_82.csv b/Analyses/turn_taking/round_robin/90_82.csv similarity index 100% rename from Analyses/turn_taking/strangers/90_82.csv rename to Analyses/turn_taking/round_robin/90_82.csv diff --git a/Analyses/turn_taking/strangers/90_83.csv b/Analyses/turn_taking/round_robin/90_83.csv similarity index 100% rename from Analyses/turn_taking/strangers/90_83.csv rename to Analyses/turn_taking/round_robin/90_83.csv diff --git a/Analyses/turn_taking/strangers/90_84.csv b/Analyses/turn_taking/round_robin/90_84.csv similarity index 100% rename from Analyses/turn_taking/strangers/90_84.csv rename to Analyses/turn_taking/round_robin/90_84.csv diff --git a/Analyses/turn_taking/strangers/90_87.csv b/Analyses/turn_taking/round_robin/90_87.csv similarity index 100% rename from Analyses/turn_taking/strangers/90_87.csv rename to Analyses/turn_taking/round_robin/90_87.csv diff --git a/Analyses/turn_taking/strangers/9_10.csv b/Analyses/turn_taking/round_robin/9_10.csv similarity index 100% rename from Analyses/turn_taking/strangers/9_10.csv rename to Analyses/turn_taking/round_robin/9_10.csv diff --git a/Analyses/turn_taking/strangers/9_11.csv b/Analyses/turn_taking/round_robin/9_11.csv similarity index 100% rename from Analyses/turn_taking/strangers/9_11.csv rename to Analyses/turn_taking/round_robin/9_11.csv diff --git a/Analyses/turn_taking/strangers/9_4.csv b/Analyses/turn_taking/round_robin/9_4.csv similarity index 100% rename from Analyses/turn_taking/strangers/9_4.csv rename to Analyses/turn_taking/round_robin/9_4.csv diff --git a/Analyses/turn_taking/strangers/9_6.csv b/Analyses/turn_taking/round_robin/9_6.csv similarity index 100% rename from Analyses/turn_taking/strangers/9_6.csv rename to Analyses/turn_taking/round_robin/9_6.csv diff --git a/Analyses/turn_taking/strangers_pure/.DS_Store b/Analyses/turn_taking/strangers_pure/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 2000].reset_index(drop=True)\n", - " \n", - " if len(turns_long) > 0:\n", - " \n", - " total_time += np.sum(turns_long['gap_length'])\n", - " \n", - "(total_time / (len(flist) * 600000)) * 100" - ] - }, - { - "cell_type": "markdown", - "id": "df8676d8", - "metadata": {}, - "source": [ - "# Friends" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3ab3f706", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.606897435897436" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_time = 0\n", - "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", - "\n", - "for file in flist:\n", - " \n", - " data = pd.read_csv(file)\n", - " \n", - " turns_long = data.loc[data['gap_length'] > 2000].reset_index(drop=True)\n", - " \n", - " if len(turns_long) > 0:\n", - " \n", - " total_time += np.sum(turns_long['gap_length'])\n", - " \n", - "(total_time / (len(flist) * 600000)) * 100" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3_8", - "language": "python", - "name": "python3_8" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git "a/Code/.ipynb_checkpoints/Study 1 - chi-square test (do friends use more \"long gaps\"?)-checkpoint.ipynb" "b/Code/.ipynb_checkpoints/Study 1 - chi-square test (do friends use more \"long gaps\"?)-checkpoint.ipynb" deleted file mode 100644 index 1154d40..0000000 --- "a/Code/.ipynb_checkpoints/Study 1 - chi-square test (do friends use more \"long gaps\"?)-checkpoint.ipynb" +++ /dev/null @@ -1,347 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1a45c883", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import os\n", - "import glob\n", - "import pandas as pd\n", - "\n", - "base_dir = os.path.dirname(os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "20e69c8f", - "metadata": {}, - "source": [ - "# Count the number of long gaps for stranger conversations" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "422bfc3c", - "metadata": {}, - "outputs": [], - "source": [ - "turns_strangers = pd.DataFrame()\n", - "turns_strangers = turns_strangers.fillna(0)\n", - "counter = 0\n", - "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", - "\n", - "for file in flist:\n", - " \n", - " name = file.split('/')[-1].split('.csv')[0]\n", - " \n", - " data = pd.read_csv(file)\n", - " \n", - " turns_long = data.loc[data['gap_length'] > 2000].reset_index(drop=True) # find gap lengths > 2 seconds long\n", - " \n", - " turns_strangers.at[counter, 'convo'] = name\n", - " turns_strangers.at[counter, 'n_long'] = len(turns_long)\n", - " turns_strangers.at[counter, 'n_not_long'] = len(data) - len(turns_long)\n", - "\n", - " counter += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "42335a7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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convon_longn_not_long
0103_1014.0164.0
124_250.0128.0
24_60.094.0
3102_1102.0199.0
4102_1043.0188.0
\n", - "
" - ], - "text/plain": [ - " convo n_long n_not_long\n", - "0 103_101 4.0 164.0\n", - "1 24_25 0.0 128.0\n", - "2 4_6 0.0 94.0\n", - "3 102_110 2.0 199.0\n", - "4 102_104 3.0 188.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "turns_strangers.head()" - ] - }, - { - "cell_type": "markdown", - "id": "7beb183c", - "metadata": {}, - "source": [ - "# Count the number of long gaps for friend conversations" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f488c005", - "metadata": {}, - "outputs": [], - "source": [ - "turns_friends = pd.DataFrame()\n", - "turns_friends = turns_friends.fillna(0)\n", - "counter = 0\n", - "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", - "\n", - "for file in flist:\n", - " \n", - " name = file.split('/')[-1].split('.csv')[0]\n", - " \n", - " data = pd.read_csv(file)\n", - " \n", - " turns_long = data.loc[data['gap_length'] > 2000].reset_index(drop=True)\n", - " \n", - " turns_friends.at[counter, 'convo'] = name\n", - " turns_friends.at[counter, 'n_long'] = len(turns_long)\n", - " turns_friends.at[counter, 'n_not_long'] = len(data) - len(turns_long)\n", - "\n", - " counter += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8e77b218", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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convon_longn_not_long
021_2110.0127.0
18_2354.0150.0
223_2600.0158.0
330_2153.0131.0
4101_2070.0160.0
\n", - "
" - ], - "text/plain": [ - " convo n_long n_not_long\n", - "0 21_211 0.0 127.0\n", - "1 8_235 4.0 150.0\n", - "2 23_260 0.0 158.0\n", - "3 30_215 3.0 131.0\n", - "4 101_207 0.0 160.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "turns_friends.head()" - ] - }, - { - "cell_type": "markdown", - "id": "b79d9b9b", - "metadata": {}, - "source": [ - "# Chi-sqaure test" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b530fbd1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[218, 9346], [346, 47614]]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = [[int(np.sum(turns_friends['n_long'])), int(np.sum(turns_friends['n_not_long']))], \n", - " [int(np.sum(turns_strangers['n_long'])), int(np.sum(turns_strangers['n_not_long']))]]\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c9402719", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import chi2_contingency\n", - "\n", - "stat, p, dof, expected = chi2_contingency(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "333d3a39", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stat: 197.7519734203746\n", - "p-value: 6.462717395100015e-45\n", - "n: 57524\n" - ] - } - ], - "source": [ - "print('stat: ' + str(stat))\n", - "print('p-value: ' + str(p))\n", - "print('n: ' + str(218 + 9346 + 346 + 47614))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3_8", - "language": "python", - "name": "python3_8" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git "a/Code/.ipynb_checkpoints/Study 1 - defining a \"long gap\"-checkpoint.ipynb" "b/Code/.ipynb_checkpoints/Study 1 - defining a \"long gap\"-checkpoint.ipynb" deleted file mode 100644 index cb25786..0000000 --- "a/Code/.ipynb_checkpoints/Study 1 - defining a \"long gap\"-checkpoint.ipynb" +++ /dev/null @@ -1,136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7260bd0a", - "metadata": {}, - "source": [ - "# Quantify distribution of all gap lengths\n", - "\n", - "Compute mean and standard deviation of all gap lengths across all conversations (friend and stranger)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "32f42732", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd \n", - "import numpy as np\n", - "import os.path\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import glob\n", - "\n", - "%matplotlib inline\n", - "sns.set_style(\"white\")\n", - "\n", - "base_dir = os.path.dirname(os.getcwd())" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bb59008b", - "metadata": {}, - "outputs": [], - "source": [ - "all_gap_lengths = []\n", - "\n", - "flist_friends = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", - "\n", - "for file in flist_friends:\n", - " data = pd.read_csv(file)\n", - " for i in range(len(data)):\n", - " all_gap_lengths.append(data.at[i, 'gap_length'])\n", - "\n", - "flist_strangers = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", - "\n", - "for file in flist_strangers:\n", - " data = pd.read_csv(file)\n", - " for i in range(len(data)):\n", - " all_gap_lengths.append(data.at[i, 'gap_length'])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e8e39762", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.displot(all_gap_lengths)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5588bdde", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean: 244.15ms\n", - "standard deviation: 595.68ms\n", - "\n", - "3 standard deviations above the mean: 2031.2ms\n" - ] - } - ], - "source": [ - "print('mean: ' + str(round(np.mean(all_gap_lengths), 2)) + 'ms')\n", - "print('standard deviation: ' + str(round(np.std(all_gap_lengths), 2)) + 'ms')\n", - "\n", - "print()\n", - "print('3 standard deviations above the mean: ' + \n", - " str(round(np.mean(all_gap_lengths) + (3*np.std(all_gap_lengths)), 2)) + 'ms')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3_8", - "language": "python", - "name": "python3_8" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Code/.ipynb_checkpoints/Study 1 - do friends use more long gaps?-checkpoint.ipynb b/Code/.ipynb_checkpoints/Study 1 - do friends use more long gaps?-checkpoint.ipynb new file mode 100644 index 0000000..71fafae --- /dev/null +++ b/Code/.ipynb_checkpoints/Study 1 - do friends use more long gaps?-checkpoint.ipynb @@ -0,0 +1,391 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "95469010", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import glob\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "3364f2d5", + "metadata": {}, + "source": [ + "# Count the number of long gaps in each conversation\n", + "\n", + "Record the number of long gaps and the total number of turns to compute frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d909916", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps = pd.DataFrame()\n", + "long_gaps = long_gaps.fillna(0)\n", + "counter = 0\n", + "\n", + "cutoffs = [500, 750, 1000, 1250, 1500, 1750, 2000,\n", + " 2250, 2500, 2750, 3000]\n", + "\n", + "folders = ['strangers_pure', 'friends']\n", + "\n", + "for folder in folders:\n", + " \n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', '{}'.format(folder), '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " name = file.split('/')[-1].split('.csv')[0]\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + " n_total = len(data)\n", + " \n", + " long_gaps.at[counter, 'convo'] = name\n", + " long_gaps.at[counter, 'subID'] = id_1\n", + " long_gaps.at[counter, 'partnerID'] = id_2\n", + " long_gaps.at[counter, 'n_total'] = n_total\n", + " long_gaps.at[counter, 'condition'] = folder\n", + " \n", + " for cutoff in cutoffs:\n", + "\n", + " turns_long = data.loc[data['gap_length'] > cutoff].reset_index(drop=True) \n", + "\n", + " long_gaps.at[counter, 'n_long_{}'.format(cutoff)] = len(turns_long)\n", + " long_gaps.at[counter, 'freq_long_{}'.format(cutoff)] = len(turns_long) / n_total\n", + "\n", + " counter += 1\n", + " \n", + " long_gaps.at[counter, 'convo'] = name\n", + " long_gaps.at[counter, 'subID'] = id_2\n", + " long_gaps.at[counter, 'partnerID'] = id_1\n", + " long_gaps.at[counter, 'n_total'] = n_total\n", + " long_gaps.at[counter, 'condition'] = folder\n", + " \n", + " for cutoff in cutoffs:\n", + "\n", + " turns_long = data.loc[data['gap_length'] > cutoff].reset_index(drop=True) \n", + "\n", + " long_gaps.at[counter, 'n_long_{}'.format(cutoff)] = len(turns_long)\n", + " long_gaps.at[counter, 'freq_long_{}'.format(cutoff)] = len(turns_long) / n_total\n", + "\n", + " counter += 1\n", + "\n", + "long_gaps.to_csv(os.path.join(base_dir, 'Analyses', 'long_gap_counts.csv'), encoding='utf-8', index=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bc4c35e7", + "metadata": {}, + "source": [ + "# How frequent are long gaps in each threshold?\n", + "\n", + "Note: These counts differ slightly from ones in FigS1 because those have added constraint of needing to be 6 seconds after the start of convo and 6 seconds before the start of convo" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "4d0d8511", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500\n", + "strangers\n", + "8508.0\n", + "0.21996432172496705\n", + "friends\n", + "2812.0\n", + "0.2940192388122125\n", + "_________\n", + "750\n", + "strangers\n", + "4197.0\n", + "0.1085084929806872\n", + "friends\n", + "1717.0\n", + "0.17952739439565035\n", + "_________\n", + "1000\n", + "strangers\n", + "2316.0\n", + "0.05987745288140851\n", + "friends\n", + "1122.0\n", + "0.11731493099121706\n", + "_________\n", + "1250\n", + "strangers\n", + "1315.0\n", + "0.03399777657126606\n", + "friends\n", + "741.0\n", + "0.0774780426599749\n", + "_________\n", + "1500\n", + "strangers\n", + "755.0\n", + "0.019519635978179375\n", + "friends\n", + "476.0\n", + "0.049769970723546636\n", + "_________\n", + "1750\n", + "strangers\n", + "455.0\n", + "0.011763489231882933\n", + "friends\n", + "343.0\n", + "0.03586365537432037\n", + "_________\n", + "2000\n", + "strangers\n", + "274.0\n", + "0.007083947361617415\n", + "friends\n", + "218.0\n", + "0.022793810121288165\n", + "_________\n", + "2250\n", + "strangers\n", + "178.0\n", + "0.004601980402802555\n", + "friends\n", + "150.0\n", + "0.015683814303638646\n", + "_________\n", + "2500\n", + "strangers\n", + "122.0\n", + "0.0031541663434938856\n", + "friends\n", + "108.0\n", + "0.011292346298619825\n", + "_________\n", + "2750\n", + "strangers\n", + "84.0\n", + "0.0021717210889630034\n", + "friends\n", + "85.0\n", + "0.008887494772061898\n", + "_________\n", + "3000\n", + "strangers\n", + "66.0\n", + "0.0017063522841852168\n", + "friends\n", + "70.0\n", + "0.007319113341698034\n", + "_________\n" + ] + } + ], + "source": [ + "cutoffs = [500, 750, 1000, 1250, 1500, 1750, 2000,\n", + " 2250, 2500, 2750, 3000]\n", + "\n", + "strangers = long_gaps.loc[long_gaps['condition'] == 'strangers_pure'].reset_index(drop=True)\n", + "friends = long_gaps.loc[long_gaps['condition'] == 'friends'].reset_index(drop=True)\n", + "\n", + "n_turns_total_strangers = np.sum(strangers['n_total']) / 2\n", + "n_turns_total_friends = np.sum(friends['n_total']) / 2\n", + "\n", + "for cutoff in cutoffs:\n", + " \n", + " n_turns_cutoff_strangers = np.sum(strangers['n_long_{}'.format(cutoff)]) / 2\n", + " n_turns_cutoff_friends = np.sum(friends['n_long_{}'.format(cutoff)]) / 2\n", + " \n", + " print(cutoff)\n", + " print('strangers')\n", + " print(n_turns_cutoff_strangers)\n", + " print(n_turns_cutoff_strangers / n_turns_total_strangers)\n", + " print('friends')\n", + " print(n_turns_cutoff_friends)\n", + " print(n_turns_cutoff_friends / n_turns_total_friends)\n", + " print('_________')\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "256c1ab7", + "metadata": {}, + "source": [ + "# Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "d82dc994", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(4,5))\n", + "\n", + "ax = sns.barplot(x=\"condition\", y=\"n_long_2000\", data=long_gaps, \n", + " palette=['#90be6d', '#43aa8b'],\n", + " saturation=1, errwidth=1,\n", + " capsize=.05)\n", + "\n", + "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('# long gaps in each conversation', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=13)\n", + "\n", + "# significance bar\n", + "lw = 2\n", + "line_color = '#594F4F' # create thinner, greyer lines\n", + "star_color = '#594F4F'\n", + "h = 0\n", + "space_between_stars_and_line = .0\n", + "star_font_size = 20\n", + "\n", + "# strangers vs friends\n", + "x1, x2 = .20, .80 \n", + "y = 4.2\n", + "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", + "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig(os.path.join(base_dir,'Figures','Fig1_counts_bar.pdf'), dpi=300)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4af4211", + "metadata": {}, + "source": [ + "# Plot individual distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "bd2e53dc", + "metadata": {}, + "outputs": [], + "source": [ + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + "for file in flist:\n", + "\n", + " name = file.split('/')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + " \n", + " ax=sns.histplot(x=data['gap_length'], color='#f9c74f', alpha=1, kde=False, binwidth=100, binrange=(-2000,2000))\n", + " ax=sns.histplot(x=data['gap_length'], color='#f94144', alpha=1, kde=False, binwidth=100, binrange=(2000,5000))\n", + " sns.despine()\n", + " plt.title(name)\n", + "\n", + " ax.set_xlabel('gap lengths (ms)', fontsize=15, labelpad=10)\n", + " ax.set_ylabel('count', fontsize=15, labelpad=10)\n", + " ax.tick_params(labelsize=12)\n", + " ax.set(xlim=(-2000, 5000))\n", + " ax.set(ylim=(0, 22))\n", + " \n", + " plt.axvline(x=2000, color='#277da1', linestyle='dotted')\n", + " \n", + " plt.tight_layout()\n", + " \n", + " plt.savefig(os.path.join(base_dir,'Figures','individual_distributions',\n", + " 'strangers', '{}.pdf'.format(name)), dpi=300)\n", + " plt.close()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "0d17c49e", + "metadata": {}, + "outputs": [], + "source": [ + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + "for file in flist:\n", + "\n", + " name = file.split('/')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + " \n", + " ax=sns.histplot(x=data['gap_length'], color='#f9c74f', alpha=1, kde=False, binwidth=100, binrange=(-2000,2000))\n", + " ax=sns.histplot(x=data['gap_length'], color='#f94144', alpha=1, kde=False, binwidth=100, binrange=(2000,5000))\n", + " sns.despine()\n", + " plt.title(name)\n", + "\n", + " ax.set_xlabel('gap lengths (ms)', fontsize=15, labelpad=10)\n", + " ax.set_ylabel('count', fontsize=15, labelpad=10)\n", + " ax.tick_params(labelsize=12)\n", + " ax.set(xlim=(-2000, 5000))\n", + " \n", + " plt.axvline(x=2000, color='#277da1', linestyle='dotted')\n", + " \n", + " plt.tight_layout()\n", + " \n", + " plt.savefig(os.path.join(base_dir,'Figures','individual_distributions',\n", + " 'friends', '{}.pdf'.format(name)), dpi=300)\n", + " plt.close()\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/Study 1 - long gaps and self-reported connection-checkpoint.ipynb b/Code/.ipynb_checkpoints/Study 1 - long gaps and self-reported connection-checkpoint.ipynb index 72e62ed..2814a83 100644 --- a/Code/.ipynb_checkpoints/Study 1 - long gaps and self-reported connection-checkpoint.ipynb +++ b/Code/.ipynb_checkpoints/Study 1 - long gaps and self-reported connection-checkpoint.ipynb @@ -29,6 +29,38 @@ "base_dir = os.path.dirname(os.getcwd())" ] }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b9f2c179", + "metadata": {}, + "outputs": [], + "source": [ + "def find_video(file):\n", + " \"\"\"Need video name to use key in assign_idenity function.\n", + "\n", + " \"\"\"\n", + " id1 = file.split('/')[-1].split('_')[0]\n", + " id2 = file.split('_')[-1].split('.csv')[0]\n", + " video_name_1 = id1 + '_' + id2\n", + " video_name_2 = id2 + '_' + id1\n", + " return video_name_1, video_name_2\n", + "\n", + "\n", + "def assign_identity(file):\n", + " \"\"\"Use key to determine which subject ID corresponds to S1 (speaker 1)\n", + " and which subject ID corresponds to S2 (speaker 2).\n", + "\n", + " \"\"\"\n", + " key = pd.read_csv(os.path.join(base_dir, 'Data', 'subject_id_key.csv'))\n", + " video_name_1, video_name_2 = find_video(file)\n", + " for i in range(len((key))):\n", + " if (key.at[i,'video_name'] == video_name_1) | (key.at[i,'video_name'] == video_name_2):\n", + " subID_S1 = key.at[i,'S1']\n", + " subID_S2 = key.at[i,'S2']\n", + " return subID_S1, subID_S2" + ] + }, { "cell_type": "markdown", "id": "d905d50f", @@ -39,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "71d71c7a", "metadata": {}, "outputs": [], @@ -48,13 +80,12 @@ "long_gap_connection = long_gap_connection.fillna(0)\n", "counter = 0\n", "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", "\n", "for file in flist:\n", " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", " convo = file.split('/')[-1].split('.csv')[0]\n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -62,8 +93,8 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", " \n", " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", @@ -77,65 +108,83 @@ " # the long gap\n", " start_of_gap = data.at[i-1, 'turn_end_msec']\n", " end_of_gap = data.at[i, 'turn_start_msec']\n", - "\n", - " start_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - " end_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_during_long_gap = np.mean(connection_1['Rating'][start_connection:end_connection])\n", - " connection_2_during_long_gap = np.mean(connection_2['Rating'][start_connection:end_connection])\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", "\n", " # 1 chunk before long gap\n", "\n", - " start_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - " end_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_1 = np.mean(connection_1['Rating'][start_connection_before_1:end_connection_before_1])\n", - " connection_2_before_long_gap_1 = np.mean(connection_2['Rating'][start_connection_before_1:end_connection_before_1])\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", "\n", " # 2 chunks before long gap\n", "\n", - " start_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - " end_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_2 = np.mean(connection_1['Rating'][start_connection_before_2:end_connection_before_2])\n", - " connection_2_before_long_gap_2 = np.mean(connection_2['Rating'][start_connection_before_2:end_connection_before_2])\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", "\n", " # 3 chunks before long gap\n", "\n", - " start_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-6000))[0])[-1]) + 1\n", - " end_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_3 = np.mean(connection_1['Rating'][start_connection_before_3:end_connection_before_3])\n", - " connection_2_before_long_gap_3 = np.mean(connection_2['Rating'][start_connection_before_3:end_connection_before_3])\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", "\n", " # 1 chunk after long gap \n", "\n", - " start_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - " end_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_1 = np.mean(connection_1['Rating'][start_connection_after_1:end_connection_after_1])\n", - " connection_2_after_long_gap_1 = np.mean(connection_2['Rating'][start_connection_after_1:end_connection_after_1])\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", "\n", " # 2 chunks after long gap \n", "\n", - " start_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - " end_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_2 = np.mean(connection_1['Rating'][start_connection_after_2:end_connection_after_2])\n", - " connection_2_after_long_gap_2 = np.mean(connection_2['Rating'][start_connection_after_2:end_connection_after_2])\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", "\n", " # 3 chunks after long gap \n", "\n", - " start_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - " end_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 6000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_3 = np.mean(connection_1['Rating'][start_connection_after_3:end_connection_after_3])\n", - " connection_2_after_long_gap_3 = np.mean(connection_2['Rating'][start_connection_after_3:end_connection_after_3])\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", "\n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", @@ -145,11 +194,18 @@ " long_gap_connection.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", "\n", " counter += 1\n", "\n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", @@ -159,6 +215,13 @@ " long_gap_connection.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", "\n", " counter += 1\n", " \n", @@ -184,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "ff7819ee", "metadata": {}, "outputs": [], @@ -197,9 +260,8 @@ "\n", "for file in flist:\n", " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", " convo = file.split('/')[-1].split('.csv')[0]\n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -207,10 +269,13 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", " \n", - " for i in list(long_gaps.index): # double-check this logic\n", + " for i in list(long_gaps.index):\n", " \n", " if (i > 0):\n", " \n", @@ -219,65 +284,83 @@ " # the long gap\n", " start_of_gap = data.at[i-1, 'turn_end_msec']\n", " end_of_gap = data.at[i, 'turn_start_msec']\n", - "\n", - " start_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - " end_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_during_long_gap = np.mean(connection_1['Rating'][start_connection:end_connection])\n", - " connection_2_during_long_gap = np.mean(connection_2['Rating'][start_connection:end_connection])\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", "\n", " # 1 chunk before long gap\n", "\n", - " start_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - " end_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_1 = np.mean(connection_1['Rating'][start_connection_before_1:end_connection_before_1])\n", - " connection_2_before_long_gap_1 = np.mean(connection_2['Rating'][start_connection_before_1:end_connection_before_1])\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", "\n", " # 2 chunks before long gap\n", "\n", - " start_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - " end_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_2 = np.mean(connection_1['Rating'][start_connection_before_2:end_connection_before_2])\n", - " connection_2_before_long_gap_2 = np.mean(connection_2['Rating'][start_connection_before_2:end_connection_before_2])\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", "\n", " # 3 chunks before long gap\n", "\n", - " start_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-6000))[0])[-1]) + 1\n", - " end_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_3 = np.mean(connection_1['Rating'][start_connection_before_3:end_connection_before_3])\n", - " connection_2_before_long_gap_3 = np.mean(connection_2['Rating'][start_connection_before_3:end_connection_before_3])\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", "\n", " # 1 chunk after long gap \n", "\n", - " start_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - " end_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_1 = np.mean(connection_1['Rating'][start_connection_after_1:end_connection_after_1])\n", - " connection_2_after_long_gap_1 = np.mean(connection_2['Rating'][start_connection_after_1:end_connection_after_1])\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", "\n", " # 2 chunks after long gap \n", "\n", - " start_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - " end_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_2 = np.mean(connection_1['Rating'][start_connection_after_2:end_connection_after_2])\n", - " connection_2_after_long_gap_2 = np.mean(connection_2['Rating'][start_connection_after_2:end_connection_after_2])\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", "\n", " # 3 chunks after long gap \n", "\n", - " start_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - " end_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 6000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_3 = np.mean(connection_1['Rating'][start_connection_after_3:end_connection_after_3])\n", - " connection_2_after_long_gap_3 = np.mean(connection_2['Rating'][start_connection_after_3:end_connection_after_3])\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", "\n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", @@ -287,11 +370,18 @@ " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", + "\n", " counter += 1\n", "\n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", @@ -301,10 +391,17 @@ " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", "\n", " counter += 1\n", " \n", - "long_gap_connection_friends['change_in_connection_3_before'] = long_gap_connection_friends['connection_3_before'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_3_before'] = long_gap_connection_friends['connection_3_before'] - long_gap_connection_friends['connection_long_gap']\n", "long_gap_connection_friends['change_in_connection_2_before'] = long_gap_connection_friends['connection_2_before'] - long_gap_connection_friends['connection_long_gap']\n", "long_gap_connection_friends['change_in_connection_1_before'] = long_gap_connection_friends['connection_1_before'] - long_gap_connection_friends['connection_long_gap']\n", "long_gap_connection_friends['change_in_connection_1_after'] = long_gap_connection_friends['connection_1_after'] - long_gap_connection_friends['connection_long_gap']\n", @@ -326,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "f400d825", "metadata": {}, "outputs": [], @@ -341,1015 +438,6 @@ " encoding='utf-8', index=False)" ] }, - { - "cell_type": "markdown", - "id": "265fbd40", - "metadata": {}, - "source": [ - "# correcting for multiple comparisions\n", - "\n", - "Sometime in between first running these analyses and returning to check them before submitting the paper, the statsmodels multitest function stopped working (https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html). I therefore copy and pasted the source code for the function below. Source code came from here: https://www.statsmodels.org/dev/_modules/statsmodels/stats/multitest.html" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e1630260", - "metadata": {}, - "outputs": [], - "source": [ - "def _ecdf(x):\n", - " '''no frills empirical cdf used in fdrcorrection\n", - " '''\n", - " nobs = len(x)\n", - " return np.arange(1,nobs+1)/float(nobs)\n", - "\n", - "multitest_methods_names = {'b': 'Bonferroni',\n", - " 's': 'Sidak',\n", - " 'h': 'Holm',\n", - " 'hs': 'Holm-Sidak',\n", - " 'sh': 'Simes-Hochberg',\n", - " 'ho': 'Hommel',\n", - " 'fdr_bh': 'FDR Benjamini-Hochberg',\n", - " 'fdr_by': 'FDR Benjamini-Yekutieli',\n", - " 'fdr_tsbh': 'FDR 2-stage Benjamini-Hochberg',\n", - " 'fdr_tsbky': 'FDR 2-stage Benjamini-Krieger-Yekutieli',\n", - " 'fdr_gbs': 'FDR adaptive Gavrilov-Benjamini-Sarkar'\n", - " }\n", - "\n", - "_alias_list = [['b', 'bonf', 'bonferroni'],\n", - " ['s', 'sidak'],\n", - " ['h', 'holm'],\n", - " ['hs', 'holm-sidak'],\n", - " ['sh', 'simes-hochberg'],\n", - " ['ho', 'hommel'],\n", - " ['fdr_bh', 'fdr_i', 'fdr_p', 'fdri', 'fdrp'],\n", - " ['fdr_by', 'fdr_n', 'fdr_c', 'fdrn', 'fdrcorr'],\n", - " ['fdr_tsbh', 'fdr_2sbh'],\n", - " ['fdr_tsbky', 'fdr_2sbky', 'fdr_twostage'],\n", - " ['fdr_gbs']\n", - " ]\n", - "\n", - "\n", - "multitest_alias = {}\n", - "for m in _alias_list:\n", - " multitest_alias[m[0]] = m[0]\n", - " for a in m[1:]:\n", - " multitest_alias[a] = m[0]\n", - "\n", - "def multipletests(pvals, alpha=0.05, method='hs', is_sorted=False,\n", - " returnsorted=False):\n", - " \"\"\"\n", - " Test results and p-value correction for multiple tests\n", - "\n", - " Parameters\n", - " ----------\n", - " pvals : array_like, 1-d\n", - " uncorrected p-values. Must be 1-dimensional.\n", - " alpha : float\n", - " FWER, family-wise error rate, e.g. 0.1\n", - " method : str\n", - " Method used for testing and adjustment of pvalues. Can be either the\n", - " full name or initial letters. Available methods are:\n", - "\n", - " - `bonferroni` : one-step correction\n", - " - `sidak` : one-step correction\n", - " - `holm-sidak` : step down method using Sidak adjustments\n", - " - `holm` : step-down method using Bonferroni adjustments\n", - " - `simes-hochberg` : step-up method (independent)\n", - " - `hommel` : closed method based on Simes tests (non-negative)\n", - " - `fdr_bh` : Benjamini/Hochberg (non-negative)\n", - " - `fdr_by` : Benjamini/Yekutieli (negative)\n", - " - `fdr_tsbh` : two stage fdr correction (non-negative)\n", - " - `fdr_tsbky` : two stage fdr correction (non-negative)\n", - "\n", - " is_sorted : bool\n", - " If False (default), the p_values will be sorted, but the corrected\n", - " pvalues are in the original order. If True, then it assumed that the\n", - " pvalues are already sorted in ascending order.\n", - " returnsorted : bool\n", - " not tested, return sorted p-values instead of original sequence\n", - "\n", - " Returns\n", - " -------\n", - " reject : ndarray, boolean\n", - " true for hypothesis that can be rejected for given alpha\n", - " pvals_corrected : ndarray\n", - " p-values corrected for multiple tests\n", - " alphacSidak : float\n", - " corrected alpha for Sidak method\n", - " alphacBonf : float\n", - " corrected alpha for Bonferroni method\n", - "\n", - " Notes\n", - " -----\n", - " There may be API changes for this function in the future.\n", - "\n", - " Except for 'fdr_twostage', the p-value correction is independent of the\n", - " alpha specified as argument. In these cases the corrected p-values\n", - " can also be compared with a different alpha. In the case of 'fdr_twostage',\n", - " the corrected p-values are specific to the given alpha, see\n", - " ``fdrcorrection_twostage``.\n", - "\n", - " The 'fdr_gbs' procedure is not verified against another package, p-values\n", - " are derived from scratch and are not derived in the reference. In Monte\n", - " Carlo experiments the method worked correctly and maintained the false\n", - " discovery rate.\n", - "\n", - " All procedures that are included, control FWER or FDR in the independent\n", - " case, and most are robust in the positively correlated case.\n", - "\n", - " `fdr_gbs`: high power, fdr control for independent case and only small\n", - " violation in positively correlated case\n", - "\n", - " **Timing**:\n", - "\n", - " Most of the time with large arrays is spent in `argsort`. When\n", - " we want to calculate the p-value for several methods, then it is more\n", - " efficient to presort the pvalues, and put the results back into the\n", - " original order outside of the function.\n", - "\n", - " Method='hommel' is very slow for large arrays, since it requires the\n", - " evaluation of n partitions, where n is the number of p-values.\n", - " \"\"\"\n", - " import gc\n", - " pvals = np.asarray(pvals)\n", - " alphaf = alpha # Notation ?\n", - "\n", - " if not is_sorted:\n", - " sortind = np.argsort(pvals)\n", - " pvals = np.take(pvals, sortind)\n", - "\n", - " ntests = len(pvals)\n", - " alphacSidak = 1 - np.power((1. - alphaf), 1./ntests)\n", - " alphacBonf = alphaf / float(ntests)\n", - " if method.lower() in ['b', 'bonf', 'bonferroni']:\n", - " reject = pvals <= alphacBonf\n", - " pvals_corrected = pvals * float(ntests)\n", - "\n", - " elif method.lower() in ['s', 'sidak']:\n", - " reject = pvals <= alphacSidak\n", - " pvals_corrected = -np.expm1(ntests * np.log1p(-pvals))\n", - "\n", - " elif method.lower() in ['hs', 'holm-sidak']:\n", - " alphacSidak_all = 1 - np.power((1. - alphaf),\n", - " 1./np.arange(ntests, 0, -1))\n", - " notreject = pvals > alphacSidak_all\n", - " del alphacSidak_all\n", - "\n", - " nr_index = np.nonzero(notreject)[0]\n", - " if nr_index.size == 0:\n", - " # nonreject is empty, all rejected\n", - " notrejectmin = len(pvals)\n", - " else:\n", - " notrejectmin = np.min(nr_index)\n", - " notreject[notrejectmin:] = True\n", - " reject = ~notreject\n", - " del notreject\n", - "\n", - " # It's eqivalent to 1 - np.power((1. - pvals),\n", - " # np.arange(ntests, 0, -1))\n", - " # but prevents the issue of the floating point precision\n", - " pvals_corrected_raw = -np.expm1(np.arange(ntests, 0, -1) *\n", - " np.log1p(-pvals))\n", - " pvals_corrected = np.maximum.accumulate(pvals_corrected_raw)\n", - " del pvals_corrected_raw\n", - "\n", - " elif method.lower() in ['h', 'holm']:\n", - " notreject = pvals > alphaf / np.arange(ntests, 0, -1)\n", - " nr_index = np.nonzero(notreject)[0]\n", - " if nr_index.size == 0:\n", - " # nonreject is empty, all rejected\n", - " notrejectmin = len(pvals)\n", - " else:\n", - " notrejectmin = np.min(nr_index)\n", - " notreject[notrejectmin:] = True\n", - " reject = ~notreject\n", - " pvals_corrected_raw = pvals * np.arange(ntests, 0, -1)\n", - " pvals_corrected = np.maximum.accumulate(pvals_corrected_raw)\n", - " del pvals_corrected_raw\n", - " gc.collect()\n", - "\n", - " elif method.lower() in ['sh', 'simes-hochberg']:\n", - " alphash = alphaf / np.arange(ntests, 0, -1)\n", - " reject = pvals <= alphash\n", - " rejind = np.nonzero(reject)\n", - " if rejind[0].size > 0:\n", - " rejectmax = np.max(np.nonzero(reject))\n", - " reject[:rejectmax] = True\n", - " pvals_corrected_raw = np.arange(ntests, 0, -1) * pvals\n", - " pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]\n", - " del pvals_corrected_raw\n", - "\n", - " elif method.lower() in ['ho', 'hommel']:\n", - " # we need a copy because we overwrite it in a loop\n", - " a = pvals.copy()\n", - " for m in range(ntests, 1, -1):\n", - " cim = np.min(m * pvals[-m:] / np.arange(1,m+1.))\n", - " a[-m:] = np.maximum(a[-m:], cim)\n", - " a[:-m] = np.maximum(a[:-m], np.minimum(m * pvals[:-m], cim))\n", - " pvals_corrected = a\n", - " reject = a <= alphaf\n", - "\n", - " elif method.lower() in ['fdr_bh', 'fdr_i', 'fdr_p', 'fdri', 'fdrp']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha,\n", - " method='indep',\n", - " is_sorted=True)\n", - " elif method.lower() in ['fdr_by', 'fdr_n', 'fdr_c', 'fdrn', 'fdrcorr']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha,\n", - " method='n',\n", - " is_sorted=True)\n", - " elif method.lower() in ['fdr_tsbky', 'fdr_2sbky', 'fdr_twostage']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha,\n", - " method='bky',\n", - " is_sorted=True)[:2]\n", - " elif method.lower() in ['fdr_tsbh', 'fdr_2sbh']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha,\n", - " method='bh',\n", - " is_sorted=True)[:2]\n", - "\n", - " elif method.lower() in ['fdr_gbs']:\n", - " #adaptive stepdown in Gavrilov, Benjamini, Sarkar, Annals of Statistics 2009\n", - "## notreject = pvals > alphaf / np.arange(ntests, 0, -1) #alphacSidak\n", - "## notrejectmin = np.min(np.nonzero(notreject))\n", - "## notreject[notrejectmin:] = True\n", - "## reject = ~notreject\n", - "\n", - " ii = np.arange(1, ntests + 1)\n", - " q = (ntests + 1. - ii)/ii * pvals / (1. - pvals)\n", - " pvals_corrected_raw = np.maximum.accumulate(q) #up requirementd\n", - "\n", - " pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]\n", - " del pvals_corrected_raw\n", - " reject = pvals_corrected <= alpha\n", - "\n", - " else:\n", - " raise ValueError('method not recognized')\n", - "\n", - " if pvals_corrected is not None: #not necessary anymore\n", - " pvals_corrected[pvals_corrected>1] = 1\n", - " if is_sorted or returnsorted:\n", - " return reject, pvals_corrected, alphacSidak, alphacBonf\n", - " else:\n", - " pvals_corrected_ = np.empty_like(pvals_corrected)\n", - " pvals_corrected_[sortind] = pvals_corrected\n", - " del pvals_corrected\n", - " reject_ = np.empty_like(reject)\n", - " reject_[sortind] = reject\n", - " return reject_, pvals_corrected_, alphacSidak, alphacBonf\n", - "\n", - "\n", - "\n", - "def fdrcorrection(pvals, alpha=0.05, method='indep', is_sorted=False):\n", - " '''\n", - " pvalue correction for false discovery rate.\n", - "\n", - " This covers Benjamini/Hochberg for independent or positively correlated and\n", - " Benjamini/Yekutieli for general or negatively correlated tests.\n", - "\n", - " Parameters\n", - " ----------\n", - " pvals : array_like, 1d\n", - " Set of p-values of the individual tests.\n", - " alpha : float, optional\n", - " Family-wise error rate. Defaults to ``0.05``.\n", - " method : {'i', 'indep', 'p', 'poscorr', 'n', 'negcorr'}, optional\n", - " Which method to use for FDR correction.\n", - " ``{'i', 'indep', 'p', 'poscorr'}`` all refer to ``fdr_bh``\n", - " (Benjamini/Hochberg for independent or positively\n", - " correlated tests). ``{'n', 'negcorr'}`` both refer to ``fdr_by``\n", - " (Benjamini/Yekutieli for general or negatively correlated tests).\n", - " Defaults to ``'indep'``.\n", - " is_sorted : bool, optional\n", - " If False (default), the p_values will be sorted, but the corrected\n", - " pvalues are in the original order. If True, then it assumed that the\n", - " pvalues are already sorted in ascending order.\n", - "\n", - " Returns\n", - " -------\n", - " rejected : ndarray, bool\n", - " True if a hypothesis is rejected, False if not\n", - " pvalue-corrected : ndarray\n", - " pvalues adjusted for multiple hypothesis testing to limit FDR\n", - "\n", - " Notes\n", - " -----\n", - " If there is prior information on the fraction of true hypothesis, then alpha\n", - " should be set to ``alpha * m/m_0`` where m is the number of tests,\n", - " given by the p-values, and m_0 is an estimate of the true hypothesis.\n", - " (see Benjamini, Krieger and Yekuteli)\n", - "\n", - " The two-step method of Benjamini, Krieger and Yekutiel that estimates the number\n", - " of false hypotheses will be available (soon).\n", - "\n", - " Both methods exposed via this function (Benjamini/Hochberg, Benjamini/Yekutieli)\n", - " are also available in the function ``multipletests``, as ``method=\"fdr_bh\"`` and\n", - " ``method=\"fdr_by\"``, respectively.\n", - "\n", - " See also\n", - " --------\n", - " multipletests\n", - "\n", - " '''\n", - " pvals = np.asarray(pvals)\n", - " assert pvals.ndim == 1, \"pvals must be 1-dimensional, that is of shape (n,)\"\n", - "\n", - " if not is_sorted:\n", - " pvals_sortind = np.argsort(pvals)\n", - " pvals_sorted = np.take(pvals, pvals_sortind)\n", - " else:\n", - " pvals_sorted = pvals # alias\n", - "\n", - " if method in ['i', 'indep', 'p', 'poscorr']:\n", - " ecdffactor = _ecdf(pvals_sorted)\n", - " elif method in ['n', 'negcorr']:\n", - " cm = np.sum(1./np.arange(1, len(pvals_sorted)+1)) #corrected this\n", - " ecdffactor = _ecdf(pvals_sorted) / cm\n", - "## elif method in ['n', 'negcorr']:\n", - "## cm = np.sum(np.arange(len(pvals)))\n", - "## ecdffactor = ecdf(pvals_sorted)/cm\n", - " else:\n", - " raise ValueError('only indep and negcorr implemented')\n", - " reject = pvals_sorted <= ecdffactor*alpha\n", - " if reject.any():\n", - " rejectmax = max(np.nonzero(reject)[0])\n", - " reject[:rejectmax] = True\n", - "\n", - " pvals_corrected_raw = pvals_sorted / ecdffactor\n", - " pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]\n", - " del pvals_corrected_raw\n", - " pvals_corrected[pvals_corrected>1] = 1\n", - " if not is_sorted:\n", - " pvals_corrected_ = np.empty_like(pvals_corrected)\n", - " pvals_corrected_[pvals_sortind] = pvals_corrected\n", - " del pvals_corrected\n", - " reject_ = np.empty_like(reject)\n", - " reject_[pvals_sortind] = reject\n", - " return reject_, pvals_corrected_\n", - " else:\n", - " return reject, pvals_corrected\n", - "\n", - "\n", - "\n", - "def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False,\n", - " is_sorted=False):\n", - " '''(iterated) two stage linear step-up procedure with estimation of number of true\n", - " hypotheses\n", - "\n", - " Benjamini, Krieger and Yekuteli, procedure in Definition 6\n", - "\n", - " Parameters\n", - " ----------\n", - " pvals : array_like\n", - " set of p-values of the individual tests.\n", - " alpha : float\n", - " error rate\n", - " method : {'bky', 'bh')\n", - " see Notes for details\n", - "\n", - " * 'bky' - implements the procedure in Definition 6 of Benjamini, Krieger\n", - " and Yekuteli 2006\n", - " * 'bh' - the two stage method of Benjamini and Hochberg\n", - "\n", - " iter : bool\n", - "\n", - " Returns\n", - " -------\n", - " rejected : ndarray, bool\n", - " True if a hypothesis is rejected, False if not\n", - " pvalue-corrected : ndarray\n", - " pvalues adjusted for multiple hypotheses testing to limit FDR\n", - " m0 : int\n", - " ntest - rej, estimated number of true hypotheses\n", - " alpha_stages : list of floats\n", - " A list of alphas that have been used at each stage\n", - "\n", - " Notes\n", - " -----\n", - " The returned corrected p-values are specific to the given alpha, they\n", - " cannot be used for a different alpha.\n", - "\n", - " The returned corrected p-values are from the last stage of the fdr_bh\n", - " linear step-up procedure (fdrcorrection0 with method='indep') corrected\n", - " for the estimated fraction of true hypotheses.\n", - " This means that the rejection decision can be obtained with\n", - " ``pval_corrected <= alpha``, where ``alpha`` is the original significance\n", - " level.\n", - " (Note: This has changed from earlier versions (<0.5.0) of statsmodels.)\n", - "\n", - " BKY described several other multi-stage methods, which would be easy to implement.\n", - " However, in their simulation the simple two-stage method (with iter=False) was the\n", - " most robust to the presence of positive correlation\n", - "\n", - " TODO: What should be returned?\n", - "\n", - " '''\n", - " pvals = np.asarray(pvals)\n", - "\n", - " if not is_sorted:\n", - " pvals_sortind = np.argsort(pvals)\n", - " pvals = np.take(pvals, pvals_sortind)\n", - "\n", - " ntests = len(pvals)\n", - " if method == 'bky':\n", - " fact = (1.+alpha)\n", - " alpha_prime = alpha / fact\n", - " elif method == 'bh':\n", - " fact = 1.\n", - " alpha_prime = alpha\n", - " else:\n", - " raise ValueError(\"only 'bky' and 'bh' are available as method\")\n", - "\n", - " alpha_stages = [alpha_prime]\n", - " rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_prime, method='indep',\n", - " is_sorted=True)\n", - " r1 = rej.sum()\n", - " if (r1 == 0) or (r1 == ntests):\n", - " return rej, pvalscorr * fact, ntests - r1, alpha_stages\n", - " ri_old = r1\n", - "\n", - " while True:\n", - " ntests0 = 1.0 * ntests - ri_old\n", - " alpha_star = alpha_prime * ntests / ntests0\n", - " alpha_stages.append(alpha_star)\n", - " #print ntests0, alpha_star\n", - " rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_star, method='indep',\n", - " is_sorted=True)\n", - " ri = rej.sum()\n", - " if (not iter) or ri == ri_old:\n", - " break\n", - " elif ri < ri_old:\n", - " # prevent cycles and endless loops\n", - " raise RuntimeError(\" oops - should not be here\")\n", - " ri_old = ri\n", - "\n", - " # make adjustment to pvalscorr to reflect estimated number of Non-Null cases\n", - " # decision is then pvalscorr < alpha (or <=)\n", - " pvalscorr *= ntests0 * 1.0 / ntests\n", - " if method == 'bky':\n", - " pvalscorr *= (1. + alpha)\n", - "\n", - " if not is_sorted:\n", - " pvalscorr_ = np.empty_like(pvalscorr)\n", - " pvalscorr_[pvals_sortind] = pvalscorr\n", - " del pvalscorr\n", - " reject = np.empty_like(rej)\n", - " reject[pvals_sortind] = rej\n", - " return reject, pvalscorr_, ntests - ri, alpha_stages\n", - " else:\n", - " return rej, pvalscorr, ntests - ri, alpha_stages" - ] - }, - { - "cell_type": "markdown", - "id": "39749b53", - "metadata": {}, - "source": [ - "## stats -- within convo: strangers" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "228e8849", - "metadata": {}, - "outputs": [], - "source": [ - "pvals = []" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "129990bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=0.7207933073688064, pvalue=0.47129386153090225)\n", - "df:655\n" - ] - } - ], - "source": [ - "first = stats.ttest_1samp(long_gap_connection['change_in_connection_3_before'].dropna(), 0) # not sig\n", - "print(first)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_3_before'].dropna())-1))\n", - "pvals.append(first[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c291ab49", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=1.8141668143047212, pvalue=0.07010944516630901)\n", - "df:655\n" - ] - } - ], - "source": [ - "second = stats.ttest_1samp(long_gap_connection['change_in_connection_2_before'].dropna(), 0) # not sig\n", - "print(second)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_2_before'].dropna())-1))\n", - "pvals.append(second[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3912b7dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=2.447324563518974, pvalue=0.014653698465096539)\n", - "df:655\n" - ] - } - ], - "source": [ - "third = stats.ttest_1samp(long_gap_connection['change_in_connection_1_before'].dropna(), 0)\n", - "print(third)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_1_before'].dropna())-1))\n", - "pvals.append(third[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0a2e10e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-3.431280445913916, pvalue=0.0006385047913464213)\n", - "df:655\n" - ] - } - ], - "source": [ - "fourth = stats.ttest_1samp(long_gap_connection['change_in_connection_1_after'].dropna(), 0) \n", - "print(fourth)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_1_after'].dropna())-1))\n", - "pvals.append(fourth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f9efe5df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-2.1848847780646423, pvalue=0.029250958369363582)\n", - "df:655\n" - ] - } - ], - "source": [ - "fifth = stats.ttest_1samp(long_gap_connection['change_in_connection_2_after'].dropna(), 0)\n", - "print(fifth)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_2_after'].dropna())-1))\n", - "pvals.append(fifth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "02139e5b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.4965486829135184, pvalue=0.13499241294136874)\n", - "df:655\n" - ] - } - ], - "source": [ - "sixth = stats.ttest_1samp(long_gap_connection['change_in_connection_3_after'].dropna(), 0) # not sig\n", - "print(sixth)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_3_after'].dropna())-1))\n", - "pvals.append(sixth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "84efcaea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([False, False, True, True, True, False]),\n", - " array([0.3299057 , 0.07361492, 0.03077277, 0.00268172, 0.04095134,\n", - " 0.11339363]),\n", - " 0.008512444610847103,\n", - " 0.008333333333333333)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#statsmodels.stats.multitest.multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)\n", - "# adjust p-values for multiple comparisions\n", - "\n", - "multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)" - ] - }, - { - "cell_type": "markdown", - "id": "790ef917", - "metadata": {}, - "source": [ - "## stats -- within convo: friends" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "584fe805", - "metadata": {}, - "outputs": [], - "source": [ - "pvals = []" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "162cee5c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-3.439430076550795, pvalue=0.0006437680286116584)\n", - "df:403\n" - ] - } - ], - "source": [ - "first = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_3_before'].dropna(), 0)\n", - "print(first)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_3_before'].dropna())-1))\n", - "pvals.append(first[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "690b2b6a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-3.3941699388897506, pvalue=0.0007566168711399177)\n", - "df:403\n" - ] - } - ], - "source": [ - "second = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_2_before'].dropna(), 0)\n", - "print(second)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_2_before'].dropna())-1))\n", - "pvals.append(second[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b762f504", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-2.2011050171376514, pvalue=0.028294935722309192)\n", - "df:403\n" - ] - } - ], - "source": [ - "third = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_1_before'].dropna(), 0)\n", - "print(third)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_1_before'].dropna())-1))\n", - "pvals.append(third[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "56abf501", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.2893702374037974, pvalue=0.19800892604632644)\n", - "df:403\n" - ] - } - ], - "source": [ - "fourth = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_1_after'].dropna(), 0) # not sig\n", - "print(fourth)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_1_after'].dropna())-1))\n", - "pvals.append(fourth[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "ddf0bdac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.5354539013954533, pvalue=0.12545706531211648)\n", - "df:403\n" - ] - } - ], - "source": [ - "fifth = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_2_after'].dropna(), 0) # not sig\n", - "print(fifth)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_2_after'].dropna())-1))\n", - "pvals.append(fifth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "ec17a494", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.7298563215221672, pvalue=0.0844215856520884)\n", - "df:403\n" - ] - } - ], - "source": [ - "sixth = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_3_after'].dropna(), 0) # not sig\n", - "print(sixth)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_3_after'].dropna())-1))\n", - "pvals.append(sixth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4291780b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ True, True, True, False, False, False]),\n", - " array([0.0015889 , 0.0015889 , 0.03961291, 0.13860625, 0.10538393,\n", - " 0.08864266]),\n", - " 0.008512444610847103,\n", - " 0.008333333333333333)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#statsmodels.stats.multitest.multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)\n", - "# adjust p-values for multiple comparisions\n", - "\n", - "multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)" - ] - }, - { - "cell_type": "markdown", - "id": "1f4cfa5e", - "metadata": {}, - "source": [ - "## stats -- between groups" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "12be0205", - "metadata": {}, - "outputs": [], - "source": [ - "pvals = []" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e89ff041", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=2.902089271177604, pvalue=0.0037868828173996723)\n", - "df:1058\n" - ] - } - ], - "source": [ - "first = stats.ttest_ind(long_gap_connection['change_in_connection_3_before'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_3_before'].dropna(), \n", - " equal_var=False)\n", - "print(first)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_3_before'].dropna()))+len(long_gap_connection_friends['change_in_connection_3_before'].dropna())-2))\n", - "pvals.append(first[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "05068f69", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=3.7201160796373545, pvalue=0.00021067494051821523)\n", - "df:1058\n" - ] - } - ], - "source": [ - "second = stats.ttest_ind(long_gap_connection['change_in_connection_2_before'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_2_before'].dropna(), \n", - " equal_var=False)\n", - "print(second)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_2_before'].dropna()))+len(long_gap_connection_friends['change_in_connection_2_before'].dropna())-2))\n", - "pvals.append(second[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "36e7ef16", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=3.2785779759682, pvalue=0.001080779911168681)\n", - "df:1058\n" - ] - } - ], - "source": [ - "third = stats.ttest_ind(long_gap_connection['change_in_connection_1_before'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_1_before'].dropna(), \n", - " equal_var=False)\n", - "print(third)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_1_before'].dropna()))+len(long_gap_connection_friends['change_in_connection_1_before'].dropna())-2))\n", - "pvals.append(third[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "3f61f58e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=-0.24237051551468813, pvalue=0.808580133654728)\n", - "df:1058\n" - ] - } - ], - "source": [ - "fourth = stats.ttest_ind(long_gap_connection['change_in_connection_1_after'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_1_after'].dropna(), \n", - " equal_var=False)\n", - "print(fourth)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_1_after'].dropna()))+len(long_gap_connection_friends['change_in_connection_1_after'].dropna())-2))\n", - "pvals.append(fourth[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "04fd934b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=0.23939954324670873, pvalue=0.8108690332092022)\n", - "df:1058\n" - ] - } - ], - "source": [ - "fifth = stats.ttest_ind(long_gap_connection['change_in_connection_2_after'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_2_after'].dropna(), \n", - " equal_var=False)\n", - "print(fifth)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_2_after'].dropna()))+len(long_gap_connection_friends['change_in_connection_2_after'].dropna())-2))\n", - "pvals.append(fifth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "29fcdd59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=0.6628757851265624, pvalue=0.5076226647425219)\n", - "df:1058\n" - ] - } - ], - "source": [ - "sixth = stats.ttest_ind(long_gap_connection['change_in_connection_3_after'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_3_after'].dropna(), \n", - " equal_var=False)\n", - "print(sixth)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_3_after'].dropna()))+len(long_gap_connection_friends['change_in_connection_3_after'].dropna())-2))\n", - "pvals.append(sixth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "f9462699", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ True, True, True, False, False, False]),\n", - " array([0.00397623, 0.00066363, 0.00170223, 0.42570624, 0.42570624,\n", - " 0.39975285]),\n", - " 0.008512444610847103,\n", - " 0.008333333333333333)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#statsmodels.stats.multitest.multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)\n", - "# adjust p-values for multiple comparisions\n", - "\n", - "multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)" - ] - }, { "cell_type": "markdown", "id": "dd16d249", @@ -1360,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "id": "4e2b15f7", "metadata": {}, "outputs": [], @@ -1369,12 +457,12 @@ "long_gap_connection = long_gap_connection.fillna(0)\n", "counter = 0\n", "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", "\n", "for file in flist:\n", " \n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -1382,8 +470,11 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", " \n", " for i in list(long_gaps.index): \n", " \n", @@ -1391,204 +482,257 @@ " \n", " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", "\n", + " # speaker info \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " speaker_S1 = 'self'\n", + " speaker_S2 = 'other'\n", + " else:\n", + " speaker_S1 = 'other'\n", + " speaker_S2 = 'self'\n", + " \n", " # the long gap\n", " \n", " start_of_gap = data.at[i-1, 'turn_end_msec']\n", " end_of_gap = data.at[i, 'turn_start_msec']\n", - "\n", - " start_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - " end_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_during_long_gap = np.mean(connection_1['Rating'][start_connection:end_connection])\n", - " connection_2_during_long_gap = np.mean(connection_2['Rating'][start_connection:end_connection])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", "\n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", "\n", " counter += 1\n", " \n", " # 1 chunk before long gap\n", "\n", - " start_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - " end_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_1 = np.mean(connection_1['Rating'][start_connection_before_1:end_connection_before_1])\n", - " connection_2_before_long_gap_1 = np.mean(connection_2['Rating'][start_connection_before_1:end_connection_before_1])\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", " # 2 chunks before long gap\n", "\n", - " start_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - " end_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_2 = np.mean(connection_1['Rating'][start_connection_before_2:end_connection_before_2])\n", - " connection_2_before_long_gap_2 = np.mean(connection_2['Rating'][start_connection_before_2:end_connection_before_2])\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", " # 3 chunks before long gap\n", "\n", - " start_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-6000))[0])[-1]) + 1\n", - " end_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_3 = np.mean(connection_1['Rating'][start_connection_before_3:end_connection_before_3])\n", - " connection_2_before_long_gap_3 = np.mean(connection_2['Rating'][start_connection_before_3:end_connection_before_3])\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", "\n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", " # 1 chunk after long gap \n", "\n", - " start_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - " end_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_1 = np.mean(connection_1['Rating'][start_connection_after_1:end_connection_after_1])\n", - " connection_2_after_long_gap_1 = np.mean(connection_2['Rating'][start_connection_after_1:end_connection_after_1])\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", " # 2 chunks after long gap \n", "\n", - " start_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - " end_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_2 = np.mean(connection_1['Rating'][start_connection_after_2:end_connection_after_2])\n", - " connection_2_after_long_gap_2 = np.mean(connection_2['Rating'][start_connection_after_2:end_connection_after_2])\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", " # 3 chunks after long gap \n", "\n", - " start_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - " end_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 6000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_3 = np.mean(connection_1['Rating'][start_connection_after_3:end_connection_after_3])\n", - " connection_2_after_long_gap_3 = np.mean(connection_2['Rating'][start_connection_after_3:end_connection_after_3])\n", - "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " " @@ -1596,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "id": "d14080f8", "metadata": {}, "outputs": [], @@ -1609,8 +753,8 @@ "\n", "for file in flist:\n", " \n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -1618,8 +762,11 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", " \n", " for i in list(long_gaps.index): \n", " \n", @@ -1627,214 +774,265 @@ " \n", " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", "\n", + " # speaker info \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " speaker_S1 = 'self'\n", + " speaker_S2 = 'other'\n", + " else:\n", + " speaker_S1 = 'other'\n", + " speaker_S2 = 'self'\n", + " \n", " # the long gap\n", " \n", " start_of_gap = data.at[i-1, 'turn_end_msec']\n", " end_of_gap = data.at[i, 'turn_start_msec']\n", - "\n", - " start_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - " end_connection = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_during_long_gap = np.mean(connection_1['Rating'][start_connection:end_connection])\n", - " connection_2_during_long_gap = np.mean(connection_2['Rating'][start_connection:end_connection])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", " # 1 chunk before long gap\n", "\n", - " start_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - " end_connection_before_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_1 = np.mean(connection_1['Rating'][start_connection_before_1:end_connection_before_1])\n", - " connection_2_before_long_gap_1 = np.mean(connection_2['Rating'][start_connection_before_1:end_connection_before_1])\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", " # 2 chunks before long gap\n", "\n", - " start_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - " end_connection_before_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-2000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_2 = np.mean(connection_1['Rating'][start_connection_before_2:end_connection_before_2])\n", - " connection_2_before_long_gap_2 = np.mean(connection_2['Rating'][start_connection_before_2:end_connection_before_2])\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", "\n", " # 3 chunks before long gap\n", "\n", - " start_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-6000))[0])[-1]) + 1\n", - " end_connection_before_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (start_of_gap-4000))[0])[-1]) + 1\n", - "\n", - " connection_1_before_long_gap_3 = np.mean(connection_1['Rating'][start_connection_before_3:end_connection_before_3])\n", - " connection_2_before_long_gap_3 = np.mean(connection_2['Rating'][start_connection_before_3:end_connection_before_3])\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", "\n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", "\n", " # 1 chunk after long gap \n", "\n", - " start_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap))[0])[-1]) + 1\n", - " end_connection_after_1 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_1 = np.mean(connection_1['Rating'][start_connection_after_1:end_connection_after_1])\n", - " connection_2_after_long_gap_1 = np.mean(connection_2['Rating'][start_connection_after_1:end_connection_after_1])\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", "\n", " # 2 chunks after long gap \n", "\n", - " start_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 2000))[0])[-1]) + 1\n", - " end_connection_after_2 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_2 = np.mean(connection_1['Rating'][start_connection_after_2:end_connection_after_2])\n", - " connection_2_after_long_gap_2 = np.mean(connection_2['Rating'][start_connection_after_2:end_connection_after_2])\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", "\n", " # 3 chunks after long gap \n", "\n", - " start_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 4000))[0])[-1]) + 1\n", - " end_connection_after_3 = (list(np.where(connection_1[\"adjustedTime\"] * 1000 < (end_of_gap + 6000))[0])[-1]) + 1\n", - "\n", - " connection_1_after_long_gap_3 = np.mean(connection_1['Rating'][start_connection_after_3:end_connection_after_3])\n", - " connection_2_after_long_gap_3 = np.mean(connection_2['Rating'][start_connection_after_3:end_connection_after_3])\n", - "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", @@ -1843,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "id": "9b67de4d", "metadata": {}, "outputs": [], diff --git a/Code/.ipynb_checkpoints/Study 1 - reference connection ratings to a common baseline-checkpoint.ipynb b/Code/.ipynb_checkpoints/Study 1 - reference connection ratings to a common baseline-checkpoint.ipynb new file mode 100644 index 0000000..3944240 --- /dev/null +++ b/Code/.ipynb_checkpoints/Study 1 - reference connection ratings to a common baseline-checkpoint.ipynb @@ -0,0 +1,595 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "243a18ec", + "metadata": {}, + "source": [ + "Reference all change scores to the first time-point (6 seconds before). " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "82926b60", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0f88818e", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection = pd.DataFrame()\n", + "long_gap_connection = long_gap_connection.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index): \n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " # the long gap\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9a11912d", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection_friends = pd.DataFrame()\n", + "long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index): \n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", + "\n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " # the long gap\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7e0fe544", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection['condition'] = 'strangers'\n", + "long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + "long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + "\n", + "long_gaps_all.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_baseline.csv'),\n", + " encoding='utf-8', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/Study 2 - analyze long gap rating task-checkpoint.ipynb b/Code/.ipynb_checkpoints/Study 2 - analyze long gap rating task-checkpoint.ipynb index 17220dc..b8b66b2 100644 --- a/Code/.ipynb_checkpoints/Study 2 - analyze long gap rating task-checkpoint.ipynb +++ b/Code/.ipynb_checkpoints/Study 2 - analyze long gap rating task-checkpoint.ipynb @@ -745,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "eaaa3048", "metadata": {}, "outputs": [ @@ -869,9 +869,18 @@ "5 [0.82, 0.91] " ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.0, the latest is 0.5.2.\n", + "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n", + " return warn(\n" + ] } ], "source": [ @@ -883,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "fd11898d", "metadata": {}, "outputs": [ @@ -1007,7 +1016,7 @@ "5 [0.77, 0.89] " ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1019,7 +1028,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "b5de541e", "metadata": {}, "outputs": [ @@ -1143,7 +1152,7 @@ "5 1.046477e-54 [0.9, 0.95] " ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1155,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "f635c9d8", "metadata": {}, "outputs": [ @@ -1279,7 +1288,7 @@ "5 [0.52, 0.86] " ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1299,7 +1308,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "27b5c490", "metadata": {}, "outputs": [], @@ -1347,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "e5da265a", "metadata": {}, "outputs": [], @@ -1364,7 +1373,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "3b6d57eb", "metadata": {}, "outputs": [ @@ -1749,7 +1758,7 @@ "4 NaN 1.0 NaN " ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1768,7 +1777,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "474437fa", "metadata": {}, "outputs": [], @@ -1778,7 +1787,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "740bcadb", "metadata": {}, "outputs": [], @@ -1827,7 +1836,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "b46128f8", "metadata": {}, "outputs": [], @@ -1845,7 +1854,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "08ba959c", "metadata": {}, "outputs": [], @@ -1854,12 +1863,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "841b5e9b", + "cell_type": "markdown", + "id": "c9c8e797", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Note: Analyses using these dataframes are in the R script \"statistical_tests.R\"" + ] } ], "metadata": { diff --git a/Code/.ipynb_checkpoints/Supplement - exploratory semantic analyses-checkpoint.ipynb b/Code/.ipynb_checkpoints/Supplement - exploratory semantic analyses-checkpoint.ipynb new file mode 100644 index 0000000..53f27ab --- /dev/null +++ b/Code/.ipynb_checkpoints/Supplement - exploratory semantic analyses-checkpoint.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ca44b226", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())\n", + "transcript_dir = '/Users/emma/Dropbox/Manuscripts/clicking/data/turn_taking' # text of transcripts\n", + " # not publicly available\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b8afd65e", + "metadata": {}, + "outputs": [], + "source": [ + "df_strangers = pd.DataFrame()\n", + "df_strangers = df_strangers.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_strangers.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " turn = data.at[i, 'turn_num']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'strangers', '{}.csv'.format(convo)))\n", + " \n", + " text_before = text_file.at[turn - 1, 'text']\n", + " \n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_strangers.at[counter, 'convo'] = convo\n", + " df_strangers.at[counter, 'turn_num'] = turn\n", + " df_strangers.at[counter, 'position'] = 'before'\n", + " df_strangers.at[counter, 'text'] = text_before\n", + " df_strangers.at[counter, 'question'] = question_before\n", + " df_strangers.at[counter, 'word_count'] = len(text_before.split())\n", + " \n", + " counter += 1\n", + " \n", + " text_after = text_file.at[turn, 'text']\n", + " \n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + " \n", + " df_strangers.at[counter, 'convo'] = convo\n", + " df_strangers.at[counter, 'turn_num'] = turn\n", + " df_strangers.at[counter, 'position'] = 'after'\n", + " df_strangers.at[counter, 'text'] = text_after\n", + " df_strangers.at[counter, 'question'] = question_after\n", + " df_strangers.at[counter, 'word_count'] = len(text_after.split())\n", + " \n", + " counter += 1\n", + "\n", + "df_strangers = df_strangers.drop_duplicates(['convo','turn_num', 'position'],keep= 'last').reset_index(drop=True)\n", + "df_strangers['condition'] = 'strangers'\n", + "df_strangers['gap'] = 'long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ebbaeacb", + "metadata": {}, + "outputs": [], + "source": [ + "df_friends = pd.DataFrame()\n", + "df_friends = df_friends.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_friends.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " turn = data.at[i, 'turn_num']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'friends', '{}.csv'.format(convo)))\n", + " \n", + " text_before = text_file.at[turn - 1, 'text']\n", + " \n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_friends.at[counter, 'convo'] = convo\n", + " df_friends.at[counter, 'turn_num'] = turn\n", + " df_friends.at[counter, 'position'] = 'before'\n", + " df_friends.at[counter, 'text'] = text_before\n", + " df_friends.at[counter, 'question'] = question_before\n", + " df_friends.at[counter, 'word_count'] = len(text_before.split())\n", + " \n", + " counter += 1\n", + " \n", + " text_after = text_file.at[turn, 'text']\n", + " \n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + " \n", + " df_friends.at[counter, 'convo'] = convo\n", + " df_friends.at[counter, 'turn_num'] = turn\n", + " df_friends.at[counter, 'position'] = 'after'\n", + " df_friends.at[counter, 'text'] = text_after\n", + " df_friends.at[counter, 'question'] = question_after\n", + " df_friends.at[counter, 'word_count'] = len(text_after.split())\n", + " \n", + " counter += 1\n", + "\n", + "df_friends = df_friends.drop_duplicates(['convo','turn_num', 'position'],keep= 'last').reset_index(drop=True)\n", + "df_friends['condition'] = 'friends'\n", + "df_friends['gap'] = 'long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3d382e20", + "metadata": {}, + "outputs": [], + "source": [ + "df_all = pd.concat([df_strangers, df_friends], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "335ec71a", + "metadata": {}, + "outputs": [], + "source": [ + "df_strangers_comparision = pd.DataFrame()\n", + "df_strangers_comparision = df_strangers_comparision.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_strangers.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'strangers', '{}.csv'.format(convo)))\n", + " \n", + " not_long_gaps = text_file.loc[text_file['turn_latency'] < 2000]\n", + " \n", + " for i in list(not_long_gaps.index):\n", + " \n", + " if i > 1:\n", + " \n", + " text_before = text_file.at[i - 1, 'text']\n", + "\n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_strangers_comparision.at[counter, 'convo'] = convo\n", + " df_strangers_comparision.at[counter, 'turn_num'] = i\n", + " df_strangers_comparision.at[counter, 'position'] = 'before'\n", + " df_strangers_comparision.at[counter, 'text'] = text_before\n", + " df_strangers_comparision.at[counter, 'question'] = question_before\n", + " df_strangers_comparision.at[counter, 'word_count'] = len(text_before.split())\n", + "\n", + " counter += 1\n", + " \n", + " text_after = text_file.at[i, 'text']\n", + "\n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + "\n", + " df_strangers_comparision.at[counter, 'convo'] = convo\n", + " df_strangers_comparision.at[counter, 'turn_num'] = i\n", + " df_strangers_comparision.at[counter, 'position'] = 'after'\n", + " df_strangers_comparision.at[counter, 'text'] = text_after\n", + " df_strangers_comparision.at[counter, 'question'] = question_after\n", + " df_strangers_comparision.at[counter, 'word_count'] = len(text_after.split())\n", + "\n", + " counter += 1\n", + "\n", + "df_strangers_comparision['condition'] = 'strangers'\n", + "df_strangers_comparision['gap'] = 'not_long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "63e25824", + "metadata": {}, + "outputs": [], + "source": [ + "df_friends_comparision = pd.DataFrame()\n", + "df_friends_comparision = df_friends_comparision.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_friends.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'friends', '{}.csv'.format(convo)))\n", + " \n", + " not_long_gaps = text_file.loc[text_file['turn_latency'] < 2000]\n", + " \n", + " for i in list(not_long_gaps.index):\n", + " \n", + " if i > 1:\n", + " \n", + " text_before = text_file.at[i - 1, 'text']\n", + "\n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_friends_comparision.at[counter, 'convo'] = convo\n", + " df_friends_comparision.at[counter, 'turn_num'] = i\n", + " df_friends_comparision.at[counter, 'position'] = 'before'\n", + " df_friends_comparision.at[counter, 'text'] = text_before\n", + " df_friends_comparision.at[counter, 'question'] = question_before\n", + " df_friends_comparision.at[counter, 'word_count'] = len(text_before.split())\n", + "\n", + " counter += 1\n", + " \n", + " text_after = text_file.at[i, 'text']\n", + "\n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + "\n", + " df_friends_comparision.at[counter, 'convo'] = convo\n", + " df_friends_comparision.at[counter, 'turn_num'] = i\n", + " df_friends_comparision.at[counter, 'position'] = 'after'\n", + " df_friends_comparision.at[counter, 'text'] = text_after\n", + " df_friends_comparision.at[counter, 'question'] = question_after\n", + " df_friends_comparision.at[counter, 'word_count'] = len(text_after.split())\n", + "\n", + " counter += 1\n", + "\n", + "df_friends_comparision['condition'] = 'friends'\n", + "df_friends_comparision['gap'] = 'not_long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7141070e", + "metadata": {}, + "outputs": [], + "source": [ + "df_all_comparison = pd.concat([df_strangers_comparision, df_friends_comparision], ignore_index=True)\n", + "df_total = pd.concat([df_all, df_all_comparison], ignore_index=True)\n", + "\n", + "df_total.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'semantic_content_all.csv'),\n", + " encoding='utf-8', index=False)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/Supplement - how do different bin intervals impact Figure 2?-checkpoint.ipynb b/Code/.ipynb_checkpoints/Supplement - how do different bin intervals impact Figure 2?-checkpoint.ipynb new file mode 100644 index 0000000..842df4d --- /dev/null +++ b/Code/.ipynb_checkpoints/Supplement - how do different bin intervals impact Figure 2?-checkpoint.ipynb @@ -0,0 +1,652 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "a5a31a17", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import glob\n", + "\n", + "%matplotlib inline\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())\n", + "\n", + "plt.rcParams['font.sans-serif'] = \"Arial\"\n", + "plt.rcParams[\"font.family\"] = \"sans-serif\"\n", + "sns.set_style(style='ticks')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "123d29f0", + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/anaconda3/envs/python3_8/lib/python3.8/site-packages/PIL/ImageFile.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileno\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 501\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: '_idat' object has no attribute 'fileno'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Figures'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Figure2B_Supplement_bin_intervals'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'{}.png'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 558\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/python3_8/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m 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colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/python3_8/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m 2309\u001b[0m \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2311\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbufsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 515\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 516\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bin_intervals = [500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]\n", + "bin_labels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "titles = ['0.5 seconds', '1 second', '2 seconds', '3 seconds', '4 seconds', \n", + " '5 seconds', '6 seconds', '7 seconds', '8 seconds', '9 seconds', '10 seconds']\n", + "\n", + "for j in range(len(bin_intervals)):\n", + "\n", + " long_gap_connection = pd.DataFrame()\n", + " long_gap_connection = long_gap_connection.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + "\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - (bin_intervals[j] * 3)\n", + " end_connection_before_3 = start_of_gap - (bin_intervals[j] * 2)\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - bin_intervals[j]\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - (bin_intervals[j] * 2) \n", + " end_connection_before_2 = start_of_gap - bin_intervals[j]\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + bin_intervals[j]\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + bin_intervals[j] \n", + " end_connection_after_2 = end_of_gap + (bin_intervals[j] * 2)\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + (bin_intervals[j] * 2)\n", + " end_connection_after_3 = end_of_gap + (bin_intervals[j] * 3)\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + "\n", + " long_gap_connection_friends = pd.DataFrame()\n", + " long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + "\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - (bin_intervals[j] * 3)\n", + " end_connection_before_3 = start_of_gap - (bin_intervals[j] * 2)\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + " \n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - bin_intervals[j]\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - (bin_intervals[j] * 2) \n", + " end_connection_before_2 = start_of_gap - bin_intervals[j]\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + bin_intervals[j]\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + bin_intervals[j] \n", + " end_connection_after_2 = end_of_gap + (bin_intervals[j] * 2)\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + (bin_intervals[j] * 2)\n", + " end_connection_after_3 = end_of_gap + (bin_intervals[j] * 3)\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection['condition'] = 'strangers'\n", + " long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + " long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + "\n", + " plt.figure(figsize=(8,5))\n", + "\n", + " timepoint_labels = ['{} seconds\\nbefore\\n(baseline)'.format(bin_labels[j]*3), \n", + " '{} seconds\\nbefore'.format(bin_labels[j]*2), \n", + " '{} seconds\\nbefore'.format(bin_labels[j]), \n", + " 'long gap',\n", + " '{} seconds\\nafter'.format(bin_labels[j]), \n", + " '{} seconds\\nafter'.format(bin_labels[j]*2), \n", + " '{} seconds\\nafter'.format(bin_labels[j]*3)]\n", + "\n", + " friend_color = '#F46036'\n", + " stranger_color = '#0FA3B1'\n", + "\n", + " ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", + " data=long_gaps_all, dodge=False,\n", + " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", + " palette = [stranger_color,friend_color], markers='o', ci=None)\n", + "\n", + " plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", + " plt.ylabel(\"connection rating\\n(compared to reference)\", fontsize=18, labelpad=10)\n", + " plt.title(\"bin interval = {}\".format(titles[j]), fontsize=18)\n", + "\n", + " ax.tick_params(labelsize=13)\n", + " #ax.set(ylim=(-4.5, 2.5)) # CHANGE LATER\n", + " plt.legend(fontsize='13')\n", + "\n", + " ax.set_xticklabels(timepoint_labels)\n", + "\n", + " ax.axvspan(2.75, 3.25, alpha=0.3, color='silver')\n", + "\n", + " sns.despine()\n", + " plt.tight_layout()\n", + "\n", + " plt.savefig(os.path.join(base_dir, 'Figures', 'Figure2B_Supplement_bin_intervals', '{}.png'.format(titles[j])), dpi=300) \n", + " plt.close()\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/Supplement - how do different thresholds impact Figure 2?-checkpoint.ipynb b/Code/.ipynb_checkpoints/Supplement - how do different thresholds impact Figure 2?-checkpoint.ipynb new file mode 100644 index 0000000..79ade5e --- /dev/null +++ b/Code/.ipynb_checkpoints/Supplement - how do different thresholds impact Figure 2?-checkpoint.ipynb @@ -0,0 +1,738 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "69a28c2c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import glob\n", + "\n", + "%matplotlib inline\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())\n", + "\n", + "plt.rcParams['font.sans-serif'] = \"Arial\"\n", + "plt.rcParams[\"font.family\"] = \"sans-serif\"\n", + "sns.set_style(style='ticks')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "54fc74d1", + "metadata": {}, + "outputs": [], + "source": [ + "lower_bounds = [500, 750, 1000, 1250, 1750, 2000, 2250, 2500, 2750, 3000] \n", + "upper_bounds = [15000, 15000, 15000, 15000, 15000, 15000, 15000, 15000, 15000, 15000] #essentially not having an upper bound\n", + "\n", + "for j in range(len(lower_bounds)):\n", + "\n", + " long_gap_connection = pd.DataFrame()\n", + " long_gap_connection = long_gap_connection.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[(data['gap_length'] > lower_bounds[j]) & (data['gap_length'] < upper_bounds[j])]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " \n", + " \n", + " long_gap_connection_friends = pd.DataFrame()\n", + " long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[(data['gap_length'] > lower_bounds[j]) & (data['gap_length'] < upper_bounds[j])]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", + "\n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + " \n", + " long_gap_connection['condition'] = 'strangers'\n", + " long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + " long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + " \n", + " long_gaps_all.to_csv(os.path.join(base_dir, 'Analyses', 'different_long_gap_cutoffs',\n", + " 'long_gap_connection_all_{}.csv'.format(lower_bounds[j])),\n", + " encoding='utf-8', index=False)\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "1fe6e73e", + "metadata": {}, + "source": [ + "# Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "71e095e9", + "metadata": {}, + "outputs": [], + "source": [ + "total_turns_strangers = 0\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "for file in flist:\n", + " data = pd.read_csv(file)\n", + " total_turns_strangers += len(data)\n", + " \n", + "total_turns_friends = 0\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "for file in flist:\n", + " data = pd.read_csv(file)\n", + " total_turns_friends += len(data)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bb5d9ce5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "threshold = 1000\n", + "strangers n = 2252.0\n", + "strangers percent = 0.05822280824219861\n", + "friends n = 1078.0\n", + "friends percent = 0.11271434546214973\n", + "____\n", + "threshold = 2250\n", + "strangers n = 170.0\n", + "strangers percent = 0.004395149822901316\n", + "friends n = 136.0\n", + "friends percent = 0.014219991635299039\n", + "____\n", + "threshold = 3000\n", + "strangers n = 64.0\n", + "strangers percent = 0.0016546446392099073\n", + "friends n = 63.0\n", + "friends percent = 0.006587202007528231\n", + "____\n", + "threshold = 500\n", + "strangers n = 8216.0\n", + "strangers percent = 0.21241500555857185\n", + "friends n = 2717.0\n", + "friends percent = 0.284086156419908\n", + "____\n", + "threshold = 2750\n", + "strangers n = 81.0\n", + "strangers percent = 0.002094159621500039\n", + "friends n = 74.0\n", + "friends percent = 0.007737348389795065\n", + "____\n", + "threshold = 2000\n", + "strangers n = 262.0\n", + "strangers percent = 0.006773701491765557\n", + "friends n = 202.0\n", + "friends percent = 0.02112086992890004\n", + "____\n", + "threshold = 1250\n", + "strangers n = 1274.0\n", + "strangers percent = 0.032937769849272214\n", + "friends n = 706.0\n", + "friends percent = 0.0738184859891259\n", + "____\n", + "threshold = 2500\n", + "strangers n = 115.0\n", + "strangers percent = 0.002973189586080302\n", + "friends n = 96.0\n", + "friends percent = 0.010037641154328732\n", + "____\n", + "threshold = 1750\n", + "strangers n = 437.0\n", + "strangers percent = 0.011298120427105148\n", + "friends n = 323.0\n", + "friends percent = 0.033772480133835214\n", + "____\n", + "threshold = 750\n", + "strangers n = 4075.0\n", + "strangers percent = 0.10535432663719331\n", + "friends n = 1660.0\n", + "friends percent = 0.17356754496026766\n", + "____\n" + ] + } + ], + "source": [ + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'different_long_gap_cutoffs', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " long_gaps_all = pd.read_csv(file)\n", + " cutoff = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " strangers = long_gaps_all.loc[long_gaps_all['condition'] == 'strangers'].reset_index(drop=True)\n", + " friends = long_gaps_all.loc[long_gaps_all['condition'] == 'friends'].reset_index(drop=True)\n", + " \n", + " print('threshold = {}'.format(cutoff))\n", + " print('strangers n = {}'.format(len(strangers) / 14))\n", + " print('strangers percent = {}'.format((len(strangers) / 14) / total_turns_strangers))\n", + " print('friends n = {}'.format(len(friends) / 14))\n", + " print('friends percent = {}'.format((len(friends) / 14) / total_turns_friends))\n", + " print('____')\n", + " \n", + " friend_color = '#F46036'\n", + " stranger_color = '#0FA3B1'\n", + " \n", + " #BOTH\n", + " plt.figure(figsize=(8,5))\n", + "\n", + " timepoint_labels = ['6 seconds\\nbefore\\n(baseline)', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap',\n", + " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", + " \n", + " ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", + " data=long_gaps_all, dodge=False,\n", + " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", + " palette = [stranger_color,friend_color], markers='o', ci=None)\n", + "\n", + " plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", + " plt.ylabel(\"connection rating\", fontsize=18, labelpad=10)\n", + " plt.title(\"long gaps > {}ms\".format(cutoff), fontsize=18)\n", + "\n", + " ax.tick_params(labelsize=13)\n", + " ax.set(ylim=(-3, 1.5)) #SET COMMON SCALE AFTER INSPECTING BOUNDS\n", + "\n", + " ax.set_xticklabels(timepoint_labels)\n", + "\n", + " ax.axvspan(2.75, 3.25, alpha=0.3, color='silver')\n", + "\n", + " sns.despine()\n", + " plt.tight_layout()\n", + "\n", + " plt.savefig(os.path.join(base_dir, 'Figures', 'Figure2B_Supplement', '{}.pdf'.format(cutoff)), dpi=300) \n", + " plt.close()\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/Supplement - how often are there speaker switches (vs speaker stays)?-checkpoint.ipynb b/Code/.ipynb_checkpoints/Supplement - how often are there speaker switches (vs speaker stays)?-checkpoint.ipynb new file mode 100644 index 0000000..6df3b97 --- /dev/null +++ b/Code/.ipynb_checkpoints/Supplement - how often are there speaker switches (vs speaker stays)?-checkpoint.ipynb @@ -0,0 +1,533 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5ce92f96", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "410c7aaf", + "metadata": {}, + "outputs": [], + "source": [ + "def find_video(file):\n", + " \"\"\"Need video name to use key in assign_idenity function.\n", + "\n", + " \"\"\"\n", + " id1 = file.split('/')[-1].split('_')[0]\n", + " id2 = file.split('_')[-1].split('.csv')[0]\n", + " video_name_1 = id1 + '_' + id2\n", + " video_name_2 = id2 + '_' + id1\n", + " return video_name_1, video_name_2\n", + "\n", + "\n", + "def assign_identity(file):\n", + " \"\"\"Use key to determine which subject ID corresponds to S1 (speaker 1)\n", + " and which subject ID corresponds to S2 (speaker 2).\n", + "\n", + " \"\"\"\n", + " key = pd.read_csv(os.path.join(base_dir, 'Data', 'subject_id_key.csv'))\n", + " video_name_1, video_name_2 = find_video(file)\n", + " for i in range(len((key))):\n", + " if (key.at[i,'video_name'] == video_name_1) | (key.at[i,'video_name'] == video_name_2):\n", + " subID_S1 = key.at[i,'S1']\n", + " subID_S2 = key.at[i,'S2']\n", + " return subID_S1, subID_S2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "40fe22bc", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection = pd.DataFrame()\n", + "long_gap_connection = long_gap_connection.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index):\n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", + " \n", + " # check if there was a speaker switch\n", + " speaker_current = data.at[i, 'speaker']\n", + " speaker_previous = data.at[i-1, 'speaker']\n", + " \n", + " if speaker_current == speaker_previous:\n", + " speaker_switch = 'no'\n", + " else:\n", + " speaker_switch = 'yes'\n", + " \n", + " # the long gap\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", + " long_gap_connection.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_1_before'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_long_gap'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_1_after'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", + " \n", + " long_gap_connection.at[counter, 'speaker_switch'] = speaker_switch\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", + " long_gap_connection.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_1_before'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_long_gap'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_1_after'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", + "\n", + " long_gap_connection.at[counter, 'speaker_switch'] = speaker_switch\n", + " \n", + " counter += 1\n", + " \n", + " \n", + " \n", + "long_gap_connection['change_in_connection_3_before'] = long_gap_connection['connection_3_before'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_2_before'] = long_gap_connection['connection_2_before'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_1_before'] = long_gap_connection['connection_1_before'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_1_after'] = long_gap_connection['connection_1_after'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_2_after'] = long_gap_connection['connection_2_after'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_3_after'] = long_gap_connection['connection_3_after'] - long_gap_connection['connection_long_gap']\n", + "\n", + "long_gap_connection.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_strangers_switches.csv'),\n", + " encoding='utf-8', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6a49e47b", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection_friends = pd.DataFrame()\n", + "long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index):\n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", + "\n", + " # check if there was a speaker switch\n", + " speaker_current = data.at[i, 'speaker']\n", + " speaker_previous = data.at[i-1, 'speaker']\n", + " \n", + " if speaker_current == speaker_previous:\n", + " speaker_switch = 'no'\n", + " else:\n", + " speaker_switch = 'yes'\n", + " \n", + " # the long gap\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", + " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_1_before'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_long_gap'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_1_after'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", + " \n", + " long_gap_connection_friends.at[counter, 'speaker_switch'] = speaker_switch\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", + " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_1_before'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_long_gap'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_1_after'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", + " \n", + " long_gap_connection_friends.at[counter, 'speaker_switch'] = speaker_switch\n", + "\n", + " counter += 1\n", + " \n", + " \n", + "long_gap_connection_friends['change_in_connection_3_before'] = long_gap_connection_friends['connection_3_before'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_2_before'] = long_gap_connection_friends['connection_2_before'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_1_before'] = long_gap_connection_friends['connection_1_before'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_1_after'] = long_gap_connection_friends['connection_1_after'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_2_after'] = long_gap_connection_friends['connection_2_after'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_3_after'] = long_gap_connection_friends['connection_3_after'] - long_gap_connection_friends['connection_long_gap']\n", + "\n", + "long_gap_connection_friends.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_friends_switches.csv'),\n", + " encoding='utf-8', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1b05a45c", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection['condition'] = 'strangers'\n", + "long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + "long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + "\n", + "long_gaps_all.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_switches.csv'),\n", + " encoding='utf-8', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "79964cf2", + "metadata": {}, + "source": [ + "# Get frequencies (overall and for each conversation type)\n", + "\n", + "Basically, this is super unlikely. Should we go ahead and limit our analyses to only speaker switches? Or just state that they are rare?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5127fc84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "248.0\n", + "14.0\n", + "0.05343511450381679\n" + ] + } + ], + "source": [ + "strangers_switch = long_gap_connection.loc[long_gap_connection['speaker_switch'] == 'yes'].reset_index(drop=True)\n", + "strangers_no_switch = long_gap_connection.loc[long_gap_connection['speaker_switch'] == 'no'].reset_index(drop=True)\n", + "\n", + "print(len(strangers_switch)/2)\n", + "print(len(strangers_no_switch)/2)\n", + "print((len(strangers_no_switch)/2) / (len(long_gap_connection)/2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "200102de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "183.0\n", + "19.0\n", + "0.09405940594059406\n" + ] + } + ], + "source": [ + "friends_switch = long_gap_connection_friends.loc[long_gap_connection_friends['speaker_switch'] == 'yes'].reset_index(drop=True)\n", + "friends_no_switch = long_gap_connection_friends.loc[long_gap_connection_friends['speaker_switch'] == 'no'].reset_index(drop=True)\n", + "\n", + "print(len(friends_switch)/2)\n", + "print(len(friends_no_switch)/2)\n", + "print((len(friends_no_switch)/2) / (len(long_gap_connection_friends)/2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23e69a33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07112068965517242" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(14+19) / (262+202)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/Supplement - investigate laughter in Study 1-checkpoint.ipynb b/Code/.ipynb_checkpoints/Supplement - investigate laughter in Study 1-checkpoint.ipynb new file mode 100644 index 0000000..7ef3f25 --- /dev/null +++ b/Code/.ipynb_checkpoints/Supplement - investigate laughter in Study 1-checkpoint.ipynb @@ -0,0 +1,232 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cbe56972", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "21bcc7eb", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(os.path.join(base_dir, 'Supplement', 'laughter_annotation.csv'))\n", + "strangers = data.loc[data['condition'] == 'strangers'].reset_index(drop=True)\n", + "friends = data.loc[data['condition'] == 'friends'].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1cd0a286", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "no 308\n", + "yes 156\n", + "Name: laughter, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['laughter'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5114cb37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "no 188\n", + "yes 74\n", + "Name: laughter, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strangers['laughter'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "24cbb1e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2824427480916031" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "74/len(strangers)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3ba64b3c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "no 120\n", + "yes 82\n", + "Name: laughter, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "friends['laughter'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a41708f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.40594059405940597" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "82/len(friends)" + ] + }, + { + "cell_type": "markdown", + "id": "4c867e8f", + "metadata": {}, + "source": [ + "Include in main dataframe to examine if laughter mediates relationship between condition and change in connection" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2a4563fa", + "metadata": {}, + "outputs": [], + "source": [ + "connection_changes = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format.csv'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "46a65ecb", + "metadata": {}, + "outputs": [], + "source": [ + "data_full = pd.merge(data, connection_changes, how='inner', on=['dyad', 'turn_num'])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4f300181", + "metadata": {}, + "outputs": [], + "source": [ + "data_full.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_laughter.csv'),\n", + " encoding='utf-8', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a6b81795", + "metadata": {}, + "outputs": [], + "source": [ + "strangers_full = data_full.loc[data_full['condition_x'] == 'strangers'].reset_index(drop=True)\n", + "friends_full = data_full.loc[data_full['condition_x'] == 'friends'].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "70ec7b93", + "metadata": {}, + "outputs": [], + "source": [ + "strangers_full.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_laughter_strangers.csv'),\n", + " encoding='utf-8', index=False)\n", + "\n", + "friends_full.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_laughter_friends.csv'),\n", + " encoding='utf-8', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/inclusion criteria for stranger dataset-checkpoint.ipynb b/Code/.ipynb_checkpoints/inclusion criteria for stranger dataset-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/Code/.ipynb_checkpoints/inclusion criteria for stranger dataset-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/.ipynb_checkpoints/plot figures-checkpoint.ipynb b/Code/.ipynb_checkpoints/plot figures-checkpoint.ipynb index 4c2b584..81df716 100644 --- a/Code/.ipynb_checkpoints/plot figures-checkpoint.ipynb +++ b/Code/.ipynb_checkpoints/plot figures-checkpoint.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "51f76132", + "id": "2b9eba0d", "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "markdown", - "id": "1cb85987", + "id": "a7065403", "metadata": {}, "source": [ "# Study 1\n", @@ -36,33 +36,37 @@ }, { "cell_type": "markdown", - "id": "9543a31d", + "id": "75b2c118", "metadata": {}, "source": [ - "## Figure 1A\n", + "## Figure 1\n", "\n", - "Distribution of gap lengths for stranger and friend conversations" + "Count differences in long gaps between friends and strangers\n", + "\n", + "For code that plots distributions of gap lengths for each conversation, see the notebook \"Study 1 - do friends use more long gaps?\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0fce672f", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_counts.csv'))" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "3cf17b21", + "execution_count": 4, + "id": "33b07174", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, { "data": { - "image/png": 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//v0mV6B9+/ZYuXIlYmJioFar4e/vj9jYWKSnp2PBggXYvn17o2WAmgETWVlZiIiIgFqtRlRUFAYPHgwAWL16NRYuXIh169ZBoVBgzZo1kMvl+Mtf/oLs7GxERkZCoVDgnnvuwdtvv93MQ0d3GlO6+PRnkuCFukSWJRP6fWBNGDJkCMrLyzF8+HBERkbikUcegaOjYb7t2bMHy5YtQ1pamkUqa2uRkZFISEiwdTXIQv6W9jO+UmUDAFY82AuuTg1bUQPbe2LYrlQAgLvCCReefNSqdSRqTUxuQb344osYN24cvLy8Gi0TGBiIkJAQs1SMyNoMu/hMGCTBUXxEFmXyOagvvvjCYGRdnfz8fAwdOhQAoFAojI62I7odGEx15Gj8HJSTXAbH2s+4RghU64UaEZlXky2o1NRUpKenAwCys7Oxfv16g9F1QM10Qpa4gJfI2gyug5LLUa1rGD4ymQxtnBxxrVoNoGYkn8JBYbU6ErUmTQZUp06dsGLFCmmo9t69e+Gg18Uhk8nQpk0bLFiwwLK1JLKCynotqJJGBkG0cXSQAqpUo4GnMwOKyBKaDKguXbpg7969AIDJkyfj3//+N9zd3a1SMSJrM+V2GwDQxmAkH3sPiCzF5EESX3zxhSXrQWRz5SbMxQfUtKDqcKAEkeU0GVC9e/fGgQMH4OXlhV69ejU5AOLEiRNmrxyRNZnagtK/FqqULSgii2kyoJYuXSpN2Lp06VKO0KM7WqUJNywEADdH3lWXyBqaDKjHH39cehwZGWnxyhDZkuFs5k108Tnd+NpwuiMiyzH5Oqjq6mp8+OGHuHDhAgDgrbfewoABA/DMM8/g6tWrFqsgkTWodTpoakerOshkcDJyzV8d3nKDyDpMDqh33nkHmzZtQnl5OVJSUvDVV1/hpZdeglqtxsqVKy1ZRyKL0z//5NrIRbpATZDpTw52sawcF0vLUVRVbcnqEbVKJgfUnj17sHr1avTo0QPffvsthgwZgueffx4LFizAwYMHLVlHIosz5VYbQM1Iv4LKKul5elEJ9uXko5QtKSKzMzmgrl+/jnvvvRdAzc0Lhw0bBgBwc3NDdTX/eqTbm6kj+ADgLr0WVgVnUSGyGJOvg7r//vtx8OBBeHt7Izc3FyNGjAAAxMfH44EHHrBYBYmsQf8aqKa6+ADDARQMKCLLMTmgZs2ahZkzZ0Kj0WDs2LHo0qUL3n77bWzatAkffvihJetIZHGVGtO6+Oovr+QoPiKLMTmgHnnkERw4cAC5ubno0aMHAOCxxx5DdHS01PVHdLvSb0HddZMWlGEXH2czJ7IUkwMKADw9PQHU3GJDCIG7774bAJCXlwcfHx/z147ISioMWlBNn5rVP0dVyS4+IosxOaB+/vlnzJ8/H1lZWQavCyEgk8lw8uRJs1eOyFoqmtOC0guoCnbxEVmMyQH1zjvvwMPDA3PnzkW7du0sWSciqzO4Duqmo/g4SILIGkwOqDNnzmDz5s3o3r27JetDZBOm3E1XWm7QxaeT7pdGROZl8nVQSqUS5eXllqwLkc2UGcwk0fTfbU5yuXTbd60QUOsYUESWYHILavbs2Vi2bBn+8Y9/4N5774VCYXgXUQ6SoNuZ/kSxbW7SggJqWlGltTOZs5uPyDJMDqh//OMfUKvVeP755w1uu8FBEnQn0L9txs0u1AVqzkOV1r6FI/mILMPkgNqwYYMl60FkU83p4gM4ko/IGkwOqMGDB0uPNRoNHE34EpsqJSUF7777Lqqrq9GtWzesWLFCulHizcpotVq8/fbbOHjwILRaLZ599llER0cDAFQqFd544w0UFRXB1dUVsbGx6NKlCwDg6NGj+Oc//4nKykq0bdsWb7/9Nvz8/My2T3R7Mejiu8koPsBwoAQv1iWyDJMHSQDAtm3bEBoaiv79++PixYtYvHgxPvjgg1uqQGFhIebNm4e1a9di79698PPzw6pVq0wus2XLFqhUKuzYsQPx8fHYuHEjjh8/DgCYM2cOJk6ciF27dmHmzJmYNWsWhBDIzc3Fyy+/jMWLFyMxMRFjxozBkiVLbmk/6PbW/C4+ThhLZGkmB9S2bduwYsUKjB8/Hg61fz12794dH3/8MT7++OMWVyAtLQ19+vRB586dAQDR0dFISkoyGLrbVJnk5GRERkbC0dER7u7uCAsLQ2JiIvLy8nDu3DmEhYUBAEaOHIny8nJkZGRgz549GD58OHr16gUAmDhxIubPn9/ifaDbX/O7+G58dXgOisgyTA6oTz75BAsXLsQLL7wAee3dRqOjo7F06VJ89dVXLa5Abm4ulEql9FypVKK0tBRlZWUmlcnJyYGvr6/BstzcXOTk5MDb21uqK1Az0jA3NxcqlQqurq545ZVXMH78ePz9739vMCqxTlxcHCIjIxEZGYmioqIW7yfZt5aM4qvDCWOJLMPkgLpw4QL69+/f4PX+/fsjLy+vxRXQ6XQGowKliukFS1Nl6kYR1hFCQC6XG32PEAIODg7QaDTYt28fZs2ahW3btmHo0KF4+eWXjdYvKioKCQkJSEhIkOYipDvPrXXx8RwUkSWYHFC+vr44depUg9e///57gxZMc/n6+iI/P196npeXB3d3d7i6uppUpv6y/Px8KJVKdOzYEVeuXDHoKqxb5u3tjQcffFDqMpwwYQJOnTqFysrKFu8H3d7Km9nFZzhIgi0oIkswOaCeffZZLFmyBP/9738hhMCRI0ewZs0arFixAk8//XSLKzBs2DAcO3YMKpUKQM2gh6CgIJPLBAUFYevWrdBoNCgpKcHOnTsRHBwMpVIJf39/7Nq1CwBw8OBByOVyBAQEYPTo0fjll19w8eJFAMA333yDrl27wsXFpcX7Qbe3Ur0WlCldfAbnoNjFR2QRJo8Vf/LJJ6HRaPDRRx+hsrISb7zxBnx8fPD6669j4sSJLa5A+/btsXLlSsTExECtVsPf3x+xsbFIT0/HggULsH379kbLADXnwbKyshAREQG1Wo2oqChpSPzq1auxcOFCrFu3DgqFAmvWrIFcLkePHj2wePFivPzyy9BoNGjXrh3WrFnT4n2g259hC8oBVTfptruLLSgii5OJJma6PHr0qNHXCwsL4eTkhLZt20qvDRo0yPy1szORkZFISEiwdTXIAnw375SCJnviWBRWVmNfTn6DcgM7eOKngiL8UVKKD06eAwDc37YNtgcNhZ+ba4PyRNRyTbagJk+eDJlMJp3HqRt0UH9gAgBOdUS3LZ0QhveDMuFC3bt400Iii2syoFJTU6XHBw4cwMcff4w33ngD/fv3h5OTE9LT07F8+XL89a9/tXhFiSylvN69oORGRozWx6mOiCyvyUESPj4+0r/169dj2bJlGDlypDSCbsiQIViyZAnef/99K1WXyPyaO8QcAFx400IiizN5FF9BQQE8PDwavK5QKFBaWmrOOhFZVXOHmAM1Lai6dlalVgeNjtdCEZmbyQE1aNAgLF++3OCi3KysLCxduhTDhw+3SOWIrKGsmbNIAIBcJjO4WPeaWtNEaSJqCZOHmS9ZsgTPPfccRo0aBU9PTwghUFRUhF69emHRokWWrCORRbWkiw8A3BwdpdZXUVW12etF1NqZHFAdO3ZEUlISDh06hLNnz0Imk6FHjx4YMmSIwbRERLeblnTxAYatraJqtVnrRETNCCgAcHR0xMiRIzFy5EhL1YfIqoqqqnGhtFx6LgdwsbTcpIEPbk43vj7FbEERmR2bPtSqlao1OFpQKD0vUauxLycf1SYMemij19piC4rI/BhQ1Orph5HCwfSvhJuTfhcfW1BE5saAolavWndjti9FM86nGrSgqtiCIjI3BhS1etV6E8O2OKDYxUdkdgwoavWqdDcGRDi3tIuPgySIzI4BRa1eS1tQbnotqGK2oIjMjgFFrZ7+IInmtKDa6A0zL+QgCSKzY0BRq1ela2kL6kYXX3GVGk3cWo2IWoABRa1eS7v4FHI5nGpvzVGl06Gcs5oTmRUDilo9g2Hmzejik8lkBt18BZXs5iMyJwYUtXotbUEBhvPxFXIkH5FZMaCo1WvpIAnAcCRfQWWV2epERAwoIsOpjprbgtLr4rvKFhSRWTGgqNWruoUuPv2RfDwHRWReDChq9ar1ZpJoziAJwLAFVVDFLj4ic2JAUatWrdVJo/jkMsC5mS2odnoBlVtRada6EbV2dhFQKSkpCA8PR0hICGJiYlBaWmpyGa1Wi+XLlyM0NBSjR4/G5s2bpfeoVCpMmjQJY8eOxYQJE5CZmdlgvZ999hkee+wxy+0c2bVr6htTFLk6OEJWe12TqdwVTtLjnHIGFJE52TygCgsLMW/ePKxduxZ79+6Fn58fVq1aZXKZLVu2QKVSYceOHYiPj8fGjRtx/PhxAMCcOXMwceJE7Nq1CzNnzsSsWbMMrvb/+eefsWHDBuvtLNkd/Tn09IeMm8qDAUVkMTYPqLS0NPTp0wedO3cGAERHRyMpKckgSJoqk5ycjMjISDg6OsLd3R1hYWFITExEXl4ezp07h7CwMADAyJEjUV5ejoyMDABAQUEBli5dildffdWq+0v2xSCgnJofUO2c9AKKXXxEZuV48yKWlZubC6VSKT1XKpUoLS1FWVkZ3NzcblomJycHvr6+BstOnz6NnJwceHt7Q653TsHHxwe5ubno3r07Zs+ejblz58LRselDEBcXh7i4OABAUVGRWfaZ7Mc1vYByvclnwZg2jg5wlMmgEQLX1RpcV2vQ1snmXyuiO4LNW1A6nc5ov79+sDRVRghhsEwIAblcbvQ9Qgg4ODjg3XffxaBBg/Dwww/ftH5RUVFISEhAQkICPD09m7NrdBu41S4+mUxmcB4ql918RGZj8z/1fH19cezYMel5Xl4e3N3d4erqalIZX19f5OfnS8vy8/OhVCrRsWNHXLlyxSDA6pYlJibCy8sL3377LcrLy5GXl4eIiAhs377dCntM9qTYoAXV/IACakby1V2ke7miAl3d3cxSN6LWzuYtqGHDhuHYsWNQqVQAagY9BAUFmVwmKCgIW7duhUajQUlJCXbu3Ing4GAolUr4+/tj165dAICDBw9CLpcjICAAaWlpSExMxPbt27Fs2TL4+/sznFqpawYtqJb9vcYWFJFl2LwF1b59e6xcuRIxMTFQq9Xw9/dHbGws0tPTsWDBAmzfvr3RMkDNgImsrCxERERArVYjKioKgwcPBgCsXr0aCxcuxLp166BQKLBmzRqDrkOia7fYxQfUG2rOgRJEZiMTvMuaySIjI5GQkGDrapAZPZ78Pb7LvQIA+GvXe9HPyx0AMLCDJ34qaDgoxtjr+3OuIDErBwDwt2734Z1BfSxca6LWgc0JatX0L9RtcQuKs0kQWQQDilq1YjOcg+LFukSWwYCiVs0so/gYUEQWwYCiVksIgRIzD5LIraiEjqd1icyCAUWtVolaA01tmCjkcji2cISnQi6XZjXXCIH8Ct52g8gcGFDUahXp3QG3pa2nOr53uUiPL5VX3NK6iKgGA4parSIzdO/V8XXVC6gyBhSROTCgqNUq1GtBtWSiWH36LaiLZeW3tC4iqsGAolar0IxdfB1d75IeX2QLisgsGFDUapmzBaV01W9BMaCIzIEBRa3WVXO2oNjFR2R2DChqtbL0gsTD2amJkjfXkYMkiMyOAUWt1oXSGwHV3llxS+vyclZAUXsdVXG1GtfVmltaHxExoKgVM2dAyWUydGpzY6DEJXbzEd0yBhS1SpVarTRvngyAp+LWAgqAQUBxoATRrWNAUat0sbQCdTPmeSic4CCX3fI6/QxaUAwoolvFgKJW6UKZ+br3AECt08Hd6cZAi9+LSnCxtNxgOiUiah4GFLVKF0rLpMdeLrceUOVaLYqqb4TRz1eLsC8nH6UcLEHUYgwoapVU12+0oDqYoQUFAN4uztLjPM5oTnTLGFDUKul38XmZKaB89C7WvVJZxftCEd0iBhS1SvotKHOcgwJq7sirf1+oQp5/IrolDChqlcw9SKKOz13s5iMyFwYUtTp5FZW4VnsvKBcHOdo63dpEsfp8XG508+VVVJptvUStEQOKWp3v8wulxz092kEmu/VroOp467WgcivZgiK6FXYRUCkpKQgPD0dISAhiYmJQWlpqchmtVovly5cjNDQUo0ePxubNm6X3qFQqTJo0CWPHjsWECROQmZkpLfvkk08QFhaGcePG4ZlnnkFWVpbld5TswqH8q9Ljge09zbpudvERmY/NA6qwsBDz5s3D2rVrsXfvXvj5+WHVqlUml9myZQtUKhV27NiB+Ph4bNy4EcePHwcAzJkzBxMnTsSuXbswc+ZMzJo1C0IIHD58GPHx8YiLi0NiYiJGjx6NefPmWX3fyTYO5RVIj//UwcOs69YfyZdXUQnBkXxELWbzgEpLS0OfPn3QuXNnAEB0dDSSkpIMvthNlUlOTkZkZCQcHR3h7u6OsLAwJCYmIi8vD+fOnUNYWBgAYOTIkSgvL0dGRgY6dOiAJUuWwM3NDQDQp08fXL582ar7TbZRVFWNjOLrAAAHmQz9vdzNun53J0c4185qXqnVoYAj+YhazOYBlZubC6VSKT1XKpUoLS1FWVmZSWVycnLg6+trsCw3Nxc5OTnw9vaGXH5jF318fJCbm4uAgAAMHjwYAFBdXY1Vq1YhNDTUkrtJdkL//NOA9h63fCfd+mQymcG9odKLrpl1/UStiXm/nS2g0+mMnqTWD5amygghDJYJISCXy42+RwgBB4cbd04tLCxETEwM3Nzc8MorrxitX1xcHOLi4gAARUVFzds5sjvf5V6RHv/Z28si27ivbRucr72Vx69Xr2FqV4tshuiOZ/MWlK+vL/Lz86XneXl5cHd3h6urq0ll6i/Lz8+HUqlEx44dceXKFYOuwrplAHDq1ClMmDABPXv2xAcffABFI7dbiIqKQkJCAhISEuDpad4T6mRdFRotvjp/SXoe6Ottke3c3/bGZ/eXq8UW2QZRa2DzgBo2bBiOHTsGlUoFoGbQQ1BQkMllgoKCsHXrVmg0GpSUlGDnzp0IDg6GUqmEv78/du3aBQA4ePAg5HI5AgICkJubi6lTp2LGjBmYP3++QauK7lxfX8iWrn/q7OaKEcoOFtlOZ7c20uOM4hKUazhhLFFL2LyLr3379li5ciViYmKgVqvh7++P2NhYpKenY8GCBdi+fXujZYCaARNZWVmIiIiAWq1GVFSUdH5p9erVWLhwIdatWweFQoE1a9ZALpfjww8/REVFBb744gt88cUXAACFQoH//e9/NjsOZFlFVdX4v1PnpOeP39sR2WUVqNBqzb4tNydH+Lg4I6+yChoh8MvVYgzzsUwYEt3JZILjYE0WGRmJhIQEW1eDWmCbKhvPpP0MoGb03psDesDNyREDO3jip4KG5xZv9fW4c5fw/ZWaARkL+nXHnD4B5toVolbD5l18RJYmhMC/Tt64SPtP7T3gZsbpjYy5r+2Nbr4UvYEZRGQ6BhTd8ZIv50uDFRxkMoTc42PxbXZzd0PdGNLD+VeRz3n5iJqNAUV3NI1Oh8W/ZkjPh3p7ob0Z7qB7M+4KJ9xf24rSCSAxK8fi2yS60zCg6I72+dksaeYIhVyO0R0tM7TcGP1ZKr6+wJlKiJqLAUV3rGvVaiw/dkp6HtzxbrgrnKy2/X5e7tIX7HD+VVwur7DatonuBAwoumP9+2QmrtbOhdfxLhc84nu3VbffTuGEQR1qLu4WAD7744JVt090u2NA0R2poLIK6/RG7r3cswsUcut/3Cfc10l6/OkfKlRa4LorojsVA4ruSKtP/IFSTU0YdHdvi7GdlDd5h2UE+d6Ne2onj71SWY2tqmyb1IPodsSAojvO70UlWH/6vPR8fr9ucDDjXXOb64nON1pRazPOIut6GYp4Gw6im2JA0R3lamUVXv7+V2hqJ0h5sL0H+nu6W2RKI1OUa7W4+y4FnOQ1AXnqWinWnzmPUjXn5yO6GQYU3VH+lXEWvxbW3INJLgPGdPTG/twrqNbpbFanNo6O0mAJADiQW9BEaSKqw4CiO8b+y/n4V8aNgRFBvt5Q6t080JZG6E0Wm15Ugqza+0URUeMYUHRHyC2vxLRDv6Bu5uMubdsg1ApTGplK6eqCbu5uAGqGnMemnwHnaSZqGgOKbns6ITD98C/SNU/tnBzxTFd/OMhtNzDCmEc7KaX5+Q7kFSDpIqc/ImoKA4pue+//fhapted1ZAAmd/FHWyfrzRhhqs5urhiqd5v5l77/DYfzr9qwRkT2jQFFt7WjVwoNpjOaFnAfutZ2pdmjx/yUaFd7q4/rag3+su8HfJeTb+NaEdknBhTdtlSlZZh68Cdoa8/lDO7giRe732fjWjXN1dERL3a/Hx2ca2ZUr9BqEfXdEey+lGvjmhHZHwYU3ZaySssxPvl7XC6vuc9SOydHfDzsT3C0wXRGzeXr6oINDz8I5V3OAIBqnQ5TD/yEpKzLvICXSI/9f5uJ6vkh/ypG7T4AVe1QbYVcjncH94UcsNkFuc3V4S5n/K3bfVJLqlqnw4uHf8XJ4hIb14zIfjCg6LayKTML4cmHpRF7DjIZpj7gjzKNBvty8m16QW5zeTkr8EL3+9DG0QEAUKrRYtqhX3CxjNdIEQEMKLpNZJWWY+qBo3jp+9+g1tWcc2rj6ICXetyPXp7tbFy7luvg4oznAjrDsXauwOzySoTuTePoPiIwoMjOVWq1iD1+GkOSvsN2vdumd23nhn/07irdVv12dn/bNng24F5pQtvs8ko89u0hrDh2CprbqEVIZG4MKLJbZ65dx6hdB7Dy+GmDc0vj/Hzx8cMD0L72/M2doKdHOzwXcK80BF0ngHfSz2DErlT858x5nCq+Dh1nnqBWxtHWFSACgMKqaqQXXsOVyipcrarG8aJriDt3SZqVHAA6ubogsvM9uL9tm9titF5z9fRohy2PDMaiXzLw09ViAEBG8XXMPpIOAFDe5Yyo+/zwVBc/dHNva8OaElkHA4qsrlqrQ0ZxCX69WoyfrxbjyJVCnCkpbbS8o0yGcH9fDPdpD7kN7+tkDe0UTniqix987nLBN9l5UOsFdG5FFdZknMWajLPo2s5N+vdAOzc80K4NurZzQ3tnBWR3+DGi1sMuAiolJQXvvvsuqqur0a1bN6xYsQJubm4mldFqtXj77bdx8OBBaLVaPPvss4iOjgYAqFQqvPHGGygqKoKrqytiY2PRpUsXAEB8fDw++eQTaDQaDB06FAsWLICTHU6PczvQ6gQqdVpUaXUoUatRWFmNgqpqXK2qRkFtiyi/ogrZ5RW4XF6JrNJyVJl4bqW/lzvG3OONjq53WXgv7IdcJsPoe7zxsI8Xfiooxulr15FdXoniarVU5o+SUvxhJNTdFU54oG0b+NzlAi9nBbycneCpUKC9swJeLjX/d3B2RnsXBVxrRw/KAMggg0xW89hBJmPIkV2weUAVFhZi3rx52Lx5Mzp37ox//vOfWLVqFZYsWWJSmS1btkClUmHHjh0oKytDVFQUevXqhb59+2LOnDmYOnUqwsPDkZqailmzZiEpKQl//PEH1q5di6+//hoeHh6YM2cOPvvsM0ybNs3kepdpNHj/97M4VtsVI+r+CdHIY0BA1P5f8xh6j2+8rve+uvc08hjS43rruFkd6nbC2PqNrE8nBLRCQCcEdALQofb/2tfNcWbEUSZDR9eaX6pujo5o4+SAgHZuePK+Tvi59hi3Nq6Ojhih7IARyg7o7+WOTZkXceRKEU4Ul0izZ9R3rVptluPl4iCHi4MDFHI5BAS0tZtTyGVwdnCAs1wOhYO85n+5HDoIaHQCGlHzv4CAg0wGR5kcjnJZbegB8toglMtktcFYoy4Qpee3vAc3mDNszVovc67LTPvY06MtXunVFe0U9vHHus0DKi0tDX369EHnzp0BANHR0YiIiMDixYulg95UmeTkZDz55JNwdHSEu7s7wsLCkJiYCB8fH5w7dw5hYWEAgJEjR+LNN99ERkYGDhw4gMDAQHh51UzcGRUVhWXLljUroLaez8Y/08+Y70C0Ml7OTvBv4wr/Nnehc9s2iPD3RXpRw4tU+Zd8DUe5HL0826GXZztUaXXIr6xEfkUVHOVy/FZYjCsV1civrDLbdWCVWh0qtRxB2Nrszc7D3S7OmNGji62rAsAOAio3NxdKpVJ6rlQqUVpairKyMqmbr6kyOTk58PX1NVh2+vRp5OTkwNvbG3K9k+k+Pj7Izc1FTk4OOnXqZPCevLw8o/WLi4tDXFwcAODMmTOIjIyUlgXe4r6bW1FRETw9PW9e0Ibq17EUwInaf8Yk2uD1oqIiJDZyHG1RH2Ov6x9Hz9p/AY2811bs/fNo7/UDbFPH5AQguRnlq6qqsHPnTovUxeYBpdPpjP6VrB8sTZURQhgsE0JALpcbfY8QAg4ODg1uFFf3HmOioqIQFRUFAIiMjERCQoLpO2dl9l4/gHU0F9bx1tl7/YDbp46WYvOxur6+vsjPv3G7gby8PLi7u8PV1dWkMvWX5efnQ6lUomPHjrhy5YpBGNUta+w9RERkP2weUMOGDcOxY8egUqkAAFu2bEFQUJDJZYKCgrB161ZoNBqUlJRg586dCA4OhlKphL+/P3bt2gUAOHjwIORyOQICAhAYGIj9+/fj6tWrEEIgLi4OwcHBVttnIiK6OZt38bVv3x4rV65ETEwM1Go1/P39ERsbi/T0dCxYsADbt29vtAxQM2AiKysLERERUKvViIqKwuDBgwEAq1evxsKFC7Fu3TooFAqsWbMGcrkc3bt3x0svvYSpU6dCrVajX79+Jg2QqOvqs1f2Xj+AdTQX1vHW2Xv9ANZRJuqfkCEiIrIDNu/iIyIiMoYBRUREdokBVev06dOYPHkyxo8fj8jISJw4UXNljlarxfLlyxEaGorRo0dj8+bN0ntUKhUmTZqEsWPHYsKECcjMzJSWxcfHY+zYsRgzZgwWL14MtVrdYJstkZycjAEDBkjP7al+27dvx7hx4xAREYGJEyciPT3d7urYlJSUFISHhyMkJAQxMTEoLW18fkBzM3bs7PW46X8G7a2Oxr7H9lTHb7/9FuHh4YiIiMCUKVOQlZVlN/UTQuC1117Df/7zHwDm/9lWVFRg9uzZePTRRxESEoLkZBOuthIkysvLxcMPPyxSUlKEEEJ8++23IiQkRAghxJdffimef/55oVarRXFxsQgJCRHHjh0TQgjxl7/8RSQmJgohhEhJSRFhYWFCp9OJ06dPixEjRoirV68KrVYrXnnlFbF+/fpbruf58+dFcHCw6N+/v/SavdQvMzNTPPzwwyIvL0/a3siRI+2qjk25evWqeOihh8T58+eFEEK88847YvHixRbbnr7Gjp09Hrf6n0F7qmNj32N7qWNFRYXo16+fUKlUQgghPv30UzFt2jS7qN/Zs2fF5MmTRb9+/cSGDRuEEOb/2cbGxooFCxYIIYTIzs4Ww4YNEzk5OU3Wiy0oAIcOHYKfnx9GjhwJoGbo+vvvvw+g5q/FyMjIBlMp5eXlNZhKqby8HBkZGdi3b580lZJcLkdUVBQSExubI8A0FRUVmDt3Ll5//XWD1+2lfgqFAsuWLYO3tzcAoHfv3igoKEB1dbXd1LEpxqbTSkpKanBRtyU0duz27NljV8fN2GfQnn62jX2P7aWOWq0WQghcv34dAFBWVgZnZ2e7qN+mTZvwxBNPIDQ0VHrN3PVKTk7GE088AQDo2LEjHn74YezevbvJetl8mLk1paam4sUXX2zw+owZM3D33Xdj/vz5OHXqFNq1a4e5c+cCgMWnUjKlfitWrMChQ4cQFRWFbt26GSyzZv1uVsfx48cDqOkqWLlyJQIDA6FQKKxex5YwZcotS+nUqZO0r/rH7syZM3Z13BYtWtTgM2hPP9vz588b/R7bSx3btGmDN998ExMnToSHhwd0Oh02b96M6dOn27x+ixYtAlAT8nXMfdzqr6/uPU1pVQE1cuRIZGRkNHh93bp1SE1Nxeeff45+/fohOTkZf/vb3/Ddd99ZfColU+q3adMmODo6YsKECbh06VKDdVurfk3VsU55eTlef/115ObmYsOGDTapY0uYMuWWpdU/dk888YTdHLfGPoP29LPVaDRGv8fOzs52UcfTp0/jgw8+wK5du+Dv74/PP/8cM2fObFAPe/l+mPtnW399wM2/X+ziA+Dt7Y0uXbqgX79+AIDg4GBotVpcvHjRLqZS+vrrr5Geno6IiAj87W9/Q2VlJSIiIpCXl2cX9atz+fJlTJw4EQ4ODvj888/Rrl07AA2nqrLH6ahMmXLLkowdO3s6bo19Bn18fOymjo19j/38/OyijmlpaXjwwQfh7+8PAJg0aRL++OMPdOzY0S7qV5+5P38tqTMDCsCIESNw6dIlaeTe0aNHIZPJ0KlTJ7uYSik+Ph47duzA9u3bsX79eri4uGD79u3w8fGxi/oBQGlpKSZPnowxY8bgvffeg4uLi7TMXurYFFOm3LKUxo6dPR23xj6Do0ePtps6NvY9Dg4Otos69uzZE0ePHkVBQQGAmnMy9vI7xhhz1ysoKEi6M0Rubi4OHjyIUaNGNVkHziRR6+jRo3jnnXdQUVEBhUKB+fPnY+DAgdBoNIiNjcXhw4elqZSee+45ADVDLRcuXIiioiIoFAosXboUvXr1AgBs3boVn376qTSV0tKlS+Hs7HzL9bx06RLCw8Px66+/AoDd1O+jjz7C+++/j4AAw5s+fPbZZ2jbtq1d1PFmUlNT8e677xpMp+Xh4WGx7dVp7Nj95z//wUcffWR3x03/M2gvn786xr7H/fv3t5s6btq0CV9++SWcnJzg7u6ORYsW4b777rOb+r3++uvo2rUrnnvuObP/bMvKyrBkyRJkZGRAq9XixRdfRERERJP1YUAREZFdYhcfERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUNdvkyZPxxhtvAAASEhLQs2dPG9fIkH79rCUlJQVnz54FUDOZardu3fDTTz+ZdRvFxcUIDQ1FUVGRWddbJzo6GsePH7fIum9G//gR1WFAEd2ivLw8TJ8+HVevXrXodlasWIEJEybA09PTIuufM2cO5s2bh+rqaousvzHWOn50+2FAEd0ia9wQIDMzE9988w2io6Mtto0//elPaNOmDRITEy22DWN4QwVqDAOKGjh16hSmTZuGgQMHonfv3ggJCcG2bdtatK6ysjK88cYbGDx4MAYPHozY2FhMnjwZa9euBVBzq/UPP/wQY8aMQe/evTFw4EDMnDkThYWFAIAff/wRPXv2xJ49exAYGIgBAwZg+vTpyMnJMbkOP/30EyZOnIi+ffsiKCgI7777LqqqqqTl3bp1Q3x8PCZNmoS+ffsiNDRUurFanQ0bNuCRRx5Bv3798NJLL2HZsmWYPHkyAGDkyJEAgClTpuD111+X3vPLL78gMjISvXv3xqOPPorvvvtOWvbbb79h4sSJ6N+/P4YMGYK5c+eiuLi40X3YuHEjRowYgTZt2gC40Y2YkpKCcePGoU+fPpgwYQLOnz+PtWvX4qGHHsLgwYOxbNkyaR0FBQV4+eWXMWTIEPTv3x/PPPMMTp48abCd0NBQfPrppyYfW1M1tb/1j1/dvv3f//0fhg4dikcffRTV1dX48ccf8fTTT2PAgAHo3bs3IiIicODAAWkbgYGB+OSTT/DCCy+gX79+CAoKwr///W+Denz99dcICQlB3759MWXKFPz73/9GYGCgtDwnJwcxMTF48MEH8ec//xmvvPIK8vLypOWTJ0/GokWLEBkZiUGDBmH//v3N/llSMwgiPWVlZWLo0KFi/vz5IjMzU5w9e1a88cYbolevXuLKlStCCCGefvppMX/+fCGEEFu3bhU9evRodH0vv/yyGDNmjPjxxx9FRkaGmDJliujWrZv417/+JYQQYsOGDWLIkCEiNTVVXLp0SaSkpIihQ4eKZcuWCSGE+OGHH0RAQIAIDg4Whw4dEr///ruIiooSY8eOFWq12ug29euXkZEh+vbtKzZs2CBUKpU4fPiwCAsLE6+//rpUPiAgQAwZMkTs3LlT2t/u3buLS5cuCSGE+Pzzz0X//v3Ftm3bRGZmpli+fLno1q2bePrpp4UQQvz+++8iICBA7N27V5SUlIiLFy+KgIAAMXz4cHHgwAFx/vx5MXPmTNG/f39RXl4uNBqNeOihh8T7778vLl68KH777TcREhJiUKf6Hn74YfG///1Pel63jbpje/LkSREUFCQGDRokXn31VZGZmSn++9//ioCAAJGSkiL9LKZPny7Onj0rMjMzxYwZM0RQUJDBds6ePSsCAgJEVlZWo3Vprpvtb2PH77HHHhNnz54VJ06cEJcvXxZ9+vQRq1evFllZWeLkyZNi+vTp4qGHHhJVVVVCCCFGjRol+vbtK7Zs2SLOnTsn3nvvPREQECCOHj0qhBAiOTlZ9OzZU2zcuFGcO3dOfPTRR6J79+5i1KhRQoiaz35QUJCYM2eOOH36tMjIyBAvvfSSCAkJkbbx9NNPi+7du4vdu3eLkydPipKSkmb/LMl0DCgyUFBQID766CNRXl4uvZaVlWXwRTc1oOred/jwYem1q1evir59+0oBtW/fPukXaJ1XX31VTJkyRQhxI6D27dsnLb9w4YIICAgQBw4cMLpd/frNnj1bzJw502D5Tz/9JAICAkReXp4Qoiag3nnnHWn5tWvXREBAgNi1a5cQouYX3/vvv2+wjsjISCmgcnJyREBAgPjhhx+EEDfCIy4uTipf90v4xIkToqioSHTr1k18+eWXQqfTCSFqguHkyZNG9yc7O1sEBASI3377TXrN2Dbefvtt0bt3b1FZWSm9NnToUPHxxx8LIYQIDw8Xc+bMkZYXFBSIH374QWi1Wqm8RqMRvXr1EomJiUbr0hI329/Gjt/mzZuldVy4cEFs2LBBer8QQnz//fciICBAXL58WQhR83OaNWuWwbYHDhwo7f+kSZPE3LlzDZbPnDlTCqivvvpK/PnPfxYajUZaXlVVJfr37y+SkpKEEDWfrSeffNLkfaNb42jrFhzZl/bt2+Opp57Ctm3bcPLkSahUKpw6dQoAoNVqm7WujIwMAED//v2l17y8vNC5c2fpeWBgIH799Ve89957OH/+PM6dO4fMzEwMHDjQYF2DBw+WHvv7+8PLywtnzpzB8OHDm6zDyZMnceHCBQwYMEB6TdSe88jMzIS3tzcAGNSpXbt2AAC1Wo2ioiJkZ2cb7AMAPPjgg9JxaYyxdVZWVsLDwwN//etf8dZbb2Ht2rV4+OGHMWrUKDz66KNG11NQUAAARgdH+Pv7S49dXV3h7e1tcNtvFxcXadDDjBkz8Nprr+Gbb77BoEGDMGLECIwfPx5y+Y2efgcHB3h4eBgdsJCYmIjFixdLz8PDwzFw4MAGr7311lsG72vu/tbx8/Mz2M/x48dj48aNOH36NC5cuCB1T+p/LvWPOQC0bdsWarUaAPD7779j7NixBssffPBBnDhxAkDN57WwsLDBZ6+iogKZmZnS806dOt3yvpFpGFBkID8/H1FRUfDx8cGoUaPwyCOPwNvbG3/5y1+avS4HBwcATZ8EX7duHdavX4/IyEgMHz4c06dPx+eff47Lly8blHN0NPyo6nQ6g1+sjXFycsL48eMxbdq0Bsvuvvtu6bFCoWiwXAghbbepfWiMsfrVree1117DpEmTkJqairS0NMybNw+JiYlYv359g/fIZDIANftcX/3j0tQxCQ0NxZ///Gekpqbi8OHD+PDDD/HZZ5/hq6++QocOHaRyGo3G6HoCAwPRr18/6bmbmxucnZ0bvGZMc/a3jn7Q/vHHH3jqqafQr18/DB06FGPHjoVGo8ELL7xg8J7Gfo5AzbFq6ufo5OSEBx54oMF5K6Am6Oq4uLjc8r6RaThIggx8++23KCsrw6ZNmzB9+nQEBgZK190095d0t27dIJPJcOzYMem14uJiXLhwQXq+ceNGxMTEYOHChXjiiSfQq1cvXLhwocG26v7KBYDz58+juLgYPXr0uGkdHnjgAWRmZuLee++V/hUWFiI2NhZlZWU3fX/btm3RsWNHg30AYHC9UF2AmCorKwuLFy/G3XffjUmTJmHdunWIjY1Famqq0ZZLXSuvbuBIS2g0GsTGxiI7Oxvh4eFYuXIldu7ciezsbBw5ckQqp9PpUFJSYhDeddzc3AyOY/v27Y2+1tz9NeX4JSQkwNfXFxs2bMBzzz2H4cOHS4MXTP1cduvWrcmfY9euXXHp0iV4eHgY7M/KlStx5swZo+ts7s+SmocBRQY8PT1RWlqKvXv3Ijs7G/v27ZO6cJp7fYyfnx/GjBmDt956C0ePHsXp06cxd+5cVFRUSL+UvLy8kJaWhszMTPzxxx9466238OuvvzbY1ptvvolffvkF6enpePXVV9GnTx+Dbr/GTJs2DcePH8fKlSuRmZmJI0eO4LXXXsP169eN/hI25vnnn8fGjRuRlJSE8+fPY/Xq1fjtt9+k5XUj606fPm3SRbSenp7YvXs3lixZgszMTGRmZmL37t3w9/c32o3n4+MDHx+fBiPumsPR0RG///47Fi1ahGPHjuHixYuIi4uDk5MTevXqJZU7deoUtFot+vbt2+Jt1Xez/TXl+Hl5eSE7OxuHDh1CdnY2tm/fjvfeew+A6Z/L559/Hjt37sSmTZugUqmwceNG7N69W1oeHh4OT09P/P3vf0d6ejrOnDmD2bNn49ixY+jatWuL9o1uDQOKDDz66KOYOnUqli1bhrCwMKxZswYzZszAvffei/T09Gavb9myZejVqxemT5+OyZMno2fPnrjnnnvg5OQEAIiNjUVJSQkef/xx/PWvf0VxcTFmz56Ns2fPoqKiQlrP+PHj8fe//x1Tp06Fv78/1q9fb1IXX7du3fDRRx/hl19+kdYxaNAgo904jXnqqafw7LPPIjY2FhEREVCpVAgODpa6k9zc3DB58mSsWrUKCxYsuOn62rZti48//hgXL17Ek08+iQkTJqCqqqrJfRo1ahR+/PFHk+tszLvvvotOnTph+vTpGDt2LJKTk/HBBx/g3nvvlcocOXIEPXr0wD333HNL29J3s/015fhNmTIFo0ePxiuvvIJx48Zh06ZNePPNN+Hq6mry5/KRRx7BggUL8J///AePPfYYvvvuOzz++OPSZ9HFxQWffvopXFxcMHXqVERHR0Oj0WDjxo1GW4am7BvdIluNzqA7X2Vlpfj2229FWVmZ9Fp1dbUYMGCA+Prrr01aR90ovpycHAvV8uZSU1NFbm6uwWvPPvusmDdvntXqcObMGdG3b19RVFRk0e2MGzdOxMfHW3QbtnLkyBGhUqkMXlu4cKE0YpTsDyOeLEahUGDJkiVYtmwZzp8/j8zMTLz55ptwcnLCiBEjbF09k23btg0zZ87EiRMncOnSJXz55Zf4/vvvER4ebrU6dO3aFcHBwdiyZYvFtnHkyBFUVlYiIiLCYtuwpQMHDmDatGn46aefkJ2djaSkJCQlJWHcuHG2rho1QiYE5xkhy/n999/xzjvv4MSJE9DpdBgwYADmzp1r0gAHoGYmiSlTpiA1NRVKpdLCtTWuqKgIK1asQFpaGsrKynD//fdj+vTpVh9KXFhYiOjoaGzevBleXl5mX//EiRPx6quv4sEHHzT7uu1BVVUVYmNj8c0336C4uBh+fn54+umnMWnSJFtXjRrBgCIiIrvELj4iIrJLDCgiIrJLDCgiIrJLDCgiIrJLDCgiIrJL/w+oZ3OCNdYZawAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] }, "metadata": {}, @@ -70,48 +74,68 @@ } ], "source": [ - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", - "data = pd.read_csv(flist[0])\n", - "all_latency_values_strangers = data['gap_length']\n", + "plt.figure(figsize=(4,5))\n", "\n", - "for file in flist[1:]:\n", - " \n", - " data = pd.read_csv(file)\n", - " all_latency_values_strangers = all_latency_values_strangers.append(data['gap_length'])\n", - " \n", - "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 3}, color='#0FA3B1')\n", - "plt.xlim(-6000, 10000)\n", + "ax = sns.barplot(x=\"condition\", y=\"n_long_2000\", data=long_gaps, \n", + " palette=['#90be6d', '#43aa8b'],\n", + " saturation=1, errwidth=1,\n", + " capsize=.05)\n", "\n", - "ax.set_xlabel('all gap lengths (ms) -- strangers', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", + "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('# long gaps in each conversation', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=13)\n", + "\n", + "# significance bar\n", + "lw = 2\n", + "line_color = '#594F4F' # create thinner, greyer lines\n", + "star_color = '#594F4F'\n", + "h = 0\n", + "space_between_stars_and_line = .0\n", + "star_font_size = 20\n", + "\n", + "# strangers vs friends\n", + "x1, x2 = .20, .80 \n", + "y = 4.2\n", + "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", + "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1A.pdf'), dpi=300)" + "plt.savefig(os.path.join(base_dir,'Figures','Fig1.pdf'), dpi=300)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f80aea3f", + "metadata": {}, + "source": [ + "## Figure 2 (top)\n", + "\n", + "How does connection change over time (from a baseline 6 seconds before a long gap)?" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "906833ff", + "execution_count": 5, + "id": "86e8263e", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps_all = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format_baseline.csv'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d6ad333c", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -119,60 +143,112 @@ } ], "source": [ - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", - "data = pd.read_csv(flist[0])\n", - "all_latency_values_friends = data['gap_length']\n", + "plt.figure(figsize=(10,5))\n", "\n", - "for file in flist[1:]:\n", - " \n", - " data = pd.read_csv(file)\n", - " all_latency_values_friends = all_latency_values_friends.append(data['gap_length'])\n", - " \n", - "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 3}, color='#F46036')\n", - "plt.xlim(-6000, 10000)\n", + "timepoint_labels = ['6 seconds\\nbefore\\n(reference)', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap',\n", + " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", "\n", - "ax.set_xlabel('all gap lengths (ms) -- friends', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", + "friend_color = '#F46036'\n", + "stranger_color = '#0FA3B1'\n", + "\n", + "ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", + " data=long_gaps_all, dodge=False,\n", + " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", + " palette = [stranger_color,friend_color], markers='o', ci=None)\n", + "\n", + "plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", + "plt.ylabel(\"change in connection\\n(compared to reference)\", fontsize=18, labelpad=10)\n", + "\n", + "ax.tick_params(labelsize=13)\n", + "plt.legend(fontsize='13')\n", + "\n", + "ax.set_xticklabels(timepoint_labels)\n", + "\n", + "ax.axvspan(2.75, 3.25, alpha=0.3, color='lightgrey')\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1B.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir,'Figures','Fig2_top.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "b26c7f3b", + "id": "92ac7a3f", "metadata": {}, "source": [ - "## Figure 1B\n", + "## Figure 2 (bottom)\n", "\n", - "Distribution of gap lengths for stranger and friend conversations, zooming in on \"long\" gap part of the distribution" + "How does connection change for friends and stangers when entering (one_before) and exiting (one_after) a long gap?" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "2494e652", + "execution_count": 17, + "id": "919e1639", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps_all = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format.csv'))\n", + "\n", + "one_before = long_gaps_all.loc[long_gaps_all['timepoint'] == '1_before'].reset_index(drop=True)\n", + "one_before['connection_change_flipped'] = one_before['connection_change'] * -1\n", + "\n", + "one_after = long_gaps_all.loc[long_gaps_all['timepoint'] == '1_after'].reset_index(drop=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "46932d55", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n", - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(4,5))\n", + "\n", + "plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", + "\n", + "friend_color = '#F46036'\n", + "stranger_color = '#0FA3B1'\n", + "\n", + "ax = sns.barplot(x=\"condition\", y=\"connection_change_flipped\", data=one_before, \n", + " palette=[stranger_color, friend_color],\n", + " saturation=1, errwidth=1,\n", + " capsize=.05)\n", + "\n", + "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('change in connection', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=13)\n", + "ax.set(ylim=(-1.35, 1))\n", + "\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig(os.path.join(base_dir,'Figures','Fig2_bottom_entering.pdf'), transparent=True, dpi=300)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "855c1c75", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -180,64 +256,74 @@ } ], "source": [ - "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 1}, color='#F46036', label = 'friend', hist_kws=dict(alpha=0.5))\n", - "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 1}, color='#0FA3B1', label = 'stranger', hist_kws=dict(alpha=0.5))\n", + "plt.figure(figsize=(4,5))\n", "\n", - "plt.xlim(-500, 2500)\n", + "plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", "\n", - "ax.set_xlabel('all gap lengths', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", + "friend_color = '#F46036'\n", + "stranger_color = '#0FA3B1'\n", "\n", - "handles, labels = plt.gca().get_legend_handles_labels()\n", - "order = [1,0]\n", - "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])\n", + "ax = sns.barplot(x=\"condition\", y=\"connection_change\", data=one_after, \n", + " palette=[stranger_color, friend_color],\n", + " saturation=1, errwidth=1,\n", + " capsize=.05)\n", + "\n", + "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('change in connection', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=13)\n", + "ax.set(ylim=(-1.35, 1))\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1B.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir,'Figures','Fig2_bottom_exiting.pdf'), transparent=True, dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "02cf7a25", + "id": "4bdd156c", "metadata": {}, "source": [ - "# Figure 1C\n", + "# Study 2\n", "\n", - "Difference in **median** gap lengths for stranger conversations compared to friend conversations." + "Impressions of long gaps from independent raters" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "e95f6eda", + "execution_count": 20, + "id": "44c268dc", "metadata": {}, "outputs": [], "source": [ - "stranger_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'turn_taking_strangers.csv'))\n", - "friend_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'turn_taking_friends.csv'))\n", + "rating_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_ratings_wide_format.csv'))\n", + "rating_data['video_num'] = rating_data['video_num'].astype(int)\n", + "data_info = pd.read_csv(os.path.join(base_dir, 'Data', 'long_gap_stimuli_info.csv'))\n", "\n", - "stranger_data['condition'] = 'stranger'\n", - "friend_data['condition'] = 'friend'\n", + "data_full = pd.merge(data_info, rating_data, how='inner', on=['video_num'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "2125d2ef", + "metadata": {}, + "source": [ + "## Figure 3A\n", "\n", - "data = pd.merge(stranger_data, friend_data, how='outer')" + "Differences in perceived awkwardness in friend vs stranger conversations" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "d8c5a83a", + "execution_count": 21, + "id": "2cbe9c59", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -245,17 +331,17 @@ } ], "source": [ - "plt.figure(figsize=(5,6))\n", + "plt.figure(figsize=(5,5))\n", "\n", - "ax = sns.barplot(x=\"condition\", y=\"median_gap_convo\", data=data, errwidth=1,\n", + "ax = sns.barplot(x=\"condition\", y=\"awkward_mean\", data=data_full, errwidth=1,\n", " capsize=.05, \n", - " palette=['#0FA3B1', '#F46036'],\n", + " palette=['#3777FF', '#EF3054'],\n", " saturation=1)\n", "\n", "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('median gap length (ms)', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('perceived awkwardness', fontsize=15, labelpad=10)\n", "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 450))\n", + "ax.set(ylim=(0, 80))\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", @@ -265,39 +351,39 @@ "line_color = '#594F4F' # create thinner, greyer lines\n", "star_color = '#594F4F'\n", "h = 0\n", - "space_between_stars_and_line = 10\n", - "star_font_size = 15\n", + "space_between_stars_and_line = -.4\n", + "star_font_size = 20\n", "\n", "# strangers vs friends\n", "x1, x2 = .20, .80 \n", - "y = 425\n", + "y = 75\n", "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"n.s.\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1C.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3A.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "4a894d88", + "id": "39a3cc6f", "metadata": {}, "source": [ - "# Figure 1D\n", + "## Figure 3B\n", "\n", - "Difference in **mean** gap lengths for stranger conversations compared to friend conversations." + "Differences in perceived connection in friend vs stranger conversations" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "1ec5461f", + "execution_count": 22, + "id": "3be96c5e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -305,17 +391,17 @@ } ], "source": [ - "plt.figure(figsize=(5,6))\n", + "plt.figure(figsize=(5,5))\n", "\n", - "ax = sns.barplot(x=\"condition\", y=\"mean_gap_convo\", data=data, errwidth=1,\n", + "ax = sns.barplot(x=\"condition\", y=\"connected_mean\", data=data_full, errwidth=1,\n", " capsize=.05, \n", - " palette=['#0FA3B1', '#F46036'],\n", + " palette=['#3777FF', '#EF3054'],\n", " saturation=1)\n", "\n", "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('mean gap length (ms)', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('perceived connection', fontsize=15, labelpad=10)\n", "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 450))\n", + "ax.set(ylim=(0, 80))\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", @@ -330,50 +416,34 @@ "\n", "# strangers vs friends\n", "x1, x2 = .20, .80 \n", - "y = 425\n", + "y = 75\n", "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1D.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3B.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "1e9945a8", - "metadata": {}, - "source": [ - "# Figure 2A\n", - "\n", - "Differences in average reported connection for friend vs stranger conversations" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "03a456ea", + "id": "b2412c3a", "metadata": {}, - "outputs": [], "source": [ - "data_strangers = pd.read_csv(os.path.join(base_dir,'Analyses','connection_strangers.csv'))\n", - "data_friends = pd.read_csv(os.path.join(base_dir,'Analyses','connection_friends.csv'))\n", - "\n", - "data_strangers['dataset'] = 'strangers'\n", - "data_friends['dataset'] = 'friends'\n", + "## Figure 3C\n", "\n", - "data = pd.merge(data_strangers, data_friends, how='outer')" + "Differences in percieved awkwardness in friend vs stranger conversations, as a function of gap length" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "3cda4a89", + "execution_count": 23, + "id": "f03f21f9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -381,71 +451,41 @@ } ], "source": [ - "plt.figure(figsize=(4,5))\n", - "\n", - "ax = sns.barplot(x=\"dataset\", y=\"avg_connection\", data=data, \n", - " palette=[stranger_color, friend_color],\n", - " saturation=1, errwidth=1,\n", - " capsize=.05)\n", - "\n", - "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('average connection rating', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=13)\n", - "\n", - "# significance bar\n", - "lw = 2\n", - "line_color = '#594F4F' # create thinner, greyer lines\n", - "star_color = '#594F4F'\n", - "h = 0\n", - "space_between_stars_and_line = -.4\n", - "star_font_size = 20\n", - "\n", - "# strangers vs friends\n", - "x1, x2 = .20, .80 \n", - "y = 80\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "sns.lmplot(x=\"interval\", y=\"awkward_mean\", hue=\"condition\", data=data_full,\n", + " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", + " scatter_kws={'alpha':0.2}, legend=False)\n", "\n", + "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", + "plt.ylabel(\"perceived awkwardness\", fontsize=15, labelpad=10)\n", + "plt.tick_params(labelsize=12)\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig2A.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3C.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "f8b80dcf", + "id": "68a840b3", "metadata": {}, "source": [ - "# Figure 2B\n", + "## Figure 3D\n", "\n", - "Explore average changes in connection ratings before and after a long gap, separately for friend and stranger conversations." + "Differences in percieved connection in friend vs stranger conversations, as a function of gap length" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "49d8bcb2", + "execution_count": 24, + "id": "085565b1", "metadata": {}, - "outputs": [], - "source": [ - "long_gaps_all = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format.csv'))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6b37315d", - "metadata": { - "scrolled": true - }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -453,135 +493,59 @@ } ], "source": [ - "plt.figure(figsize=(8,5))\n", - "\n", - "plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", - "\n", - "timepoint_labels = ['6 seconds\\nbefore', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap\\n(reference)',\n", - " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", - "\n", - "friend_color = '#b7094c'\n", - "stranger_color = '#0091ad'\n", - "\n", - "ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", - " data=long_gaps_all, dodge=True,\n", - " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", - " palette = [stranger_color,friend_color], capsize=0.12, markers='o', errwidth=1.5)\n", - "\n", - "plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", - "plt.ylabel(\"connection rating\\n(compared to reference)\", fontsize=18, labelpad=10)\n", - "\n", - "ax.tick_params(labelsize=13)\n", - "plt.legend(fontsize='13')\n", - "\n", - "ax.set_xticklabels(timepoint_labels)\n", - "\n", - "ax.axvspan(2.75, 3.25, alpha=0.3, color='silver')\n", - "\n", - "## significance for friends\n", - "lw = 1\n", - "line_color = friend_color \n", - "star_color = friend_color\n", - "h = 0\n", - "space_between_stars_and_line = -.1\n", - "star_font_size = 25\n", - "\n", - "# long gap vs 3 before\n", - "x1, x2 = 0.1, 2.9 \n", - "y = -1.9\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"**\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "# long gap vs 2 before\n", - "x1, x2 = 1.1, 2.9 \n", - "y = -1.6\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"**\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "# long gap vs 1 before\n", - "x1, x2 = 2.1, 2.9 \n", - "y = -1.005\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"*\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "## significance for strangers\n", - "lw = 1\n", - "line_color = stranger_color \n", - "star_color = stranger_color\n", - "h = 0\n", - "space_between_stars_and_line = -.1\n", - "star_font_size = 25\n", - "\n", - "# long gap vs 1 before\n", - "x1, x2 = 2.1, 2.9 \n", - "y = 1.0\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"*\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - " \n", - "# long gap vs 1 after\n", - "x1, x2 = 3.1, 3.9 \n", - "y = 0.8\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"**\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "# long gap vs 2 after\n", - "x1, x2 = 3.1, 4.9 \n", - "y = 0.5\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"*\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "sns.lmplot(x=\"interval\", y=\"connected_mean\", hue=\"condition\", data=data_full,\n", + " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", + " scatter_kws={'alpha':0.2}, legend=False)\n", "\n", + "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", + "plt.ylabel(\"perceived connection\", fontsize=15, labelpad=10)\n", + "plt.tick_params(labelsize=12)\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig2B.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3D.pdf'), dpi=300)\n" ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "dab2ce6e", + "cell_type": "markdown", + "id": "438a8592", "metadata": {}, - "outputs": [], "source": [ - "long_gaps_strangers = long_gaps_all.loc[long_gaps_all['condition'] == 'strangers'].reset_index(drop=True)\n", - "long_gaps_friends = long_gaps_all.loc[long_gaps_all['condition'] == 'friends'].reset_index(drop=True)" + "# Supplemental Figures" ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "5e5793b3", + "cell_type": "markdown", + "id": "8df6c2a5", "metadata": {}, + "source": [ + "## Figure S1A\n", + "\n", + "Distribution of gap lengths for stranger conversations" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "af1f2dbd", + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "No handles with labels found to put in legend.\n" + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { + "image/png": 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\n", "text/plain": [ - "[Text(0, 0, '6 seconds\\nbefore'),\n", - " Text(1, 0, '4 seconds\\nbefore'),\n", - " Text(2, 0, '2 seconds\\nbefore'),\n", - " Text(3, 0, 'long gap\\n(reference)'),\n", - " Text(4, 0, '2 seconds\\nafter'),\n", - " Text(5, 0, '4 seconds\\nafter'),\n", - " Text(6, 0, '6 seconds\\nafter')]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
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" ] }, "metadata": {}, @@ -589,64 +553,58 @@ } ], "source": [ - "plt.figure(figsize=(8,5))\n", - "\n", - "#plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", - "\n", - "timepoint_labels = ['6 seconds\\nbefore', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap\\n(reference)',\n", - " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "data = pd.read_csv(flist[0])\n", + "all_latency_values_strangers = data['gap_length']\n", "\n", - "friend_color = '#b7094c'\n", - "stranger_color = '#0091ad'\n", + "for file in flist[1:]:\n", + " \n", + " data = pd.read_csv(file)\n", + " all_latency_values_strangers = all_latency_values_strangers.append(data['gap_length'])\n", + " \n", + "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True,\n", + " kde_kws = {'linewidth': 3}, color='#0FA3B1')\n", + "plt.xlim(-6000, 10000)\n", "\n", - "ax = sns.pointplot(x=\"timepoint\", y=\"connection\",\n", - " data=long_gaps_strangers,\n", - " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", - " color = stranger_color, capsize=0.12, markers='o', errwidth=1.5)\n", + "ax.set_xlabel('all gap lengths (ms) -- strangers', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=12)\n", "\n", - "plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", - "plt.ylabel(\"connection rating\", fontsize=18, labelpad=10)\n", + "sns.despine()\n", + "plt.tight_layout()\n", "\n", - "ax.tick_params(labelsize=13)\n", - "plt.legend(fontsize='13')\n", + "#plt.savefig(os.path.join(base_dir,'Figures','FigS1A.pdf'), dpi=300)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7985dc1e", + "metadata": {}, + "source": [ + "## Figure S1B\n", "\n", - "ax.set_xticklabels(timepoint_labels)" + "Distribution of gap lengths for friend conversations" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "a454f61a", + "execution_count": 26, + "id": "85875018", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "No handles with labels found to put in legend.\n" + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { + "image/png": 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MqlWraNu2Lf/617+YP38+s2bNcqhMcnIyOTk5rF+/npKSEhISEujUqRNdu3Zl0qRJjB49mtjYWLZs2cL48eNZt24d+fn5PP3007z77rt06tSJZcuWMWvWLN555x2n3pu4ijg6HxT2CUqeQQnhOpf1DMrZyQlg27ZtdOnShbZt2wIwYsQI1q1bZzdKdF1lMjIyiI+Px8/Pj+DgYGJiYkhPTyc/P5/Dhw8TExMDQL9+/SgtLWXv3r18/PHH9OnTh06dOgEwfPhwpkyZ4vR7E1cPVeF4gsJ2Tqji3zAXn3VRVEL4tjprUJ07d+aLL76gWbNmdOrUqc4OEHv27KlXAHl5eYSHh1vXw8PDKS4upqSkxNrMV1eZ3NxcIiIi7PYdOHCA3NxcQkND7Tp2hIWFkZeXR05ODoGBgUyYMIEjR44QERFRa4JKSUkhJSUFgKKionrdo7gKXEYNCptmblV8BspLIKiJa+ISwofVmaBmz55tTRKzZ892SQ89s9lc43ltE0tdZZRSdvuUUuh0uhqPUUqh1+sxGo18/vnnrFy5krZt27J8+XKefvpp0tLSLrpGQkICCQkJAMTHx9f7PoX3UiYjGA2WFU0HOn3dBzS06ShUUS5zQgnhInUmqAceeMC67KpfzhEREezevdu6np+fT3BwsF1vwbrKREREUFBQYN1XUFBAeHg4LVu25Ndff7VLYFX7QkNDueWWW6xNhsOGDWPu3LmUl5fTsGF1843wEba1JwdeldD8/C21LKMBlBnKS10coBC+yeFnUJWVlbzxxhscPXoUgBdeeIEePXrw8MMPc/r06XoH0Lt3b3bv3k1OTg4AycnJREdHO1wmOjqaNWvWYDQaOXv2LBs2bGDgwIGEh4cTGRnJxo0bAdi6dSs6nY6oqCgGDRrE999/z/HjxwHYvHkz7du3l+Tko+xGMvdvUHtBWw2uqz6+RJ5BCeEKDo8k8fLLL/PRRx/Rv39/MjMz+eCDD/j73//O559/zrx585g/f369AmjevDnz5s0jMTERg8FAZGQkSUlJZGVlMW3aNNLS0motA5YOE8eOHSMuLg6DwUBCQgK9evUC4JVXXmH69OksXryYgIAAXn/9dXQ6HR07dmTmzJk8/fTTGI1GmjRpwuuvv16v+MU1wKaDhBbg2B8pWsPA6sQkCUoIl9CUgw3ovXv3ZsGCBdx2221MnTqVvLw83nnnHfbt28fDDz/M9u3bXR2rx8XHx5OamurpMISTmY8fomzGSAC0sEj8h/zJuk/XrjPmbPsOQLp2nal4cwbq5GEAAh6Zgn/fOPcFLISPcLiJ79y5c7Rp0wawTF7Yu3dvAIKCgqisrHRNdEK4gd04fAGONfFpNh0llHQzF8IlHG7i+/3vf8/WrVsJDQ0lLy+Pvn37ArB69WpuvPFGlwUohMvZjiLhYIKigU2CkiY+IVzC4QQ1fvx4xo0bh9FoZOjQobRr146XXnqJlStX8sYbb7gyRiFcyu4lXQc7SWgNqztJUPybkyMSQsBlJKi77rqLL774gry8PDp27AjAvffey4gRI6xNf0JclerRxGf7LpTUoIRwDYcTFEDTpk0By/tESilatGgBWN5LCgsLc350QriB3UCxjtag7Jr4pAYlhCs4nKC+++47pkyZwrFjx+y2V70Iu2/fPqcHJ4RbVJRVL9enBiWdJIRwict6DyokJIRnnnmGJk1k3DFx7ahXDcp2uCNp4hPCJRxOUAcPHmTVqlXcdNNNroxHCPersBlJwsEXdWkoI0kI4WoOvwcVHh5OaamMOSauQZX1aOLzbwBVAxpXltu/SyWEcAqHE9TEiROZM2cO27Zt4/jx4+Tn59v9E+JqZdvNXPMPcOgYTdPs34U6d8bZYQnh8xxu4vvHP/6BwWDg8ccfv2h6C+kkIa5q9elmzvnx+MosU76r3wqhefgljhBCXA6HE9Tbb7/tyjiE8Bj7oY4uY0T76xrB+Tks1dlC5wYlhHA8QVWNEA5gNBrx87usV6iE8F4VF/TiU2aHDtOuC6JqpGX12ykXBCaEb3P4GRTAhx9+yJAhQ+jevTvHjx9n5syZLFq0yFWxCeEWdjUoR+eDAksNquocv9V/TjQhRM0cTlAffvghL774Ivfffz96vWVK7Jtuuom33nqLt956y2UBCuFy9X0GFRhkXVZnJEEJ4WwOJ6h3332X6dOn87e//Q3d+e61I0aMYPbs2XzwwQcuC1AIV7PrxXcZCUpqUEK4lsMJ6ujRo3Tv3v2i7d27d5du5uLqVs8mPu06mxqUPIMSwukcTlARERHs37//ou1ff/01ERERTg1KCLeqvPwp3wE0qUEJ4VIOd8V79NFHmTVrFr/++itKKXbs2EFqaipLly5l4sSJroxRCJdSdoPFOvaiLgDX2T+DqnonUAjhHA4nqD/+8Y8YjUbefPNNysvLmTp1KmFhYTz33HMMHz7clTEK4Vr1beLzDwA/fzAawFgJZcUQ2NgFAQrhm+pMUDt37rRbb9++PfPnz6ewsBB/f38aN25sLdezZ0/XRSmEiyijAUwmy4pOj+bnf3knuC4Izlne1lVnTqFJghLCaepMUKNGjULTNJSyvI5Y1XxRU1OGDHUkrkrlNgMg206h4SDtukaoqgT122lo+TtnRSaEz6szQW3ZssW6/MUXX/DWW28xdepUunfvjr+/P1lZWcydO5dHHnnE5YEK4QqqvMS6rNUjQdl1NZd3oYRwqjp78YWFhVn/LVmyhDlz5tCvXz+Cg4MJDAzktttuY9asWbz22mtuClcIJyu7whqUTZOeuajAGREJIc5zuJv5qVOnCAkJuWh7QEAAxcXFzoxJCLdRNk18tt3GHaU1qk5QqlDeBxTCmRxOUD179mTu3Ll2L+UeO3aM2bNn06dPH5cEJ4Sr2Tbx1acGRaMm1eeSBCWEUznczXzWrFk89thj9O/fn6ZNm6KUoqioiE6dOjFjxgxXxiiE69jWoOrVxGeToKSJTwincjhBtWzZknXr1vHll19y6NAhNE2jY8eO3Hbbbdax+YS42qgym04SV9jEZ5YalBBOdVmTOvn5+dGvXz/69evnqniEcC+7Jr7LT1BcFwSazjKH1NkilKHS4WnjhRB1k6qP8GnqSpv4dDq0Jk2rz1f0q1PiEkJIghK+7gpf1AXQQq63LktHCSGcxysSVGZmJrGxsQwePJjExMQau63XVsZkMjF37lyGDBnCoEGDWLVqlfWYnJwcRo4cydChQxk2bBjZ2dkXnfe9997j3nvvdd3NCa92pd3MAbTg5tXnkwQlhNN4PEEVFhYyefJkFi5cyKZNm2jdujXz5893uExycjI5OTmsX7+e1atXs2zZMn788UcAJk2axPDhw9m4cSPjxo1j/Pjx1mGbAL777jvefvtt992s8Dq2nSScUYOSjhJCOI/HE9S2bdvo0qULbdu2BSyz9K5bt84ukdRVJiMjg/j4ePz8/AgODiYmJob09HTy8/M5fPgwMTExAPTr14/S0lL27t0LWF48nj17Nv/85z/der/Cy9g9g6pnDcq2iU+6mgvhNB5PUHl5eYSHh1vXw8PDKS4upqSkxKEyubm5dhMmhoeHk5eXR25uLqGhoXZd4MPCwsjLy8NkMjFx4kSeeeYZwsLCXHyHwptd8Vh8gBZs+wxKEpQQznJZ3cxdwWw21zjJm21iqavMhSOrK6XQ6XQ1HqOUQq/Xs2DBAnr27Mmdd97J9u3b64wvJSWFlJQUAIqKii7r3sRVoOwKu5kjnSSEcBWPJ6iIiAh2795tXc/Pz7cORutImYiICAoKqv9qLSgoIDw8nJYtW1pn/61KVFX70tPTadasGZ988gmlpaXk5+cTFxdHWlraRfElJCSQkJAAQHx8vNPvX3iWfSeJK69BmaUGJYTTeLyJr3fv3uzevZucnBzA0ukhOjra4TLR0dGsWbMGo9HI2bNn2bBhAwMHDiQ8PJzIyEg2btwIwNatW9HpdERFRbFt2zbS09NJS0tjzpw5REZG1pichA9wQjdzGgeDXm9ZLj6Dsp2hVwhRbx6vQTVv3px58+aRmJiIwWAgMjKSpKQksrKymDZtGmlpabWWAUuHiWPHjhEXF4fBYCAhIYFevXoB8MorrzB9+nQWL15MQEAAr7/+ugzLJOzYDXXUsBGq5Oxln0PT6dFCQlGncy3nLCxAC490WoxC+CpN2XaXE3WKj48nNTXV02EIJ1FmE6WP3WFdD3zna1RhPqY931i36dp1xpy9x+64C7fpO/ak4v9Nw5xjmVU6YOwL6Dvfji6oCUKI+pPqhPBd5WXVyw0D0epZu1aVZXbrpp+224/xJ4SoF0lQwmc5o4u5lc28UJScu7JzCSEASVDClzmhi3kVu5l16/EcSwhxMUlQwmc5swalBdomKKlBCeEMkqCEz1LF1TUdrdEVdmgICq5eLv7tys4lhAAkQQkfpkpsEol/AOZTuajKinqdS2scUn3e4jNI51ghrpwkKOG7bGo6qrzE0r3cZKjXqbSAhhDQwLJiMsK5M04IUAjfJglK+Cxl2xTX4LorPp8WFGJdlmk3hLhykqCEz7J7BuWEBIVtM1+RJCghrpQkKOGznF+Dqu4ooU5LghLiSkmCEj7L9n0lZ9SgbJv4ZNoNIa6cJCjhu1xZg5JpN4S4YpKghM+y7WbulBqU7TOowrwrPp8Qvk4SlPBZ9s+gGl75CYOC4fzkmOrMKZShfu9UCSEsJEEJn6QMlVBxfhRyTQP/Bld8Tk3vB43ON/Mphco/ccXnFMKXSYISPsluQNcG16Gdr/lcKS24mXXZnJvjlHMK4askQQnf5OQOElW0JjYJKu+Y084rhC+SBCV8ku3zJ6e8pFt1ruDm1dfIO+q08wrhiyRBCZ/k9A4S50kNSgjnkQQlfJIqPmNddm4NyvYZ1FEZ1VyIKyAJSvgkdeZU9cp1jWsveLmuCwK/AMtyWTHqt9POO7cQPkYSlPBJtiM92E7XfqU0TUNrer113Xz8Z6edWwhfIwlK+CRVZJOgAp1YgwK0ZmHWZfPRA049txC+RBKU8ElmmwSFE2tQcEGCOiY1KCHqSxKU8El2TXyurEEdkxqUEPUlCUr4HFVWAmXFlhU/f6e+qAugNW0BOsuPlio4YbmeEOKySYISPked+dW6rAU3d9owR9Zz6v3QQludv5iSjhJC1JMkKOFz7Jr3bEZ+cCZdqxuty6aff3TJNYS41kmCEj7HtoOEFnx9HSXrT/e7m6uv9/MPLrmGENc6SVDC59jVoEJcVIOySVCmg7tRZpNLriPEtUwSlPA56lSuddlVTXxa01AICrGslBVjOijNfEJcLq9IUJmZmcTGxjJ48GASExMpLi52uIzJZGLu3LkMGTKEQYMGsWrVKusxOTk5jBw5kqFDhzJs2DCys7Ot+959911iYmK47777ePjhhzl2TAb29BXmX45Yl7UWrVxzEUM5WnOb7ub7v3XNdYS4hnk8QRUWFjJ58mQWLlzIpk2baN26NfPnz3e4THJyMjk5Oaxfv57Vq1ezbNkyfvzR8tfqpEmTGD58OBs3bmTcuHGMHz8epRRfffUVq1evJiUlhfT0dAYNGsTkyZPdfu/C/ZRSdglKF9baZdfShbexLpsO7HLZdYS4Vnk8QW3bto0uXbrQtm1bAEaMGMG6devsRoGuq0xGRgbx8fH4+fkRHBxMTEwM6enp5Ofnc/jwYWJiYgDo168fpaWl7N27l+uvv55Zs2YRFBQEQJcuXfjll1/cet/CM9SZU9XvQF0XBE2auuxauht+b102Z2ehDBUuu5YQ1yKPJ6i8vDzCw8Ot6+Hh4RQXF1NSUuJQmdzcXCIiIuz25eXlkZubS2hoKDpd9S2GhYWRl5dHVFQUvXr1AqCyspL58+czZMgQV96m8BLKtvbU8ndOfwfKltY4BKrmhzJUYtovtSghLoefpwMwm801/pKwTSx1lVFK2e1TSqHT6Wo8RimFXq+3rhcWFpKYmEhQUBATJkyoMb6UlBRSUlIAKCoqurybE17HfEGCcjXdDb/HfLYQANOPX+LX5f9cfk0hrhUer0FFRERQUFDd7Tc/P5/g4GACAwMdKnPhvoKCAsLDw2nZsiW//vqrXVNh1T6A/fv3M2zYMG6++WYWLVpEQEBAjfElJCSQmppKamoqTZu6rjlIuIf55GHrsu4GNySoVu2sy6bvMlFms8uvKcS1wuMJqnfv3uzevZucnBzA0ukhOjra4TLR0dGsWbMGo9HI2bNn2bBhAwMHDiQ8PJzIyEg2btwIwNatW9HpdERFRZGXl8fo0aN58sknmTJlil2tSlzbzCcOWZc1N9SgtPBI65TyqqgA8+GfXH5NIa4VHm/ia968OfPmzSMxMRGDwUBkZCRJSUlkZWUxbdo00tLSai0Dlg4Tx44dIy4uDoPBQEJCgvX50iuvvML06dNZvHgxAQEBvP766+h0Ot544w3Kysp4//33ef/99wEICAjgf//7n8c+B+F6qqIcc84+67q+bUdURZlLr6np9OgiozCfH+7IuDMD/Y1dXHpNIa4VmrJtAxN1io+PJzU11dNhiHoy7fuW8pefAiy1p8C5yZhP5WLa8421jK5dZ8zZe2pdr08Z88nDGDM+sFy3aSjXzU9D03m88UIIryc/JcJnmA78YF3Wd+jhtutqEW3g/JxT0swnhOMkQQmfYTpY3c1bH9XdbdfVdHr0nW+zrht3fuq2awtxNZMEJXyCqijDfKh6PDzt+paYT+WiKt3z8qy+653WZdPOT6U3nxAOkAQlfIIp62swVAKghbTAfOKQ5dmTyeCW6+tu7AJBwYClmc+0d4dbrivE1UwSlPAJxm8/ty5rbaLcfn0NDX2PvtZ1w+ZkzMVn3R6HEFcTSVDimqcMlZh2b7Ou6yLdn6BUZRm6Zjajm+/Zjso/7vY4hLiaSIIS1zzT95lQXmpZCQqxzNXkAVrI9Wih56f3UGaMX6R5JA4hrhaSoMQ1z/BFunVZf2MXlw4Qeyn6Tja9+b7+GHXujMdiEcLbSYIS1zRzwQnMe3daVjSdpbOCB2mtb0Rr2sKyYqjA8EmyR+MRwptJghLXNMNHK6zLuptuQWvUxIPRgKZp6LrcYV03ZPwPVXrxDNJCCElQ4hpmLirAuG29dd2vb5wHo6mma9Ohep6osmIMn8oYkELURBKUuGYZVi8Go+U9J127LujadfZwRBaaTofeZl4ow8crMZ08LN3OhbiAJChxTTLt+xbjVxut6/73P+7RzhEX0v2+E1rI+WdRpecwrF0C5SV1HySEj5EEJa456mwRFW/Nsq7ru96BLryN24Y1coSm06PvG2tdN/203aviE8IbSIIS1xRlNlG+ZAaq6FfLhgYN0XXo4dZhjRyl794HqjptlJdi2r7JswEJ4WUkQYlriiF9Keafqse58+sdi3Z+qgtvo/n5241ybshcizJILUqIKpKgxDXDdPgnDOnvWNf9BgxD16qdByO6NF37bnBdkGXlbBHGres8G5AQXkQSlLgmKEMFFe/MBmWZxkLXoQd+d4/wcFSXpun97GtRG5ajjN7VFCmEp0iCEteEyv+9gfrliGUloCH+8X8Dk9GzQTlIF9UdGgYCoArzMX65wbMBCeElJEGJq57p8F6MGR9Y1/U9+lqSlZd1iqiN5udvN0afYcMylPHqSK5CuJIkKHFVU4ZKKt55wdq0p4VHouvQw8NRXT5dhx5wvjOH+vUXDJtXeTgiITxPEpS4qhk+fKu6ac/PH787hnrVC7mO0vwD8Ov/oHXd8OFbmI//7MGIhPA8SVDiqmU6sAvDR+9b1/V/uAutcYjnArpCfn3uRRfZwbJiqKDspScwbFqF6eAuVEWZZ4MTwgP8PB2AEPWhSosto0UoBVi6a+s63OLZoK6Qhob/g09QsXiKZYLF0nNUJr9m2ennj/4P/QlIGIfOQxMuCuFuUoMSVx2lFBXLX0KdzrNsaNSEgITEq7Jpz5aqLEMV5uEX/ZC1V5+V0YBp+2bKpg7HmPW1ZwIUws0kQYmrilIKw+o3MG3/xLot4IGx1S+7XgN010fg/8Bf8Yt9BF3bjmjB11fvLCuh4tV/yBQdwidIE5+4aqjSc1SsmI/p64+t23RR3UGvv2q6lDtKC2iIvuetmJuFAWDOO4px2wYoOQvKTOWK+ZjzjhEw4u9oOr2HoxXCNaQGJbyeUgrj9s2UTUmwS05aq3bob7vbg5G5jy68Df5D/4LWur11mzHjA8pffhpzbo7nAhPChaQGJbyWUgrz3p1Upv4/zId/stun7xmNrsMtaDrf+RtLCwyiwZjnqVz9H8w/fgWA+cD3lE0dgf7WAfgPHYW+7U0ejlII55EEJbyGUgp1Khdzzj7MOfsxZX198btATZrif99j6G66FfXzDx6J07PMlmk6NM2SpJQCZca0MwPTzgx0bTvi1/c+/G67Gy3w2nkuJ3yTJCjhEUop1K8nMefstySjowcwH91vecZSE50e3U23oO/WG03vh2Y2otwbstfQNA2/7n0wt26P+eAPmA/+YN1nztlHZc4+KpNfQ9+tD35/6Ie+6x1o11AnEuE7JEEJt1BmE+qXHEz7v8e0/1tMB3ZB8W+XPtDPH32PvujadECrmtxPAKBrHo7fnydh3JGB+acdmI8eALPJsrOywlqrws8ffYdb0LXviu73nSzvUTVqgqbXW0ZONxotnUxMJggMQmvcFM0/wLM3JwRekqAyMzNZsGABlZWVdOjQgRdffJGgoCCHyphMJl566SW2bt2KyWTi0UcfZcQIyzQLOTk5TJ06laKiIgIDA0lKSqJdO8v8QKtXr+bdd9/FaDRy++23M23aNPz9/d1+795KmU1QWowqOYsqLQZNQ/Pzh6p/ej0YDGCoQBkqLWXPFaHOnUEVn7H8X1YMZSWownzMuUfBkcn4rgtC17ItXNcIXfMItJa/Q9/xD5iz97j8nq9Wuubh6Prehyovw3zkJ8zHDqLyjlUXMBow/bQd00/bHT9poyboro9Aa9ES3fUt0a6PAL0fVFZYJlWsLEdVlIOhEvwD0Bo0hICGaA0D0QKbQFATtMDGaAENLMfZ/NP0fue/h/zAbESVl0FFGaqy3PI95t8AAhpYkqR/A/Dzv+rfcRP14/EEVVhYyOTJk1m1ahVt27blX//6F/Pnz2fWrFkOlUlOTiYnJ4f169dTUlJCQkICnTp1omvXrkyaNInRo0cTGxvLli1bGD9+POvWrePnn39m4cKFrF27lpCQECZNmsR7773HmDFjPPdBuJGqLMewcQXmw3tQJiMYDZapKYwGVFkJquQslJ6zjtLgMg0aojULR9eus6XZrnk4BAWjv7GLJKR60Bpeh77jrfjFjMb0XSbq2EFLsirMv/yTlZzFXHIWjh7A5PxQL4+mQVWy0ust3eo1DXR60OlA01X/rwFolv3n/6/ObbbbsStjLWS3jQv2175Nu/A8cP75oEKhsLRHK+s2attWFWfVvel0lvttcB3+/ePR39zTVZ+yV/J4gtq2bRtdunShbdu2AIwYMYK4uDhmzpxp/auprjIZGRn88Y9/xM/Pj+DgYGJiYkhPTycsLIzDhw8TExMDQL9+/Xj++efZu3cvX3zxBQMGDKBZs2YAJCQkMGfOHJ9JUKZvP8eQ9pb7LxwUgq5NFFqjYLTwSLSQ69E0DV27zpKQnEjTNHRNW0DTFui73YnWPBzjjgzUr7+gigos4/pV/VFy/heh1uA6S3NfZTmUl1lHh/cKSkFlheUfXPazx2vlWaXpwC4CX9vgU++9eTxB5eXlER4ebl0PDw+nuLiYkpISazNfXWVyc3OJiIiw23fgwAFyc3MJDQ1FZ9MNOSwsjLy8PHJzc2nVqpXdMfn5Nf+VmZKSQkpKCgAHDx4kPj7eOTfuAkVFRTRt2tTB0je4NJYa/QZFe/acj3Gv+6/voMv7HD3jymJseP7fhfTnt4fUNyw73v45ent8cEGMvwHDHvJoPDWpqKhgwwbXTLLp8QRlNptrbF+2TSx1lVFK2e1TSqHT6Wo8RimFXq9HXdB0VXVMTRISEkhISAAgPj6e1NRUx2/Ozbw9PpAYnUVivHLeHh9cPTG6isffcoyIiKCgoMC6np+fT3BwMIGBgQ6VuXBfQUEB4eHhtGzZkl9//dUuGVXtq+0YIYQQ3sPjCap3797s3r2bnJwcAJKTk4mOjna4THR0NGvWrMFoNHL27Fk2bNjAwIEDCQ8PJzIyko0bNwKwdetWdDodUVFRDBgwgM8++4zTp0+jlCIlJYWBAwe67Z6FEEJcmseb+Jo3b868efNITEzEYDAQGRlJUlISWVlZTJs2jbS0tFrLgKXDxLFjx4iLi8NgMJCQkECvXr0AeOWVV5g+fTqLFy8mICCA119/HZ1Ox0033cRTTz3F6NGjMRgMdOvWzaEOElVNfd7K2+MDidFZJMYr5+3xgcSoqQsfyAghhBBewONNfEIIIURNJEEJIYTwSpKgzjtw4ACjRo3i/vvvJz4+nj17LC+Omkwm5s6dy5AhQxg0aBCrVq2yHpOTk8PIkSMZOnQow4YNIzs727pv9erVDB06lLvvvpuZM2diMDhnQr2MjAx69OhhXfem+NLS0rjvvvuIi4tj+PDhZGVleV2MdcnMzCQ2NpbBgweTmJhIcXGxS69nq6bPzls/N9vvQW+LsaafY2+K8ZNPPiE2Npa4uDj+8pe/cOzYMa+JTynFs88+yzvvvAM4/2tbVlbGxIkTueeeexg8eDAZGRkOBeXzSktL1Z133qkyMzOVUkp98sknavDgwUoppVasWKEef/xxZTAY1JkzZ9TgwYPV7t27lVJKPfjggyo9PV0ppVRmZqaKiYlRZrNZHThwQPXt21edPn1amUwmNWHCBLVkyZIrjvPIkSNq4MCBqnv37tZt3hJfdna2uvPOO1V+fr71ev369fOqGOty+vRp9X//93/qyJEjSimlXn75ZTVz5kyXXc9WbZ+dN35uF34PelOMtf0ce0uMZWVlqlu3bionJ0cppdTSpUvVmDFjvCK+Q4cOqVGjRqlu3bqpt99+Wynl/K9tUlKSmjZtmlJKqZMnT6revXur3NzcOuOSGhTw5Zdf0rp1a/r16wdYuq6/9tprgOWvxfj4+IuGUsrPz79oKKXS0lL27t3Lp59+ah1KSafTkZCQQHp6+hXFWFZWxjPPPMNzzz1nt91b4gsICGDOnDmEhoYC0LlzZ06dOkVlZaXXxFiXmobTWrdu3UUvdbtCbZ/dxx9/7FWfW03fg970ta3t59hbYjSZTCilOHfuHAAlJSU0aNDAK+JbuXIlDz30EEOGDLFuc3ZcGRkZPPSQZSSMli1bcuedd/LRRx/VGZfHu5m705YtW3jiiScu2v7kk0/SokULpkyZwv79+2nSpAnPPPMMgMuHUnIkvhdffJEvv/yShIQEOnToYLfPnfFdKsb7778fsDQVzJs3jwEDBhAQEOD2GOvDkSG3XKVVq1bWe7X97A4ePOhVn9uMGTMu+h70pq/tkSNHavw59pYYGzVqxPPPP8/w4cMJCQnBbDazatUqxo4d6/H4ZsyYAViSfBVnf24Xnq/qmLr4VILq168fe/dePAbc4sWL2bJlC8uXL6dbt25kZGTw17/+lc8//9zlQyk5Et/KlSvx8/Nj2LBhnDhx4qJzuyu+umKsUlpaynPPPUdeXh5vv/22R2KsD0eG3HK1Cz+7hx56yGs+t9q+B73pa2s0Gmv8OW7QoIFXxHjgwAEWLVrExo0biYyMZPny5YwbN+6iOLzl58PZX9sLzweX/vmSJj4gNDSUdu3a0a1bNwAGDhyIyWTi+PHjXjGU0tq1a8nKyiIuLo6//vWvlJeXExcXR35+vlfEV+WXX35h+PDh6PV6li9fTpMmlgkGvSnG2jgy5JYr1fTZedPnVtv3YFhYmNfEWNvPcevWrb0ixm3btnHLLbcQGRkJwMiRI/n5559p2bKlV8R3IWd//9UnZklQQN++fTlx4oS1597OnTvRNI1WrVp5xVBKq1evZv369aSlpbFkyRIaNmxIWloaYWFhXhEfQHFxMaNGjeLuu+/m1VdfpWHD6tGyvSXGujgy5Jar1PbZedPnVtv34KBBg7wmxtp+jgcOHOgVMd58883s3LmTU6dOAZZnMt7yO6Ymzo4rOjraOjNEXl4eW7dupX///nXGICNJnLdz505efvllysrKCAgIYMqUKdx6660YjUaSkpL46quvrEMpPfbYY4Clq+X06dMpKioiICCA2bNn06lTJwDWrFnD0qVLrUMpzZ49mwYNGlxxnCdOnCA2NpZdu3YBeE18b775Jq+99hpRUVF229977z0aN27sFTFeypYtW1iwYIHdcFohISEuu16V2j67d955hzfffNPrPjfb70Fv+f6rUtPPcffu3b0mxpUrV7JixQr8/f0JDg5mxowZ/O53v/Oa+J577jnat2/PY4895vSvbUlJCbNmzWLv3r2YTCaeeOIJ4uLi6oxHEpQQQgivJE18QgghvJIkKCGEEF5JEpQQQgivJAlKCCGEV5IEJYQQwitJghJCCOGVJEEJIYTwSpKghBBCeCVJUEIIIbySJCghhBBeSRKUEEIIryQJSrjEqFGjmDp1KgCpqancfPPNHo7Inm187pKZmcmhQ4cAy4CrHTp04Ntvv3XqNc6cOcOQIUMoKipy6nmrjBgxgh9//NEl5/7tt9945JFH6NKli3Xm1QstXLiQQYMGueT6Vd544w0GDBjg0msIx0iCEsIN8vPzGTt2LKdPn3bpdV588UWGDRtG06ZNXXL+SZMmMXnyZCorK51+7vXr17Nz505WrlzJf/7znxrLPProo9YpG8S1TxKUEG7gjkkDsrOz2bx5MyNGjHDZNf7whz/QqFEj0tPTnX7us2fP0qJFC7p27UpYWFiNZRo1akSzZs2cfm3hnSRBiXrZv38/Y8aM4dZbb6Vz584MHjyYDz/8sF7nKikpYerUqfTq1YtevXqRlJTEqFGjWLhwIWCZjv2NN97g7rvvpnPnztx6662MGzeOwsJCALZv387NN9/Mxx9/zIABA+jRowdjx44lNzfX4Ri+/fZbhg8fTteuXYmOjmbBggVUVFRY93fo0IHVq1czcuRIunbtypAhQy76S/7tt9/mrrvuolu3bjz11FPMmTOHUaNGAdCvXz8A/vKXv/Dcc89Zj/n++++Jj4+nc+fO3HPPPXz++efWfT/88APDhw+ne/fu3HbbbTzzzDOcOXOm1ntYtmwZffv2pVGjRkB1M2JmZib33XcfXbp0YdiwYRw5coSFCxfyf//3f/Tq1Ys5c+ZYz3Hq1CmefvppbrvtNrp3787DDz/Mvn377K4zZMgQli5d6vBn64jnnnuO1157jV9++YUOHTqQmprKwoULGTVqFImJidxyyy28+uqrFzXx5ebmWvffcccdTJgwgfz8fOv+UaNGsWDBAp555hluueUW+vbty+zZszEajdYyGzdu5J577qFr1648/vjjFzWPpqamcs8999C5c2f69+/Pv//9b8xms1PvX9RMEpS4bKWlpTz66KOEhobywQcfkJaWRs+ePZk2bZp1ttDL8dxzz/Htt9/yn//8h2XLlrF371527txp3b906VKWL1/OtGnT2LRpEwsWLOC7775j8eLF1jImk4kFCxYwZ84cVq5cyW+//cbjjz9u94uoNvv27eOxxx5j0KBBrFu3jjlz5vD5558za9Ysu3Lz589n5MiRrF27lltvvZVZs2Zx8uRJAN5//30WLVrEhAkTWLt2LTfccAMrVqywHrt27VrA8gzF9tnXihUrmDBhAuvXr6d9+/b84x//oKyszDqh2+2338769etZsmQJWVlZJCUl1Xofn332GX379r1o+7x585g2bRr/+9//OHPmDAkJCZw4cYL//ve/TJgwgffff58tW7YA8Pzzz2M0Gvnvf/9LamoqjRo1Yty4cXbn69evH4cOHeL48eOX/GwdNXXqVMaMGUN4eDjbtm1j6NChAOzYsYPWrVuzdu1ahg0bZndMaWkpo0aNokGDBiQnJ/POO+9gMBgYPXq0XRPk0qVL+d3vfseaNWsYO3YsK1euZMOGDYBlgsN//OMfPPDAA6SlpdG7d29WrlxpPXb//v3MmDGDCRMmsHnzZqZMmcI777zjkhqkqIES4jKdOnVKvfnmm6q0tNS67dixYyoqKkrt3LlTKaXUn//8ZzVlyhSllFJr1qxRHTt2rPFcVcd99dVX1m2nT59WXbt2Vf/+97+VUkp9+umnKjMz0+64f/7zn+ovf/mLUkqpb775RkVFRalPP/3Uuv/o0aMqKipKffHFFzVe1za+iRMnqnHjxtnt//bbb1VUVJTKz89XSikVFRWlXn75Zev+3377TUVFRamNGzcqpZTq37+/eu211+zOER8fr/785z8rpZTKzc1VUVFR6ptvvlFKKXX8+HEVFRWlUlJSrOV/+uknFRUVpfbs2aOKiopUhw4d1IoVK5TZbFZKKXXo0CG1b9++Gu/n5MmTKioqSv3www/WbTVd46WXXlKdO3dW5eXl1m233367euutt5RSSsXGxqpJkyZZ9586dUp98803ymQyWcsbjUbVqVMnlZ6eXmMs9bVo0SLVv39/6/q///1v1aFDB1VWVma3beDAgUoppT744AN1xx13KKPRaN1fUVGhunfvrtatW6eUsnydhw0bZned++67T82aNUsppdT48eOt30dVEhMTrXFs3rxZde7cWWVlZVn3f/fdd+rkyZPOuGVxCX6eTpDi6tO8eXP+9Kc/8eGHH7Jv3z5ycnLYv38/YKnJXI69e/cC0L17d+u2Zs2a0bZtW+v6gAED2LVrF6+++ipHjhzh8OHDZGdnc+utt9qdq1evXtblyMhImjVrxsGDB+nTp0+dMezbt4+jR4/So0cP6zZ1/plRdnY2oaGhAHYxNWnSBACDwUBRUREnT560uweAW265xfq51Kamc5aXlxMSEsIjjzzCCy+8wMKFC7nzzjvp378/99xzT43nqaq51tQ5IjIy0rocGBhIaGio3dTgDRs2tNY4nnzySZ599lk2b95Mz5496du3L/fffz86XXVji16vJyQkpMYOH+np6cycOdO6Hhsby6233nrRthdeeKHOz6VKixYtaNiwYY379u7dS2Fh4UXfB2VlZWRnZ1vXbT9jsHzOBoMBgJ9//tna/Fqle/fuZGVlAdCnTx+6devGgw8+SJs2bejduzdDhw6lZcuWDsUvrowkKHHZCgoKSEhIICwsjP79+3PXXXcRGhrKgw8+eNnn0uv1QN2dCBYvXsySJUuIj4+nT58+jB07luXLl/PLL7/YlfPzs/92NpvNdr9Ya+Pv78/999/PmDFjLtrXokUL63JAQMBF+5VS1uvWdQ+1qSm+qvM8++yzjBw5ki1btrBt2zYmT55Meno6S5YsuegYTdMAanw2cuHnUtdnMmTIEO644w62bNnCV199xRtvvMF7773HBx98wPXXX28tZzQaazzPgAED6Natm3U9KCiIBg0aXLTNUbUlJ7B83W688cYae/w1btzYulzb1w0sn9uFXzd/f3+7669YsYKsrCy++OILtm7dyn//+18mTpxY4/eLcC55BiUu2yeffEJJSQkrV65k7NixDBgwwPpg+XJ/SXfo0AFN09i9e7d125kzZzh69Kh1fdmyZSQmJjJ9+nQeeughOnXqxNGjRy+61p49e6zLR44c4cyZM3Ts2PGSMdx4441kZ2fTpk0b67/CwkKSkpIoKSm55PGNGzemZcuWdvcA2L0vVJVAHHXs2DFmzpxJixYtGDlyJIsXLyYpKYktW7bUWHOpquVVdRypD6PRSFJSEidPniQ2NpZ58+axYcMGTp48yY4dO6zlzGaztcfdhYKCguw+x+bNm9e4zRnat2/PiRMnCAkJsTv3vHnzOHjwoEPnuOmmm9i1a5fdNtvvoy+//JJFixbRpUsXnnrqKZKTkxk+fLj1maJwLUlQ4rI1bdqU4uJiNm3axMmTJ/n000+tTTiX+35M69atufvuu3nhhRfYuXMnBw4c4JlnnqGsrMz6S71Zs2Zs27aN7Oxsfv75Z1544QV27dp10bWef/55vv/+e7KysvjnP/9Jly5d7Jr9ajNmzBh+/PFH5s2bR3Z2Njt27ODZZ5/l3LlzNf4Srsnjjz/OsmXLWLduHUeOHOGVV17hhx9+sO6v6ll34MABh16ibdq0KR999BGzZs0iOzub7OxsPvroIyIjI2tsxgsLCyMsLOyiHneXw8/Pj59++okZM2awe/dujh8/TkpKCv7+/nTq1Mlabv/+/ZhMJrp27VrvazlDbGwsTZs25e9//ztZWVkcPHiQiRMnsnv3btq3b+/QOUaPHk1WVpa1+Tg5OZmNGzda9/v7+7No0SKWL1/O8ePH2bVrF9u3b7erEQrXkQQlLts999zD6NGjmTNnDjExMbz++us8+eSTtGnTxtp2fznmzJlDp06dGDt2LKNGjeLmm2/mhhtusDa1JCUlcfbsWR544AEeeeQRzpw5w8SJEzl06BBlZWXW89x///38/e9/Z/To0URGRrJkyRKHmvg6dOjAm2++yffff289R8+ePWt9WbQmf/rTn3j00UdJSkoiLi6OnJwcBg4caG1eCgoKYtSoUcyfP59p06Zd8nyNGzfmrbfe4vjx4/zxj39k2LBhVFRU1HlP/fv3Z/v27Q7HXJMFCxbQqlUrxo4dy9ChQ8nIyGDRokW0adPGWmbHjh107NiRG2644YqudaUaNmzI0qVLadiwIaNHj2bEiBEYjUaWLVvmcC2tS5cuLF682NoVPy0tjUceecS6v1evXrz44ot88MEHxMTE8NRTT9GzZ0+3j0LiszzWPUMIpVR5ebn65JNPVElJiXVbZWWl6tGjh1q7dq1D56jqxZebm+uiKC9ty5YtKi8vz27bo48+qiZPnuy2GA4ePKi6du2qioqKXHqd++67T61evdql1xBCKaWkBiU8KiAggFmzZjFnzhyOHDlCdnY2zz//PP7+/jW+0+OtPvzwQ8aNG8eePXs4ceIEK1as4OuvvyY2NtZtMbRv356BAweSnJzssmvs2LGD8vJy4uLiXHYNIapIghIepWkab775JidPnmTYsGEMGzaMX375hffee++qGtJm+vTptGnThjFjxjB06FBWr17NggULuP32290ax9SpU1m7du0VdZaoyyuvvMK8efMu6hkohCtoSrlhkDAhhBDiMkkNSgghhFeSBCWEEMIrSYISQgjhlSRBCSGE8EqSoIQQQnil/w+aME/Z4ZchfAAAAABJRU5ErkJggg==\n", "text/plain": [ - "[Text(0, 0, '6 seconds\\nbefore'),\n", - " Text(1, 0, '4 seconds\\nbefore'),\n", - " Text(2, 0, '2 seconds\\nbefore'),\n", - " Text(3, 0, 'long gap\\n(reference)'),\n", - " Text(4, 0, '2 seconds\\nafter'),\n", - " Text(5, 0, '4 seconds\\nafter'),\n", - " Text(6, 0, '6 seconds\\nafter')]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -654,57 +612,60 @@ } ], "source": [ - "plt.figure(figsize=(8,5))\n", - "\n", - "#plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", - "\n", - "timepoint_labels = ['6 seconds\\nbefore', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap\\n(reference)',\n", - " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "data = pd.read_csv(flist[0])\n", + "all_latency_values_friends = data['gap_length']\n", "\n", - "friend_color = '#b7094c'\n", - "stranger_color = '#0091ad'\n", + "for file in flist[1:]:\n", + " \n", + " data = pd.read_csv(file)\n", + " all_latency_values_friends = all_latency_values_friends.append(data['gap_length'])\n", + " \n", + "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True,\n", + " kde_kws = {'linewidth': 3}, color='#F46036')\n", + "plt.xlim(-6000, 10000)\n", "\n", - "ax = sns.pointplot(x=\"timepoint\", y=\"connection\",\n", - " data=long_gaps_friends,\n", - " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", - " color = friend_color, capsize=0.12, markers='o', errwidth=1.5)\n", + "ax.set_xlabel('all gap lengths (ms) -- friends', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=12)\n", "\n", - "plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", - "plt.ylabel(\"connection rating\", fontsize=18, labelpad=10)\n", + "sns.despine()\n", + "plt.tight_layout()\n", "\n", - "ax.tick_params(labelsize=13)\n", - "plt.legend(fontsize='13')\n", + "#plt.savefig(os.path.join(base_dir,'Figures','FigS1B.pdf'), dpi=300)\n" + ] + }, + { + "cell_type": "markdown", + "id": "765f49ea", + "metadata": {}, + "source": [ + "## Figure S1C\n", "\n", - "ax.set_xticklabels(timepoint_labels)" + "A zoomed in version of both distributions, highlighting the long gaps " ] }, { "cell_type": "code", - "execution_count": 4, - "id": "e8d2b2a3", + "execution_count": 27, + "id": "6440e6d9", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[Text(0, 0, '6 seconds\\nbefore'),\n", - " Text(1, 0, '4 seconds\\nbefore'),\n", - " Text(2, 0, '2 seconds\\nbefore'),\n", - " Text(3, 0, 'long gap\\n(reference)'),\n", - " Text(4, 0, '2 seconds\\nafter'),\n", - " Text(5, 0, '4 seconds\\nafter'),\n", - " Text(6, 0, '6 seconds\\nafter')]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -712,195 +673,102 @@ } ], "source": [ - "plt.figure(figsize=(8,5))\n", - "\n", - "plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", - "\n", - "timepoint_labels = ['6 seconds\\nbefore', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap\\n(reference)',\n", - " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", + "min_value = 2000\n", + "max_value = 6000\n", + "\n", + "bins = []\n", + "interval = 75\n", + "counter = 0\n", + "new_value = 0\n", + "while new_value < max_value:\n", + " new_value = min_value+(interval*counter)\n", + " bins.append(new_value)\n", + " counter += 1\n", + " \n", + "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True, bins=bins,\n", + " kde_kws = {'linewidth': 0}, color='#F46036', label = 'friend', hist_kws=dict(alpha=0.5))\n", + "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True, bins=bins,\n", + " kde_kws = {'linewidth': 0}, color='#0FA3B1', label = 'stranger', hist_kws=dict(alpha=0.5))\n", "\n", - "friend_color = '#b7094c'\n", - "stranger_color = '#0091ad'\n", + "plt.xlim(min_value, max_value)\n", "\n", - "ax = sns.pointplot(x=\"timepoint\", y=\"connection\", hue=\"condition\",\n", - " data=long_gaps_all, dodge=True,\n", - " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", - " palette = [stranger_color,friend_color], capsize=0.12, markers='o', errwidth=1.5)\n", + "ax.set_xlabel('all gap lengths', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=12)\n", "\n", - "plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", - "plt.ylabel(\"connection rating\\n(compared to reference)\", fontsize=18, labelpad=10)\n", + "handles, labels = plt.gca().get_legend_handles_labels()\n", + "order = [1,0]\n", + "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])\n", "\n", - "ax.tick_params(labelsize=13)\n", - "plt.legend(fontsize='13')\n", + "sns.despine()\n", + "plt.tight_layout()\n", "\n", - "ax.set_xticklabels(timepoint_labels)" + "#plt.savefig(os.path.join(base_dir,'Figures','FigS1C.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "0386c943", + "id": "f87b90f2", "metadata": {}, "source": [ - "# Study 2\n", + "## Figure S2\n", "\n", - "Independent raters' perceptions of long gaps in conversation" + "See the notebook \"Supplement - how do different bin intervals impact Figure 2?\"" ] }, { - "cell_type": "code", - "execution_count": 34, - "id": "f4476707", + "cell_type": "markdown", + "id": "d82737d9", "metadata": {}, - "outputs": [], "source": [ - "rating_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_ratings_wide_format.csv'))\n", - "rating_data['video_num'] = rating_data['video_num'].astype(int)\n", - "data_info = pd.read_csv(os.path.join(base_dir, 'Data', 'long_gap_stimuli_info.csv'))\n", + "## Figure S3\n", "\n", - "data_full = pd.merge(data_info, rating_data, how='inner', on=['video_num'])" + "See the notebook \"Supplement - how do different thresholds impact Figure 2?\"" ] }, { "cell_type": "markdown", - "id": "a48c2656", + "id": "b2815a47", "metadata": {}, "source": [ - "## Figure 3A\n", + "## Figure S4A\n", "\n", - "Differences in perceived awkwardness in friend vs stranger conversations" + "Percentage of turns containing a question mark split by relationship type (stranger vs friend), gap length (> 2000ms (indicating a long gap) vs ≤ 2000ms) and position (before the gap vs after the gap)" ] }, { "cell_type": "code", - "execution_count": 35, - "id": "0face793", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(5,5))\n", - "\n", - "ax = sns.barplot(x=\"condition\", y=\"awkward_mean\", data=data_full, errwidth=1,\n", - " capsize=.05, \n", - " palette=['#3777FF', '#EF3054'],\n", - " saturation=1)\n", - "\n", - "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('perceived awkwardness', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 80))\n", - "\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "\n", - "# significance bar\n", - "lw = 2\n", - "line_color = '#594F4F' # create thinner, greyer lines\n", - "star_color = '#594F4F'\n", - "h = 0\n", - "space_between_stars_and_line = -.4\n", - "star_font_size = 20\n", - "\n", - "# strangers vs friends\n", - "x1, x2 = .20, .80 \n", - "y = 75\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3A.pdf'), dpi=300)\n" - ] - }, - { - "cell_type": "markdown", - "id": "83286a36", + "execution_count": 30, + "id": "9620e798", "metadata": {}, + "outputs": [], "source": [ - "## Figure 3B\n", - "\n", - "Differences in perceived connection in friend vs stranger conversations" + "df_total = pd.read_csv(os.path.join(base_dir, 'Analyses',\n", + " 'semantic_content_all.csv'))\n", + "df_total['question_numeric'] = df_total['question'].replace({'yes':1, 'no':0})" ] }, { "cell_type": "code", - "execution_count": 36, - "id": "27dbf862", + "execution_count": 31, + "id": "cf2165d1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "" ] }, + "execution_count": 31, "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(5,5))\n", - "\n", - "ax = sns.barplot(x=\"condition\", y=\"connected_mean\", data=data_full, errwidth=1,\n", - " capsize=.05, \n", - " palette=['#3777FF', '#EF3054'],\n", - " saturation=1)\n", - "\n", - "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('perceived connection', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 80))\n", - "\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "\n", - "# significance bar\n", - "lw = 2\n", - "line_color = '#594F4F' # create thinner, greyer lines\n", - "star_color = '#594F4F'\n", - "h = 0\n", - "space_between_stars_and_line = -.4\n", - "star_font_size = 20\n", - "\n", - "# strangers vs friends\n", - "x1, x2 = .20, .80 \n", - "y = 75\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3B.pdf'), dpi=300)\n" - ] - }, - { - "cell_type": "markdown", - "id": "4441b2d3", - "metadata": {}, - "source": [ - "## Figure 3C\n", - "\n", - "Differences in percieved awkwardness in friend vs stranger conversations, as a function of gap length" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "355eb455", - "metadata": {}, - "outputs": [ + "output_type": "execute_result" + }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, @@ -908,41 +776,46 @@ } ], "source": [ - "sns.lmplot(x=\"interval\", y=\"awkward_mean\", hue=\"condition\", data=data_full,\n", - " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", - " scatter_kws={'alpha':0.2}, legend=False)\n", - "\n", - "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", - "plt.ylabel(\"perceived awkwardness\", fontsize=15, labelpad=10)\n", - "plt.tick_params(labelsize=12)\n", - "\n", - "sns.despine()\n", - "plt.tight_layout()\n", + "sns.catplot(x=\"gap\", y=\"question_numeric\", hue=\"position\",\n", + " col=\"condition\", data=df_total,\n", + " height=5, aspect=.8, kind='bar', \n", + " saturation=1, errwidth=1, capsize=.05,\n", + " palette=['#7678ed', '#3d348b'])\n", "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3C.pdf'), dpi=300)\n" + "#plt.savefig(os.path.join(base_dir,'Figures','supplement_questions.pdf'), dpi=300)" ] }, { "cell_type": "markdown", - "id": "3a374263", + "id": "817d13ee", "metadata": {}, "source": [ - "## Figure 3D\n", + "## Figure S4B\n", "\n", - "Differences in percieved connection in friend vs stranger conversations, as a function of gap length" + "Word count of turns containing a question mark split by relationship type (stranger vs friend), gap length (> 2000ms (indicating a long gap) vs ≤ 2000ms) and position (before the gap vs after the gap). " ] }, { "cell_type": "code", - "execution_count": 38, - "id": "b7aeb7a5", + "execution_count": 32, + "id": "2da950fe", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -950,18 +823,13 @@ } ], "source": [ - "sns.lmplot(x=\"interval\", y=\"connected_mean\", hue=\"condition\", data=data_full,\n", - " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", - " scatter_kws={'alpha':0.2}, legend=False)\n", - "\n", - "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", - "plt.ylabel(\"perceived connection\", fontsize=15, labelpad=10)\n", - "plt.tick_params(labelsize=12)\n", + "sns.catplot(x=\"gap\", y=\"word_count\", hue=\"position\",\n", + " col=\"condition\", data=df_total,\n", + " height=5, aspect=.8, kind='bar', \n", + " saturation=1, errwidth=1, capsize=.05,\n", + " palette=['#cae9ff', '#5fa8d3'])\n", "\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3D.pdf'), dpi=300)\n" + "#plt.savefig(os.path.join(base_dir,'Figures','supplement_word_count.pdf'), dpi=300)" ] } ], diff --git a/Code/Discussion - percent of conversations that are long gaps.ipynb b/Code/Discussion - percent of conversations that are long gaps.ipynb deleted file mode 100644 index 5c94e6a..0000000 --- a/Code/Discussion - percent of conversations that are long gaps.ipynb +++ /dev/null @@ -1,134 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e2fec2de", - "metadata": {}, - "source": [ - "On average, what is the percentage of conversations that contain long gaps? Compute separately for friends and strangers." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "320bfaf0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import os\n", - "import glob\n", - "import pandas as pd\n", - "\n", - "base_dir = os.path.dirname(os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "e692062f", - "metadata": {}, - "source": [ - "# Strangers" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "812741ae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4768975155279503" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_time = 0\n", - "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", - "\n", - "for file in flist:\n", - " \n", - " data = pd.read_csv(file)\n", - " \n", - " turns_long = data.loc[data['gap_length'] > 2000].reset_index(drop=True)\n", - " \n", - " if len(turns_long) > 0:\n", - " \n", - " total_time += np.sum(turns_long['gap_length'])\n", - " \n", - "(total_time / (len(flist) * 600000)) * 100" - ] - }, - { - "cell_type": "markdown", - "id": "df8676d8", - "metadata": {}, - "source": [ - "# Friends" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3ab3f706", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.606897435897436" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_time = 0\n", - "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", - "\n", - "for file in flist:\n", - " \n", - " data = pd.read_csv(file)\n", - " \n", - " turns_long = data.loc[data['gap_length'] > 2000].reset_index(drop=True)\n", - " \n", - " if len(turns_long) > 0:\n", - " \n", - " total_time += np.sum(turns_long['gap_length'])\n", - " \n", - "(total_time / (len(flist) * 600000)) * 100" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3_8", - "language": "python", - "name": "python3_8" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git "a/Code/Study 1 - chi-square test (do friends use more \"long gaps\"?).ipynb" "b/Code/Study 1 - chi-square test (do friends use more \"long gaps\"?).ipynb" deleted file mode 100644 index f8b13a4..0000000 --- "a/Code/Study 1 - chi-square test (do friends use more \"long gaps\"?).ipynb" +++ /dev/null @@ -1,347 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1a45c883", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import os\n", - "import glob\n", - "import pandas as pd\n", - "\n", - "base_dir = os.path.dirname(os.getcwd())" - ] - }, - { - "cell_type": "markdown", - "id": "20e69c8f", - "metadata": {}, - "source": [ - "## Count the number of long gaps for stranger conversations" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "422bfc3c", - "metadata": {}, - "outputs": [], - "source": [ - "turns_strangers = pd.DataFrame()\n", - "turns_strangers = turns_strangers.fillna(0)\n", - "counter = 0\n", - "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", - "\n", - "for file in flist:\n", - " \n", - " name = file.split('/')[-1].split('.csv')[0]\n", - " \n", - " data = pd.read_csv(file)\n", - " \n", - " turns_long = data.loc[data['gap_length'] > 2000].reset_index(drop=True) # find gap lengths > 2 seconds long\n", - " \n", - " turns_strangers.at[counter, 'convo'] = name\n", - " turns_strangers.at[counter, 'n_long'] = len(turns_long)\n", - " turns_strangers.at[counter, 'n_not_long'] = len(data) - len(turns_long)\n", - "\n", - " counter += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "42335a7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " convo n_long n_not_long\n", - "0 103_101 4.0 164.0\n", - "1 24_25 0.0 128.0\n", - "2 4_6 0.0 94.0\n", - "3 102_110 2.0 199.0\n", - "4 102_104 3.0 188.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "turns_strangers.head()" - ] - }, - { - "cell_type": "markdown", - "id": "7beb183c", - "metadata": {}, - "source": [ - "## Count the number of long gaps for friend conversations" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f488c005", - "metadata": {}, - "outputs": [], - "source": [ - "turns_friends = pd.DataFrame()\n", - "turns_friends = turns_friends.fillna(0)\n", - "counter = 0\n", - "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", - "\n", - "for file in flist:\n", - " \n", - " name = file.split('/')[-1].split('.csv')[0]\n", - " \n", - " data = pd.read_csv(file)\n", - " \n", - " turns_long = data.loc[data['gap_length'] > 2000].reset_index(drop=True) # find gap lengths > 2 seconds long\n", - " \n", - " turns_friends.at[counter, 'convo'] = name\n", - " turns_friends.at[counter, 'n_long'] = len(turns_long)\n", - " turns_friends.at[counter, 'n_not_long'] = len(data) - len(turns_long)\n", - "\n", - " counter += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8e77b218", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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convon_longn_not_long
021_2110.0127.0
18_2354.0150.0
223_2600.0158.0
330_2153.0131.0
4101_2070.0160.0
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" - ], - "text/plain": [ - " convo n_long n_not_long\n", - "0 21_211 0.0 127.0\n", - "1 8_235 4.0 150.0\n", - "2 23_260 0.0 158.0\n", - "3 30_215 3.0 131.0\n", - "4 101_207 0.0 160.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "turns_friends.head()" - ] - }, - { - "cell_type": "markdown", - "id": "b79d9b9b", - "metadata": {}, - "source": [ - "## Chi-sqaure test" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b530fbd1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[218, 9346], [346, 47614]]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = [[int(np.sum(turns_friends['n_long'])), int(np.sum(turns_friends['n_not_long']))], \n", - " [int(np.sum(turns_strangers['n_long'])), int(np.sum(turns_strangers['n_not_long']))]]\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c9402719", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import chi2_contingency\n", - "\n", - "stat, p, dof, expected = chi2_contingency(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "333d3a39", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stat: 197.7519734203746\n", - "p-value: 6.462717395100015e-45\n", - "n: 57524\n" - ] - } - ], - "source": [ - "print('stat: ' + str(stat))\n", - "print('p-value: ' + str(p))\n", - "print('n: ' + str(218 + 9346 + 346 + 47614))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3_8", - "language": "python", - "name": "python3_8" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git "a/Code/Study 1 - defining a \"long gap\".ipynb" "b/Code/Study 1 - defining a \"long gap\".ipynb" index cb25786..d17817d 100644 --- "a/Code/Study 1 - defining a \"long gap\".ipynb" +++ "b/Code/Study 1 - defining a \"long gap\".ipynb" @@ -46,7 +46,7 @@ " for i in range(len(data)):\n", " all_gap_lengths.append(data.at[i, 'gap_length'])\n", "\n", - "flist_strangers = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", + "flist_strangers = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", "\n", "for file in flist_strangers:\n", " data = pd.read_csv(file)\n", @@ -56,23 +56,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "e8e39762", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "5588bdde", "metadata": {}, "outputs": [ @@ -95,10 +95,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "mean: 244.15ms\n", - "standard deviation: 595.68ms\n", + "mean: 248.2ms\n", + "standard deviation: 598.02ms\n", "\n", - "3 standard deviations above the mean: 2031.2ms\n" + "3 standard deviations above the mean: 2042.24ms\n" ] } ], diff --git a/Code/Study 1 - do friends use more long gaps?.ipynb b/Code/Study 1 - do friends use more long gaps?.ipynb new file mode 100644 index 0000000..dad5e59 --- /dev/null +++ b/Code/Study 1 - do friends use more long gaps?.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "95469010", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import glob\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "id": "3364f2d5", + "metadata": {}, + "source": [ + "# Count the number of long gaps in each conversation\n", + "\n", + "Record the number of long gaps and the total number of turns to compute frequency. Use different cutoffs for defining a long gap. Main text defines gaps over 2000ms as long." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d909916", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps = pd.DataFrame()\n", + "long_gaps = long_gaps.fillna(0)\n", + "counter = 0\n", + "\n", + "cutoffs = [500, 750, 1000, 1250, 1500, 1750, 2000,\n", + " 2250, 2500, 2750, 3000]\n", + "\n", + "folders = ['strangers_pure', 'friends']\n", + "\n", + "for folder in folders:\n", + " \n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', '{}'.format(folder), '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " name = file.split('/')[-1].split('.csv')[0]\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + " n_total = len(data)\n", + " \n", + " long_gaps.at[counter, 'convo'] = name\n", + " long_gaps.at[counter, 'subID'] = id_1\n", + " long_gaps.at[counter, 'partnerID'] = id_2\n", + " long_gaps.at[counter, 'n_total'] = n_total\n", + " long_gaps.at[counter, 'condition'] = folder\n", + " \n", + " for cutoff in cutoffs:\n", + "\n", + " turns_long = data.loc[data['gap_length'] > cutoff].reset_index(drop=True) \n", + "\n", + " long_gaps.at[counter, 'n_long_{}'.format(cutoff)] = len(turns_long)\n", + " long_gaps.at[counter, 'freq_long_{}'.format(cutoff)] = len(turns_long) / n_total\n", + "\n", + " counter += 1\n", + " \n", + " long_gaps.at[counter, 'convo'] = name\n", + " long_gaps.at[counter, 'subID'] = id_2\n", + " long_gaps.at[counter, 'partnerID'] = id_1\n", + " long_gaps.at[counter, 'n_total'] = n_total\n", + " long_gaps.at[counter, 'condition'] = folder\n", + " \n", + " for cutoff in cutoffs:\n", + "\n", + " turns_long = data.loc[data['gap_length'] > cutoff].reset_index(drop=True) \n", + "\n", + " long_gaps.at[counter, 'n_long_{}'.format(cutoff)] = len(turns_long)\n", + " long_gaps.at[counter, 'freq_long_{}'.format(cutoff)] = len(turns_long) / n_total\n", + "\n", + " counter += 1\n", + "\n", + "long_gaps.to_csv(os.path.join(base_dir, 'Analyses', 'long_gap_counts.csv'), encoding='utf-8', index=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bc4c35e7", + "metadata": {}, + "source": [ + "# How frequent are long gaps in each threshold?" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "4d0d8511", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500\n", + "strangers\n", + "8508.0\n", + "0.21996432172496705\n", + "friends\n", + "2812.0\n", + "0.2940192388122125\n", + "_________\n", + "750\n", + "strangers\n", + "4197.0\n", + "0.1085084929806872\n", + "friends\n", + "1717.0\n", + "0.17952739439565035\n", + "_________\n", + "1000\n", + "strangers\n", + "2316.0\n", + "0.05987745288140851\n", + "friends\n", + "1122.0\n", + "0.11731493099121706\n", + "_________\n", + "1250\n", + "strangers\n", + "1315.0\n", + "0.03399777657126606\n", + "friends\n", + "741.0\n", + "0.0774780426599749\n", + "_________\n", + "1500\n", + "strangers\n", + "755.0\n", + "0.019519635978179375\n", + "friends\n", + "476.0\n", + "0.049769970723546636\n", + "_________\n", + "1750\n", + "strangers\n", + "455.0\n", + "0.011763489231882933\n", + "friends\n", + "343.0\n", + "0.03586365537432037\n", + "_________\n", + "2000\n", + "strangers\n", + "274.0\n", + "0.007083947361617415\n", + "friends\n", + "218.0\n", + "0.022793810121288165\n", + "_________\n", + "2250\n", + "strangers\n", + "178.0\n", + "0.004601980402802555\n", + "friends\n", + "150.0\n", + "0.015683814303638646\n", + "_________\n", + "2500\n", + "strangers\n", + "122.0\n", + "0.0031541663434938856\n", + "friends\n", + "108.0\n", + "0.011292346298619825\n", + "_________\n", + "2750\n", + "strangers\n", + "84.0\n", + "0.0021717210889630034\n", + "friends\n", + "85.0\n", + "0.008887494772061898\n", + "_________\n", + "3000\n", + "strangers\n", + "66.0\n", + "0.0017063522841852168\n", + "friends\n", + "70.0\n", + "0.007319113341698034\n", + "_________\n" + ] + } + ], + "source": [ + "cutoffs = [500, 750, 1000, 1250, 1500, 1750, 2000,\n", + " 2250, 2500, 2750, 3000]\n", + "\n", + "strangers = long_gaps.loc[long_gaps['condition'] == 'strangers_pure'].reset_index(drop=True)\n", + "friends = long_gaps.loc[long_gaps['condition'] == 'friends'].reset_index(drop=True)\n", + "\n", + "n_turns_total_strangers = np.sum(strangers['n_total']) / 2\n", + "n_turns_total_friends = np.sum(friends['n_total']) / 2\n", + "\n", + "for cutoff in cutoffs:\n", + " \n", + " n_turns_cutoff_strangers = np.sum(strangers['n_long_{}'.format(cutoff)]) / 2\n", + " n_turns_cutoff_friends = np.sum(friends['n_long_{}'.format(cutoff)]) / 2\n", + " \n", + " print(cutoff)\n", + " print('strangers')\n", + " print(n_turns_cutoff_strangers)\n", + " print(n_turns_cutoff_strangers / n_turns_total_strangers)\n", + " print('friends')\n", + " print(n_turns_cutoff_friends)\n", + " print(n_turns_cutoff_friends / n_turns_total_friends)\n", + " print('_________')\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "a4af4211", + "metadata": {}, + "source": [ + "# Plot individual distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "bd2e53dc", + "metadata": {}, + "outputs": [], + "source": [ + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + "for file in flist:\n", + "\n", + " name = file.split('/')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + " \n", + " ax=sns.histplot(x=data['gap_length'], color='#f9c74f', alpha=1, kde=False, binwidth=100, binrange=(-2000,2000))\n", + " ax=sns.histplot(x=data['gap_length'], color='#f94144', alpha=1, kde=False, binwidth=100, binrange=(2000,5000))\n", + " sns.despine()\n", + " plt.title(name)\n", + "\n", + " ax.set_xlabel('gap lengths (ms)', fontsize=15, labelpad=10)\n", + " ax.set_ylabel('count', fontsize=15, labelpad=10)\n", + " ax.tick_params(labelsize=12)\n", + " ax.set(xlim=(-2000, 5000))\n", + " ax.set(ylim=(0, 22))\n", + " \n", + " plt.axvline(x=2000, color='#277da1', linestyle='dotted')\n", + " \n", + " plt.tight_layout()\n", + " \n", + " plt.savefig(os.path.join(base_dir,'Figures','individual_distributions',\n", + " 'strangers', '{}.pdf'.format(name)), dpi=300)\n", + " plt.close()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "0d17c49e", + "metadata": {}, + "outputs": [], + "source": [ + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + "for file in flist:\n", + "\n", + " name = file.split('/')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + " \n", + " ax=sns.histplot(x=data['gap_length'], color='#f9c74f', alpha=1, kde=False, binwidth=100, binrange=(-2000,2000))\n", + " ax=sns.histplot(x=data['gap_length'], color='#f94144', alpha=1, kde=False, binwidth=100, binrange=(2000,5000))\n", + " sns.despine()\n", + " plt.title(name)\n", + "\n", + " ax.set_xlabel('gap lengths (ms)', fontsize=15, labelpad=10)\n", + " ax.set_ylabel('count', fontsize=15, labelpad=10)\n", + " ax.tick_params(labelsize=12)\n", + " ax.set(xlim=(-2000, 5000))\n", + " \n", + " plt.axvline(x=2000, color='#277da1', linestyle='dotted')\n", + " \n", + " plt.tight_layout()\n", + " \n", + " plt.savefig(os.path.join(base_dir,'Figures','individual_distributions',\n", + " 'friends', '{}.pdf'.format(name)), dpi=300)\n", + " plt.close()\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Study 1 - long gaps and self-reported connection.ipynb b/Code/Study 1 - long gaps and self-reported connection.ipynb index 180067c..cc24c09 100644 --- a/Code/Study 1 - long gaps and self-reported connection.ipynb +++ b/Code/Study 1 - long gaps and self-reported connection.ipynb @@ -29,6 +29,38 @@ "base_dir = os.path.dirname(os.getcwd())" ] }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b9f2c179", + "metadata": {}, + "outputs": [], + "source": [ + "def find_video(file):\n", + " \"\"\"Need video name to use key in assign_idenity function.\n", + "\n", + " \"\"\"\n", + " id1 = file.split('/')[-1].split('_')[0]\n", + " id2 = file.split('_')[-1].split('.csv')[0]\n", + " video_name_1 = id1 + '_' + id2\n", + " video_name_2 = id2 + '_' + id1\n", + " return video_name_1, video_name_2\n", + "\n", + "\n", + "def assign_identity(file):\n", + " \"\"\"Use key to determine which subject ID corresponds to S1 (speaker 1)\n", + " and which subject ID corresponds to S2 (speaker 2).\n", + "\n", + " \"\"\"\n", + " key = pd.read_csv(os.path.join(base_dir, 'Data', 'subject_id_key.csv'))\n", + " video_name_1, video_name_2 = find_video(file)\n", + " for i in range(len((key))):\n", + " if (key.at[i,'video_name'] == video_name_1) | (key.at[i,'video_name'] == video_name_2):\n", + " subID_S1 = key.at[i,'S1']\n", + " subID_S2 = key.at[i,'S2']\n", + " return subID_S1, subID_S2" + ] + }, { "cell_type": "markdown", "id": "d905d50f", @@ -39,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "71d71c7a", "metadata": {}, "outputs": [], @@ -48,13 +80,12 @@ "long_gap_connection = long_gap_connection.fillna(0)\n", "counter = 0\n", "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", "\n", "for file in flist:\n", " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", " convo = file.split('/')[-1].split('.csv')[0]\n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -62,8 +93,8 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", " \n", " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", @@ -152,8 +183,8 @@ " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", "\n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", @@ -163,11 +194,18 @@ " long_gap_connection.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", "\n", " counter += 1\n", "\n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", @@ -177,6 +215,13 @@ " long_gap_connection.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", "\n", " counter += 1\n", " \n", @@ -202,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "ff7819ee", "metadata": {}, "outputs": [], @@ -215,9 +260,8 @@ "\n", "for file in flist:\n", " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", " convo = file.split('/')[-1].split('.csv')[0]\n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -225,8 +269,8 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", " \n", " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", @@ -315,8 +359,8 @@ " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", "\n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", @@ -326,11 +370,18 @@ " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", "\n", " counter += 1\n", "\n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", @@ -340,6 +391,13 @@ " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", "\n", " counter += 1\n", " \n", @@ -365,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "f400d825", "metadata": {}, "outputs": [], @@ -380,1015 +438,6 @@ " encoding='utf-8', index=False)" ] }, - { - "cell_type": "markdown", - "id": "265fbd40", - "metadata": {}, - "source": [ - "# correcting for multiple comparisions\n", - "\n", - "Sometime in between first running these analyses and returning to check them before submitting the paper, the statsmodels multitest function stopped working (https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html). I therefore copy and pasted the source code for the function below. Source code came from here: https://www.statsmodels.org/dev/_modules/statsmodels/stats/multitest.html" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e1630260", - "metadata": {}, - "outputs": [], - "source": [ - "def _ecdf(x):\n", - " '''no frills empirical cdf used in fdrcorrection\n", - " '''\n", - " nobs = len(x)\n", - " return np.arange(1,nobs+1)/float(nobs)\n", - "\n", - "multitest_methods_names = {'b': 'Bonferroni',\n", - " 's': 'Sidak',\n", - " 'h': 'Holm',\n", - " 'hs': 'Holm-Sidak',\n", - " 'sh': 'Simes-Hochberg',\n", - " 'ho': 'Hommel',\n", - " 'fdr_bh': 'FDR Benjamini-Hochberg',\n", - " 'fdr_by': 'FDR Benjamini-Yekutieli',\n", - " 'fdr_tsbh': 'FDR 2-stage Benjamini-Hochberg',\n", - " 'fdr_tsbky': 'FDR 2-stage Benjamini-Krieger-Yekutieli',\n", - " 'fdr_gbs': 'FDR adaptive Gavrilov-Benjamini-Sarkar'\n", - " }\n", - "\n", - "_alias_list = [['b', 'bonf', 'bonferroni'],\n", - " ['s', 'sidak'],\n", - " ['h', 'holm'],\n", - " ['hs', 'holm-sidak'],\n", - " ['sh', 'simes-hochberg'],\n", - " ['ho', 'hommel'],\n", - " ['fdr_bh', 'fdr_i', 'fdr_p', 'fdri', 'fdrp'],\n", - " ['fdr_by', 'fdr_n', 'fdr_c', 'fdrn', 'fdrcorr'],\n", - " ['fdr_tsbh', 'fdr_2sbh'],\n", - " ['fdr_tsbky', 'fdr_2sbky', 'fdr_twostage'],\n", - " ['fdr_gbs']\n", - " ]\n", - "\n", - "\n", - "multitest_alias = {}\n", - "for m in _alias_list:\n", - " multitest_alias[m[0]] = m[0]\n", - " for a in m[1:]:\n", - " multitest_alias[a] = m[0]\n", - "\n", - "def multipletests(pvals, alpha=0.05, method='hs', is_sorted=False,\n", - " returnsorted=False):\n", - " \"\"\"\n", - " Test results and p-value correction for multiple tests\n", - "\n", - " Parameters\n", - " ----------\n", - " pvals : array_like, 1-d\n", - " uncorrected p-values. Must be 1-dimensional.\n", - " alpha : float\n", - " FWER, family-wise error rate, e.g. 0.1\n", - " method : str\n", - " Method used for testing and adjustment of pvalues. Can be either the\n", - " full name or initial letters. Available methods are:\n", - "\n", - " - `bonferroni` : one-step correction\n", - " - `sidak` : one-step correction\n", - " - `holm-sidak` : step down method using Sidak adjustments\n", - " - `holm` : step-down method using Bonferroni adjustments\n", - " - `simes-hochberg` : step-up method (independent)\n", - " - `hommel` : closed method based on Simes tests (non-negative)\n", - " - `fdr_bh` : Benjamini/Hochberg (non-negative)\n", - " - `fdr_by` : Benjamini/Yekutieli (negative)\n", - " - `fdr_tsbh` : two stage fdr correction (non-negative)\n", - " - `fdr_tsbky` : two stage fdr correction (non-negative)\n", - "\n", - " is_sorted : bool\n", - " If False (default), the p_values will be sorted, but the corrected\n", - " pvalues are in the original order. If True, then it assumed that the\n", - " pvalues are already sorted in ascending order.\n", - " returnsorted : bool\n", - " not tested, return sorted p-values instead of original sequence\n", - "\n", - " Returns\n", - " -------\n", - " reject : ndarray, boolean\n", - " true for hypothesis that can be rejected for given alpha\n", - " pvals_corrected : ndarray\n", - " p-values corrected for multiple tests\n", - " alphacSidak : float\n", - " corrected alpha for Sidak method\n", - " alphacBonf : float\n", - " corrected alpha for Bonferroni method\n", - "\n", - " Notes\n", - " -----\n", - " There may be API changes for this function in the future.\n", - "\n", - " Except for 'fdr_twostage', the p-value correction is independent of the\n", - " alpha specified as argument. In these cases the corrected p-values\n", - " can also be compared with a different alpha. In the case of 'fdr_twostage',\n", - " the corrected p-values are specific to the given alpha, see\n", - " ``fdrcorrection_twostage``.\n", - "\n", - " The 'fdr_gbs' procedure is not verified against another package, p-values\n", - " are derived from scratch and are not derived in the reference. In Monte\n", - " Carlo experiments the method worked correctly and maintained the false\n", - " discovery rate.\n", - "\n", - " All procedures that are included, control FWER or FDR in the independent\n", - " case, and most are robust in the positively correlated case.\n", - "\n", - " `fdr_gbs`: high power, fdr control for independent case and only small\n", - " violation in positively correlated case\n", - "\n", - " **Timing**:\n", - "\n", - " Most of the time with large arrays is spent in `argsort`. When\n", - " we want to calculate the p-value for several methods, then it is more\n", - " efficient to presort the pvalues, and put the results back into the\n", - " original order outside of the function.\n", - "\n", - " Method='hommel' is very slow for large arrays, since it requires the\n", - " evaluation of n partitions, where n is the number of p-values.\n", - " \"\"\"\n", - " import gc\n", - " pvals = np.asarray(pvals)\n", - " alphaf = alpha # Notation ?\n", - "\n", - " if not is_sorted:\n", - " sortind = np.argsort(pvals)\n", - " pvals = np.take(pvals, sortind)\n", - "\n", - " ntests = len(pvals)\n", - " alphacSidak = 1 - np.power((1. - alphaf), 1./ntests)\n", - " alphacBonf = alphaf / float(ntests)\n", - " if method.lower() in ['b', 'bonf', 'bonferroni']:\n", - " reject = pvals <= alphacBonf\n", - " pvals_corrected = pvals * float(ntests)\n", - "\n", - " elif method.lower() in ['s', 'sidak']:\n", - " reject = pvals <= alphacSidak\n", - " pvals_corrected = -np.expm1(ntests * np.log1p(-pvals))\n", - "\n", - " elif method.lower() in ['hs', 'holm-sidak']:\n", - " alphacSidak_all = 1 - np.power((1. - alphaf),\n", - " 1./np.arange(ntests, 0, -1))\n", - " notreject = pvals > alphacSidak_all\n", - " del alphacSidak_all\n", - "\n", - " nr_index = np.nonzero(notreject)[0]\n", - " if nr_index.size == 0:\n", - " # nonreject is empty, all rejected\n", - " notrejectmin = len(pvals)\n", - " else:\n", - " notrejectmin = np.min(nr_index)\n", - " notreject[notrejectmin:] = True\n", - " reject = ~notreject\n", - " del notreject\n", - "\n", - " # It's eqivalent to 1 - np.power((1. - pvals),\n", - " # np.arange(ntests, 0, -1))\n", - " # but prevents the issue of the floating point precision\n", - " pvals_corrected_raw = -np.expm1(np.arange(ntests, 0, -1) *\n", - " np.log1p(-pvals))\n", - " pvals_corrected = np.maximum.accumulate(pvals_corrected_raw)\n", - " del pvals_corrected_raw\n", - "\n", - " elif method.lower() in ['h', 'holm']:\n", - " notreject = pvals > alphaf / np.arange(ntests, 0, -1)\n", - " nr_index = np.nonzero(notreject)[0]\n", - " if nr_index.size == 0:\n", - " # nonreject is empty, all rejected\n", - " notrejectmin = len(pvals)\n", - " else:\n", - " notrejectmin = np.min(nr_index)\n", - " notreject[notrejectmin:] = True\n", - " reject = ~notreject\n", - " pvals_corrected_raw = pvals * np.arange(ntests, 0, -1)\n", - " pvals_corrected = np.maximum.accumulate(pvals_corrected_raw)\n", - " del pvals_corrected_raw\n", - " gc.collect()\n", - "\n", - " elif method.lower() in ['sh', 'simes-hochberg']:\n", - " alphash = alphaf / np.arange(ntests, 0, -1)\n", - " reject = pvals <= alphash\n", - " rejind = np.nonzero(reject)\n", - " if rejind[0].size > 0:\n", - " rejectmax = np.max(np.nonzero(reject))\n", - " reject[:rejectmax] = True\n", - " pvals_corrected_raw = np.arange(ntests, 0, -1) * pvals\n", - " pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]\n", - " del pvals_corrected_raw\n", - "\n", - " elif method.lower() in ['ho', 'hommel']:\n", - " # we need a copy because we overwrite it in a loop\n", - " a = pvals.copy()\n", - " for m in range(ntests, 1, -1):\n", - " cim = np.min(m * pvals[-m:] / np.arange(1,m+1.))\n", - " a[-m:] = np.maximum(a[-m:], cim)\n", - " a[:-m] = np.maximum(a[:-m], np.minimum(m * pvals[:-m], cim))\n", - " pvals_corrected = a\n", - " reject = a <= alphaf\n", - "\n", - " elif method.lower() in ['fdr_bh', 'fdr_i', 'fdr_p', 'fdri', 'fdrp']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha,\n", - " method='indep',\n", - " is_sorted=True)\n", - " elif method.lower() in ['fdr_by', 'fdr_n', 'fdr_c', 'fdrn', 'fdrcorr']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha,\n", - " method='n',\n", - " is_sorted=True)\n", - " elif method.lower() in ['fdr_tsbky', 'fdr_2sbky', 'fdr_twostage']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha,\n", - " method='bky',\n", - " is_sorted=True)[:2]\n", - " elif method.lower() in ['fdr_tsbh', 'fdr_2sbh']:\n", - " # delegate, call with sorted pvals\n", - " reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha,\n", - " method='bh',\n", - " is_sorted=True)[:2]\n", - "\n", - " elif method.lower() in ['fdr_gbs']:\n", - " #adaptive stepdown in Gavrilov, Benjamini, Sarkar, Annals of Statistics 2009\n", - "## notreject = pvals > alphaf / np.arange(ntests, 0, -1) #alphacSidak\n", - "## notrejectmin = np.min(np.nonzero(notreject))\n", - "## notreject[notrejectmin:] = True\n", - "## reject = ~notreject\n", - "\n", - " ii = np.arange(1, ntests + 1)\n", - " q = (ntests + 1. - ii)/ii * pvals / (1. - pvals)\n", - " pvals_corrected_raw = np.maximum.accumulate(q) #up requirementd\n", - "\n", - " pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]\n", - " del pvals_corrected_raw\n", - " reject = pvals_corrected <= alpha\n", - "\n", - " else:\n", - " raise ValueError('method not recognized')\n", - "\n", - " if pvals_corrected is not None: #not necessary anymore\n", - " pvals_corrected[pvals_corrected>1] = 1\n", - " if is_sorted or returnsorted:\n", - " return reject, pvals_corrected, alphacSidak, alphacBonf\n", - " else:\n", - " pvals_corrected_ = np.empty_like(pvals_corrected)\n", - " pvals_corrected_[sortind] = pvals_corrected\n", - " del pvals_corrected\n", - " reject_ = np.empty_like(reject)\n", - " reject_[sortind] = reject\n", - " return reject_, pvals_corrected_, alphacSidak, alphacBonf\n", - "\n", - "\n", - "\n", - "def fdrcorrection(pvals, alpha=0.05, method='indep', is_sorted=False):\n", - " '''\n", - " pvalue correction for false discovery rate.\n", - "\n", - " This covers Benjamini/Hochberg for independent or positively correlated and\n", - " Benjamini/Yekutieli for general or negatively correlated tests.\n", - "\n", - " Parameters\n", - " ----------\n", - " pvals : array_like, 1d\n", - " Set of p-values of the individual tests.\n", - " alpha : float, optional\n", - " Family-wise error rate. Defaults to ``0.05``.\n", - " method : {'i', 'indep', 'p', 'poscorr', 'n', 'negcorr'}, optional\n", - " Which method to use for FDR correction.\n", - " ``{'i', 'indep', 'p', 'poscorr'}`` all refer to ``fdr_bh``\n", - " (Benjamini/Hochberg for independent or positively\n", - " correlated tests). ``{'n', 'negcorr'}`` both refer to ``fdr_by``\n", - " (Benjamini/Yekutieli for general or negatively correlated tests).\n", - " Defaults to ``'indep'``.\n", - " is_sorted : bool, optional\n", - " If False (default), the p_values will be sorted, but the corrected\n", - " pvalues are in the original order. If True, then it assumed that the\n", - " pvalues are already sorted in ascending order.\n", - "\n", - " Returns\n", - " -------\n", - " rejected : ndarray, bool\n", - " True if a hypothesis is rejected, False if not\n", - " pvalue-corrected : ndarray\n", - " pvalues adjusted for multiple hypothesis testing to limit FDR\n", - "\n", - " Notes\n", - " -----\n", - " If there is prior information on the fraction of true hypothesis, then alpha\n", - " should be set to ``alpha * m/m_0`` where m is the number of tests,\n", - " given by the p-values, and m_0 is an estimate of the true hypothesis.\n", - " (see Benjamini, Krieger and Yekuteli)\n", - "\n", - " The two-step method of Benjamini, Krieger and Yekutiel that estimates the number\n", - " of false hypotheses will be available (soon).\n", - "\n", - " Both methods exposed via this function (Benjamini/Hochberg, Benjamini/Yekutieli)\n", - " are also available in the function ``multipletests``, as ``method=\"fdr_bh\"`` and\n", - " ``method=\"fdr_by\"``, respectively.\n", - "\n", - " See also\n", - " --------\n", - " multipletests\n", - "\n", - " '''\n", - " pvals = np.asarray(pvals)\n", - " assert pvals.ndim == 1, \"pvals must be 1-dimensional, that is of shape (n,)\"\n", - "\n", - " if not is_sorted:\n", - " pvals_sortind = np.argsort(pvals)\n", - " pvals_sorted = np.take(pvals, pvals_sortind)\n", - " else:\n", - " pvals_sorted = pvals # alias\n", - "\n", - " if method in ['i', 'indep', 'p', 'poscorr']:\n", - " ecdffactor = _ecdf(pvals_sorted)\n", - " elif method in ['n', 'negcorr']:\n", - " cm = np.sum(1./np.arange(1, len(pvals_sorted)+1)) #corrected this\n", - " ecdffactor = _ecdf(pvals_sorted) / cm\n", - "## elif method in ['n', 'negcorr']:\n", - "## cm = np.sum(np.arange(len(pvals)))\n", - "## ecdffactor = ecdf(pvals_sorted)/cm\n", - " else:\n", - " raise ValueError('only indep and negcorr implemented')\n", - " reject = pvals_sorted <= ecdffactor*alpha\n", - " if reject.any():\n", - " rejectmax = max(np.nonzero(reject)[0])\n", - " reject[:rejectmax] = True\n", - "\n", - " pvals_corrected_raw = pvals_sorted / ecdffactor\n", - " pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]\n", - " del pvals_corrected_raw\n", - " pvals_corrected[pvals_corrected>1] = 1\n", - " if not is_sorted:\n", - " pvals_corrected_ = np.empty_like(pvals_corrected)\n", - " pvals_corrected_[pvals_sortind] = pvals_corrected\n", - " del pvals_corrected\n", - " reject_ = np.empty_like(reject)\n", - " reject_[pvals_sortind] = reject\n", - " return reject_, pvals_corrected_\n", - " else:\n", - " return reject, pvals_corrected\n", - "\n", - "\n", - "\n", - "def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False,\n", - " is_sorted=False):\n", - " '''(iterated) two stage linear step-up procedure with estimation of number of true\n", - " hypotheses\n", - "\n", - " Benjamini, Krieger and Yekuteli, procedure in Definition 6\n", - "\n", - " Parameters\n", - " ----------\n", - " pvals : array_like\n", - " set of p-values of the individual tests.\n", - " alpha : float\n", - " error rate\n", - " method : {'bky', 'bh')\n", - " see Notes for details\n", - "\n", - " * 'bky' - implements the procedure in Definition 6 of Benjamini, Krieger\n", - " and Yekuteli 2006\n", - " * 'bh' - the two stage method of Benjamini and Hochberg\n", - "\n", - " iter : bool\n", - "\n", - " Returns\n", - " -------\n", - " rejected : ndarray, bool\n", - " True if a hypothesis is rejected, False if not\n", - " pvalue-corrected : ndarray\n", - " pvalues adjusted for multiple hypotheses testing to limit FDR\n", - " m0 : int\n", - " ntest - rej, estimated number of true hypotheses\n", - " alpha_stages : list of floats\n", - " A list of alphas that have been used at each stage\n", - "\n", - " Notes\n", - " -----\n", - " The returned corrected p-values are specific to the given alpha, they\n", - " cannot be used for a different alpha.\n", - "\n", - " The returned corrected p-values are from the last stage of the fdr_bh\n", - " linear step-up procedure (fdrcorrection0 with method='indep') corrected\n", - " for the estimated fraction of true hypotheses.\n", - " This means that the rejection decision can be obtained with\n", - " ``pval_corrected <= alpha``, where ``alpha`` is the original significance\n", - " level.\n", - " (Note: This has changed from earlier versions (<0.5.0) of statsmodels.)\n", - "\n", - " BKY described several other multi-stage methods, which would be easy to implement.\n", - " However, in their simulation the simple two-stage method (with iter=False) was the\n", - " most robust to the presence of positive correlation\n", - "\n", - " TODO: What should be returned?\n", - "\n", - " '''\n", - " pvals = np.asarray(pvals)\n", - "\n", - " if not is_sorted:\n", - " pvals_sortind = np.argsort(pvals)\n", - " pvals = np.take(pvals, pvals_sortind)\n", - "\n", - " ntests = len(pvals)\n", - " if method == 'bky':\n", - " fact = (1.+alpha)\n", - " alpha_prime = alpha / fact\n", - " elif method == 'bh':\n", - " fact = 1.\n", - " alpha_prime = alpha\n", - " else:\n", - " raise ValueError(\"only 'bky' and 'bh' are available as method\")\n", - "\n", - " alpha_stages = [alpha_prime]\n", - " rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_prime, method='indep',\n", - " is_sorted=True)\n", - " r1 = rej.sum()\n", - " if (r1 == 0) or (r1 == ntests):\n", - " return rej, pvalscorr * fact, ntests - r1, alpha_stages\n", - " ri_old = r1\n", - "\n", - " while True:\n", - " ntests0 = 1.0 * ntests - ri_old\n", - " alpha_star = alpha_prime * ntests / ntests0\n", - " alpha_stages.append(alpha_star)\n", - " #print ntests0, alpha_star\n", - " rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_star, method='indep',\n", - " is_sorted=True)\n", - " ri = rej.sum()\n", - " if (not iter) or ri == ri_old:\n", - " break\n", - " elif ri < ri_old:\n", - " # prevent cycles and endless loops\n", - " raise RuntimeError(\" oops - should not be here\")\n", - " ri_old = ri\n", - "\n", - " # make adjustment to pvalscorr to reflect estimated number of Non-Null cases\n", - " # decision is then pvalscorr < alpha (or <=)\n", - " pvalscorr *= ntests0 * 1.0 / ntests\n", - " if method == 'bky':\n", - " pvalscorr *= (1. + alpha)\n", - "\n", - " if not is_sorted:\n", - " pvalscorr_ = np.empty_like(pvalscorr)\n", - " pvalscorr_[pvals_sortind] = pvalscorr\n", - " del pvalscorr\n", - " reject = np.empty_like(rej)\n", - " reject[pvals_sortind] = rej\n", - " return reject, pvalscorr_, ntests - ri, alpha_stages\n", - " else:\n", - " return rej, pvalscorr, ntests - ri, alpha_stages" - ] - }, - { - "cell_type": "markdown", - "id": "39749b53", - "metadata": {}, - "source": [ - "## stats -- within convo: strangers" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "228e8849", - "metadata": {}, - "outputs": [], - "source": [ - "pvals = []" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "129990bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=0.7112813876828835, pvalue=0.4771631015885298)\n", - "df:655\n" - ] - } - ], - "source": [ - "first = stats.ttest_1samp(long_gap_connection['change_in_connection_3_before'].dropna(), 0) # not sig\n", - "print(first)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_3_before'].dropna())-1))\n", - "pvals.append(first[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c291ab49", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=1.8164183514423367, pvalue=0.06976319071928287)\n", - "df:655\n" - ] - } - ], - "source": [ - "second = stats.ttest_1samp(long_gap_connection['change_in_connection_2_before'].dropna(), 0) # not sig\n", - "print(second)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_2_before'].dropna())-1))\n", - "pvals.append(second[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "3912b7dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=2.45281395795911, pvalue=0.014434035309558887)\n", - "df:655\n" - ] - } - ], - "source": [ - "third = stats.ttest_1samp(long_gap_connection['change_in_connection_1_before'].dropna(), 0)\n", - "print(third)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_1_before'].dropna())-1))\n", - "pvals.append(third[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0a2e10e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-3.422122610641923, pvalue=0.0006600081343947048)\n", - "df:655\n" - ] - } - ], - "source": [ - "fourth = stats.ttest_1samp(long_gap_connection['change_in_connection_1_after'].dropna(), 0) \n", - "print(fourth)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_1_after'].dropna())-1))\n", - "pvals.append(fourth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f9efe5df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-2.1745623441672635, pvalue=0.030020105042069565)\n", - "df:655\n" - ] - } - ], - "source": [ - "fifth = stats.ttest_1samp(long_gap_connection['change_in_connection_2_after'].dropna(), 0)\n", - "print(fifth)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_2_after'].dropna())-1))\n", - "pvals.append(fifth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "02139e5b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.485605118746222, pvalue=0.13786472575297623)\n", - "df:655\n" - ] - } - ], - "source": [ - "sixth = stats.ttest_1samp(long_gap_connection['change_in_connection_3_after'].dropna(), 0) # not sig\n", - "print(sixth)\n", - "print('df:' + str(len(long_gap_connection['change_in_connection_3_after'].dropna())-1))\n", - "pvals.append(sixth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "84efcaea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([False, False, True, True, True, False]),\n", - " array([0.33401417, 0.07325135, 0.03031147, 0.00277203, 0.04202815,\n", - " 0.11580637]),\n", - " 0.008512444610847103,\n", - " 0.008333333333333333)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#statsmodels.stats.multitest.multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)\n", - "# adjust p-values for multiple comparisions\n", - "\n", - "multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)" - ] - }, - { - "cell_type": "markdown", - "id": "790ef917", - "metadata": {}, - "source": [ - "## stats -- within convo: friends" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "584fe805", - "metadata": {}, - "outputs": [], - "source": [ - "pvals = []" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "162cee5c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-3.4391851938662317, pvalue=0.000644333906067079)\n", - "df:403\n" - ] - } - ], - "source": [ - "first = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_3_before'].dropna(), 0)\n", - "print(first)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_3_before'].dropna())-1))\n", - "pvals.append(first[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "690b2b6a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-3.390324204538273, pvalue=0.0007670103842778143)\n", - "df:403\n" - ] - } - ], - "source": [ - "second = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_2_before'].dropna(), 0)\n", - "print(second)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_2_before'].dropna())-1))\n", - "pvals.append(second[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "b762f504", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-2.204406394522009, pvalue=0.028060279059486454)\n", - "df:403\n" - ] - } - ], - "source": [ - "third = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_1_before'].dropna(), 0)\n", - "print(third)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_1_before'].dropna())-1))\n", - "pvals.append(third[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "56abf501", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.289175349303128, pvalue=0.19807658995481292)\n", - "df:403\n" - ] - } - ], - "source": [ - "fourth = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_1_after'].dropna(), 0) # not sig\n", - "print(fourth)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_1_after'].dropna())-1))\n", - "pvals.append(fourth[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "ddf0bdac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.541104955960675, pvalue=0.1240760578888544)\n", - "df:403\n" - ] - } - ], - "source": [ - "fifth = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_2_after'].dropna(), 0) # not sig\n", - "print(fifth)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_2_after'].dropna())-1))\n", - "pvals.append(fifth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ec17a494", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_1sampResult(statistic=-1.726476765154216, pvalue=0.08502803635775963)\n", - "df:403\n" - ] - } - ], - "source": [ - "sixth = stats.ttest_1samp(long_gap_connection_friends['change_in_connection_3_after'].dropna(), 0) # not sig\n", - "print(sixth)\n", - "print('df:' + str(len(long_gap_connection_friends['change_in_connection_3_after'].dropna())-1))\n", - "pvals.append(sixth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "4291780b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ True, True, True, False, False, False]),\n", - " array([0.00161072, 0.00161072, 0.03928439, 0.13865361, 0.10422389,\n", - " 0.08927944]),\n", - " 0.008512444610847103,\n", - " 0.008333333333333333)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#statsmodels.stats.multitest.multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)\n", - "# adjust p-values for multiple comparisions\n", - "\n", - "multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)" - ] - }, - { - "cell_type": "markdown", - "id": "1f4cfa5e", - "metadata": {}, - "source": [ - "## stats -- between groups" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "12be0205", - "metadata": {}, - "outputs": [], - "source": [ - "pvals = []" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "e89ff041", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=2.8933453972411227, pvalue=0.003892976144933679)\n", - "df:1058\n" - ] - } - ], - "source": [ - "first = stats.ttest_ind(long_gap_connection['change_in_connection_3_before'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_3_before'].dropna(), \n", - " equal_var=False)\n", - "print(first)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_3_before'].dropna()))+len(long_gap_connection_friends['change_in_connection_3_before'].dropna())-2))\n", - "pvals.append(first[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "05068f69", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=3.718819831849168, pvalue=0.00021174307311687412)\n", - "df:1058\n" - ] - } - ], - "source": [ - "second = stats.ttest_ind(long_gap_connection['change_in_connection_2_before'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_2_before'].dropna(), \n", - " equal_var=False)\n", - "print(second)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_2_before'].dropna()))+len(long_gap_connection_friends['change_in_connection_2_before'].dropna())-2))\n", - "pvals.append(second[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "36e7ef16", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=3.284849749162399, pvalue=0.0010572542304095652)\n", - "df:1058\n" - ] - } - ], - "source": [ - "third = stats.ttest_ind(long_gap_connection['change_in_connection_1_before'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_1_before'].dropna(), \n", - " equal_var=False)\n", - "print(third)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_1_before'].dropna()))+len(long_gap_connection_friends['change_in_connection_1_before'].dropna())-2))\n", - "pvals.append(third[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3f61f58e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=-0.23761030902318284, pvalue=0.8122686542506321)\n", - "df:1058\n" - ] - } - ], - "source": [ - "fourth = stats.ttest_ind(long_gap_connection['change_in_connection_1_after'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_1_after'].dropna(), \n", - " equal_var=False)\n", - "print(fourth)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_1_after'].dropna()))+len(long_gap_connection_friends['change_in_connection_1_after'].dropna())-2))\n", - "pvals.append(fourth[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "04fd934b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=0.24934347000150373, pvalue=0.8031715596488865)\n", - "df:1058\n" - ] - } - ], - "source": [ - "fifth = stats.ttest_ind(long_gap_connection['change_in_connection_2_after'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_2_after'].dropna(), \n", - " equal_var=False)\n", - "print(fifth)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_2_after'].dropna()))+len(long_gap_connection_friends['change_in_connection_2_after'].dropna())-2))\n", - "pvals.append(fifth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "29fcdd59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ttest_indResult(statistic=0.66735379988914, pvalue=0.5047606588354114)\n", - "df:1058\n" - ] - } - ], - "source": [ - "sixth = stats.ttest_ind(long_gap_connection['change_in_connection_3_after'].dropna(), \n", - " long_gap_connection_friends['change_in_connection_3_after'].dropna(), \n", - " equal_var=False)\n", - "print(sixth)\n", - "print('df:' + str((len(long_gap_connection['change_in_connection_3_after'].dropna()))+len(long_gap_connection_friends['change_in_connection_3_after'].dropna())-2))\n", - "pvals.append(sixth[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "f9462699", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ True, True, True, False, False, False]),\n", - " array([0.00408762, 0.00066699, 0.00166518, 0.42644104, 0.42644104,\n", - " 0.39749902]),\n", - " 0.008512444610847103,\n", - " 0.008333333333333333)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#statsmodels.stats.multitest.multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)\n", - "# adjust p-values for multiple comparisions\n", - "\n", - "multipletests(pvals, alpha=0.05, method='fdr_tsbky', is_sorted=False, returnsorted=False)" - ] - }, { "cell_type": "markdown", "id": "dd16d249", @@ -1399,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 12, "id": "4e2b15f7", "metadata": {}, "outputs": [], @@ -1408,12 +457,12 @@ "long_gap_connection = long_gap_connection.fillna(0)\n", "counter = 0\n", "\n", - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", "\n", "for file in flist:\n", " \n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -1421,8 +470,8 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", " \n", " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", @@ -1433,6 +482,15 @@ " \n", " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", "\n", + " # speaker info \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " speaker_S1 = 'self'\n", + " speaker_S2 = 'other'\n", + " else:\n", + " speaker_S1 = 'other'\n", + " speaker_S2 = 'self'\n", + " \n", " # the long gap\n", " \n", " start_of_gap = data.at[i-1, 'turn_end_msec']\n", @@ -1444,23 +502,27 @@ " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", "\n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", "\n", " counter += 1\n", " \n", @@ -1475,23 +537,27 @@ " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", @@ -1506,23 +572,27 @@ " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", @@ -1537,23 +607,27 @@ " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", @@ -1568,24 +642,27 @@ " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", @@ -1600,23 +677,27 @@ " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", @@ -1631,31 +712,39 @@ " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", - " long_gap_connection.at[counter, 'subID'] = id_1\n", - " long_gap_connection.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection.at[counter, 'subID'] = id_2\n", - " long_gap_connection.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", + " \n", + "long_gap_connection.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_strangers.csv'),\n", + " encoding='utf-8', index=False)\n", " " ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 13, "id": "d14080f8", "metadata": {}, "outputs": [], @@ -1668,8 +757,8 @@ "\n", "for file in flist:\n", " \n", - " id_1 = file.split('/')[-1].split('_')[0]\n", - " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", " \n", " data = pd.read_csv(file)\n", " \n", @@ -1677,8 +766,8 @@ " \n", " if len(long_gaps) > 0:\n", " \n", - " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", - " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", " \n", " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", @@ -1689,6 +778,15 @@ " \n", " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", "\n", + " # speaker info \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " speaker_S1 = 'self'\n", + " speaker_S2 = 'other'\n", + " else:\n", + " speaker_S1 = 'other'\n", + " speaker_S2 = 'self'\n", + " \n", " # the long gap\n", " \n", " start_of_gap = data.at[i-1, 'turn_end_msec']\n", @@ -1700,23 +798,27 @@ " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", @@ -1731,25 +833,27 @@ " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", "\n", @@ -1764,24 +868,27 @@ " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", - "\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", @@ -1797,24 +904,28 @@ " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", @@ -1830,24 +941,28 @@ " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", @@ -1863,24 +978,28 @@ " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", @@ -1896,33 +1015,41 @@ " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", "\n", - " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S1\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", " \n", - " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", - " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", - " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_during_long_gap\n", " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " long_gap_connection_friends.at[counter, 'speaker'] = speaker_S2\n", + " long_gap_connection_friends.at[counter, 'turn_num'] = i\n", "\n", " \n", " counter += 1\n", + " \n", + "long_gap_connection_friends.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_friends.csv'),\n", + " encoding='utf-8', index=False)\n", " " ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 14, "id": "9b67de4d", "metadata": {}, "outputs": [], diff --git a/Code/Study 1 - reference connection ratings to a common baseline.ipynb b/Code/Study 1 - reference connection ratings to a common baseline.ipynb new file mode 100644 index 0000000..55cb855 --- /dev/null +++ b/Code/Study 1 - reference connection ratings to a common baseline.ipynb @@ -0,0 +1,595 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "243a18ec", + "metadata": {}, + "source": [ + "Reference all change scores to the first time-point (6 seconds before). Used to visualize the temporal dynamics in Figure 2." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "82926b60", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0f88818e", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection = pd.DataFrame()\n", + "long_gap_connection = long_gap_connection.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index): \n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " # the long gap\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9a11912d", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection_friends = pd.DataFrame()\n", + "long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index): \n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + " \n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " # the long gap\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " \n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " \n", + " counter += 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7e0fe544", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection['condition'] = 'strangers'\n", + "long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + "long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + "\n", + "long_gaps_all.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_baseline.csv'),\n", + " encoding='utf-8', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Study 2 - analyze long gap rating task.ipynb b/Code/Study 2 - analyze long gap rating task.ipynb index 041694b..b8b66b2 100644 --- a/Code/Study 2 - analyze long gap rating task.ipynb +++ b/Code/Study 2 - analyze long gap rating task.ipynb @@ -877,7 +877,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.0, the latest is 0.5.1.\n", + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.0, the latest is 0.5.2.\n", "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n", " return warn(\n" ] @@ -1777,7 +1777,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "474437fa", "metadata": {}, "outputs": [], @@ -1787,7 +1787,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "740bcadb", "metadata": {}, "outputs": [], @@ -1836,7 +1836,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "b46128f8", "metadata": {}, "outputs": [], @@ -1854,7 +1854,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "08ba959c", "metadata": {}, "outputs": [], diff --git a/Code/Supplement - exploratory semantic analyses.ipynb b/Code/Supplement - exploratory semantic analyses.ipynb new file mode 100644 index 0000000..364bc25 --- /dev/null +++ b/Code/Supplement - exploratory semantic analyses.ipynb @@ -0,0 +1,351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ccc97058", + "metadata": {}, + "source": [ + "As a preliminary investigation into the content of what participants tend to say near a long gap, we examined the text of the turns immediately preceding and immediately following each gap. \n", + "\n", + "Gaps were categorized based on their lengths (>2000ms (indicating a long gap) and ≤ 2000ms (all other gaps)). \n", + "\n", + "We coded for (i) the number of words (as a proxy for minimal responses) and (ii) the presence of a question mark (as a proxy for question asking). " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ca44b226", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())\n", + "transcript_dir = '/Users/emma/Dropbox/Manuscripts/clicking/data/turn_taking' # text of transcripts\n", + " # not publicly available\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "7d06a865", + "metadata": {}, + "source": [ + "# Extract information from gaps > 2000ms (long gaps)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b8afd65e", + "metadata": {}, + "outputs": [], + "source": [ + "df_strangers = pd.DataFrame()\n", + "df_strangers = df_strangers.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_strangers.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " turn = data.at[i, 'turn_num']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'strangers', '{}.csv'.format(convo)))\n", + " \n", + " text_before = text_file.at[turn - 1, 'text']\n", + " \n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_strangers.at[counter, 'convo'] = convo\n", + " df_strangers.at[counter, 'turn_num'] = turn\n", + " df_strangers.at[counter, 'position'] = 'before'\n", + " df_strangers.at[counter, 'text'] = text_before\n", + " df_strangers.at[counter, 'question'] = question_before\n", + " df_strangers.at[counter, 'word_count'] = len(text_before.split())\n", + " \n", + " counter += 1\n", + " \n", + " text_after = text_file.at[turn, 'text']\n", + " \n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + " \n", + " df_strangers.at[counter, 'convo'] = convo\n", + " df_strangers.at[counter, 'turn_num'] = turn\n", + " df_strangers.at[counter, 'position'] = 'after'\n", + " df_strangers.at[counter, 'text'] = text_after\n", + " df_strangers.at[counter, 'question'] = question_after\n", + " df_strangers.at[counter, 'word_count'] = len(text_after.split())\n", + " \n", + " counter += 1\n", + "\n", + "df_strangers = df_strangers.drop_duplicates(['convo','turn_num', 'position'],keep= 'last').reset_index(drop=True)\n", + "df_strangers['condition'] = 'strangers'\n", + "df_strangers['gap'] = 'long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ebbaeacb", + "metadata": {}, + "outputs": [], + "source": [ + "df_friends = pd.DataFrame()\n", + "df_friends = df_friends.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_friends.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " turn = data.at[i, 'turn_num']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'friends', '{}.csv'.format(convo)))\n", + " \n", + " text_before = text_file.at[turn - 1, 'text']\n", + " \n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_friends.at[counter, 'convo'] = convo\n", + " df_friends.at[counter, 'turn_num'] = turn\n", + " df_friends.at[counter, 'position'] = 'before'\n", + " df_friends.at[counter, 'text'] = text_before\n", + " df_friends.at[counter, 'question'] = question_before\n", + " df_friends.at[counter, 'word_count'] = len(text_before.split())\n", + " \n", + " counter += 1\n", + " \n", + " text_after = text_file.at[turn, 'text']\n", + " \n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + " \n", + " df_friends.at[counter, 'convo'] = convo\n", + " df_friends.at[counter, 'turn_num'] = turn\n", + " df_friends.at[counter, 'position'] = 'after'\n", + " df_friends.at[counter, 'text'] = text_after\n", + " df_friends.at[counter, 'question'] = question_after\n", + " df_friends.at[counter, 'word_count'] = len(text_after.split())\n", + " \n", + " counter += 1\n", + "\n", + "df_friends = df_friends.drop_duplicates(['convo','turn_num', 'position'],keep= 'last').reset_index(drop=True)\n", + "df_friends['condition'] = 'friends'\n", + "df_friends['gap'] = 'long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3d382e20", + "metadata": {}, + "outputs": [], + "source": [ + "df_all = pd.concat([df_strangers, df_friends], ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "d21fea1b", + "metadata": {}, + "source": [ + "# Extract information from gaps ≤ 2000ms" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "335ec71a", + "metadata": {}, + "outputs": [], + "source": [ + "df_strangers_comparision = pd.DataFrame()\n", + "df_strangers_comparision = df_strangers_comparision.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_strangers.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'strangers', '{}.csv'.format(convo)))\n", + " \n", + " not_long_gaps = text_file.loc[text_file['turn_latency'] < 2000]\n", + " \n", + " for i in list(not_long_gaps.index):\n", + " \n", + " if i > 1:\n", + " \n", + " text_before = text_file.at[i - 1, 'text']\n", + "\n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_strangers_comparision.at[counter, 'convo'] = convo\n", + " df_strangers_comparision.at[counter, 'turn_num'] = i\n", + " df_strangers_comparision.at[counter, 'position'] = 'before'\n", + " df_strangers_comparision.at[counter, 'text'] = text_before\n", + " df_strangers_comparision.at[counter, 'question'] = question_before\n", + " df_strangers_comparision.at[counter, 'word_count'] = len(text_before.split())\n", + "\n", + " counter += 1\n", + " \n", + " text_after = text_file.at[i, 'text']\n", + "\n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + "\n", + " df_strangers_comparision.at[counter, 'convo'] = convo\n", + " df_strangers_comparision.at[counter, 'turn_num'] = i\n", + " df_strangers_comparision.at[counter, 'position'] = 'after'\n", + " df_strangers_comparision.at[counter, 'text'] = text_after\n", + " df_strangers_comparision.at[counter, 'question'] = question_after\n", + " df_strangers_comparision.at[counter, 'word_count'] = len(text_after.split())\n", + "\n", + " counter += 1\n", + "\n", + "df_strangers_comparision['condition'] = 'strangers'\n", + "df_strangers_comparision['gap'] = 'not_long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "63e25824", + "metadata": {}, + "outputs": [], + "source": [ + "df_friends_comparision = pd.DataFrame()\n", + "df_friends_comparision = df_friends_comparision.fillna(0)\n", + "counter = 0\n", + "\n", + "file = os.path.join(base_dir, 'Analyses', 'long_gap_connection_friends.csv')\n", + "data = pd.read_csv(file)\n", + "\n", + "for i in range(len(data)):\n", + " \n", + " convo = data.at[i, 'dyad']\n", + " \n", + " text_file = pd.read_csv(os.path.join(transcript_dir, 'friends', '{}.csv'.format(convo)))\n", + " \n", + " not_long_gaps = text_file.loc[text_file['turn_latency'] < 2000]\n", + " \n", + " for i in list(not_long_gaps.index):\n", + " \n", + " if i > 1:\n", + " \n", + " text_before = text_file.at[i - 1, 'text']\n", + "\n", + " if len(text_before.split('?')) > 1:\n", + " question_before = 'yes'\n", + " else:\n", + " question_before = 'no'\n", + " \n", + " df_friends_comparision.at[counter, 'convo'] = convo\n", + " df_friends_comparision.at[counter, 'turn_num'] = i\n", + " df_friends_comparision.at[counter, 'position'] = 'before'\n", + " df_friends_comparision.at[counter, 'text'] = text_before\n", + " df_friends_comparision.at[counter, 'question'] = question_before\n", + " df_friends_comparision.at[counter, 'word_count'] = len(text_before.split())\n", + "\n", + " counter += 1\n", + " \n", + " text_after = text_file.at[i, 'text']\n", + "\n", + " if len(text_after.split('?')) > 1:\n", + " question_after = 'yes'\n", + " else:\n", + " question_after = 'no'\n", + "\n", + " df_friends_comparision.at[counter, 'convo'] = convo\n", + " df_friends_comparision.at[counter, 'turn_num'] = i\n", + " df_friends_comparision.at[counter, 'position'] = 'after'\n", + " df_friends_comparision.at[counter, 'text'] = text_after\n", + " df_friends_comparision.at[counter, 'question'] = question_after\n", + " df_friends_comparision.at[counter, 'word_count'] = len(text_after.split())\n", + "\n", + " counter += 1\n", + "\n", + "df_friends_comparision['condition'] = 'friends'\n", + "df_friends_comparision['gap'] = 'not_long'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7141070e", + "metadata": {}, + "outputs": [], + "source": [ + "df_all_comparison = pd.concat([df_strangers_comparision, df_friends_comparision], ignore_index=True)\n", + "df_total = pd.concat([df_all, df_all_comparison], ignore_index=True)\n", + "\n", + "df_total.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'semantic_content_all.csv'),\n", + " encoding='utf-8', index=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d2c8cae4", + "metadata": {}, + "source": [ + "Note that the 'text' variable has been removed from the .csv file in this repo because it may contain identifying information about participants." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Supplement - how do different bin intervals impact Figure 2?.ipynb b/Code/Supplement - how do different bin intervals impact Figure 2?.ipynb new file mode 100644 index 0000000..9e614a6 --- /dev/null +++ b/Code/Supplement - how do different bin intervals impact Figure 2?.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ea7857df", + "metadata": {}, + "source": [ + "Re-plot Figure 2 during different bin intervals. See Fig S3." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a5a31a17", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import glob\n", + "\n", + "%matplotlib inline\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())\n", + "\n", + "plt.rcParams['font.sans-serif'] = \"Arial\"\n", + "plt.rcParams[\"font.family\"] = \"sans-serif\"\n", + "sns.set_style(style='ticks')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "123d29f0", + "metadata": {}, + "outputs": [], + "source": [ + "bin_intervals = [500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]\n", + "bin_labels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "titles = ['0.5 seconds', '1 second', '2 seconds', '3 seconds', '4 seconds', \n", + " '5 seconds', '6 seconds', '7 seconds', '8 seconds', '9 seconds', '10 seconds']\n", + "\n", + "for j in range(len(bin_intervals)):\n", + "\n", + " long_gap_connection = pd.DataFrame()\n", + " long_gap_connection = long_gap_connection.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + "\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - (bin_intervals[j] * 3)\n", + " end_connection_before_3 = start_of_gap - (bin_intervals[j] * 2)\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - bin_intervals[j]\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - (bin_intervals[j] * 2) \n", + " end_connection_before_2 = start_of_gap - bin_intervals[j]\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + bin_intervals[j]\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + bin_intervals[j] \n", + " end_connection_after_2 = end_of_gap + (bin_intervals[j] * 2)\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + (bin_intervals[j] * 2)\n", + " end_connection_after_3 = end_of_gap + (bin_intervals[j] * 3)\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + "\n", + " long_gap_connection_friends = pd.DataFrame()\n", + " long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + "\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - (bin_intervals[j] * 3)\n", + " end_connection_before_3 = start_of_gap - (bin_intervals[j] * 2)\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + " \n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - bin_intervals[j]\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - (bin_intervals[j] * 2) \n", + " end_connection_before_2 = start_of_gap - bin_intervals[j]\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + bin_intervals[j]\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + bin_intervals[j] \n", + " end_connection_after_2 = end_of_gap + (bin_intervals[j] * 2)\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + (bin_intervals[j] * 2)\n", + " end_connection_after_3 = end_of_gap + (bin_intervals[j] * 3)\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection['condition'] = 'strangers'\n", + " long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + " long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + "\n", + " plt.figure(figsize=(8,5))\n", + "\n", + " timepoint_labels = ['{} seconds\\nbefore\\n(baseline)'.format(bin_labels[j]*3), \n", + " '{} seconds\\nbefore'.format(bin_labels[j]*2), \n", + " '{} seconds\\nbefore'.format(bin_labels[j]), \n", + " 'long gap',\n", + " '{} seconds\\nafter'.format(bin_labels[j]), \n", + " '{} seconds\\nafter'.format(bin_labels[j]*2), \n", + " '{} seconds\\nafter'.format(bin_labels[j]*3)]\n", + "\n", + " friend_color = '#F46036'\n", + " stranger_color = '#0FA3B1'\n", + "\n", + " ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", + " data=long_gaps_all, dodge=False,\n", + " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", + " palette = [stranger_color,friend_color], markers='o', ci=None)\n", + "\n", + " plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", + " plt.ylabel(\"connection rating\", fontsize=18, labelpad=10)\n", + " plt.title(\"bin interval = {}\".format(titles[j]), fontsize=18)\n", + "\n", + " ax.tick_params(labelsize=13)\n", + " ax.set(ylim=(-1.5, 4)) # CHANGE LATER\n", + " plt.legend(fontsize='13')\n", + "\n", + " ax.set_xticklabels(timepoint_labels)\n", + "\n", + " ax.axvspan(2.75, 3.25, alpha=0.3, color='silver')\n", + "\n", + " sns.despine()\n", + " plt.tight_layout()\n", + "\n", + " plt.savefig(os.path.join(base_dir, 'Figures', 'Supplement_different_intervals', '{}.png'.format(titles[j])), dpi=300) \n", + " plt.close()\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Supplement - how do different thresholds impact Figure 2?.ipynb b/Code/Supplement - how do different thresholds impact Figure 2?.ipynb new file mode 100644 index 0000000..3de49a7 --- /dev/null +++ b/Code/Supplement - how do different thresholds impact Figure 2?.ipynb @@ -0,0 +1,746 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "758d2795", + "metadata": {}, + "source": [ + "Re-plot Figure 2 with different thresholds for what constitutes a long gap. See Fig S2." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "69a28c2c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import glob\n", + "\n", + "%matplotlib inline\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())\n", + "\n", + "plt.rcParams['font.sans-serif'] = \"Arial\"\n", + "plt.rcParams[\"font.family\"] = \"sans-serif\"\n", + "sns.set_style(style='ticks')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "54fc74d1", + "metadata": {}, + "outputs": [], + "source": [ + "lower_bounds = [500, 750, 1000, 1250, 1750, 2000, 2250, 2500, 2750, 3000] \n", + "upper_bounds = [15000, 15000, 15000, 15000, 15000, 15000, 15000, 15000, 15000, 15000] #essentially not having an upper bound\n", + "\n", + "for j in range(len(lower_bounds)):\n", + "\n", + " long_gap_connection = pd.DataFrame()\n", + " long_gap_connection = long_gap_connection.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[(data['gap_length'] > lower_bounds[j]) & (data['gap_length'] < upper_bounds[j])]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): \n", + "\n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_1\n", + " long_gap_connection.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = id_2\n", + " long_gap_connection.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + " \n", + " \n", + " \n", + " long_gap_connection_friends = pd.DataFrame()\n", + " long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + " counter = 0\n", + "\n", + " flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + " for file in flist:\n", + "\n", + " id_1 = file.split('/')[-1].split('_')[0]\n", + " id_2 = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " data = pd.read_csv(file)\n", + "\n", + " long_gaps = data.loc[(data['gap_length'] > lower_bounds[j]) & (data['gap_length'] < upper_bounds[j])]\n", + "\n", + " if len(long_gaps) > 0:\n", + "\n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_1, id_2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(id_2, id_1)))\n", + "\n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + "\n", + " for i in list(long_gaps.index): \n", + "\n", + " if (i > 0):\n", + "\n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", + "\n", + " # define the long gap\n", + " \n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + "\n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # the long gap\n", + "\n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + "\n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_during_long_gap - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = 'long_gap'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_during_long_gap - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + "\n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + "\n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + "\n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + "\n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_before_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_before'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_before_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + "\n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + "\n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_1 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '1_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_1 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + "\n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + "\n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_2 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '2_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_2 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + "\n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + "\n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_1_after_long_gap_3 - connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = id_2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = id_1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = id_1 + '_' + id_2\n", + " long_gap_connection_friends.at[counter, 'timepoint'] = '3_after'\n", + " long_gap_connection_friends.at[counter, 'connection'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_change'] = connection_2_after_long_gap_3 - connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + "\n", + "\n", + " counter += 1\n", + " \n", + " long_gap_connection['condition'] = 'strangers'\n", + " long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + " long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + " \n", + " long_gaps_all.to_csv(os.path.join(base_dir, 'Analyses', 'different_long_gap_cutoffs',\n", + " 'long_gap_connection_all_{}.csv'.format(lower_bounds[j])),\n", + " encoding='utf-8', index=False)\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "1fe6e73e", + "metadata": {}, + "source": [ + "# Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "71e095e9", + "metadata": {}, + "outputs": [], + "source": [ + "total_turns_strangers = 0\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "for file in flist:\n", + " data = pd.read_csv(file)\n", + " total_turns_strangers += len(data)\n", + " \n", + "total_turns_friends = 0\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "for file in flist:\n", + " data = pd.read_csv(file)\n", + " total_turns_friends += len(data)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bb5d9ce5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "threshold = 1000\n", + "strangers n = 2252.0\n", + "strangers percent = 0.05822280824219861\n", + "friends n = 1078.0\n", + "friends percent = 0.11271434546214973\n", + "____\n", + "threshold = 2250\n", + "strangers n = 170.0\n", + "strangers percent = 0.004395149822901316\n", + "friends n = 136.0\n", + "friends percent = 0.014219991635299039\n", + "____\n", + "threshold = 3000\n", + "strangers n = 64.0\n", + "strangers percent = 0.0016546446392099073\n", + "friends n = 63.0\n", + "friends percent = 0.006587202007528231\n", + "____\n", + "threshold = 500\n", + "strangers n = 8216.0\n", + "strangers percent = 0.21241500555857185\n", + "friends n = 2717.0\n", + "friends percent = 0.284086156419908\n", + "____\n", + "threshold = 2750\n", + "strangers n = 81.0\n", + "strangers percent = 0.002094159621500039\n", + "friends n = 74.0\n", + "friends percent = 0.007737348389795065\n", + "____\n", + "threshold = 2000\n", + "strangers n = 262.0\n", + "strangers percent = 0.006773701491765557\n", + "friends n = 202.0\n", + "friends percent = 0.02112086992890004\n", + "____\n", + "threshold = 1250\n", + "strangers n = 1274.0\n", + "strangers percent = 0.032937769849272214\n", + "friends n = 706.0\n", + "friends percent = 0.0738184859891259\n", + "____\n", + "threshold = 2500\n", + "strangers n = 115.0\n", + "strangers percent = 0.002973189586080302\n", + "friends n = 96.0\n", + "friends percent = 0.010037641154328732\n", + "____\n", + "threshold = 1750\n", + "strangers n = 437.0\n", + "strangers percent = 0.011298120427105148\n", + "friends n = 323.0\n", + "friends percent = 0.033772480133835214\n", + "____\n", + "threshold = 750\n", + "strangers n = 4075.0\n", + "strangers percent = 0.10535432663719331\n", + "friends n = 1660.0\n", + "friends percent = 0.17356754496026766\n", + "____\n" + ] + } + ], + "source": [ + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'different_long_gap_cutoffs', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " long_gaps_all = pd.read_csv(file)\n", + " cutoff = file.split('_')[-1].split('.csv')[0]\n", + "\n", + " strangers = long_gaps_all.loc[long_gaps_all['condition'] == 'strangers'].reset_index(drop=True)\n", + " friends = long_gaps_all.loc[long_gaps_all['condition'] == 'friends'].reset_index(drop=True)\n", + " \n", + " print('threshold = {}'.format(cutoff))\n", + " print('strangers n = {}'.format(len(strangers) / 14))\n", + " print('strangers percent = {}'.format((len(strangers) / 14) / total_turns_strangers))\n", + " print('friends n = {}'.format(len(friends) / 14))\n", + " print('friends percent = {}'.format((len(friends) / 14) / total_turns_friends))\n", + " print('____')\n", + " \n", + " friend_color = '#F46036'\n", + " stranger_color = '#0FA3B1'\n", + " \n", + " #BOTH\n", + " plt.figure(figsize=(8,5))\n", + "\n", + " timepoint_labels = ['6 seconds\\nbefore\\n(baseline)', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap',\n", + " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", + " \n", + " ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", + " data=long_gaps_all, dodge=False,\n", + " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", + " palette = [stranger_color,friend_color], markers='o', ci=None)\n", + "\n", + " plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", + " plt.ylabel(\"connection rating\", fontsize=18, labelpad=10)\n", + " plt.title(\"long gaps > {}ms\".format(cutoff), fontsize=18)\n", + "\n", + " ax.tick_params(labelsize=13)\n", + " ax.set(ylim=(-3, 1.5)) #SET COMMON SCALE AFTER INSPECTING BOUNDS\n", + "\n", + " ax.set_xticklabels(timepoint_labels)\n", + "\n", + " ax.axvspan(2.75, 3.25, alpha=0.3, color='silver')\n", + "\n", + " sns.despine()\n", + " plt.tight_layout()\n", + "\n", + " plt.savefig(os.path.join(base_dir, 'Figures', 'Figure2B_Supplement', '{}.pdf'.format(cutoff)), dpi=300) \n", + " plt.close()\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Supplement - how often are there speaker switches (vs speaker stays)?.ipynb b/Code/Supplement - how often are there speaker switches (vs speaker stays)?.ipynb new file mode 100644 index 0000000..e6406a3 --- /dev/null +++ b/Code/Supplement - how often are there speaker switches (vs speaker stays)?.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e0cc66b6", + "metadata": {}, + "source": [ + "In two-person conversations, people typically take turns back and forth. After one person stops speaking, the other person begins speaking. Occasionally, the speaker who last spoke is the one who decides to speak again next. This would be an example of a ‘speaker-stay’ (vs a ‘speaker-switch’).\n", + "\n", + "Here we examined the frequency of speaker-switches vs speaker-stays for the turns surrounding the long gaps in our stranger and friend datasets. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5ce92f96", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "410c7aaf", + "metadata": {}, + "outputs": [], + "source": [ + "def find_video(file):\n", + " \"\"\"Need video name to use key in assign_idenity function.\n", + "\n", + " \"\"\"\n", + " id1 = file.split('/')[-1].split('_')[0]\n", + " id2 = file.split('_')[-1].split('.csv')[0]\n", + " video_name_1 = id1 + '_' + id2\n", + " video_name_2 = id2 + '_' + id1\n", + " return video_name_1, video_name_2\n", + "\n", + "\n", + "def assign_identity(file):\n", + " \"\"\"Use key to determine which subject ID corresponds to S1 (speaker 1)\n", + " and which subject ID corresponds to S2 (speaker 2).\n", + "\n", + " \"\"\"\n", + " key = pd.read_csv(os.path.join(base_dir, 'Data', 'subject_id_key.csv'))\n", + " video_name_1, video_name_2 = find_video(file)\n", + " for i in range(len((key))):\n", + " if (key.at[i,'video_name'] == video_name_1) | (key.at[i,'video_name'] == video_name_2):\n", + " subID_S1 = key.at[i,'S1']\n", + " subID_S2 = key.at[i,'S2']\n", + " return subID_S1, subID_S2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "40fe22bc", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection = pd.DataFrame()\n", + "long_gap_connection = long_gap_connection.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'strangers', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index):\n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", + " \n", + " # check if there was a speaker switch\n", + " speaker_current = data.at[i, 'speaker']\n", + " speaker_previous = data.at[i-1, 'speaker']\n", + " \n", + " if speaker_current == speaker_previous:\n", + " speaker_switch = 'no'\n", + " else:\n", + " speaker_switch = 'yes'\n", + " \n", + " # the long gap\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", + " long_gap_connection.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_1_before'] = connection_1_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_long_gap'] = connection_1_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_1_after'] = connection_1_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", + " \n", + " long_gap_connection.at[counter, 'speaker_switch'] = speaker_switch\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection.at[counter, 'dyad'] = convo\n", + " long_gap_connection.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", + " long_gap_connection.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_1_before'] = connection_2_before_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_long_gap'] = connection_2_during_long_gap\n", + " long_gap_connection.at[counter, 'connection_1_after'] = connection_2_after_long_gap_1\n", + " long_gap_connection.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", + " long_gap_connection.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", + " long_gap_connection.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection.at[counter, 'speaker'] = 'other'\n", + "\n", + " long_gap_connection.at[counter, 'speaker_switch'] = speaker_switch\n", + " \n", + " counter += 1\n", + " \n", + " \n", + " \n", + "long_gap_connection['change_in_connection_3_before'] = long_gap_connection['connection_3_before'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_2_before'] = long_gap_connection['connection_2_before'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_1_before'] = long_gap_connection['connection_1_before'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_1_after'] = long_gap_connection['connection_1_after'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_2_after'] = long_gap_connection['connection_2_after'] - long_gap_connection['connection_long_gap']\n", + "long_gap_connection['change_in_connection_3_after'] = long_gap_connection['connection_3_after'] - long_gap_connection['connection_long_gap']\n", + "\n", + "long_gap_connection.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_strangers_switches.csv'),\n", + " encoding='utf-8', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6a49e47b", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection_friends = pd.DataFrame()\n", + "long_gap_connection_friends = long_gap_connection_friends.fillna(0)\n", + "counter = 0\n", + "\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "\n", + "for file in flist:\n", + " \n", + " subID_S1, subID_S2 = assign_identity(file)\n", + " convo = file.split('/')[-1].split('.csv')[0]\n", + " \n", + " data = pd.read_csv(file)\n", + " \n", + " long_gaps = data.loc[data['gap_length'] > 2000]\n", + " \n", + " if len(long_gaps) > 0:\n", + " \n", + " connection_1 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S1, subID_S2)))\n", + " connection_2 = pd.read_csv(os.path.join(base_dir, 'Data', 'continuous_connection_ratings', 'friends', '{}_{}.csv'.format(subID_S2, subID_S1)))\n", + " \n", + " connection_1['time_msec'] = connection_1['adjustedTime'] * 1000\n", + " connection_2['time_msec'] = connection_2['adjustedTime'] * 1000\n", + " \n", + " for i in list(long_gaps.index):\n", + " \n", + " if (i > 0):\n", + " \n", + " if (data.at[i-1, 'turn_end_msec'] > 6000) & (data.at[i, 'turn_start_msec'] < 594000): # might change and just give NaNs for ones that are too close\n", + "\n", + " # check if there was a speaker switch\n", + " speaker_current = data.at[i, 'speaker']\n", + " speaker_previous = data.at[i-1, 'speaker']\n", + " \n", + " if speaker_current == speaker_previous:\n", + " speaker_switch = 'no'\n", + " else:\n", + " speaker_switch = 'yes'\n", + " \n", + " # the long gap\n", + " start_of_gap = data.at[i-1, 'turn_end_msec']\n", + " end_of_gap = data.at[i, 'turn_start_msec']\n", + " \n", + " connection_1_long_gap_subset = connection_1.loc[(connection_1['time_msec'] > start_of_gap) & (connection_1['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_1_during_long_gap = np.mean(connection_1_long_gap_subset['Rating'])\n", + " \n", + " connection_2_long_gap_subset = connection_2.loc[(connection_2['time_msec'] > start_of_gap) & (connection_2['time_msec'] < end_of_gap)].reset_index(drop=True)\n", + " connection_2_during_long_gap = np.mean(connection_2_long_gap_subset['Rating'])\n", + "\n", + " # 1 chunk before long gap\n", + "\n", + " start_connection_before_1 = start_of_gap - 2000\n", + " end_connection_before_1 = start_of_gap\n", + " \n", + " connection_1_before_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_1) & (connection_1['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_1_before_long_gap_1 = np.mean(connection_1_before_1_subset['Rating'])\n", + " \n", + " connection_2_before_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_1) & (connection_2['time_msec'] < end_connection_before_1)].reset_index(drop=True)\n", + " connection_2_before_long_gap_1 = np.mean(connection_2_before_1_subset['Rating'])\n", + "\n", + " # 2 chunks before long gap\n", + "\n", + " start_connection_before_2 = start_of_gap - 4000 \n", + " end_connection_before_2 = start_of_gap - 2000\n", + " \n", + " connection_1_before_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_2) & (connection_1['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_1_before_long_gap_2 = np.mean(connection_1_before_2_subset['Rating'])\n", + " \n", + " connection_2_before_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_2) & (connection_2['time_msec'] < end_connection_before_2)].reset_index(drop=True)\n", + " connection_2_before_long_gap_2 = np.mean(connection_2_before_2_subset['Rating'])\n", + "\n", + " # 3 chunks before long gap\n", + "\n", + " start_connection_before_3 = start_of_gap - 6000\n", + " end_connection_before_3 = start_of_gap - 4000\n", + "\n", + " connection_1_before_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_before_3) & (connection_1['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_1_before_long_gap_3 = np.mean(connection_1_before_3_subset['Rating'])\n", + " \n", + " connection_2_before_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_before_3) & (connection_2['time_msec'] < end_connection_before_3)].reset_index(drop=True)\n", + " connection_2_before_long_gap_3 = np.mean(connection_2_before_3_subset['Rating'])\n", + " \n", + "\n", + " # 1 chunk after long gap \n", + "\n", + " start_connection_after_1 = end_of_gap\n", + " end_connection_after_1 = end_of_gap + 2000\n", + " \n", + " connection_1_after_1_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_1) & (connection_1['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_1_after_long_gap_1 = np.mean(connection_1_after_1_subset['Rating'])\n", + " \n", + " connection_2_after_1_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_1) & (connection_2['time_msec'] < end_connection_after_1)].reset_index(drop=True)\n", + " connection_2_after_long_gap_1 = np.mean(connection_2_after_1_subset['Rating'])\n", + "\n", + " # 2 chunks after long gap \n", + "\n", + " start_connection_after_2 = end_of_gap + 2000 \n", + " end_connection_after_2 = end_of_gap + 4000\n", + " \n", + " connection_1_after_2_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_2) & (connection_1['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_1_after_long_gap_2 = np.mean(connection_1_after_2_subset['Rating'])\n", + " \n", + " connection_2_after_2_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_2) & (connection_2['time_msec'] < end_connection_after_2)].reset_index(drop=True)\n", + " connection_2_after_long_gap_2 = np.mean(connection_2_after_2_subset['Rating'])\n", + "\n", + " # 3 chunks after long gap \n", + "\n", + " start_connection_after_3 = end_of_gap + 4000\n", + " end_connection_after_3 = end_of_gap + 6000\n", + " \n", + " connection_1_after_3_subset = connection_1.loc[(connection_1['time_msec'] > start_connection_after_3) & (connection_1['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_1_after_long_gap_3 = np.mean(connection_1_after_3_subset['Rating'])\n", + " \n", + " connection_2_after_3_subset = connection_2.loc[(connection_2['time_msec'] > start_connection_after_3) & (connection_2['time_msec'] < end_connection_after_3)].reset_index(drop=True)\n", + " connection_2_after_long_gap_3 = np.mean(connection_2_after_3_subset['Rating'])\n", + "\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", + " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_1_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_1_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_1_before'] = connection_1_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_long_gap'] = connection_1_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_1_after'] = connection_1_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_1_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_1_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S1':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", + " \n", + " long_gap_connection_friends.at[counter, 'speaker_switch'] = speaker_switch\n", + "\n", + " counter += 1\n", + "\n", + " long_gap_connection_friends.at[counter, 'subID'] = subID_S2\n", + " long_gap_connection_friends.at[counter, 'partnerID'] = subID_S1\n", + " long_gap_connection_friends.at[counter, 'dyad'] = convo\n", + " long_gap_connection_friends.at[counter, 'connection_3_before'] = connection_2_before_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'connection_2_before'] = connection_2_before_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_1_before'] = connection_2_before_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_long_gap'] = connection_2_during_long_gap\n", + " long_gap_connection_friends.at[counter, 'connection_1_after'] = connection_2_after_long_gap_1\n", + " long_gap_connection_friends.at[counter, 'connection_2_after'] = connection_2_after_long_gap_2\n", + " long_gap_connection_friends.at[counter, 'connection_3_after'] = connection_2_after_long_gap_3\n", + " long_gap_connection_friends.at[counter, 'gap_length'] = data.at[i, 'gap_length']\n", + " \n", + " speaker = data.at[i, 'speaker']\n", + " if speaker == 'S2':\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'self'\n", + " else:\n", + " long_gap_connection_friends.at[counter, 'speaker'] = 'other'\n", + " \n", + " long_gap_connection_friends.at[counter, 'speaker_switch'] = speaker_switch\n", + "\n", + " counter += 1\n", + " \n", + " \n", + "long_gap_connection_friends['change_in_connection_3_before'] = long_gap_connection_friends['connection_3_before'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_2_before'] = long_gap_connection_friends['connection_2_before'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_1_before'] = long_gap_connection_friends['connection_1_before'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_1_after'] = long_gap_connection_friends['connection_1_after'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_2_after'] = long_gap_connection_friends['connection_2_after'] - long_gap_connection_friends['connection_long_gap']\n", + "long_gap_connection_friends['change_in_connection_3_after'] = long_gap_connection_friends['connection_3_after'] - long_gap_connection_friends['connection_long_gap']\n", + "\n", + "long_gap_connection_friends.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_friends_switches.csv'),\n", + " encoding='utf-8', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1b05a45c", + "metadata": {}, + "outputs": [], + "source": [ + "long_gap_connection['condition'] = 'strangers'\n", + "long_gap_connection_friends['condition'] = 'friends'\n", + "\n", + "long_gaps_all = pd.merge(long_gap_connection, long_gap_connection_friends, how='outer')\n", + "\n", + "long_gaps_all.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_switches.csv'),\n", + " encoding='utf-8', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "79964cf2", + "metadata": {}, + "source": [ + "# Get frequencies (overall and for each conversation type)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5127fc84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "248.0\n", + "14.0\n", + "0.05343511450381679\n" + ] + } + ], + "source": [ + "strangers_switch = long_gap_connection.loc[long_gap_connection['speaker_switch'] == 'yes'].reset_index(drop=True)\n", + "strangers_no_switch = long_gap_connection.loc[long_gap_connection['speaker_switch'] == 'no'].reset_index(drop=True)\n", + "\n", + "print(len(strangers_switch)/2)\n", + "print(len(strangers_no_switch)/2)\n", + "print((len(strangers_no_switch)/2) / (len(long_gap_connection)/2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "200102de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "183.0\n", + "19.0\n", + "0.09405940594059406\n" + ] + } + ], + "source": [ + "friends_switch = long_gap_connection_friends.loc[long_gap_connection_friends['speaker_switch'] == 'yes'].reset_index(drop=True)\n", + "friends_no_switch = long_gap_connection_friends.loc[long_gap_connection_friends['speaker_switch'] == 'no'].reset_index(drop=True)\n", + "\n", + "print(len(friends_switch)/2)\n", + "print(len(friends_no_switch)/2)\n", + "print((len(friends_no_switch)/2) / (len(long_gap_connection_friends)/2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23e69a33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07112068965517242" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(14+19) / (262+202)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Supplement - investigate laughter in Study 1.ipynb b/Code/Supplement - investigate laughter in Study 1.ipynb new file mode 100644 index 0000000..c26a223 --- /dev/null +++ b/Code/Supplement - investigate laughter in Study 1.ipynb @@ -0,0 +1,242 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6d2320b5", + "metadata": {}, + "source": [ + "We examined the video footage of all of the long gaps and annotated whether participants laughed during the gap (laughter_annotation.csv).\n", + "\n", + "We then added this info into 'long_gap_connection_all_long_format.csv' to investigate whether laughter during the long gap might mediate the effect between relationship type (friend vs stranger) and change in connection. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cbe56972", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np\n", + "import os.path\n", + "import glob\n", + "from scipy import stats\n", + "import statsmodels\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "21bcc7eb", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(os.path.join(base_dir, 'Supplement', 'laughter_annotation.csv'))\n", + "strangers = data.loc[data['condition'] == 'strangers'].reset_index(drop=True)\n", + "friends = data.loc[data['condition'] == 'friends'].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1cd0a286", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "no 308\n", + "yes 156\n", + "Name: laughter, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['laughter'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5114cb37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "no 188\n", + "yes 74\n", + "Name: laughter, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strangers['laughter'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "24cbb1e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2824427480916031" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "74/len(strangers)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3ba64b3c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "no 120\n", + "yes 82\n", + "Name: laughter, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "friends['laughter'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a41708f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.40594059405940597" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "82/len(friends)" + ] + }, + { + "cell_type": "markdown", + "id": "4c867e8f", + "metadata": {}, + "source": [ + "Include in main dataframe to examine if laughter mediates relationship between condition and change in connection" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2a4563fa", + "metadata": {}, + "outputs": [], + "source": [ + "connection_changes = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format.csv'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "46a65ecb", + "metadata": {}, + "outputs": [], + "source": [ + "data_full = pd.merge(data, connection_changes, how='inner', on=['dyad', 'turn_num'])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4f300181", + "metadata": {}, + "outputs": [], + "source": [ + "data_full.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_laughter.csv'),\n", + " encoding='utf-8', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a6b81795", + "metadata": {}, + "outputs": [], + "source": [ + "strangers_full = data_full.loc[data_full['condition_x'] == 'strangers'].reset_index(drop=True)\n", + "friends_full = data_full.loc[data_full['condition_x'] == 'friends'].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "70ec7b93", + "metadata": {}, + "outputs": [], + "source": [ + "strangers_full.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_laughter_strangers.csv'),\n", + " encoding='utf-8', index=False)\n", + "\n", + "friends_full.to_csv(os.path.join(base_dir, 'Analyses',\n", + " 'long_gap_connection_all_long_format_laughter_friends.csv'),\n", + " encoding='utf-8', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/inclusion criteria for stranger dataset.ipynb b/Code/inclusion criteria for stranger dataset.ipynb new file mode 100644 index 0000000..1f63b22 --- /dev/null +++ b/Code/inclusion criteria for stranger dataset.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3c1f69a9", + "metadata": {}, + "source": [ + "Because this paper specifically investigates strangers vs friends, we want to ensure that all dyads from the round-robin dataset were actually strangers. \n", + "\n", + "Conversation partners in this dataser were assigned by an experimenter. To ensure that participants did not know each other we asked them “How well did you know your study partner before today?” (0 = Not well at all, 50 = Moderately well, and 100 = Extremely well). \n", + "\n", + "In order to limit our analyses to true strangers who do not know each other, we excluded 61 dyads where both dyad members indicated a response greater than 0 on this question. The analyses reported in this paper come from 261 stranger dyads. However, note that results are similar with all dyads included. \n", + "\n", + "This script identifies the true stranger dyads and copies their data from the 'round_robin' folder to the 'strangers_pure' folder." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "97f8f733", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import glob\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline\n", + "sns.set_style(\"white\")\n", + "\n", + "base_dir = os.path.dirname(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b0cea461", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(os.path.join(base_dir, 'Data', 'post_convo_survey', \n", + " 'strangers', 'data_common_questions.csv')) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "df5155de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([516., 28., 19., 15., 8., 23., 12., 9., 8., 6.]),\n", + " array([ 0. , 9.9, 19.8, 29.7, 39.6, 49.5, 59.4, 69.3, 79.2, 89.1, 99. ]),\n", + " )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data['knew_before'])" + ] + }, + { + "cell_type": "markdown", + "id": "c39a7ffc", + "metadata": {}, + "source": [ + "### Exclude dyads where both people endorsed knowing their partner" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "855da4c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "61\n", + "18.94\n" + ] + } + ], + "source": [ + "dyads = []\n", + "\n", + "for i in data['dyad'].unique():\n", + " \n", + " data_subset = data.loc[data['dyad'] == i].reset_index(drop=True)\n", + " data_knew_before = data_subset.loc[data_subset['knew_before'] > 0].reset_index(drop=True)\n", + " if len(data_knew_before) == 2:\n", + " dyads.append(i)\n", + " \n", + "print(len(dyads))\n", + "print(round((len(dyads) / 322)*100,2))" + ] + }, + { + "cell_type": "markdown", + "id": "415e27d0", + "metadata": {}, + "source": [ + "# Create a new folder of stranger turn-taking data\n", + "\n", + "Limiting it to the \"true\" strangers\n", + "\n", + "Make a subset of data to the pure strangers. Create a filename by joining subID and partnerID. Check to see if that file exists in the turn_taking folder. If it does, copy it to the other folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45e3a02b", + "metadata": {}, + "outputs": [], + "source": [ + "pure_strangers = data.loc[~data['dyad'].isin(dyads)].reset_index(drop=True)\n", + "\n", + "for i in range(len(pure_strangers)):\n", + " file_name = str(pure_strangers.at[i, 'subID']) + '_' + str(pure_strangers.at[i, 'partnerID'])\n", + " \n", + " path_to_file_original = os.path.join(base_dir, 'Analyses', 'turn_taking', 'round_robin', '{}.csv'.format(file_name))\n", + " \n", + " if os.path.exists(path_to_file_original):\n", + " path_to_file_copy = os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '{}.csv'.format(file_name))\n", + " shutil.copyfile(path_to_file_original, path_to_file_copy)\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3_8", + "language": "python", + "name": "python3_8" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/plot figures.ipynb b/Code/plot figures.ipynb index d565824..53c3897 100644 --- a/Code/plot figures.ipynb +++ b/Code/plot figures.ipynb @@ -2,8 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, - "id": "51f76132", + "execution_count": 1, + "id": "2b9eba0d", "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "markdown", - "id": "1cb85987", + "id": "a7065403", "metadata": {}, "source": [ "# Study 1\n", @@ -36,33 +36,37 @@ }, { "cell_type": "markdown", - "id": "9543a31d", + "id": "75b2c118", "metadata": {}, "source": [ - "## Figure 1A\n", + "## Figure 1\n", + "\n", + "Count differences in long gaps between friends and strangers\n", "\n", - "Distribution of gap lengths for stranger and friend conversations" + "For code that plots distributions of gap lengths for each conversation, see the notebook \"Study 1 - do friends use more long gaps?\"" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "3cf17b21", + "execution_count": 2, + "id": "0fce672f", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_counts.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "33b07174", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, @@ -70,48 +74,68 @@ } ], "source": [ - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers', '*.csv'))\n", - "data = pd.read_csv(flist[0])\n", - "all_latency_values_strangers = data['gap_length']\n", + "plt.figure(figsize=(4,5))\n", "\n", - "for file in flist[1:]:\n", - " \n", - " data = pd.read_csv(file)\n", - " all_latency_values_strangers = all_latency_values_strangers.append(data['gap_length'])\n", - " \n", - "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 3}, color='#0FA3B1')\n", - "plt.xlim(-6000, 10000)\n", + "ax = sns.barplot(x=\"condition\", y=\"n_long_2000\", data=long_gaps, \n", + " palette=['#90be6d', '#43aa8b'],\n", + " saturation=1, errwidth=1,\n", + " capsize=.05)\n", "\n", - "ax.set_xlabel('all gap lengths (ms) -- strangers', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", + "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('# long gaps in each conversation', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=13)\n", + "\n", + "# significance bar\n", + "lw = 2\n", + "line_color = '#594F4F' # create thinner, greyer lines\n", + "star_color = '#594F4F'\n", + "h = 0\n", + "space_between_stars_and_line = .0\n", + "star_font_size = 20\n", + "\n", + "# strangers vs friends\n", + "x1, x2 = .20, .80 \n", + "y = 4.2\n", + "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", + "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1A_strangers.pdf'), dpi=300)" + "plt.savefig(os.path.join(base_dir,'Figures','Fig1.pdf'), dpi=300)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f80aea3f", + "metadata": {}, + "source": [ + "## Figure 2 (top)\n", + "\n", + "How does connection change over time (from a baseline 6 seconds before a long gap)?" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "906833ff", + "execution_count": 4, + "id": "86e8263e", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps_all = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format_baseline.csv'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6ad333c", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -119,60 +143,112 @@ } ], "source": [ - "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", - "data = pd.read_csv(flist[0])\n", - "all_latency_values_friends = data['gap_length']\n", + "plt.figure(figsize=(10,5))\n", "\n", - "for file in flist[1:]:\n", - " \n", - " data = pd.read_csv(file)\n", - " all_latency_values_friends = all_latency_values_friends.append(data['gap_length'])\n", - " \n", - "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 3}, color='#F46036')\n", - "plt.xlim(-6000, 10000)\n", + "timepoint_labels = ['6 seconds\\nbefore\\n(reference)', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap',\n", + " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", "\n", - "ax.set_xlabel('all gap lengths (ms) -- friends', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", + "friend_color = '#F46036'\n", + "stranger_color = '#0FA3B1'\n", + "\n", + "ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", + " data=long_gaps_all, dodge=False,\n", + " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", + " palette = [stranger_color,friend_color], markers='o', ci=None)\n", + "\n", + "plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", + "plt.ylabel(\"change in connection\\n(compared to reference)\", fontsize=18, labelpad=10)\n", + "\n", + "ax.tick_params(labelsize=13)\n", + "plt.legend(fontsize='13')\n", + "\n", + "ax.set_xticklabels(timepoint_labels)\n", + "\n", + "ax.axvspan(2.75, 3.25, alpha=0.3, color='lightgrey')\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1A_friends.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir,'Figures','Fig2_top.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "b26c7f3b", + "id": "92ac7a3f", "metadata": {}, "source": [ - "## Figure 1B\n", + "## Figure 2 (bottom)\n", "\n", - "Distribution of gap lengths for stranger and friend conversations, zooming in on \"long\" gap part of the distribution" + "How does connection change for friends and stangers when entering (one_before) and exiting (one_after) a long gap?" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "2494e652", + "execution_count": 6, + "id": "919e1639", + "metadata": {}, + "outputs": [], + "source": [ + "long_gaps_all = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format.csv'))\n", + "\n", + "one_before = long_gaps_all.loc[long_gaps_all['timepoint'] == '1_before'].reset_index(drop=True)\n", + "one_before['connection_change_flipped'] = one_before['connection_change'] * -1\n", + "\n", + "one_after = long_gaps_all.loc[long_gaps_all['timepoint'] == '1_after'].reset_index(drop=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "46932d55", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n", - "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(4,5))\n", + "\n", + "plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", + "\n", + "friend_color = '#F46036'\n", + "stranger_color = '#0FA3B1'\n", + "\n", + "ax = sns.barplot(x=\"condition\", y=\"connection_change_flipped\", data=one_before, \n", + " palette=[stranger_color, friend_color],\n", + " saturation=1, errwidth=1,\n", + " capsize=.05)\n", + "\n", + "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('change in connection', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=13)\n", + "ax.set(ylim=(-1.35, 1))\n", + "\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig(os.path.join(base_dir,'Figures','Fig2_bottom_entering.pdf'), transparent=True, dpi=300)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "855c1c75", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -180,64 +256,74 @@ } ], "source": [ - "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 1}, color='#F46036', label = 'friend', hist_kws=dict(alpha=0.5))\n", - "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True,\n", - " kde_kws = {'linewidth': 1}, color='#0FA3B1', label = 'stranger', hist_kws=dict(alpha=0.5))\n", + "plt.figure(figsize=(4,5))\n", "\n", - "plt.xlim(-500, 2500)\n", + "plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", "\n", - "ax.set_xlabel('all gap lengths', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=12)\n", + "friend_color = '#F46036'\n", + "stranger_color = '#0FA3B1'\n", "\n", - "handles, labels = plt.gca().get_legend_handles_labels()\n", - "order = [1,0]\n", - "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])\n", + "ax = sns.barplot(x=\"condition\", y=\"connection_change\", data=one_after, \n", + " palette=[stranger_color, friend_color],\n", + " saturation=1, errwidth=1,\n", + " capsize=.05)\n", + "\n", + "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('change in connection', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=13)\n", + "ax.set(ylim=(-1.35, 1))\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1B.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir,'Figures','Fig2_bottom_exiting.pdf'), transparent=True, dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "02cf7a25", + "id": "4bdd156c", "metadata": {}, "source": [ - "# Figure 1C\n", + "# Study 2\n", "\n", - "Difference in **median** gap lengths for stranger conversations compared to friend conversations." + "Impressions of long gaps from independent raters" ] }, { "cell_type": "code", "execution_count": 9, - "id": "e95f6eda", + "id": "44c268dc", "metadata": {}, "outputs": [], "source": [ - "stranger_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'turn_taking_strangers.csv'))\n", - "friend_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'turn_taking_friends.csv'))\n", + "rating_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_ratings_wide_format.csv'))\n", + "rating_data['video_num'] = rating_data['video_num'].astype(int)\n", + "data_info = pd.read_csv(os.path.join(base_dir, 'Data', 'long_gap_stimuli_info.csv'))\n", "\n", - "stranger_data['condition'] = 'stranger'\n", - "friend_data['condition'] = 'friend'\n", + "data_full = pd.merge(data_info, rating_data, how='inner', on=['video_num'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "2125d2ef", + "metadata": {}, + "source": [ + "## Figure 3A\n", "\n", - "data = pd.merge(stranger_data, friend_data, how='outer')" + "Differences in perceived awkwardness in friend vs stranger conversations" ] }, { "cell_type": "code", "execution_count": 10, - "id": "d8c5a83a", + "id": "2cbe9c59", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -245,17 +331,17 @@ } ], "source": [ - "plt.figure(figsize=(5,6))\n", + "plt.figure(figsize=(5,5))\n", "\n", - "ax = sns.barplot(x=\"condition\", y=\"median_gap_convo\", data=data, errwidth=1,\n", + "ax = sns.barplot(x=\"condition\", y=\"awkward_mean\", data=data_full, errwidth=1,\n", " capsize=.05, \n", - " palette=['#0FA3B1', '#F46036'],\n", + " palette=['#3777FF', '#EF3054'],\n", " saturation=1)\n", "\n", "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('median gap length (ms)', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('perceived awkwardness', fontsize=15, labelpad=10)\n", "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 450))\n", + "ax.set(ylim=(0, 80))\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", @@ -265,39 +351,39 @@ "line_color = '#594F4F' # create thinner, greyer lines\n", "star_color = '#594F4F'\n", "h = 0\n", - "space_between_stars_and_line = 10\n", - "star_font_size = 15\n", + "space_between_stars_and_line = -.4\n", + "star_font_size = 20\n", "\n", "# strangers vs friends\n", "x1, x2 = .20, .80 \n", - "y = 425\n", + "y = 75\n", "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"n.s.\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1C.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3A.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "4a894d88", + "id": "39a3cc6f", "metadata": {}, "source": [ - "# Figure 1D\n", + "## Figure 3B\n", "\n", - "Difference in **mean** gap lengths for stranger conversations compared to friend conversations." + "Differences in perceived connection in friend vs stranger conversations" ] }, { "cell_type": "code", "execution_count": 11, - "id": "1ec5461f", + "id": "3be96c5e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -305,17 +391,17 @@ } ], "source": [ - "plt.figure(figsize=(5,6))\n", + "plt.figure(figsize=(5,5))\n", "\n", - "ax = sns.barplot(x=\"condition\", y=\"mean_gap_convo\", data=data, errwidth=1,\n", + "ax = sns.barplot(x=\"condition\", y=\"connected_mean\", data=data_full, errwidth=1,\n", " capsize=.05, \n", - " palette=['#0FA3B1', '#F46036'],\n", + " palette=['#3777FF', '#EF3054'],\n", " saturation=1)\n", "\n", "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('mean gap length (ms)', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('perceived connection', fontsize=15, labelpad=10)\n", "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 450))\n", + "ax.set(ylim=(0, 80))\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", @@ -330,50 +416,76 @@ "\n", "# strangers vs friends\n", "x1, x2 = .20, .80 \n", - "y = 425\n", + "y = 75\n", "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig1D.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3B.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "1e9945a8", + "id": "b2412c3a", "metadata": {}, "source": [ - "# Figure 2A\n", + "## Figure 3C\n", "\n", - "Differences in average reported connection for friend vs stranger conversations" + "Differences in percieved awkwardness in friend vs stranger conversations, as a function of gap length" ] }, { "cell_type": "code", "execution_count": 12, - "id": "03a456ea", + "id": "f03f21f9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "data_strangers = pd.read_csv(os.path.join(base_dir,'Analyses','connection_strangers.csv'))\n", - "data_friends = pd.read_csv(os.path.join(base_dir,'Analyses','connection_friends.csv'))\n", + "sns.lmplot(x=\"interval\", y=\"awkward_mean\", hue=\"condition\", data=data_full,\n", + " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", + " scatter_kws={'alpha':0.2}, legend=False)\n", + "\n", + "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", + "plt.ylabel(\"perceived awkwardness\", fontsize=15, labelpad=10)\n", + "plt.tick_params(labelsize=12)\n", "\n", - "data_strangers['dataset'] = 'strangers'\n", - "data_friends['dataset'] = 'friends'\n", + "sns.despine()\n", + "plt.tight_layout()\n", "\n", - "data = pd.merge(data_strangers, data_friends, how='outer')" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3C.pdf'), dpi=300)\n" + ] + }, + { + "cell_type": "markdown", + "id": "68a840b3", + "metadata": {}, + "source": [ + "## Figure 3D\n", + "\n", + "Differences in percieved connection in friend vs stranger conversations, as a function of gap length" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "3cda4a89", + "execution_count": 13, + "id": "085565b1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -381,74 +493,59 @@ } ], "source": [ - "plt.figure(figsize=(4,5))\n", - "\n", - "friend_color = '#b7094c'\n", - "stranger_color = '#0091ad'\n", - "\n", - "ax = sns.barplot(x=\"dataset\", y=\"avg_connection\", data=data, \n", - " palette=[stranger_color, friend_color],\n", - " saturation=1, errwidth=1,\n", - " capsize=.05)\n", - "\n", - "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('average connection rating', fontsize=15, labelpad=10)\n", - "ax.tick_params(labelsize=13)\n", - "\n", - "# significance bar\n", - "lw = 2\n", - "line_color = '#594F4F' # create thinner, greyer lines\n", - "star_color = '#594F4F'\n", - "h = 0\n", - "space_between_stars_and_line = -.4\n", - "star_font_size = 20\n", - "\n", - "# strangers vs friends\n", - "x1, x2 = .20, .80 \n", - "y = 80\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "sns.lmplot(x=\"interval\", y=\"connected_mean\", hue=\"condition\", data=data_full,\n", + " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", + " scatter_kws={'alpha':0.2}, legend=False)\n", "\n", + "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", + "plt.ylabel(\"perceived connection\", fontsize=15, labelpad=10)\n", + "plt.tick_params(labelsize=12)\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig2A.pdf'), dpi=300)\n" + "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3D.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "f8b80dcf", + "id": "438a8592", "metadata": {}, "source": [ - "# Figure 2B\n", - "\n", - "Explore average changes in connection ratings before and after a long gap, separately for friend and stranger conversations." + "# Supplemental Figures" ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "49d8bcb2", + "cell_type": "markdown", + "id": "8df6c2a5", "metadata": {}, - "outputs": [], "source": [ - "long_gaps_all = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_connection_all_long_format.csv'))" + "## Figure S1A\n", + "\n", + "Distribution of gap lengths for stranger conversations" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "6b37315d", + "execution_count": 25, + "id": "af1f2dbd", "metadata": { "scrolled": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, { "data": { - "image/png": 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\n", 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jR1y/ft0owKqXffHFFxg7dizatWsHAJg+fTpCQkIaVTfdfUy5DopdfETWY3IL6vnnn0dSUhI2b96MwMBAo3CqNnbsWBw+fLhRBQwfPhwpKSlQq9UAKgc9BAQEmLxOQEAAdu/eDa1Wi8LCQuzduxeBgYFQqVTw8/PDvn37AACHDx+GXC6Hv78/evfujcTERKkb8dtvv8WAAQMaVTfdfUpNuA6Ks5kTWY/JLajPP/8ckydPrvV6Tk4OQkND8eOPP0KpVDa6gHbt2mHt2rWIiIiARqOBn58fIiMjkZqaiqVLlyI2NrbedYDKARMZGRkIDQ2FRqNBeHg4hgwZAgDYsGEDli1bhi1btkCpVGLjxo2Qy+V47LHHcPXqVYSFhUGpVKJTp0546623Gl073V0afR0UW1BEFiUThsPlakhKSkJqaioA4L333sPs2bONut6AymuNDh06hGPHjlm2UjsQFhaGmJgYW5dBFvJk4jHsuZIFAPjHPZ0xpIMnAGBwew+cuFE5QKZMq8Orv/wBAHBROODa1GDbFEvUAjTYgurcuTPefPNNacj3gQMH4ODgIC2XyWRo3bo1li5datkqiaygXG/CXHw1uvhqDtIhIvNpMKC6deuGAwcOAABmzJiB9957D25ublYpjMjaTDkH5SCTwUEmg04ICFR2C7ZSONS5LhE1T6POQRHdzcq0t78OCgCcHeQorlq3SKtlQBFZSIMB1bdvXxw6dAienp7o06dPg10Zp06dMntxRNZk2MVX33VQAODs4CAF1E2NBh2cOQsJkSU0GFBvvPEGXF1dpcfsa6e7mdFMEvVMdQRUtqCqFVZoLVoTUUvWYEA9+uij0uOwsDCLF0NkS2VawxZUQwF1q0vvpoYBRWQpJl+oW1FRgc2bN+PSpUsAgNdffx2DBg3CU089hdzcXIsVSGQtZSbMxQcYt6AYUESWY3JAvf3229ixYwdKSkqQmJiIL774Ai+88AI0Gg3Wrl1ryRqJrMJwqqPbnYOqVqjRWLQmopbM5FF833zzDTZs2IBevXph+/btGDp0KJ555hkMGzYMTz31lAVLJLIOU2YzB9jFR2QtJregbt68iS5dugCovHnh8OHDAQCurq6oqKiwTHVEVqLR66GtuiBdhsrrnerDLj4i6zC5BXXPPffg8OHD8PLyQlZWFkaOHAkAiI6Oxr333muxAomsobTGNVANjVg1bkGxi4/IUkwOqPnz52PevHnQarWYOHEiunXrhrfeegs7duzA5s2bLVkjkcWVmTCLRDW2oIisw+SAGj16NA4dOoSsrCz06tULAPDII49g2rRpUtcf0Z2qxISbFVZzMhokwYAishSTAwqAdEfZnJwcCCHQoUMHAJU3EPT29jZ/dURWUrOLryHGF+qyi4/IUkwOqF9++QVLlixBRkaG0evVszmfOXPG7MURWYtRQDUwiwQAtOIoPiKrMDmg3n77bbi7u2PRokVo27atJWsisrpSnWmzSACAs4LnoIisweSAOnfuHHbu3ImePXtash4imyhtxDkojuIjsg6Tr4NSqVQoKSmxZC1ENlOivdUSasw5KLagiCzH5BbUggULsHr1avznP/9Bly5doFQqjZZzkATdyRozSMKJ56CIrMLkgPrPf/4DjUaDZ555xugiRg6SoLtBWSPOQTnKZJDLAL2ovIdUuU5nFFpEZB4mB9TWrVstWQeRTTXmOiiZTIZWRjct1DKgiCzA5IAaMmSI9Fir1UKhaNQlVER2rTFdfEDNu+pq0Z531SUyO5MHSQDA119/jaCgIAwcOBCXL1/GihUr8P7771uqNiKrKW3EVEcA4GR4sS7PQxFZhMkB9fXXX+PNN9/E5MmT4VDVndGzZ0989NFH+Oijj5pVRGJiIkJCQjBhwgRERESgqKjI5HV0Oh3WrFmDoKAgjBs3Djt37pTeo1arMX36dEycOBFTpkxBenq6tOz48eN44oknMGnSJEyfPh2XL19u1jHQna0xF+oCHGpOZA0mB9THH3+MZcuW4bnnnoO86l+Y06ZNwxtvvIEvvviiyQXk5eVh8eLF2LRpEw4cOABfX1+sW7fO5HV27doFtVqNPXv2IDo6Gtu2bcPJkycBAAsXLsTUqVOxb98+zJs3D/Pnz4cQAllZWXjxxRexYsUKxMXFYfz48Vi5cmWTj4HufEYtqAZmMq/GoeZElmdyQF26dAkDBw6s9frAgQORnZ3d5AKSk5PRr18/dO3aFUBl6MXHx0NU3ZvnduskJCQgLCwMCoUCbm5uCA4ORlxcHLKzs3HhwgUEBwcDAEaNGoWSkhKcPn0a33zzDUaMGIE+ffoAAKZOnYolS5Y0+RjozlfSrBYUA4rIEkwe6eDj44OzZ8/C19fX6PUff/wRPj4+TS4gKysLKpVKeq5SqVBUVITi4mK4urredp3MzEyj/atUKqSlpSEzMxNeXl5Saw+ovFYrKysLarUaLi4ueOmll3Dx4kX4+PjUG1BRUVGIiooCAOTn5zf5OMm+GV6oa8o5KOMWFLv4iCzB5ICaPXs2Vq5cievXr0MIgWPHjiEmJgaffPIJFixY0OQC9Hp9nTeHMwyWhtapvg6rmhACcrm8zvcIIeDg4ACtVosffvgBO3bsQNeuXfHZZ5/hxRdfRGxsbK19hIeHIzw8HAAQFhbW5OMk+1Zs0IJybmQLqrCCLSgiSzA5oJ544glotVp88MEHKCsrw2uvvQZvb2+8+uqrmDp1apML8PHxQUpKivQ8Ozsbbm5ucHFxMWkdHx8f5OTkSMtycnKgUqnQsWNHKUyrg6p6mZeXF+677z6py3DKlClYs2YNysrK4Ozs3ORjoTtXsUE3nVOjW1AMKCJLaDCgjh8/bvS8e/fuWLduHfLy8uDo6Ig2bdpI691///1NKmD48OGIjIyEWq1G165dsWvXLgQEBJi8TkBAAHbv3o0xY8agpKQEe/fuxapVq6BSqeDn54d9+/YhODgYhw8fhlwuh7+/P4QQiIqKwuXLl+Hr64tvv/0W3bt3Zzi1YEWGc/E1sgX1F7v4iCyiwYCaMWMGZDKZNGChuiVSs1sNQJOnOmrXrh3Wrl2LiIgIaDQa+Pn5ITIyEqmpqVi6dCliY2PrXQeoHDCRkZGB0NBQaDQahIeHSxcVb9iwAcuWLcOWLVugVCqxceNGyOVy9OrVCytWrMCLL74IrVaLtm3bYuPGjU2qn+4Oxl18t58VorXi1jr55RUWqYmopZMJw+FyNRiOzjt06BA++ugjvPbaaxg4cCAcHR2RmpqKNWvW4J///CceffRRqxRsS2FhYYiJibF1GWQB/tEHkFNWDgBYNagX3JSO0rLB7T1w4obxAJmzBTfxf2kXAQCjVO0RG/iQ9YolaiEa7Mvw9vaW/vvwww+xevVqjBo1Sjr/M3ToUKxcuRLvvvuulcolsoxigy4+JxO6+Fo73mpB5bIFRWQRJl8HdePGDbi7u9d6XalU1jnzA9GdQi+EURefKXPxtTaYi5JdfESWYXJA3X///VizZo1Rt19GRgbeeOMNjBgxwiLFEVmD0UW6chnkJswkYXgOii0oIssweZj5ypUr8fTTT2PMmDHw8PCAEAL5+fno06cPli9fbskaiSyq2OhuuqbdNkMpl0Mpl6NCr0eZTo8SrRYunOGfyKxM/o3q2LEj4uPjceTIEZw/fx4ymQy9evXC0KFDjS6qJbrTNPb8E1A5otVd6SgNrMgtr2BAEZlZo36jFAoFRo0ahVGjRlmqHiKrK9Lc6uIzNaAAGAdUWQV8W7vc5h1E1Bhs+lCLZ3iRrimzSFTzMBiKnl/B81BE5saAohbPaJqjRrSgDK+Vyi1jQBGZGwOKWjzDIeZOJg6SAAAPJ6X0OK+C0x0RmRsDilq8xs7DV83N0bAFVW7WmoiIAUXU5C4+D6dbAcUWFJH5MaCoxTPu4mvaOag8noMiMjsGFLV4RU1tQRkOkihnFx+RuTGgqMUzGmZuwq02qnkoDQZJlLOLj8jcGFDU4hU38Toooy4+zsdHZHYMKGrxig1mkmjMKD4PBhSRRTGgqMVragvKReEAR3nlzOclOh1KDQZbEFHzMaCoxStqwmSxQOWEse0MLta9wYESRGbFgKIWr7iJk8Vq9HqjgRInc//C5aIS3sCQyEwYUNTiGc0k0YguvhKdDnKDexsezMzBwcwco2HrRNR0DChq8YxnkjB9mDkAtDWY7qhQw6HmRObEgKIWz3CQhHMjb77ZVnnrlmp/VbDlRGRODChq0fRCGE111Jhh5gBbUESWxICiFq1Up4OoeuzsIIdcJmtw/ZoML9ZlC4rIvOwioBITExESEoIJEyYgIiICRUVFJq+j0+mwZs0aBAUFYdy4cdi5c6f0HrVajenTp2PixImYMmUK0tPTa233008/xSOPPGK5gyO7Znj+yaWR558AoK3jrS4+tqCIzMvmAZWXl4fFixdj06ZNOHDgAHx9fbFu3TqT19m1axfUajX27NmD6OhobNu2DSdPngQALFy4EFOnTsW+ffswb948zJ8/H0IIabu//PILtm7dar2DJbtjOIKvlULRwJp1M2xBFfKWG0RmZfOASk5ORr9+/dC1a1cAwLRp0xAfH28UJA2tk5CQgLCwMCgUCri5uSE4OBhxcXHIzs7GhQsXEBwcDAAYNWoUSkpKcPr0aQDAjRs38MYbb+Dll1+26vGSfTHslnNVNL4F5eqoQHWnYJFWB51eNLg+EZmu8f9kNLOsrCyoVCrpuUqlQlFREYqLi+Hq6nrbdTIzM+Hj42O0LC0tDZmZmfDy8oLcYFSWt7c3srKy0LNnTyxYsACLFi2C4jb/ao6KikJUVBQAID8/3yzHTPbjhsGdcA1v4W4qB5kMbRwVKKzqKrzJbj4is7F5C0qv10NWx4lpw2BpaB0hhNEyIQTkcnmd7xFCwMHBAevXr8f999+PYcOG3ba+8PBwxMTEICYmBh4eHo05NLoDXDcIKM8mBBRgfB7qL16kS2Q2Nm9B+fj4ICUlRXqenZ0NNzc3uLi4mLSOj48PcnJypGU5OTlQqVTo2LEjrl+/bhRg1cvi4uLg6emJ7777DiUlJcjOzkZoaChiY2OtcMRkT3INpiXyNDif1BhtlY5ASRkAnociMiebt6CGDx+OlJQUqNVqAJWDHgICAkxeJyAgALt374ZWq0VhYSH27t2LwMBAqFQq+Pn5Yd++fQCAw4cPQy6Xw9/fH8nJyYiLi0NsbCxWr14NPz8/hlMLdcPgVu1Nb0EZDDVnC4rIbGzegmrXrh3Wrl2LiIgIaDQa+Pn5ITIyEqmpqVi6dCliY2PrXQeoHDCRkZGB0NBQaDQahIeHY8iQIQCADRs2YNmyZdiyZQuUSiU2btxo1HVIdL2Z56AA49kkONScyHxkwnC4HDUoLCwMMTExti6DzCj8h59x4Go2AGDj0P6o65dhcHsPnLhRe4BM9etHsnPxpfoqAGBoBw9sHfZ3+Lq61FqfiBqHzQlq0QxH8Xkqm9aCcjc4d1XAc1BEZsOAohbtuhnOQXk43Qqo/HIGFJG5MKCoRcstb/45KHeDlldBRQXYa05kHgwoarFKtFppJnNHuaxJM0kAQCsHOZyqBt9U6AX+4kAJIrNgQFGLZTjEvIOzU50Xg5tCJpMZdfNllZQ3sDYRmYoBRS2W4UW67ZrYvVfNsJsvs7SsWdsiokoMKGqxDK+B6uDs1KxtGbagGFBE5sGAohbLcIh5+2a2oDyUhl18DCgic2BAUYtleA6qfXNbUOziIzI7BhS1WDcMhpg3O6DYxUdkdgwoarGMW1DN7eIzaEGxi4/ILBhQ1GJlGbR0mjtIwk156866N8rKodHrm7U9ImJAUQt24Wax9LhrMyd3VcjlaFN140I9gGtsRRE1GwOKWqQKnR4ZxSXS866urZu9TcNrqS4ahB8RNQ0DilqkjOIS6KumzOvs0gqtmjjNkaEOrW51E54vLGr29ohaOgYUtUiG3Xt/a9P81hMAeBmcxzrPFhRRszGgqEVKNwiQbmYKKMOBFulsQRE1GwOKWpz88gqk5BZIzz2dHHG5qASlOl2ztmvUgmJAETUbA4panCKNFr/nFUjPCyo0OJiZg4pmDg1v56yUhppfKi5BhY5DzYmagwFFLdL1GrfaMAelXC7d/l0vAHURz0MRNQcDilocjV6PPMNbbTRzFglDHYy6+RhQRM3BgKIW52pJKapvyu6udIRSbr5fA8PzUOk3eR6KqDkYUNTinMovlB57m6l7r5rhtVB/cqAEUbPYRUAlJiYiJCQEEyZMQEREBIqKav9i17eOTqfDmjVrEBQUhHHjxmHnzp3Se9RqNaZPn46JEydiypQpSE9Pl5Z9/PHHCA4OxqRJk/DUU08hIyPD8gdKduEXgxF85roGqpph4P1hEIRE1Hg2D6i8vDwsXrwYmzZtwoEDB+Dr64t169aZvM6uXbugVquxZ88eREdHY9u2bTh58iQAYOHChZg6dSr27duHefPmYf78+RBC4OjRo4iOjkZUVBTi4uIwbtw4LF682OrHTrbxy4186XG3tuYNqM6tW0mPT+UXciQfUTPYPKCSk5PRr18/dO3aFQAwbdo0xMfHQwhh0joJCQkICwuDQqGAm5sbgoODERcXh+zsbFy4cAHBwcEAgFGjRqGkpASnT59G+/btsXLlSri6ugIA+vXrh2vXrln1uMk2rpeV42JR5Rx8DjIZujRzktiaXB0V6OTiDAAo1+tx5q+bZt0+UUuisHUBWVlZUKlU0nOVSoWioiIUFxdLAdLQOpmZmfDx8TFalpaWhszMTHh5eUFucALc29sbWVlZCAgIkF6rqKjAunXrEBQUVGd9UVFRiIqKAgDk5+fXuQ7dOY7m5EqP/Vq3MusAiWq93dviatVs5r/nFmCAp5vZ90HUEti8BaXX6yGTyWq9bhgsDa0jhDBaJoSAXC6v8z1CCDg43JoUNC8vD7Nnz4aLiwteeumlOusLDw9HTEwMYmJi4OHh0ejjI/tyNPtWQN3b1tUi++jj3lZ6/JvB+S4iahybB5SPjw9ycnKk59nZ2XBzc4OLi4tJ69RclpOTA5VKhY4dO+L69etGXYXVywDg7NmzmDJlCnr37o33338fSqX5roUh+6TR6xGXkSk9v8fMAySq9fEwCCiDGSuIqHFsHlDDhw9HSkoK1Go1gMpBD4ZdcLdbJyAgALt374ZWq0VhYSH27t2LwMBAqFQq+Pn5Yd++fQCAw4cPQy6Xw9/fH1lZWZg1axbmzp2LJUuWGLWq6O4Vn5GJzKq76LZxVKC7mQdIVOvl1kZ6fLqgEGXNnOOPqKWy+Tmodu3aYe3atYiIiIBGo4Gfnx8iIyORmpqKpUuXIjY2tt51gMoBExkZGQgNDYVGo0F4eDiGDBkCANiwYQOWLVuGLVu2QKlUYuPGjZDL5di8eTNKS0vx+eef4/PPPwcAKJVKfPnllzb7HMjyPky7KD1+yMsTCgucfwIAF4UD/Fq3QkZxKTR6gQNXsnFfO3e4Oirg4cSWOpGpZMKwD4waFBYWhpiYGFuXQU3wU04ugr49AgBQyGRYNrAn3Krmzas2uL0HTtyoPRCmKa8vPJaKn67nAQACfDogxM8HAT5e8DXzqEGiu5nNu/iILE2nF3j1xCnpeVAn71rhZG593A27+TjUnKgpGFB019uRnoHf8/4CADg7yPFir24W36e/mysUVaNIM0vLjCanJSLTMKDorlZQocGq389Iz+f3vhedDGZ7sBQnBwejYeynCzjtEVFjMaDorvbWyTTkVrVeOrduhfl97rXavg27+X7L/ctq+yW6WzCg6K514noePjIYufdS73uRW1bR7Fu7m6qfp5v0C5Z+sxhneS6KqFEYUHTXWn/qT+iqBql2b9sachnMcmt3U7krHY2mOfo8nTPmEzUGA4ruSmcKCvHN1WzpeYivT53TZVnaKJ8O0uP9V7OQXXWhMBHdHgOK7jpCCKxOOSvdNbe3exv42ej6o66uLuhatW+NXmDrObVN6iC6EzGg6K6zW30Vey9nSc+DOnnbsBpgtKq99Pjjc2qUajn1EZEpGFB0V8koKsGC46nS8wc6eNqs9VStn6cbPKouDM4tr8CXF6/YtB6iOwUDiu4ahRUaTE38GX9VaAAAnVycMbmLz23eZXkOMhlGGrSiNp4+D62VBmoQ3ckYUHRXEELgX0d+laYVcpTLsPbvfeFsJzPVP9DBE20cK+dmTr9ZjC/VV21cEZH9Y0DRXeGjc2qjUXv/+8BADGrnbruCamilcMCMbn7S87dPpkHDVhRRgxhQdMf7KScXS3/5Q3r+ZDdfDPdqZ7ULck0V/rfOaFvVirpYVIKXfkrB5aIS5HOePqI6MaDojlaq1eHFn36XLr7t6OKMgZ5uVr0g11QOchnGGFwXtf3CZaz6/QyKNFobVkVkvxhQdMcSQuC1X07hfGExgMrzTjO7+VnsRoTmMErVHv0Nbgkfrb6KD9MugrdlI6rNfn+TiW7jf1LP4eM/L0nPJ/t1hMrF2YYV3Z5cJsP0br7o7HJrRvVNZ9Kx6HgqdHqGFJEhBhTdcbR6PZacOIU3T6ZJrw30dMNDXp42rMp0Tg4OeLHXPehucDuOrefUeOrwCZTZ2XkzIltiQNEdJbesHGHf/4TNZy9Irz3YwRNPdvO1yVx7TeWscMCcHl0xqN2tyWTjL2ci7OBPKOCgCSIADCi6Q+iFQOylaxi1/xAOZd2QXp/YWYV3hw6w6/NO9VHI5ZjRzQ/T7/GVXjuak4vAbw7jdD5vcEh05/1WU4sihMB3V7Mxet8hzDp8AleKS6Vlc3veg7V/74M7qOFUi1wmw/O97kGo360ZL87fLMYj3x1BwrUcG1ZGZHsMKLJbGUUlmJZ4DI//8DNO5t+6I62zgxzP+HeBv5srfsi6bnfDyZtijE8HPNnNF/KqsM2r0GDK9z9hRtJxJGVe5wSz1CIpbF0AEVDZhXe1pBSZJWW4VFSCw9k3sOvCFaPwcXaQ4yGvdgjw6YDWjnffV3dwew+4Kx3x2fkMFFZdGxV/ORPxlzOhlMvx9/bumNhZhce6dkJHg1GARHeru++3nO4If1Vo8HtuAZJzcnE0Oxe/5xWguIFWwmNdOuFp/y44V1hkxSqt7962rni5nz+Ss3ONpm6q0OvxY04efszJw/JfT2O4d3sM826He9q0lv7zdFLasHIi87OLgEpMTMT69etRUVGBHj164M0334Srq6tJ6+h0Orz11ls4fPgwdDodZs+ejWnTpgEA1Go1XnvtNeTn58PFxQWRkZHo1q0bACA6Ohoff/wxtFotHnzwQSxduhSOjo5WP/Y7iRACGr2ARugr/1+vh0avR4VeD23V8wq9gFavR5FGi7yKCuSWVSCvvAK55RXIL6/AlZIynC8sQk5ZuUn77Orqgkl+PrinTWu0VbaMn4+rowJLB/ZEH482+Pl6Ps4XFiGr9NbnJQAczr6Bw9k3jN7nrnTEPW1aw8vZCW5KR7hX/efhpISXsxO8WjmhvZMTnBVyOMsd4OQgh5ODHApZZU+/TAbIUHleTH4nn9iju4bNAyovLw+LFy/Gzp070bVrV/zP//wP1q1bh5UrV5q0zq5du6BWq7Fnzx4UFxcjPDwcffr0Qf/+/bFw4ULMmjULISEhSEpKwvz58xEfH48///wTmzZtwldffQV3d3csXLgQn376KZ599tlmHcvBazn47PwllGp1EKj8Q6IXovJx9f8DEAIQENBX/X/V/yAgqpYZvq/q9RqP9VUzD4gG31t7n3Vuz2Ab+urH4tZ+tHpRGUIWnu3AzdERnVu3QjsnJZwd5Oju5op727S+o4aPm5Nvaxf4tq68l9VNjQanC24ivbAYx2/ko66fREGFBr/mFphl3wqZDK0UDnCqGh1Z/T1ylMng6CCHk1wOpVwOR7kcSgc5hBDQCQGtENDpq/6/6vtyK/QAOWRVQVj1XCYzCkU5KoOycj2Z8XsNnwMt9nthSX6tW+HffbqjU2v76EK2eUAlJyejX79+6Nq1KwBg2rRpCA0NxYoVK6QvYEPrJCQk4IknnoBCoYCbmxuCg4MRFxcHb29vXLhwAcHBwQCAUaNGYdWqVTh9+jQOHTqEsWPHwtOz8sLO8PBwrF69ulkBVaHTY3byL9K9iKhhjnIZurR2gXcrZ3RrW9lF5eaogEwmw+D2HjhxI9/WJdqVNo6OGNrBEy/06oaD13JwpuAmrpeV40ZZBa6XlyOvvAJlOvMNFtEKgZsaLW6abYt0p9ADWD+kv63LAGAHAZWVlQWVSiU9V6lUKCoqQnFxsdTN19A6mZmZ8PHxMVqWlpaGzMxMeHl5QW5wfYy3tzeysrKQmZmJzp07G70nO/tWf7+hqKgoREVFAQDOnTuHsLCweo/l7408dnPLz8+Hh4eHjatoWF01Xqn6r1pcPe+11us1a7RFPabW2A5Au3rWtTV7/z7ae32AbWq8CKD+v3K1lZeXY+/evRapxeYBpdfr62yqGwZLQ+sIIYyWCSEgl8vrfI8QAg4ODrUm5qx+T13Cw8MRHh4OAAgLC0NMTIzpB2dl9l4fwBrNhTU2n73XB9w5NVqKza+D8vHxQU7OrQsSs7Oz4ebmBhcXF5PWqbksJycHKpUKHTt2xPXr143CqHpZfe8hIiL7YfOAGj58OFJSUqBWqwEAu3btQkBAgMnrBAQEYPfu3dBqtSgsLMTevXsRGBgIlUoFPz8/7Nu3DwBw+PBhyOVy+Pv7Y+zYsfj++++Rm5sLIQSioqIQGBhotWMmIqLbs3kXX7t27bB27VpERERAo9HAz88PkZGRSE1NxdKlSxEbG1vvOkDlgImMjAyEhoZCo9EgPDwcQ4YMAQBs2LABy5Ytw5YtW6BUKrFx40bI5XL07NkTL7zwAmbNmgWNRoMBAwaYNECiuqvPXtl7fQBrNBfW2Hz2Xh/AGmWCd0ojIiI7ZPMuPiIiorowoIiIyC4xoKqkpaVhxowZmDx5MsLCwnDq1CkAgE6nw5o1axAUFIRx48Zh586d0nvUajWmT5+OiRMnYsqUKUhPT5eWRUdHY+LEiRg/fjxWrFgBjcY8F/AmJCRg0KBB0nN7qi82NhaTJk1CaGgopk6ditTUVLursSGJiYkICQnBhAkTEBERgaIi6837V9dnZ6+fm+F30N5qrOv32J5q/O677xASEoLQ0FDMnDkTGRkZdlOfEAKvvPIK/vvf/wIw/8+2tLQUCxYswMMPP4wJEyYgISHBpKJavJKSEjFs2DCRmJgohBDiu+++ExMmTBBCCLF9+3bxzDPPCI1GIwoKCsSECRNESkqKEEKIxx57TMTFxQkhhEhMTBTBwcFCr9eLtLQ0MXLkSJGbmyt0Op146aWXxIcfftjsOi9evCgCAwPFwIEDpdfspb709HQxbNgwkZ2dLe1v1KhRdlVjQ3Jzc8UDDzwgLl68KIQQ4u233xYrVqyw2P4M1ffZ2ePnVvM7aE811vd7bC81lpaWigEDBgi1Wi2EEOKTTz4Rzz77rF3Ud/78eTFjxgwxYMAAsXXrViGE+X+2kZGRYunSpUIIIa5evSqGDx8uMjMzG6yLLSgAR44cga+vL0aNGgWgcuj6u+++C6DyX4thYWG1plLKzs6uNZVSSUkJTp8+jYMHD0pTKcnlcoSHhyMurr65AUxTWlqKRYsW4dVXXzV63V7qUyqVWL16Nby8vAAAffv2xY0bN1BRUWE3NTakrum04uPja13UbQn1fXbffPONXX1udX0H7elnW9/vsb3UqNPpIITAzZuVE0gVFxfDycnJLurbsWMHHn/8cQQFBUmvmbuuhIQEPP744wCAjh07YtiwYdi/f3+Dddl8mLk1JSUl4fnnn6/1+ty5c9GhQwcsWbIEZ8+eRdu2bbFo0SIAsPhUSqbU9+abb+LIkSMIDw9Hjx49jJZZs77b1Th58mQAlV0Fa9euxdixY6FUKq1eY1OYMuWWpXTu3Fk6VsPP7ty5c3b1uS1fvrzWd9CefrYXL16s8/fYXmps3bo1Vq1ahalTp8Ld3R16vR47d+7EnDlzbF7f8uXLAVSGfDVzf241t1f9noa0qIAaNWoUTp8+Xev1LVu2ICkpCZ999hkGDBiAhIQE/Otf/8IPP/xg8amUTKlvx44dUCgUmDJlCq5cuWK0zJr1NVRjtZKSErz66qvIysrC1q1bbVJjU5gy5Zal1fzsHn/8cbv53Or7DtrTz1ar1db5e+zk5GQXNaalpeH999/Hvn374Ofnh88++wzz5s2rVYe9/H6Y+2dbc3vA7X+/2MUHwMvLC926dcOAAQMAAIGBgdDpdLh8+bJdTKX01VdfITU1FaGhofjXv/6FsrIyhIaGIjs72y7qq3bt2jVMnToVDg4O+Oyzz9C2bVsAtaeqssfpqEyZcsuS6vrs7Olzq+876O3tbTc11vd77Ovraxc1Jicn47777oOfnx8AYPr06fjzzz/RsWNHu6ivJnN//5pSMwMKwMiRI3HlyhVp5N7x48chk8nQuXNnu5hKKTo6Gnv27EFsbCw+/PBDODs7IzY2Ft7e3nZRHwAUFRVhxowZGD9+PN555x04OztLy+ylxoaYMuWWpdT32dnT51bfd3DcuHF2U2N9v8eBgYF2UWPv3r1x/Phx3LhReaPJhIQEu/kbUxdz1xUQECDdGSIrKwuHDx/GmDFjGqyBM0lUOX78ON5++22UlpZCqVRiyZIlGDx4MLRaLSIjI3H06FFpKqWnn34aQOVQy2XLliE/Px9KpRJvvPEG+vTpAwDYvXs3PvnkE2kqpTfeeANOTk7NrvPKlSsICQnBb7/9BgB2U98HH3yAd999F/7+/kavf/rpp2jTpo1d1Hg7SUlJWL9+vdF0Wu7u7hbbX7X6Prv//ve/+OCDD+zuczP8DtrL969aXb/HAwcOtJsad+zYge3bt8PR0RFubm5Yvnw5/va3v9lNfa+++iq6d++Op59+2uw/2+LiYqxcuRKnT5+GTqfD888/j9DQ0AbrYUAREZFdYhcfERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUERHZJQYUNdqMGTPw2muvAQBiYmLQu3dvG1dkzLA+a0lMTMT58+cBVE6m2qNHD5w4ccKs+ygoKEBQUBDy8/PNut1q06ZNw8mTJy2y7dsx/PyIqjGgiJopOzsbc+bMQW5urkX38+abb2LKlCnw8PCwyPYXLlyIxYsXo6KiwiLbr4+1Pj+68zCgiJrJGjcESE9Px7fffotp06ZZbB9///vf0bp1a8TFxVlsH3XhDRWoPgwoquXs2bN49tlnMXjwYPTt2xcTJkzA119/3aRtFRcX47XXXsOQIUMwZMgQREZGYsaMGdi0aROAylutb968GePHj0ffvn0xePBgzJs3D3l5eQCAn3/+Gb1798Y333yDsWPHYtCgQZgzZw4yMzNNruHEiROYOnUq+vfvj4CAAKxfvx7l5eXS8h49eiA6OhrTp09H//79ERQUJN1YrdrWrVsxevRoDBgwAC+88AJWr16NGTNmAABGjRoFAJg5cyZeffVV6T2//vorwsLC0LdvXzz88MP44YcfpGW///47pk6dioEDB2Lo0KFYtGgRCgoK6j2Gbdu2YeTIkWjdujWAW92IiYmJmDRpEvr164cpU6bg4sWL2LRpEx544AEMGTIEq1evlrZx48YNvPjiixg6dCgGDhyIp556CmfOnDHaT1BQED755BOTP1tTNXS8NT+/6mP7v//7Pzz44IN4+OGHUVFRgZ9//hlPPvkkBg0ahL59+yI0NBSHDh2S9jF27Fh8/PHHeO655zBgwAAEBATgvffeM6rjq6++woQJE9C/f3/MnDkT7733HsaOHSstz8zMREREBO677z489NBDeOmll5CdnS0tnzFjBpYvX46wsDDcf//9+P777xv9s6RGEEQGiouLxYMPPiiWLFki0tPTxfnz58Vrr70m+vTpI65fvy6EEOLJJ58US5YsEUIIsXv3btGrV696t/fiiy+K8ePHi59//lmcPn1azJw5U/To0UP87//+rxBCiK1bt4qhQ4eKpKQkceXKFZGYmCgefPBBsXr1aiGEED/99JPw9/cXgYGB4siRI+KPP/4Q4eHhYuLEiUKj0dS5T8P6Tp8+Lfr37y+2bt0q1Gq1OHr0qAgODhavvvqqtL6/v78YOnSo2Lt3r3S8PXv2FFeuXBFCCPHZZ5+JgQMHiq+//lqkp6eLNWvWiB49eognn3xSCCHEH3/8Ifz9/cWBAwdEYWGhuHz5svD39xcjRowQhw4dEhcvXhTz5s0TAwcOFCUlJUKr1YoHHnhAvPvuu+Ly5cvi999/FxMmTDCqqaZhw4aJL7/8UnpevY/qz/bMmTMiICBA3H///eLll18W6enp4v/9v/8n/P39RWJiovSzmDNnjjh//rxIT08Xc+fOFQEBAUb7OX/+vPD39xcZGRn11tJYtzve+j6/Rx55RJw/f16cOnVKXLt2TfTr109s2LBBZGRkiDNnzog5c+aIBx54QJSXlwshhBgzZozo37+/2LVrl7hw4YJ45513hL+/vzh+/LgQQoiEhATRu3dvsW3bNnHhwgXxwQcfiJ49e4oxY8YIISq/+wEBAWLhwoUiLS1NnD59WrzwwgtiwoQJ0j6efPJJ0bNnT7F//35x5swZUVhY2OifJZmOAUVGbty4IT744ANRUlIivZaRkWH0i25qQFW/7+jRo9Jrubm5on///lJAHTx4UPoDWu3ll18WM2fOFELcCqiDBw9Kyy9duiT8/f3FoUOH6tyvYX0LFiwQ8+bNM1p+4sQJ4e/vL7Kzs4UQlQH19ttvS8v/+usv4e/vL/bt2yeEqPzD9+677xptIywsTAqozMxM4e/vL3766SchxK3wiIqKktav/iN86tQpkZ+fL3r06CG2b98u9Hq9EKIyGM6cOVPn8Vy9elX4+/uL33//XXqtrn289dZbom/fvqKsrEx67cEHHxQfffSREEKIkJAQsXDhQmn5jRs3xE8//SR0Op20vlarFX369BFxcXF11tIUtzve+j6/nTt3Stu4dOmS2Lp1q/R+IYT48ccfhb+/v7h27ZoQovLnNH/+fKN9Dx48WDr+6dOni0WLFhktnzdvnhRQX3zxhXjooYeEVquVlpeXl4uBAweK+Ph4IUTld+uJJ54w+dioeRS2bsGRfWnXrh3+8Y9/4Ouvv8aZM2egVqtx9uxZAIBOp2vUtk6fPg0AGDhwoPSap6cnunbtKj0fO3YsfvvtN7zzzju4ePEiLly4gPT0dAwePNhoW0OGDJEe+/n5wdPTE+fOncOIESMarOHMmTO4dOkSBg0aJL0mqs55pKenw8vLCwCMamrbti0AQKPRID8/H1evXjU6BgC47777pM+lPnVts6ysDO7u7vjnP/+J119/HZs2bcKwYcMwZswYPPzww3Vu58aNGwBQ5+AIPz8/6bGLiwu8vLyMbvvt7OwsDXqYO3cuXnnlFXz77be4//77MXLkSEyePBly+a2efgcHB7i7u9c5YCEuLg4rVqyQnoeEhGDw4MG1Xnv99deN3tfY463m6+trdJyTJ0/Gtm3bkJaWhkuXLkndk4bfS8PPHADatGkDjUYDAPjjjz8wceJEo+X33XcfTp06BaDy+5qXl1fru1daWor09HTpeefOnZt9bGQaBhQZycnJQXh4OLy9vTFmzBiMHj0aXl5eeOyxxxq9LQcHBwANnwTfsmULPvzwQ4SFhWHEiBGYM2cOPvvsM1y7ds1oPYXC+Kuq1+uN/rDWx9HREZMnT8azzz5ba1mHDh2kx0qlstZyIYS034aOoT511Ve9nVdeeQXTp09HUlISkpOTsXjxYsTFxeHDDz+s9R6ZTAag8phrqvm5NPSZBAUF4aGHHkJSUhKOHj2KzZs349NPP8UXX3yB9u3bS+tptdo6tzN27FgMGDBAeu7q6gonJ6dar9WlMcdbzTBo//zzT/zjH//AgAED8OCDD2LixInQarV47rnnjN5T388RqPysGvo5Ojo64t5776113gqoDLpqzs7OzT42Mg0HSZCR7777DsXFxdixYwfmzJmDsWPHStfdNPaPdI8ePSCTyZCSkiK9VlBQgEuXLknPt23bhoiICCxbtgyPP/44+vTpg0uXLtXaV/W/cgHg4sWLKCgoQK9evW5bw7333ov09HR06dJF+i8vLw+RkZEoLi6+7fvbtGmDjh07Gh0DAKPrhaoDxFQZGRlYsWIFOnTogOnTp2PLli2IjIxEUlJSnS2X6lZe9cCRptBqtYiMjMTVq1cREhKCtWvXYu/evbh69SqOHTsmrafX61FYWGgU3tVcXV2NPsd27drV+Vpjj9eUzy8mJgY+Pj7YunUrnn76aYwYMUIavGDq97JHjx4N/hy7d++OK1euwN3d3eh41q5di3PnztW5zcb+LKlxGFBkxMPDA0VFRThw4ACuXr2KgwcPSl04jb0+xtfXF+PHj8frr7+O48ePIy0tDYsWLUJpaan0R8nT0xPJyclIT0/Hn3/+iddffx2//fZbrX2tWrUKv/76K1JTU/Hyyy+jX79+Rt1+9Xn22Wdx8uRJrF27Funp6Th27BheeeUV3Lx5s84/wnV55plnsG3bNsTHx+PixYvYsGEDfv/9d2l59ci6tLQ0ky6i9fDwwP79+7Fy5Uqkp6cjPT0d+/fvh5+fX53deN7e3vD29q414q4xFAoF/vjjDyxfvhwpKSm4fPkyoqKi4OjoiD59+kjrnT17FjqdDv3792/yvmq63fGa8vl5enri6tWrOHLkCK5evYrY2Fi88847AEz/Xj7zzDPYu3cvduzYAbVajW3btmH//v3S8pCQEHh4eODf//43UlNTce7cOSxYsAApKSno3r17k46NmocBRUYefvhhzJo1C6tXr0ZwcDA2btyIuXPnokuXLkhNTW309lavXo0+ffpgzpw5mDFjBnr37o1OnTrB0dERABAZGYnCwkI8+uij+Oc//4mCggIsWLAA58+fR2lpqbSdyZMn49///jdmzZoFPz8/fPjhhyZ18fXo0QMffPABfv31V2kb999/f53dOPX5xz/+gdmzZyMyMhKhoaFQq9UIDAyUupNcXV0xY8YMrFu3DkuXLr3t9tq0aYOPPvoIly9fxhNPPIEpU6agvLy8wWMaM2YMfv75Z5Nrrsv69evRuXNnzJkzBxMnTkRCQgLef/99dOnSRVrn2LFj6NWrFzp16tSsfRm63fGa8vnNnDkT48aNw0svvYRJkyZhx44dWLVqFVxcXEz+Xo4ePRpLly7Ff//7XzzyyCP44Ycf8Oijj0rfRWdnZ3zyySdwdnbGrFmzMG3aNGi1Wmzbtq3OlqEpx0bNZKvRGXT3KysrE999950oLi6WXquoqBCDBg0SX331lUnbqB7Fl5mZaaEqby8pKUlkZWUZvTZ79myxePFiq9Vw7tw50b9/f5Gfn2/R/UyaNElER0dbdB+2cuzYMaFWq41eW7ZsmTRilOwPI54sRqlUYuXKlVi9ejUuXryI9PR0rFq1Co6Ojhg5cqStyzPZ119/jXnz5uHUqVO4cuUKtm/fjh9//BEhISFWq6F79+4IDAzErl27LLaPY8eOoaysDKGhoRbbhy0dOnQIzz77LE6cOIGrV68iPj4e8fHxmDRpkq1Lo3rIhOA8I2Q5f/zxB95++22cOnUKer0egwYNwqJFi0wa4ABUziQxc+ZMJCUlQaVSWbjauuXn5+PNN99EcnIyiouLcc8992DOnDlWH0qcl5eHadOmYefOnfD09DT79qdOnYqXX34Z9913n9m3bQ/Ky8sRGRmJb7/9FgUFBfD19cWTTz6J6dOn27o0qgcDioiI7BK7+IiIyC4xoIiIyC4xoIiIyC4xoIiIyC4xoIiIyC79f+Tyedb2yk48AAAAAElFTkSuQmCC\n", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -456,135 +553,58 @@ } ], "source": [ - "plt.figure(figsize=(8,5))\n", - "\n", - "plt.axhline(y=0, color='#87919E', linestyle='dotted')\n", - "\n", - "timepoint_labels = ['6 seconds\\nbefore', '4 seconds\\nbefore', '2 seconds\\nbefore', 'long gap\\n(reference)',\n", - " '2 seconds\\nafter', '4 seconds\\nafter', '6 seconds\\nafter']\n", - "\n", - "friend_color = '#b7094c'\n", - "stranger_color = '#0091ad'\n", - "\n", - "ax = sns.pointplot(x=\"timepoint\", y=\"connection_change\", hue=\"condition\",\n", - " data=long_gaps_all, dodge=True,\n", - " order=[\"3_before\", \"2_before\", \"1_before\", 'long_gap', '1_after', '2_after', '3_after'],\n", - " palette = [stranger_color,friend_color], capsize=0.12, markers='o', errwidth=1.5)\n", - "\n", - "plt.xlabel(\"timepoint\", fontsize=18, labelpad=20)\n", - "plt.ylabel(\"connection rating\\n(compared to reference)\", fontsize=18, labelpad=10)\n", - "\n", - "ax.tick_params(labelsize=13)\n", - "plt.legend(fontsize='13')\n", - "\n", - "ax.set_xticklabels(timepoint_labels)\n", - "\n", - "ax.axvspan(2.75, 3.25, alpha=0.3, color='silver')\n", - "\n", - "## significance for friends\n", - "lw = 1\n", - "line_color = friend_color \n", - "star_color = friend_color\n", - "h = 0\n", - "space_between_stars_and_line = -.1\n", - "star_font_size = 25\n", - "\n", - "# long gap vs 3 before\n", - "x1, x2 = 0.1, 2.9 \n", - "y = -1.9\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"**\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "# long gap vs 2 before\n", - "x1, x2 = 1.1, 2.9 \n", - "y = -1.6\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"**\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "# long gap vs 1 before\n", - "x1, x2 = 2.1, 2.9 \n", - "y = -1.005\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"*\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "## significance for strangers\n", - "lw = 1\n", - "line_color = stranger_color \n", - "star_color = stranger_color\n", - "h = 0\n", - "space_between_stars_and_line = -.1\n", - "star_font_size = 25\n", - "\n", - "# long gap vs 1 before\n", - "x1, x2 = 2.1, 2.9 \n", - "y = 1.0\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"*\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - " \n", - "# long gap vs 1 after\n", - "x1, x2 = 3.1, 3.9 \n", - "y = 0.8\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"**\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'strangers_pure', '*.csv'))\n", + "data = pd.read_csv(flist[0])\n", + "all_latency_values_strangers = data['gap_length']\n", "\n", - "# long gap vs 2 after\n", - "x1, x2 = 3.1, 4.9 \n", - "y = 0.5\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"*\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "for file in flist[1:]:\n", + " \n", + " data = pd.read_csv(file)\n", + " all_latency_values_strangers = all_latency_values_strangers.append(data['gap_length'])\n", + " \n", + "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True,\n", + " kde_kws = {'linewidth': 3}, color='#0FA3B1')\n", + "plt.xlim(-6000, 10000)\n", "\n", + "ax.set_xlabel('all gap lengths (ms) -- strangers', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", + "ax.tick_params(labelsize=12)\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "plt.savefig(os.path.join(base_dir,'Figures','Fig2B.pdf'), dpi=300)\n" - ] - }, - { - "cell_type": "markdown", - "id": "0386c943", - "metadata": {}, - "source": [ - "# Study 2\n", - "\n", - "Independent raters' perceptions of long gaps in conversation" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "f4476707", - "metadata": {}, - "outputs": [], - "source": [ - "rating_data = pd.read_csv(os.path.join(base_dir, 'Analyses', 'long_gap_ratings_wide_format.csv'))\n", - "rating_data['video_num'] = rating_data['video_num'].astype(int)\n", - "data_info = pd.read_csv(os.path.join(base_dir, 'Data', 'long_gap_stimuli_info.csv'))\n", - "\n", - "data_full = pd.merge(data_info, rating_data, how='inner', on=['video_num'])" + "#plt.savefig(os.path.join(base_dir,'Figures','FigS1A.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "a48c2656", + "id": "60145da1", "metadata": {}, "source": [ - "## Figure 3A\n", + "## Figure S1B\n", "\n", - "Differences in perceived awkwardness in friend vs stranger conversations" + "Distribution of gap lengths for friend conversations" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "0face793", + "execution_count": 26, + "id": "85875018", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -592,59 +612,60 @@ } ], "source": [ - "plt.figure(figsize=(5,5))\n", + "flist = glob.glob(os.path.join(base_dir, 'Analyses', 'turn_taking', 'friends', '*.csv'))\n", + "data = pd.read_csv(flist[0])\n", + "all_latency_values_friends = data['gap_length']\n", "\n", - "ax = sns.barplot(x=\"condition\", y=\"awkward_mean\", data=data_full, errwidth=1,\n", - " capsize=.05, \n", - " palette=['#3777FF', '#EF3054'],\n", - " saturation=1)\n", + "for file in flist[1:]:\n", + " \n", + " data = pd.read_csv(file)\n", + " all_latency_values_friends = all_latency_values_friends.append(data['gap_length'])\n", + " \n", + "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True,\n", + " kde_kws = {'linewidth': 3}, color='#F46036')\n", + "plt.xlim(-6000, 10000)\n", "\n", - "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('perceived awkwardness', fontsize=15, labelpad=10)\n", + "ax.set_xlabel('all gap lengths (ms) -- friends', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 80))\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "# significance bar\n", - "lw = 2\n", - "line_color = '#594F4F' # create thinner, greyer lines\n", - "star_color = '#594F4F'\n", - "h = 0\n", - "space_between_stars_and_line = -.4\n", - "star_font_size = 20\n", - "\n", - "# strangers vs friends\n", - "x1, x2 = .20, .80 \n", - "y = 75\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", - "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3A.pdf'), dpi=300)\n" + "#plt.savefig(os.path.join(base_dir,'Figures','FigS1B.pdf'), dpi=300)\n" ] }, { "cell_type": "markdown", - "id": "83286a36", + "id": "4b5c52c6", "metadata": {}, "source": [ - "## Figure 3B\n", + "## Figure S1C\n", "\n", - "Differences in perceived connection in friend vs stranger conversations" + "A zoomed in version of both distributions, highlighting the long gaps " ] }, { "cell_type": "code", - "execution_count": 19, - "id": "27dbf862", + "execution_count": 27, + "id": "6440e6d9", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/Users/emma/anaconda3/envs/python3_8/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -652,59 +673,102 @@ } ], "source": [ - "plt.figure(figsize=(5,5))\n", + "min_value = 2000\n", + "max_value = 6000\n", + "\n", + "bins = []\n", + "interval = 75\n", + "counter = 0\n", + "new_value = 0\n", + "while new_value < max_value:\n", + " new_value = min_value+(interval*counter)\n", + " bins.append(new_value)\n", + " counter += 1\n", + " \n", + "ax = sns.distplot(all_latency_values_friends, hist = True, kde = True, bins=bins,\n", + " kde_kws = {'linewidth': 0}, color='#F46036', label = 'friend', hist_kws=dict(alpha=0.5))\n", + "ax = sns.distplot(all_latency_values_strangers, hist = True, kde = True, bins=bins,\n", + " kde_kws = {'linewidth': 0}, color='#0FA3B1', label = 'stranger', hist_kws=dict(alpha=0.5))\n", "\n", - "ax = sns.barplot(x=\"condition\", y=\"connected_mean\", data=data_full, errwidth=1,\n", - " capsize=.05, \n", - " palette=['#3777FF', '#EF3054'],\n", - " saturation=1)\n", + "plt.xlim(min_value, max_value)\n", "\n", - "ax.set_xlabel('type of conversation', fontsize=15, labelpad=10)\n", - "ax.set_ylabel('perceived connection', fontsize=15, labelpad=10)\n", + "ax.set_xlabel('all gap lengths', fontsize=15, labelpad=10)\n", + "ax.set_ylabel('density', fontsize=15, labelpad=10)\n", "ax.tick_params(labelsize=12)\n", - "ax.set(ylim=(0, 80))\n", + "\n", + "handles, labels = plt.gca().get_legend_handles_labels()\n", + "order = [1,0]\n", + "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", - "# significance bar\n", - "lw = 2\n", - "line_color = '#594F4F' # create thinner, greyer lines\n", - "star_color = '#594F4F'\n", - "h = 0\n", - "space_between_stars_and_line = -.4\n", - "star_font_size = 20\n", + "#plt.savefig(os.path.join(base_dir,'Figures','FigS1C.pdf'), dpi=300)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f87b90f2", + "metadata": {}, + "source": [ + "## Figure S2\n", "\n", - "# strangers vs friends\n", - "x1, x2 = .20, .80 \n", - "y = 75\n", - "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=lw, c=line_color)\n", - "plt.text((x1+x2)*.5, y+space_between_stars_and_line, \"***\", ha='center', va='bottom', color=star_color, fontsize=star_font_size)\n", + "See the notebook \"Supplement - how do different bin intervals impact Figure 2?\"" + ] + }, + { + "cell_type": "markdown", + "id": "7d5d271a", + "metadata": {}, + "source": [ + "## Figure S3\n", "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3B.pdf'), dpi=300)\n" + "See the notebook \"Supplement - how do different thresholds impact Figure 2?\"" ] }, { "cell_type": "markdown", - "id": "4441b2d3", + "id": "b2815a47", "metadata": {}, "source": [ - "## Figure 3C\n", + "## Figure S4A\n", "\n", - "Differences in percieved awkwardness in friend vs stranger conversations, as a function of gap length" + "Percentage of turns containing a question mark split by relationship type (stranger vs friend), gap length (> 2000ms (indicating a long gap) vs ≤ 2000ms) and position (before the gap vs after the gap)" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "355eb455", + "execution_count": 30, + "id": "9620e798", + "metadata": {}, + "outputs": [], + "source": [ + "df_total = pd.read_csv(os.path.join(base_dir, 'Analyses',\n", + " 'semantic_content_all.csv'))\n", + "df_total['question_numeric'] = df_total['question'].replace({'yes':1, 'no':0})" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "cf2165d1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -712,41 +776,46 @@ } ], "source": [ - "sns.lmplot(x=\"interval\", y=\"awkward_mean\", hue=\"condition\", data=data_full,\n", - " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", - " scatter_kws={'alpha':0.2}, legend=False)\n", - "\n", - "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", - "plt.ylabel(\"perceived awkwardness\", fontsize=15, labelpad=10)\n", - "plt.tick_params(labelsize=12)\n", - "\n", - "sns.despine()\n", - "plt.tight_layout()\n", + "sns.catplot(x=\"gap\", y=\"question_numeric\", hue=\"position\",\n", + " col=\"condition\", data=df_total,\n", + " height=5, aspect=.8, kind='bar', \n", + " saturation=1, errwidth=1, capsize=.05,\n", + " palette=['#7678ed', '#3d348b'])\n", "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3C.pdf'), dpi=300)\n" + "#plt.savefig(os.path.join(base_dir,'Figures','supplement_questions.pdf'), dpi=300)" ] }, { "cell_type": "markdown", - "id": "3a374263", + "id": "18d7a8d3", "metadata": {}, "source": [ - "## Figure 3D\n", + "## Figure S4B\n", "\n", - "Differences in percieved connection in friend vs stranger conversations, as a function of gap length" + "Word count of turns containing a question mark split by relationship type (stranger vs friend), gap length (> 2000ms (indicating a long gap) vs ≤ 2000ms) and position (before the gap vs after the gap). " ] }, { "cell_type": "code", - "execution_count": 21, - "id": "b7aeb7a5", + "execution_count": 32, + "id": "2da950fe", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -754,18 +823,13 @@ } ], "source": [ - "sns.lmplot(x=\"interval\", y=\"connected_mean\", hue=\"condition\", data=data_full,\n", - " palette=['#3777FF', '#EF3054'], x_jitter=.1, y_jitter=None,\n", - " scatter_kws={'alpha':0.2}, legend=False)\n", + "sns.catplot(x=\"gap\", y=\"word_count\", hue=\"position\",\n", + " col=\"condition\", data=df_total,\n", + " height=5, aspect=.8, kind='bar', \n", + " saturation=1, errwidth=1, capsize=.05,\n", + " palette=['#cae9ff', '#5fa8d3'])\n", "\n", - "plt.xlabel(\"gap length interval (seconds)\", fontsize=15, labelpad=10)\n", - "plt.ylabel(\"perceived connection\", fontsize=15, labelpad=10)\n", - "plt.tick_params(labelsize=12)\n", - "\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(os.path.join(base_dir, 'Figures', 'Fig3D.pdf'), dpi=300)\n" + "#plt.savefig(os.path.join(base_dir,'Figures','supplement_word_count.pdf'), dpi=300)" ] } ], diff --git a/Code/statistical_tests.R b/Code/statistical_tests.R index 877307d..a3be926 100644 --- a/Code/statistical_tests.R +++ b/Code/statistical_tests.R @@ -1,38 +1,94 @@ -## Long gaps paper -## Emma Templeton +## Paper Title: Long gaps between turns are awkward for strangers but not for friends +## Author of this script: Emma Templeton # Code to reproduce all analyses, in order that they are reported install.packages(c('lme4','lmerTest','lsr')) +install.packages("lmerTest",dependencies=TRUE) library(lme4) library(lmerTest) library(lsr) -setwd("/Users/Emma/Dropbox/Manuscripts/long_gaps_repo") +setwd("/Users/Emma/Dropbox/Manuscripts/long_gaps_repo_revision") #################### STUDY 1 ########################### # ______________________________________________________ -# Frequency of long gaps by conversation type +# Count of long gaps by conversation type + +data <- read.csv("./Analyses/long_gap_counts.csv") +data$subID <- as.factor(data$subID) +data$partnerID <- as.factor(data$partnerID) +data$condition <- as.factor(data$condition) + +# poisson regression approach +model <- glmer(n_long_2000 ~ condition + log(n_total) + (1 | subID), family="poisson"(link = "log"), data=data) +summary(model) + +# check for overdispersion +rd <- glm(n_long_2000 ~ condition + log(n_total), data = data, family = poisson(link = "log")) +dispersiontest(rd) + +# check zero-inflation +m <- glmer(n_long_2000 ~ condition + log(n_total) + (1 | subID), family = poisson(link = "log"), data = data) +check_zeroinflation(m) + +# zero-inflated negative binomial approach +# (because poisson suffers from overdispersion and zero-inflation) + +install.packages(c('glmmTMB')) +library(glmmTMB) + +eq <- n_long_2000 ~ condition + offset(log(n_total)) + (1 | subID) +#eq <- n_long_2000 ~ condition + log(n_total) + (1 | subID) +zinbinom2b <- glmmTMB(eq, + data=data, + ziformula=~1, + family=nbinom2) +summary(zinbinom2b) + +# SUPPLEMENTAL ANALYSIS + +# How do different thresholds impact results? +# thresholds tested (ms): 500, 750, 1000, 1250, 1500, 1750, 2250, 2500, 2750, 3000 +# All cutoffs provide the same result: friends have more long gaps than strangers + +# poisson regression approach +model_supp <- glmer(n_long_3000 ~ condition + log(n_total) + (1 | subID), family="poisson"(link = "log"), data=data) +summary(model_supp) + +# zero-inflated negative binomial approach +eq_supp <- n_long_500 ~ condition + offset(log(n_total)) + (1 | subID) +#eq_supp <- n_long_500 ~ condition + log(n_total) + (1 | subID) +zinbinom2b_supp <- glmmTMB(eq_supp, + data=data, + ziformula=~1, + family=nbinom2) +summary(zinbinom2b_supp) -data_friends <- read.csv("./Analyses/turn_taking_friends.csv") -data_strangers <- read.csv("./Analyses/turn_taking_strangers.csv") +# ______________________________________________________ +# Long gaps and overall enjoyment (strangers) -t.test(data_friends$mean_gap_convo, data_strangers$mean_gap_convo, var.equal = FALSE) -cohensD(data_friends$mean_gap_convo, data_strangers$mean_gap_convo, method = 'unequal') +data_counts <- read.csv("./Analyses/long_gap_counts.csv") +data_enjoyment <- read.csv("./Data/post_convo_survey/strangers/data_common_questions.csv") +merged <- merge(data_counts, data_enjoyment, by=c("subID", "partnerID")) -t.test(data_friends$median_gap_convo, data_strangers$median_gap_convo, var.equal = FALSE) -cohensD(data_friends$median_gap_convo, data_strangers$median_gap_convo, method = 'unequal') +merged$subID <- as.factor(merged$subID) +merged$partnerID <- as.factor(merged$partnerID) +merged$convo <- as.factor(merged$convo) -# chi-square test is in the notebook "Study 1 - chi-square test (do friends use more "long gaps"?)" -# in the project's Github repo +model <- lmer(convo_enjoy ~ n_long_2000 + n_total + (1 | subID) + (1 | convo), data=merged) +summary(model) # ______________________________________________________ -# Reported connection by conversation type +# Reported connection by relationship type connection_friends <- read.csv("./Analyses/connection_friends.csv") -connection_strangers <- read.csv("./Analyses/connection_strangers.csv") + +connection_strangers_all <- read.csv("./Analyses/connection_strangers.csv") +strangers_inclusion_criteria <- read.csv("./Analyses/long_gap_counts.csv") +connection_strangers <- merge(connection_strangers_all, strangers_inclusion_criteria, by=c("subID", "partnerID")) mean(connection_friends$avg_connection) sd(connection_friends$avg_connection) @@ -44,10 +100,167 @@ t.test(connection_friends$avg_connection, connection_strangers$avg_connection, v cohensD(connection_friends$avg_connection, connection_strangers$avg_connection, method = 'unequal') # ______________________________________________________ -# Long gaps and self-reported connection +# Changes in connection when entering and exiting long gaps + +# First, examine strangers and friends separately +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format_strangers.csv") +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format_friends.csv") + +stacked$subID <- as.factor(stacked$subID) +stacked$partnerID <- as.factor(stacked$partnerID) +stacked$dyad <- as.factor(stacked$dyad) + +three_before <- stacked[ which(stacked$timepoint=='3_before'), ] +two_before <- stacked[ which(stacked$timepoint=='2_before'), ] +one_before <- stacked[ which(stacked$timepoint=='1_before'), ] +one_after <- stacked[ which(stacked$timepoint=='1_after'), ] +two_after <- stacked[ which(stacked$timepoint=='2_after'), ] +three_after <- stacked[ which(stacked$timepoint=='3_after'), ] + +model <- lmer(connection_change ~ 1 + + (1 | subID), data=three_before) +summary(model) + +model <- lmer(connection_change ~ 1 + + (1 | subID), data=two_before) +summary(model) + +model <- lmer(connection_change ~ 1 + + (1 | subID), data=one_before) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ 1 + + (1 | subID), data=one_after) +summary(model) # exiting a long gap + +model <- lmer(connection_change ~ 1 + + (1 | subID), data=two_after) +summary(model) + +model <- lmer(connection_change ~ 1 + + (1 | subID), data=three_after) +summary(model) + +# Next, examine group differences +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format.csv") + +stacked$subID <- as.factor(stacked$subID) +stacked$partnerID <- as.factor(stacked$partnerID) +stacked$dyad <- as.factor(stacked$dyad) +stacked$condition <- as.factor(stacked$condition) + +three_before <- stacked[ which(stacked$timepoint=='3_before'), ] +two_before <- stacked[ which(stacked$timepoint=='2_before'), ] +one_before <- stacked[ which(stacked$timepoint=='1_before'), ] +one_after <- stacked[ which(stacked$timepoint=='1_after'), ] +two_after <- stacked[ which(stacked$timepoint=='2_after'), ] +three_after <- stacked[ which(stacked$timepoint=='3_after'), ] + +model <- lmer(connection_change ~ condition + + (1 | subID), data=three_before) +summary(model) + +model <- lmer(connection_change ~ condition + + (1 | subID), data=two_before) +summary(model) + +model <- lmer(connection_change ~ condition + + (1 | subID), data=one_before) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ condition + + (1 | subID), data=one_after) +summary(model) # exiting a long gap + +model <- lmer(connection_change ~ condition + + (1 | subID), data=two_after) +summary(model) + +model <- lmer(connection_change ~ condition + + (1 | subID), data=three_after) +summary(model) -# stats for this section is in the notebook "Study 1 - long gaps and self-reported connection" -# in the project's Github repo +# ______________________________________________________ +# Changes in connection when entering and exiting long gaps: Continuous Gap Length +# Answers the question: Does connection change more as long gaps get *longer*? + +# First, examine strangers and friends separately +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format_strangers.csv") +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format_friends.csv") + +stacked$subID <- as.factor(stacked$subID) +stacked$partnerID <- as.factor(stacked$partnerID) +stacked$dyad <- as.factor(stacked$dyad) + +three_before <- stacked[ which(stacked$timepoint=='3_before'), ] +two_before <- stacked[ which(stacked$timepoint=='2_before'), ] +one_before <- stacked[ which(stacked$timepoint=='1_before'), ] +one_after <- stacked[ which(stacked$timepoint=='1_after'), ] +two_after <- stacked[ which(stacked$timepoint=='2_after'), ] +three_after <- stacked[ which(stacked$timepoint=='3_after'), ] + +model <- lmer(connection_change ~ log(gap_length) + + (1 | subID), data=three_before) +summary(model) + +model <- lmer(connection_change ~ log(gap_length) + + (1 | subID), data=two_before) +summary(model) + +model <- lmer(connection_change ~ log(gap_length) + + (1 | subID), data=one_before) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ log(gap_length) + + (1 | subID), data=one_after) +summary(model) # exiting a long gap + +model <- lmer(connection_change ~ log(gap_length) + + (1 | subID), data=two_after) +summary(model) + +model <- lmer(connection_change ~ log(gap_length) + + (1 | subID), data=three_after) +summary(model) + +# Next, examine group differences +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format.csv") + +stacked$subID <- as.factor(stacked$subID) +stacked$partnerID <- as.factor(stacked$partnerID) +stacked$dyad <- as.factor(stacked$dyad) +stacked$condition <- as.factor(stacked$condition) + +three_before <- stacked[ which(stacked$timepoint=='3_before'), ] +two_before <- stacked[ which(stacked$timepoint=='2_before'), ] +one_before <- stacked[ which(stacked$timepoint=='1_before'), ] +one_after <- stacked[ which(stacked$timepoint=='1_after'), ] +two_after <- stacked[ which(stacked$timepoint=='2_after'), ] +three_after <- stacked[ which(stacked$timepoint=='3_after'), ] + +model <- lmer(connection_change ~ log(gap_length) * condition + + (1 | subID), data=three_before) +summary(model) + +model <- lmer(connection_change ~ log(gap_length) * condition + + (1 | subID), data=two_before) +summary(model) + +model <- lmer(connection_change ~ log(gap_length) * condition + + (1 | subID), data=one_before) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ log(gap_length) * condition + + (1 | subID), data=one_after) +summary(model) # exiting a long gap + +model <- lmer(connection_change ~ log(gap_length) * condition + + (1 | subID), data=two_after) +summary(model) + +model <- lmer(connection_change ~ log(gap_length) * condition + + (1 | subID), data=three_after) +summary(model) #################### STUDY 2 ########################### @@ -70,7 +283,7 @@ merged$gestures_present <- as.factor(merged$gestures_present) merged$rater_know_person <- as.factor(merged$rater_know_person) # Difference by condition (friend / stranger) -# (for continous variables) +# (for continuous variables) model_awkward <- lmer(scale(awkward) ~ condition + (1 | rater), data=merged) @@ -138,3 +351,187 @@ table(merged$condition, merged$laughter_who_consensus_binary) chisq.test(table(merged$condition, merged$laughter_who_consensus_binary)) chisq.test(table(merged$condition, merged$laughter_who_consensus_binary), simulate.p.value = TRUE) +#################################################### +#SUPPLEMENTAL ANALYSES + +# ______________________________________________________ +# Laughter as a mediator? + +# STRANGERS ONLY +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format_laughter_strangers.csv") +stacked$subID <- as.factor(stacked$subID) +stacked$partnerID <- as.factor(stacked$partnerID) + +three_before <- stacked[ which(stacked$timepoint=='3_before'), ] +two_before <- stacked[ which(stacked$timepoint=='2_before'), ] +one_before <- stacked[ which(stacked$timepoint=='1_before'), ] +one_after <- stacked[ which(stacked$timepoint=='1_after'), ] +two_after <- stacked[ which(stacked$timepoint=='2_after'), ] +three_after <- stacked[ which(stacked$timepoint=='3_after'), ] + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=three_before) +summary(model) + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=two_before) +summary(model) + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=one_before) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=one_after) +summary(model) # exiting a long gap + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=two_after) +summary(model) + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=three_after) +summary(model) + +# FRIENDS ONLY +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format_laughter_friends.csv") +stacked$subID <- as.factor(stacked$subID) +stacked$partnerID <- as.factor(stacked$partnerID) + +three_before <- stacked[ which(stacked$timepoint=='3_before'), ] +two_before <- stacked[ which(stacked$timepoint=='2_before'), ] +one_before <- stacked[ which(stacked$timepoint=='1_before'), ] +one_after <- stacked[ which(stacked$timepoint=='1_after'), ] +two_after <- stacked[ which(stacked$timepoint=='2_after'), ] +three_after <- stacked[ which(stacked$timepoint=='3_after'), ] + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=three_before) +summary(model) + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=two_before) +summary(model) + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=one_before) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=one_after) +summary(model) # exiting a long gap + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=two_after) +summary(model) + +model <- lmer(connection_change ~ laughter + + (1 | subID), data=three_after) +summary(model) + +# BOTH +stacked <- read.csv("./Analyses/long_gap_connection_all_long_format_laughter.csv") +stacked$subID <- as.factor(stacked$subID) +stacked$partnerID <- as.factor(stacked$partnerID) +stacked$laughter <- as.factor(stacked$laughter) + +stacked$laughter<-ifelse(stacked$laughter=="yes",1,0) + +three_before <- stacked[ which(stacked$timepoint=='3_before'), ] +two_before <- stacked[ which(stacked$timepoint=='2_before'), ] +one_before <- stacked[ which(stacked$timepoint=='1_before'), ] +one_after <- stacked[ which(stacked$timepoint=='1_after'), ] +two_after <- stacked[ which(stacked$timepoint=='2_after'), ] +three_after <- stacked[ which(stacked$timepoint=='3_after'), ] + +model <- lmer(connection_change ~ condition_x * laughter + + (1 | subID) + (1 | dyad), data=three_before) +summary(model) + +model <- lmer(connection_change ~ condition_x * laughter + + (1 | subID) + (1 | dyad), data=two_before) +summary(model) + +model <- lmer(connection_change ~ condition_x * laughter + + (1 | subID), data=one_before) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ condition_x * laughter + + (1 | subID), data=one_after) +summary(model) # entering a long gap + +model <- lmer(connection_change ~ condition_x * laughter + + (1 | subID) + (1 | dyad), data=two_after) +summary(model) + +model <- lmer(connection_change ~ condition_x * laughter + + (1 | subID) + (1 | dyad), data=three_after) +summary(model) + +# Test for mediation effects! +# Step 1: condition --> change in connection +fit.totaleffect <- lmer(connection_change ~ condition_x + + (1 | subID), data=one_before) +summary(fit.totaleffect) + +# Step 2: condition --> laughter +fit.mediator <- glmer(laughter ~ condition_x + (1 | subID), + data = one_before, + family = binomial) +summary(fit.mediator) + +# Step 3: condition + laughter --> change in connection +fit.dv <- lmer(connection_change ~ condition_x + laughter + + (1 | subID), data=one_before) +summary(fit.dv) + +# Step 4 +install.packages("mediation") +library(mediation) + +results = mediate(fit.mediator, fit.dv, treat = "condition_x", mediator = "laughter", + control.value = "strangers", treat.value = "friends") +summary(results) + +# ______________________________________________________ +# Semantic Content (question marks and word counts) + +data <- read.csv("./Analyses/semantic_content_all.csv") + +data$convo <- as.factor(data$convo) +data$position <- as.factor(data$position) +data$question <- as.factor(data$question) +data$condition <- as.factor(data$condition) +data$gap <- as.factor(data$gap) + +strangers <- data[ which(data$condition=='strangers'), ] +friends <- data[ which(data$condition=='friends'), ] + +# questions +model_1 <- glm(question ~ gap * position * condition, + data = data, + family = binomial) +summary(model_1) + +model_1 <- glmer(question ~ gap * position + (1 | convo), + data = strangers, + family = binomial) +summary(model_1) + +model_1 <- glmer(question ~ gap * position + (1 | convo), + data = friends, + family = binomial) +summary(model_1) + +# word count +model_1 <- lmer(word_count ~ position * gap * condition + (1 | convo), + data = data) +summary(model_1) + +model_1 <- lmer(word_count ~ position * gap + (1 | convo), + data = strangers) +summary(model_1) + +model_1 <- lmer(word_count ~ position * gap + (1 | convo), + data = friends) +summary(model_1) diff --git a/Data/.DS_Store b/Data/.DS_Store index 3bea9ca95e398de322061710f99cb3f414506d08..922918ec6d5beab518f52e05f262e93eac2e07f9 100644 GIT binary patch delta 334 zcmZoMXfc=|#>B!ku~2NHo+2aH#(>?7i#IScF>+1*$7HwJi^-p{UXY=HA)ld`p@box zAsNWZV<-cX@j#JMh9V$4m7y{x-7q*gKevDZ1Q_QO14$xu<>tG%B<18M0abGV@iqzR zLynj_>#B)qu~2NHo+2ab#(>?7jI5LYvDj_)V)19(yqYbBabtrJ^JaDqeh#3z a&4L`?nJ4p$IC3xm0V4wg%jO7?HOv6T@DYXp diff --git a/Data/post_convo_survey/.DS_Store b/Data/post_convo_survey/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..624cb00696a4e3dbafac6d215bc3a449d0a84bc1 GIT binary patch literal 6148 zcmeHKK~BR!475un+QOknjw|s2Dy|$t6<*K}&=M#}ZBnJcIcI*wC-?)efbrU`ND~kz 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+238,29,100,100,54,79,59,100,0,52,100,0,100,51,90,0,100,3,,50,67,1 +86,239,73,83,30,81,78,74,0,68,71,73,100,68,69,65,100,2,,91,93,2 +239,86,97,96,71,100,97,95,0,73,82,36,100,100,76,100,100,2.6,,100,100,2 +11,240,93,100,31,87,74,78,6,61,100,88,90,42,89,86,90,3,,71,67,1 +240,11,89,89,51,78,73,92,2,65,85,39,89,47,65,68,95,1.8,,75,68,1 +11,241,99,100,50,94,22,90,0,50,100,100,94,60,76,94,100,3,,64,67,1 +241,11,74,77,59,56,40,72,0,64,57,58,61,57,53,56,76,2.5,,72,63,1 +29,242,64,74,47,75,76,77,0,78,80,65,75,70,61,84,78,3,,74,85,1 +242,29,55,64,76,81,60,65,46,44,63,56,53,82,58,60,72,2.5,,73,79,1 +8,243,67,65,46,73,62,66,20,95,61,65,52,60,61,58,79,1,,100,81,1 +243,8,83,100,61,100,100,100,9,66,100,58,100,93,100,62,100,3,,100,95,1 +8,244,100,100,65,86,67,100,19,99,97,95,100,87,95,84,100,3,,100,94,1 +244,8,92,99,51,93,77,100,8,70,100,22,100,74,75,27,100,3,,89,94,1 +245,30,100,100,50,69,39,34,7,100,100,72,100,100,100,31,100,1,,100,69,2 +30,245,98,100,72,96,95,83,72,95,96,36,94,98,95,68,95,1,,91,87,1 +44,246,98,96,41,88,46,53,7,53,98,72,75,64,73,56,95,1.7,,100,71,2 +246,44,67,65,49,35,26,59,0,79,62,63,74,43,45,37,90,1.6,,98,51,2 +247,66,81,80,61,100,82,87,85,33,67,41,76,75,62,25,75,2,,98,96,2 +66,247,65,100,37,100,67,53,56,68,77,63,100,42,56,38,100,2.4,,100,100,1 +11,248,75,68,28,76,89,78,0,51,73,39,50,30,31,17,51,1,,43,39,1 +248,11,62,67,96,68,35,70,59,54,64,64,80,71,65,89,78,3,,59,67,2 +66,249,100,100,50,100,100,100,0,37,100,80,100,53,100,39,100,2.8,,3,66,1 +249,66,94,94,52,81,60,83,0,37,85,18,84,60,45,25,73,3,,14,33,1 +66,250,100,100,51,63,37,100,0,77,100,66,100,76,100,84,100,2,,1,67,1 +250,66,93,92,59,52,49,59,0,52,61,35,98,64,66,14,96,1.8,,15,46,1 +86,251,100,100,53,95,80,86,0,79,96,81,100,73,81,70,100,2.5,,60,93,2 +251,86,98,100,60,100,12,93,0,87,90,21,98,79,81,22,98,3.1,,100,95,2 +44,252,91,91,45,89,46,53,9,55,81,80,75,49,59,42,91,1.6,,66,82,2 +252,44,84,84,46,64,70,50,30,58,62,44,84,55,51,73,83,1.2,,73,71,2 +44,253,100,99,49,43,46,59,42,73,74,57,77,61,65,56,91,1.7,,59,59,2 +253,44,94,78,58,77,63,53,49,63,65,53,99,70,65,66,99,2,,74,58,1 +23,254,100,88,51,85,100,100,50,100,100,18,87,100,90,34,100,1,,100,100,1 +254,23,99,100,39,91,93,100,86,12,99,95,100,38,81,72,100,1.5,,100,86,1 +85,255,92,100,51,92,88,89,12,90,100,60,100,91,75,30,100,1.7,,94,79,2 +255,85,85,87,54,75,76,57,19,74,80,73,88,87,87,60,97,1.2,,100,69,2 +85,256,81,91,51,93,83,97,77,89,96,11,83,56,70,5,91,5,I've known her for 7 years,84,93,2 +256,85,70,71,54,61,46,62,41,77,73,58,73,73,70,61,75,5,ABOUT 7/8 YEARS,84,83,1 +23,257,74,64,52,70,17,81,82,27,56,33,72,33,35,32,76,1,,100,68,1 +257,23,63,73,56,60,58,79,80,51,58,33,68,36,44,25,72,1,,100,74,2 +4,258,98,98,34,98,92,94,86,89,89,91,92,94,75,38,100,3.1,,100,97,1 +258,4,100,100,51,70,37,67,0,100,100,67,100,100,100,100,100,2,,100,100,1 +107,259,100,100,57,89,92,86,28,69,97,86,100,82,74,92,100,0.7,,80,87,2 +259,107,95,83,46,41,71,84,7,96,92,59,97,58,38,16,82,0.6,,100,68,1 +23,260,100,86,51,100,59,100,72,100,100,25,100,100,100,22,100,1,,100,100,1 +260,23,80,93,79,94,62,89,52,74,93,76,95,90,88,65,96,1.5,,99,90,1 +85,261,92,94,51,85,76,65,9,60,77,16,76,63,70,16,100,2,,70,68,2 +261,85,85,92,37,93,95,100,0,74,85,29,89,65,73,31,85,1.8,,56,58,2 +68,262,96,96,24,67,37,94,0,100,100,100,100,13,55,57,100,1.3,,93,68,1 +262,68,100,92,69,72,59,100,39,38,85,69,93,79,74,100,100,2,,100,84,1 +68,263,98,77,44,26,0,72,0,100,64,0,71,41,48,0,67,2,,60,65,1 +263,68,90,97,85,65,25,100,5,24,100,29,61,95,52,55,97,2.5,,91,83,1 +68,264,43,46,31,27,37,65,0,77,60,8,42,30,29,0,36,1.5,,30,32,1 +264,68,78,88,91,75,76,96,68,72,81,65,70,38,36,89,72,2,,74,70,1 +4,265,44,44,29,41,33,63,18,66,54,76,80,36,57,24,57,3.1,,51,60,1 +265,4,69,63,68,90,64,96,25,57,62,54,61,100,46,99,97,2.7,,69,87,1 \ No newline at end of file diff --git a/Data/post_convo_survey/strangers/clean_data.csv b/Data/post_convo_survey/strangers/clean_data.csv new file mode 100644 index 0000000..1afb73d --- /dev/null +++ b/Data/post_convo_survey/strangers/clean_data.csv @@ -0,0 +1,645 @@ +subID,partnerID,convo_flow,convo_enjoy,friends,speak,knew_before,knew_before_text,know_now,common,similar,attractive,attracted_to,extraverted,fun,disclosed,comfortable,extraverted_self,fun_self,disclosed_self,comfortable_self,length_self,length_partner,convo_order,round_robin_num +1,2,66,58,63,10,58,We're both sophomores in the same sorority,69,44,58,74,45,70,62,57,,,,,57,,,1,1 +1,4,100,87,94,67,0,,65,58,37,55,49,36,60,55,,,,,88,,,2,1 +1,3,88,74,79,65,0,,71,58,72,88,53,57,70,37,,,,,62,,,3,1 +1,5,96,93,100,80,78,freshman friends,74,82,88,63,52,61,73,79,,,,,92,,,4,1 +1,6,90,87,89,44,66,sorority,68,28,27,64,53,90,65,92,,,,,61,,,5,1 +1,10,81,68,84,68,0,,68,32,60,62,45,31,65,66,,,,,70,,,6,1 +1,7,94,77,63,28,0,,56,13,67,54,44,73,69,85,,,,,87,,,7,1 +1,11,82,79,64,60,0,,58,33,61,57,38,41,62,29,,,,,64,,,8,1 +1,9,75,63,62,22,0,,79,63,70,60,46,46,46,76,,,,,86,,,9,1 +1,8,92,97,100,41,0,,87,40,78,60,56,100,89,77,,,,,72,,,10,1 +2,6,77,80,80,62,62,She's in my sorority and in my neuroscience class.,65,65,41,70,70,74,74,61,,,,,74,,,1,1 +2,8,67,81,66,82,0,,20,34,34,34,27,34,70,62,,,,,70,,,2,1 +2,7,77,72,78,59,0,,16,60,71,75,76,37,72,59,,,,,71,,,3,1 +2,10,36,61,64,73,0,,10,67,58,41,37,40,54,35,,,,,22,,,4,1 +2,1,91,91,91,77,58,Same sorority as me.,65,42,58,44,30,41,71,41,,,,,79,,,5,1 +2,3,36,17,23,64,0,,8,19,21,57,57,33,20,11,,,,,19,,,6,1 +2,4,73,84,80,50,0,,24,64,81,40,36,34,72,60,,,,,78,,,7,1 +2,11,59,38,39,61,0,,9,59,39,24,20,38,56,42,,,,,64,,,8,1 +2,5,98,83,83,68,71,one of my good friends; in my sorority,71,59,59,41,27,61,80,60,,,,,76,,,9,1 +2,9,77,76,67,78,8,my friend's trip leader,22,65,63,44,27,19,78,65,,,,,78,,,10,1 +3,6,80,85,70,38,0,,34,51,48,61,27,66,64,65,,,,,58,,,1,1 +3,4,81,96,73,47,3,,39,59,66,64,25,50,80,50,,,,,69,,,2,1 +3,2,65,69,72,42,10,,36,55,42,60,45,61,70,60,,,,,70,,,3,1 +3,1,64,72,39,49,17,,34,41,39,41,27,61,51,48,,,,,80,,,4,1 +3,8,72,78,74,47,15,,37,47,43,48,25,52,54,45,,,,,63,,,5,1 +3,11,52,57,72,58,1,,23,41,44,41,9,44,46,31,,,,,71,,,6,1 +3,5,59,80,64,52,6,,28,63,53,46,13,46,60,32,,,,,65,,,7,1 +3,7,60,65,68,52,17,,51,60,58,56,21,54,73,57,,,,,75,,,8,1 +3,9,77,70,71,53,13,,33,55,55,59,14,48,68,36,,,,,66,,,9,1 +3,10,63,61,76,53,8,,27,59,56,41,12,54,58,35,,,,,77,,,10,1 +4,10,82,88,86,65,25,Classmate in big lecture course,63,87,70,71,5,84,80,49,,,,,83,,,1,1 +4,11,79,69,53,45,0,,52,77,36,56,4,55,48,53,,,,,86,,,2,1 +4,5,68,59,44,43,0,,45,31,35,53,1,79,71,40,,,,,83,,,3,1 +4,3,74,69,67,54,0,,53,44,52,78,3,39,56,11,,,,,86,,,4,1 +4,1,100,100,100,50,0,,60,87,82,95,2,96,100,58,,,,,100,,,5,1 +4,2,100,100,100,47,4,I've seen her in my neuro class,54,93,94,100,4,98,100,63,,,,,97,,,6,1 +4,8,92,88,62,46,0,,52,63,33,78,5,71,44,53,,,,,95,,,7,1 +4,6,95,86,47,46,7,She sits behind me in neuro class,45,73,30,75,6,70,58,77,,,,,95,,,8,1 +4,7,98,95,83,56,0,,53,90,86,92,4,39,87,72,,,,,89,,,9,1 +4,9,93,96,72,46,0,,54,76,65,77,5,70,62,38,,,,,87,,,10,1 +5,11,83,84,84,50,0,,51,79,79,73,0,26,81,27,,,,,100,,,1,1 +5,4,72,68,59,51,0,,33,66,41,42,0,18,66,28,,,,,69,,,2,1 +5,1,99,98,100,51,93,"friends since early freshman year, same sorority, same psych class",95,100,90,91,0,100,100,53,,,,,100,,,3,1 +5,10,98,97,97,85,0,,50,72,72,72,0,23,74,19,,,,,79,,,4,1 +5,6,100,100,100,34,78,same sorority,71,85,71,94,0,31,68,82,,,,,98,,,5,1 +5,8,89,85,69,34,0,,33,32,45,77,0,23,69,27,,,,,81,,,6,1 +5,2,80,76,79,51,90,same sorority,90,72,67,89,0,52,63,66,,,,,88,,,7,1 +5,3,60,60,63,62,0,,24,50,51,95,0,28,68,25,,,,,63,,,8,1 +5,9,76,86,83,51,15,"took a class together, hadn't really spoken before",50,77,77,67,0,38,73,66,,,,,97,,,9,1 +5,7,81,90,89,51,0,,56,66,59,49,0,35,70,51,,,,,83,,,10,1 +6,3,66,63,56,58,0,,28,29,60,59,36,71,72,51,,,,,100,,,1,1 +6,2,100,80,80,65,71,Sorority sisters,51,65,78,70,51,76,100,75,,,,,100,,,2,1 +6,9,53,60,50,50,0,,26,30,43,27,0,51,51,35,,,,,76,,,3,1 +6,10,50,50,22,50,1,Study group,24,28,37,7,0,52,52,46,,,,,100,,,4,1 +6,1,87,68,80,49,52,Sorority sister,63,52,61,46,7,81,69,50,,,,,100,,,5,1 +6,5,67,100,92,49,51,Sorority sister,62,48,56,42,0,75,75,58,,,,,100,,,6,1 +6,8,68,56,50,67,0,,72,34,67,18,0,76,45,47,,,,,100,,,7,1 +6,4,31,18,6,35,0,,12,17,35,0,0,47,17,32,,,,,100,,,8,1 +6,7,15,7,19,51,0,,26,27,6,9,0,51,27,39,,,,,45,,,9,1 +6,11,100,78,72,60,0,,41,38,68,49,0,81,70,32,,,,,49,,,10,1 +7,8,100,100,100,71,6,"I'd seen her before, but we had never spoken. ",24,63,65,62,0,74,76,54,,,,,100,,,1,1 +7,2,100,100,84,50,0,,12,61,36,77,0,100,78,52,,,,,100,,,2,1 +7,11,61,52,11,51,0,,15,29,0,17,0,64,58,0,,,,,79,,,3,1 +7,10,100,100,100,51,0,,45,69,100,75,0,84,100,5,,,,,100,,,4,1 +7,4,100,82,58,37,0,,33,54,39,43,0,75,71,48,,,,,100,,,5,1 +7,1,100,100,100,51,0,,52,100,100,69,0,100,100,29,,,,,100,,,6,1 +7,9,100,85,51,51,0,,26,24,34,0,0,54,71,27,,,,,100,,,7,1 +7,3,100,100,100,51,0,,47,66,70,52,0,76,76,34,,,,,100,,,8,1 +7,5,100,100,100,51,5,,36,100,100,53,0,82,94,39,,,,,100,,,9,1 +7,6,82,60,21,45,0,,19,27,27,68,0,58,27,32,,,,,78,,,10,1 +8,2,90,89,58,32,0,,19,31,24,76,0,86,100,5,,,,,100,,,1,1 +8,7,90,88,87,58,0,,57,87,78,28,0,41,89,56,,,,,95,,,2,1 +8,9,99,97,94,50,0,,54,38,59,58,0,78,90,43,,,,,96,,,3,1 +8,10,96,96,75,55,0,,60,78,66,54,0,62,88,42,,,,,89,,,4,1 +8,11,87,74,31,61,23,I taught her in PE skating however never really talked to her. ,52,29,21,25,0,73,71,29,,,,,62,,,5,1 +8,3,88,89,68,52,0,,23,37,26,69,0,34,62,15,,,,,71,,,6,1 +8,4,72,42,9,55,0,,32,18,15,21,0,25,38,24,,,,,44,,,7,1 +8,6,81,71,4,52,0,,38,24,22,21,0,68,62,55,,,,,65,,,8,1 +8,5,28,31,18,95,0,,23,20,20,17,0,33,38,33,,,,,25,,,9,1 +8,1,100,100,90,69,0,,31,32,77,59,0,97,100,12,,,,,97,,,10,1 +9,8,93,90,84,64,0,,59,38,60,58,4,75,77,61,,,,,84,,,1,1 +9,11,84,78,67,66,0,,59,71,36,61,1,57,66,33,,,,,64,,,2,1 +9,10,85,77,80,69,0,,62,71,38,66,0,74,68,39,,,,,74,,,3,1 +9,6,43,55,44,54,0,,39,57,40,69,0,43,37,39,,,,,64,,,4,1 +9,2,53,46,59,55,67,She is friends with my trippees,40,57,39,62,2,59,60,28,,,,,34,,,5,1 +9,4,77,65,60,60,1,,57,62,38,59,0,62,69,57,,,,,67,,,6,1 +9,1,85,79,78,65,10,,64,57,63,71,1,61,69,70,,,,,77,,,7,1 +9,7,58,56,57,67,1,,65,56,38,57,0,41,61,40,,,,,59,,,8,1 +9,5,73,78,65,71,29,We had a class together last Spring,63,72,57,61,0,61,68,57,,,,,73,,,9,1 +9,3,65,40,40,68,1,,44,39,29,57,0,26,61,33,,,,,78,,,10,1 +10,11,75,76,52,58,0,,51,71,67,35,0,61,64,57,,,,,81,,,1,1 +10,8,95,97,98,51,0,,51,73,73,62,0,67,100,50,,,,,100,,,2,1 +10,9,36,61,53,20,0,,35,28,22,49,0,34,55,56,,,,,55,,,3,1 +10,2,93,92,84,36,0,,49,53,37,90,0,60,79,51,,,,,97,,,4,1 +10,4,56,75,79,39,23,"Mutual friend, Same class",56,42,39,78,0,38,61,69,,,,,60,,,5,1 +10,6,49,52,44,28,43,We are in the same clas,49,36,37,72,0,55,49,46,,,,,20,,,6,1 +10,5,59,72,72,51,0,,59,61,48,60,0,76,74,56,,,,,58,,,7,1 +10,1,53,47,24,49,0,,44,21,0,49,0,60,37,20,,,,,21,,,8,1 +10,7,100,100,100,51,0,,69,59,76,78,0,58,100,76,,,,,100,,,9,1 +10,3,68,68,55,31,0,,35,21,26,86,0,74,75,73,,,,,60,,,10,1 +11,10,100,100,72,44,0,,25,86,78,58,49,60,61,74,,,,,100,,,1,1 +11,9,100,83,83,46,0,,17,77,56,71,52,74,100,72,,,,,100,,,2,1 +11,8,100,99,77,51,7,One of my PE class instructers,24,55,47,70,50,63,100,58,,,,,100,,,3,1 +11,4,82,88,86,52,0,,62,74,80,75,51,57,84,16,,,,,100,,,4,1 +11,5,87,89,74,57,0,"We were Link Up Buddies but I accidentally never replied to her emails, so we knew each other in name only",18,70,61,77,51,71,79,67,,,,,84,,,5,1 +11,3,84,90,86,59,0,,4,42,60,72,50,63,92,32,,,,,93,,,6,1 +11,2,91,93,93,46,0,,9,65,62,76,50,78,95,23,,,,,100,,,7,1 +11,7,92,99,95,51,0,,8,60,65,71,52,66,100,7,,,,,100,,,8,1 +11,1,100,100,100,50,0,,35,71,82,72,50,80,100,36,,,,,96,,,9,1 +11,6,100,99,84,40,0,,36,64,51,69,51,80,95,39,,,,,100,,,10,1 +20,21,60,61,62,55,52,We have a class together.,56,45,52,72,0,56,58,54,59,53,44,43,54,,,1,2 +20,25,100,100,100,51,0,,64,100,76,66,0,80,100,83,100,100,72,79,71,,,2,2 +20,26,53,53,51,52,0,,45,46,47,70,45,38,42,55,54,55,55,43,54,,,3,2 +20,28,79,67,57,37,0,,64,67,53,39,0,75,76,52,83,62,60,39,85,,,4,2 +20,22,85,78,74,53,0,,68,58,62,67,0,80,78,57,79,58,66,46,67,,,5,2 +20,29,66,67,58,32,0,,70,43,57,36,0,63,64,53,82,67,42,50,68,,,6,2 +20,30,100,80,66,31,0,,70,70,67,52,0,81,73,35,82,72,64,60,72,,,7,2 +20,24,53,54,47,36,0,,65,35,44,35,0,56,53,47,72,53,56,64,59,,,8,2 +21,27,92,57,25,43,9,Seeing in class,29,55,27,26,0,54,70,54,85,53,62,55,85,,,1,2 +21,22,89,83,76,68,0,,41,34,67,73,1,45,61,9,71,66,79,58,86,,,2,2 +21,28,95,87,65,64,1,Briefly seen on Trips,39,35,59,54,0,62,63,66,100,85,87,64,87,,,3,2 +21,20,90,91,95,42,38,Small class together this term,54,46,67,90,11,84,87,52,95,72,77,18,95,,,4,2 +21,23,89,96,96,50,33,Classes together,45,66,66,60,2,58,64,53,84,72,82,82,83,,,5,2 +21,25,86,99,97,49,20,Classes together,37,65,69,71,9,64,75,32,77,75,71,27,91,,,6,2 +21,26,26,26,23,55,0,,10,19,53,69,1,62,10,0,60,26,20,8,39,,,7,2 +21,24,73,60,20,43,31,Previous attempt at this study,42,8,5,3,0,38,46,4,57,32,37,14,44,,,8,2 +21,29,100,97,99,52,0,,49,59,19,52,2,19,75,58,60,79,77,63,91,,,9,2 +21,30,83,64,61,45,1,,41,56,61,79,2,84,77,87,88,77,64,71,68,,,10,2 +22,29,76,68,52,49,0,,18,55,56,75,13,52,69,45,84,53,54,21,69,,,1,2 +22,26,63,62,62,50,0,,50,63,64,76,4,78,79,57,61,57,60,57,67,,,2,2 +22,21,69,68,66,51,0,,51,64,64,82,6,93,76,60,72,60,54,50,73,,,3,2 +22,28,100,100,75,42,0,,65,29,61,63,0,95,96,54,98,60,60,42,95,,,4,2 +22,24,74,63,38,46,0,,50,26,27,48,0,50,52,48,75,47,47,52,82,,,5,2 +22,20,100,96,95,51,0,,67,83,88,75,3,95,97,66,89,69,71,56,96,,,6,2 +22,23,96,97,98,42,0,,66,89,91,72,0,90,91,80,89,67,67,41,91,,,7,2 +22,25,98,84,87,50,0,,57,91,90,66,0,74,77,61,90,75,75,52,96,,,8,2 +22,30,100,98,99,50,51,Friend of a friend; live in same building,79,95,95,81,0,87,97,72,99,70,72,63,97,,,9,2 +22,27,26,24,21,63,0,,35,27,22,48,0,28,30,41,18,56,47,35,21,,,10,2 +23,21,92,69,61,51,13,Classmate,30,59,59,66,0,83,65,38,75,82,75,15,92,,,1,2 +23,24,20,14,13,70,0,,7,9,9,16,0,15,13,23,19,65,66,10,0,,,2,2 +23,25,80,71,71,51,0,,51,67,68,71,0,72,78,24,74,77,77,15,82,,,3,2 +23,29,87,80,69,50,0,,51,67,65,61,0,56,63,27,69,82,69,13,84,,,4,2 +23,22,89,84,69,51,0,,51,72,75,89,0,88,77,19,78,82,82,16,75,,,5,2 +23,27,63,65,51,51,0,,51,53,48,49,0,42,57,33,56,63,66,14,57,,,6,2 +23,30,85,70,61,48,0,,52,59,59,59,0,87,67,70,66,71,71,31,65,,,7,2 +23,28,56,47,37,74,0,,29,36,37,51,0,41,50,37,52,57,44,36,36,,,8,2 +23,26,59,51,57,60,0,,33,46,55,85,0,64,60,32,55,63,61,24,54,,,9,2 +24,29,100,100,86,60,23,"I met her once or twice during our first-year orientation week. We are also both in the same LLC (the Thought Project), so we've had a few short conversations through that.",35,66,35,76,58,35,91,78,78,37,62,70,91,,,1,2 +24,30,84,68,56,70,0,N/A,23,55,27,60,52,39,65,16,59,63,58,60,91,,,2,2 +24,28,99,99,99,51,0,N/A,33,66,31,59,51,99,98,86,66,57,60,56,92,,,3,2 +24,27,66,58,43,59,0,N/A,60,37,38,60,53,36,61,35,55,36,55,41,59,,,4,2 +24,23,70,68,53,55,0,N/A,58,58,29,55,56,62,59,53,54,43,48,56,56,,,5,2 +24,21,68,72,59,54,25,We had had a conversation before through this study.,71,39,37,59,62,69,69,55,57,38,44,47,55,,,6,2 +24,25,72,72,64,40,0,N/A,62,69,68,58,58,41,59,45,57,44,58,45,60,,,7,2 +24,26,75,76,65,61,0,N/A,40,62,56,57,58,61,60,63,61,55,56,41,56,,,8,2 +24,22,65,69,71,57,0,N/A,57,67,71,55,55,61,64,59,60,46,57,58,58,,,9,2 +24,20,82,81,70,63,0,N/A,32,69,40,64,62,80,80,16,63,40,45,80,60,,,10,2 +25,20,88,99,95,63,0,,35,76,65,70,62,82,91,76,41,64,69,69,80,,,1,2 +25,23,89,74,88,63,0,,25,18,66,30,5,74,83,27,87,73,84,15,90,,,2,2 +25,26,13,22,65,41,36,We went to high school and lived in the same dorm,40,18,35,71,65,70,85,39,27,42,34,86,27,,,3,2 +25,21,85,82,67,70,0,,27,77,80,39,9,69,82,16,64,72,77,15,89,,,4,2 +25,24,5,7,0,72,0,,8,3,0,0,0,19,15,4,11,63,30,7,5,,,5,2 +25,28,87,100,100,64,0,,66,100,79,62,62,83,97,31,74,72,77,37,79,,,6,2 +25,29,26,9,3,82,0,,18,19,5,8,4,16,27,9,19,29,17,13,22,,,7,2 +25,22,74,87,89,41,0,,23,66,70,87,73,76,87,24,67,64,68,24,71,,,8,2 +25,27,34,34,17,69,0,,16,24,19,12,0,35,17,37,32,62,53,11,30,,,9,2 +25,30,80,85,81,31,0,,68,33,50,65,42,94,91,94,96,69,71,30,83,,,10,2 +26,22,94,97,97,49,0,,11,86,90,92,0,100,100,33,100,84,83,23,67,,,1,2 +26,29,100,100,100,50,0,,45,75,62,78,3,23,85,77,94,98,89,46,99,,,2,2 +26,27,100,100,94,50,0,,59,100,75,69,2,63,97,94,100,100,96,11,100,,,3,2 +26,25,100,100,100,42,92,"Same boarding school, lived in same dorm",98,100,95,92,4,95,100,100,100,100,85,18,100,,,4,2 +26,21,62,89,84,44,0,,59,58,77,87,23,41,80,44,74,94,87,0,97,,,5,2 +26,20,100,100,100,28,0,,79,92,95,92,5,100,100,44,100,97,98,22,97,,,6,2 +26,24,100,100,94,34,0,,75,79,32,52,1,57,98,68,98,99,99,26,100,,,7,2 +26,28,99,99,93,11,0,,85,9,45,64,2,95,99,94,95,78,83,5,97,,,8,2 +26,30,86,90,88,44,0,,76,71,76,76,2,100,91,14,100,24,25,23,91,,,9,2 +26,23,98,100,98,43,0,,81,87,95,88,1,100,100,30,100,90,86,19,96,,,10,2 +27,21,78,79,75,65,11,,32,74,70,57,23,64,65,57,64,76,62,56,62,,,1,2 +27,26,82,86,82,63,18,,68,90,84,72,22,67,75,56,73,71,67,57,67,,,2,2 +27,24,62,69,61,61,9,,69,64,70,56,6,60,87,64,82,69,74,58,76,,,3,2 +27,23,75,73,74,72,10,,68,65,76,65,35,63,66,64,70,62,63,61,71,,,4,2 +27,30,65,66,58,66,23,,60,66,62,42,42,59,70,55,67,65,58,65,62,,,5,2 +27,29,70,69,69,71,60,mutual friends,65,68,73,63,34,66,74,63,74,68,70,63,70,,,6,2 +27,25,58,64,57,68,31,,55,60,63,69,39,61,63,59,69,61,63,58,66,,,7,2 +27,22,78,80,78,61,17,,77,67,70,69,42,64,85,67,72,66,66,57,67,,,8,2 +27,28,59,61,79,71,22,,76,68,67,57,36,64,69,62,67,64,62,63,68,,,9,2 +28,30,95,95,96,31,0,,20,58,70,91,0,100,100,5,100,85,76,14,90,,,1,2 +28,24,97,100,100,56,0,,15,61,60,82,0,69,100,23,84,88,71,20,92,,,2,2 +28,21,94,94,100,47,2,,20,38,64,66,0,100,100,15,94,77,58,12,80,,,3,2 +28,22,100,100,100,90,0,,5,8,63,82,0,100,100,38,100,100,100,24,100,,,4,2 +28,20,85,100,100,84,0,,8,77,63,98,0,100,100,0,100,74,75,15,97,,,5,2 +28,29,100,100,100,51,34,We are in two of the same clubs,30,52,62,74,0,24,85,30,68,100,72,24,100,,,6,2 +28,25,99,95,100,38,0,,16,67,65,77,0,95,93,2,93,82,72,9,84,,,7,2 +28,26,55,53,59,55,0,,10,13,37,75,0,84,75,4,94,64,56,13,80,,,8,2 +28,23,31,41,35,47,0,,3,16,35,67,0,66,54,9,84,64,32,3,57,,,9,2 +28,27,70,89,94,56,6,,24,59,58,85,0,45,97,26,84,32,64,8,79,,,10,2 +29,22,92,86,81,73,0,,44,78,80,76,0,45,82,62,77,79,78,82,82,,,1,2 +29,24,62,78,57,31,45,"Met during orientation, sit near each other in psych. ",69,72,63,68,0,31,67,85,61,43,64,65,43,,,2,2 +29,26,32,45,45,82,0,,30,52,52,76,0,86,68,13,55,64,12,57,40,,,3,2 +29,23,87,88,84,50,0,,63,86,77,75,0,88,90,60,83,70,63,64,74,,,4,2 +29,21,84,87,85,35,10,Have just seen her around,73,70,39,76,0,88,89,97,90,37,61,70,71,,,5,2 +29,30,86,90,83,57,0,,66,73,83,75,0,46,87,65,83,79,81,75,86,,,6,2 +29,28,91,99,96,26,30,Growing Change center for service program,70,87,89,74,0,84,88,81,89,63,79,71,79,,,7,2 +29,27,81,82,83,53,46,"My friend's roommate, we met a couple of times",60,87,86,77,0,46,82,63,62,72,73,64,68,,,8,2 +29,25,87,89,92,20,0,,53,75,73,82,0,91,90,64,88,58,74,13,70,,,9,2 +29,20,87,94,85,80,0,,65,81,82,83,0,81,91,63,83,93,86,83,86,,,10,2 +30,24,72,59,25,43,0,,12,17,17,33,0,61,43,70,65,65,57,26,36,,,1,2 +30,28,78,59,44,55,0,,23,42,32,54,0,61,57,35,63,60,61,17,58,,,2,2 +30,29,67,59,23,64,0,,26,32,32,39,0,33,47,32,63,67,66,26,47,,,3,2 +30,21,69,68,68,54,0,,28,57,40,55,0,57,58,59,63,75,58,64,64,,,4,2 +30,27,68,62,22,60,0,,14,20,20,15,0,59,29,41,62,56,38,31,67,,,5,2 +30,22,67,69,64,59,39,mutual friends,40,61,60,90,0,69,71,31,67,69,70,43,72,,,6,2 +30,20,70,73,78,67,0,,29,68,69,73,0,64,72,26,70,77,77,38,75,,,7,2 +30,25,74,75,79,67,0,,24,86,86,87,0,75,69,19,64,81,66,38,77,,,8,2 +30,23,79,77,70,71,0,,20,78,78,78,0,58,62,26,62,78,68,19,69,,,9,2 +30,26,72,65,61,67,0,,15,58,41,78,0,67,58,27,43,73,66,15,64,,,10,2 +40,49,90,83,66,31,0,,28,35,54,32,0,41,58,45,57,57,54,41,63,,,1,3 +40,42,58,53,42,38,0,,23,25,26,27,0,66,57,39,58,42,52,25,53,,,2,3 +40,48,37,55,65,55,0,,23,24,50,66,0,59,41,14,38,43,43,37,65,,,3,3 +40,44,78,64,56,53,6,Met once quickly,14,31,50,15,0,43,59,36,61,57,54,57,59,,,4,3 +40,41,37,45,43,55,0,,22,32,29,23,0,27,36,29,36,55,52,39,43,,,5,3 +40,47,23,20,4,32,0,,24,0,0,16,0,7,18,60,27,58,42,35,37,,,6,3 +40,45,67,70,73,45,80,Friend,81,69,68,63,0,61,63,41,68,56,55,44,75,,,7,3 +40,46,27,16,5,24,0,,17,14,9,20,0,14,17,15,19,56,56,39,59,,,8,3 +40,43,93,91,92,55,30,Friend,56,85,85,55,0,85,86,61,77,74,76,43,71,,,9,3 +40,50,75,65,55,38,0,,22,42,43,20,0,44,57,44,45,67,59,37,73,,,10,3 +41,49,77,76,75,29,0,,21,60,59,48,3,85,93,73,85,86,88,34,87,,,1,3 +41,46,37,55,59,78,0,,26,35,35,20,0,15,35,21,24,87,86,29,83,,,2,3 +41,45,70,69,70,51,14,Met once before,44,51,51,52,0,82,80,49,86,77,74,62,75,,,3,3 +41,50,39,66,67,50,0,,12,29,35,50,0,34,64,26,57,48,58,26,68,,,4,3 +41,44,73,74,74,50,0,,37,81,42,24,0,30,60,66,61,56,55,60,84,,,5,3 +41,43,82,64,72,61,0,,24,51,51,53,0,65,64,47,81,76,75,59,80,,,6,3 +41,40,60,40,50,51,0,,38,31,39,48,0,65,64,51,87,58,67,66,87,,,7,3 +41,47,45,70,51,41,0,,36,51,26,13,0,18,35,26,46,41,43,32,75,,,8,3 +41,42,60,68,74,52,0,,29,30,55,4,0,41,62,38,69,51,58,23,82,,,9,3 +41,48,36,35,42,65,0,,25,32,35,58,0,64,39,25,73,67,53,30,68,,,10,3 +42,50,85,79,51,67,0,,14,13,5,48,0,56,62,27,67,68,54,57,79,,,1,3 +42,40,51,63,62,67,0,,26,29,38,67,0,63,61,28,56,63,53,64,63,,,2,3 +42,44,62,71,57,60,0,,24,8,23,25,0,59,65,66,68,64,57,60,70,,,3,3 +42,49,88,80,72,55,0,,27,62,62,46,0,74,75,41,72,76,54,25,79,,,4,3 +42,43,82,73,68,67,0,,26,31,44,40,0,66,70,34,64,83,56,74,74,,,5,3 +42,48,63,71,64,65,0,,26,64,55,74,0,68,61,44,69,67,54,57,75,,,6,3 +42,47,56,58,29,44,0,,24,39,17,19,0,42,43,65,61,54,47,52,63,,,7,3 +42,41,69,60,37,52,3,"In my study group, but barely",24,34,45,22,0,55,55,59,77,58,53,57,69,,,8,3 +42,45,58,55,51,60,0,,16,31,32,63,0,62,51,56,47,60,53,53,65,,,9,3 +42,46,22,45,20,46,0,,29,9,4,11,0,6,19,56,14,54,42,46,42,,,10,3 +43,50,82,66,10,67,0,,10,40,33,9,0,72,57,60,77,89,84,69,64,,,1,3 +43,45,100,100,100,51,68,Friends,85,100,90,64,0,100,100,54,100,100,99,53,100,,,2,3 +43,49,91,87,50,35,0,,5,57,59,47,0,93,87,10,88,66,50,8,78,,,3,3 +43,41,77,57,20,58,0,,11,34,12,25,0,34,31,58,90,72,33,6,88,,,4,3 +43,42,91,76,56,33,0,,18,61,58,45,0,78,80,70,76,77,35,28,93,,,5,3 +43,44,79,69,56,65,0,,21,27,23,10,0,54,71,38,84,83,70,64,83,,,6,3 +43,46,13,3,0,85,0,,3,0,0,0,0,0,8,44,11,88,59,31,16,,,7,3 +43,48,81,86,69,50,0,,12,58,62,70,0,89,79,55,74,71,66,36,86,,,8,3 +43,40,96,92,93,48,14,Met him a couple times through friends,30,82,76,67,0,91,82,66,82,76,82,53,90,,,9,3 +43,47,59,29,0,54,0,,10,10,10,10,0,15,15,6,26,71,38,15,41,,,10,3 +44,50,95,94,91,56,43,PSYCHOLOGY CLASS AND WE PLAYED POOL TOGETHER ONCE,62,69,67,23,83,44,78,61,69,30,30,54,71,,,1,3 +44,42,71,71,44,27,0,,66,42,44,53,19,65,81,55,90,39,58,40,62,,,2,3 +44,49,79,80,98,23,3,,71,65,60,63,29,73,88,68,69,59,62,40,65,,,3,3 +44,40,62,52,40,50,31,We met during an event at orientation,55,55,53,47,15,62,60,64,65,69,43,57,57,,,4,3 +44,41,70,68,64,56,0,,62,65,67,54,19,45,65,47,68,64,68,65,69,,,5,3 +44,43,59,60,58,52,0,,59,44,47,57,16,59,59,53,64,56,58,52,72,,,6,3 +44,45,69,63,60,52,0,,64,58,56,54,12,63,65,54,68,60,57,57,68,,,7,3 +44,46,18,16,20,79,0,,58,30,31,26,20,12,17,37,31,60,45,52,33,,,8,3 +44,47,60,55,22,19,0,,75,42,40,10,2,3,56,63,59,43,53,46,54,,,9,3 +44,48,93,87,87,42,0,,64,40,57,55,20,91,90,56,94,58,63,59,80,,,10,3 +45,46,73,60,35,27,0,,51,7,9,22,0,36,59,52,64,59,51,14,73,,,1,3 +45,50,98,89,79,50,53,Acquaintences,67,71,70,44,0,91,83,40,88,86,79,38,87,,,2,3 +45,43,100,88,78,43,85,friends,86,85,68,73,0,100,85,54,89,78,83,56,93,,,3,3 +45,41,86,76,67,50,24,had met once before,49,69,65,49,0,78,77,71,69,61,61,57,86,,,4,3 +45,48,76,68,62,52,0,,30,40,52,86,12,72,66,51,80,60,62,47,69,,,5,3 +45,47,92,86,69,40,0,,51,69,35,32,0,41,62,65,72,62,71,64,77,,,6,3 +45,40,84,82,89,50,80,friends,85,79,78,85,3,77,83,61,96,74,74,62,97,,,7,3 +45,49,84,77,71,11,0,,41,64,57,62,0,100,81,57,79,41,62,31,77,,,8,3 +45,42,69,65,49,33,0,,42,25,39,62,0,74,72,72,66,60,64,62,75,,,9,3 +45,44,77,71,64,62,0,,51,66,60,27,0,75,73,60,66,74,65,58,75,,,10,3 +46,45,89,74,44,66,0,,58,21,36,63,18,62,62,49,81,81,59,59,74,,,1,3 +46,50,51,51,37,46,0,,53,29,55,53,14,61,56,54,53,45,48,52,58,,,2,3 +46,48,42,47,28,68,0,,25,19,31,64,11,56,43,41,63,59,44,60,53,,,3,3 +46,41,69,56,52,41,0,,47,35,55,47,11,58,56,49,63,58,50,59,68,,,4,3 +46,43,33,36,20,44,0,,35,18,34,63,4,62,30,36,59,42,41,53,38,,,5,3 +46,47,40,58,58,49,0,,54,71,62,27,1,38,44,55,49,44,48,55,40,,,6,3 +46,40,42,52,55,62,0,,34,46,47,63,3,46,46,41,43,58,48,62,54,,,7,3 +46,49,55,58,45,82,0,,11,37,51,58,3,44,57,13,56,74,53,68,60,,,8,3 +46,44,33,38,32,52,0,,31,17,37,55,1,53,37,38,36,42,36,53,18,,,9,3 +46,42,43,54,29,52,0,,26,26,39,63,3,55,52,45,50,54,55,53,57,,,10,3 +47,41,85,68,79,63,0,,58,57,36,77,0,59,72,57,68,65,41,64,72,,,1,3 +47,46,42,40,44,59,15,,55,24,28,59,7,36,43,59,42,59,41,64,56,,,2,3 +47,45,68,69,62,43,10,,65,57,43,66,43,73,73,73,59,57,40,62,78,,,3,3 +47,40,59,58,56,57,3,,67,39,38,64,18,64,56,56,40,44,40,54,66,,,4,3 +47,50,78,78,77,48,16,,73,62,55,76,19,73,75,58,68,57,39,62,81,,,5,3 +47,42,55,55,57,58,6,,56,38,33,60,8,62,59,76,39,30,33,42,42,,,6,3 +47,49,75,76,72,45,9,,68,57,58,59,9,76,74,76,65,57,40,61,83,,,7,3 +47,43,65,66,57,61,6,,62,25,25,57,3,59,61,80,53,57,39,62,58,,,8,3 +47,44,68,81,62,44,6,,71,64,43,72,4,74,71,71,67,60,40,60,67,,,9,3 +47,48,61,60,60,91,2,,63,43,40,77,18,65,64,35,43,44,38,74,70,,,10,3 +48,40,37,36,61,49,0,,19,46,36,59,0,73,56,0,22,0,19,11,4,,,1,3 +48,46,63,65,27,7,0,,20,0,10,13,0,12,55,6,46,58,59,0,63,,,2,3 +48,45,100,74,85,51,0,,51,83,79,85,17,73,74,16,75,66,67,5,80,,,3,3 +48,50,100,100,93,54,0,,72,41,41,41,0,69,100,69,99,95,76,69,100,,,4,3 +48,42,93,70,75,0,0,,61,77,70,71,0,92,85,74,81,9,13,14,73,,,5,3 +48,49,100,100,100,49,76,"On football team together, taking class together, in same fraternity",80,92,73,29,0,78,95,23,88,65,68,68,83,,,6,3 +48,43,98,97,93,36,14,"In class together, friend of a friend",47,66,31,68,9,92,71,12,96,66,61,13,84,,,7,3 +48,47,80,63,67,6,0,,26,30,0,0,0,13,66,67,69,25,26,0,83,,,8,3 +48,41,78,86,63,51,0,,35,26,31,26,0,65,67,19,69,58,58,20,82,,,9,3 +48,44,90,100,100,63,0,,61,33,34,9,0,20,74,61,86,78,78,31,100,,,10,3 +49,41,92,87,55,51,0,,10,67,67,52,0,37,66,38,77,43,57,15,87,,,1,3 +49,40,42,57,44,68,0,,31,81,31,51,0,55,57,64,40,53,33,19,46,,,2,3 +49,44,93,100,83,87,0,,50,79,68,49,0,56,100,32,83,58,74,19,100,,,3,3 +49,43,72,81,97,76,0,,40,77,89,49,0,79,93,15,74,65,64,14,84,,,4,3 +49,42,100,100,90,51,0,,48,84,100,50,0,72,100,30,88,63,66,28,100,,,5,3 +49,50,89,82,61,97,0,,25,56,42,50,0,44,100,21,65,60,52,13,93,,,6,3 +49,45,64,86,82,95,3,,14,85,80,50,0,63,83,14,64,85,62,67,95,,,7,3 +49,46,87,84,46,39,0,,30,41,34,49,0,31,69,41,50,43,37,16,74,,,8,3 +49,47,57,63,34,55,0,,43,61,40,50,0,28,62,75,57,65,44,35,86,,,9,3 +49,48,93,100,100,72,71,He's my football teammate and friend,76,90,90,50,0,85,100,68,93,67,84,33,100,,,10,3 +50,42,100,89,81,32,0,,47,59,51,54,0,62,85,62,100,95,86,59,100,,,1,3 +50,44,100,95,91,52,14,We are both students of the same psychology class,64,65,55,57,0,76,87,57,100,91,93,58,94,,,2,3 +50,46,42,36,28,72,0,,33,31,31,17,0,0,34,20,60,100,82,50,41,,,3,3 +50,43,100,100,100,51,0,,63,67,68,69,6,100,100,61,75,100,85,50,100,,,4,3 +50,45,100,100,100,51,63,Orientation friends,97,94,100,100,18,100,100,63,100,100,100,69,100,,,5,3 +50,49,100,92,94,51,0,,72,64,70,57,0,100,83,64,100,100,100,62,100,,,6,3 +50,48,100,100,100,52,0,,100,100,100,100,17,100,100,88,100,100,100,95,100,,,7,3 +50,41,62,56,60,59,0,,54,56,55,43,0,61,62,56,66,76,78,62,59,,,8,3 +50,47,78,64,69,65,0,,50,53,52,48,0,40,48,31,66,83,84,60,74,,,9,3 +50,40,100,91,84,51,0,,70,77,80,63,11,86,84,67,100,93,91,74,100,,,10,3 +60,63,97,100,100,63,0,,51,77,44,63,0,81,99,57,100,100,100,57,100,89,54,1,4 +60,62,53,59,57,58,0,,46,43,39,43,0,13,34,48,35,39,43,57,46,29,69,2,4 +60,70,100,100,100,71,78,Classmates and both part of FYSEP Program,71,79,75,72,0,57,78,100,100,72,61,99,100,100,100,3,4 +60,68,98,89,100,54,0,,50,60,61,88,0,63,73,28,94,70,65,57,100,83,42,4,4 +60,66,92,78,91,87,0,,29,79,63,36,0,34,42,12,69,65,70,27,100,49,54,5,4 +60,64,67,70,15,52,0,,7,0,12,100,0,99,73,14,79,74,62,14,93,78,48,6,4 +60,69,59,76,100,37,34,We had already been introduced to each other.,43,17,24,100,0,59,69,0,97,68,72,73,92,51,31,7,4 +60,61,100,100,100,32,0,,51,91,100,11,0,100,100,75,100,87,82,58,100,100,84,8,4 +60,65,100,100,100,66,0,,70,32,100,100,0,70,100,68,86,77,75,81,89,100,51,9,4 +61,68,83,76,87,50,0,,21,78,41,79,0,38,51,46,35,56,56,67,78,75,75,1,4 +61,63,41,40,41,51,0,,69,40,27,37,0,52,44,71,73,63,67,70,61,48,74,2,4 +61,67,100,100,100,50,12,We're in the same study session.,64,63,82,83,0,100,100,66,81,80,73,67,100,89,71,3,4 +61,62,44,35,29,65,0,,37,26,28,30,0,37,37,36,65,61,32,30,40,39,38,4,4 +61,70,57,38,38,60,1,I saw her once at a study session.,50,36,37,29,0,61,62,67,70,59,42,44,60,51,50,5,4 +61,65,40,35,23,57,0,,31,33,33,39,0,40,38,41,48,51,51,51,42,38,40,6,4 +61,69,60,51,50,50,0,,45,41,41,61,0,50,50,37,64,57,40,41,60,50,50,7,4 +61,64,76,75,81,50,0,,65,68,65,79,0,71,73,59,68,62,62,64,84,60,60,8,4 +61,60,88,77,78,56,0,,63,50,51,47,0,68,77,76,75,64,64,85,81,69,60,9,4 +61,66,51,27,18,65,0,,29,25,23,27,0,34,36,36,41,40,61,39,44,35,35,10,4 +62,70,76,77,72,36,0,,34,61,68,68,8,28,64,27,74,43,49,22,77,51,50,1,4 +62,60,63,64,58,43,0,,28,63,57,68,5,44,65,39,67,35,51,32,68,51,49,2,4 +62,68,63,64,64,51,0,,29,37,62,75,3,43,66,24,73,37,51,19,77,50,50,3,4 +62,69,43,44,50,61,0,,25,50,48,62,1,34,51,22,51,40,41,18,77,26,25,4,4 +62,64,54,68,51,51,0,,30,59,40,76,0,62,61,36,72,45,36,23,75,51,49,5,4 +62,61,71,67,60,43,0,,41,50,67,73,0,38,70,37,72,45,54,29,82,51,49,6,4 +62,65,67,71,66,43,0,,34,68,51,74,0,69,68,30,74,41,54,28,77,49,50,7,4 +62,63,81,76,60,64,0,,51,51,64,65,0,31,67,30,79,59,56,27,79,59,51,8,4 +62,66,74,75,75,44,51,Freshman floormate,55,66,65,64,0,51,72,21,94,58,64,29,98,49,50,9,4 +63,70,89,48,82,100,88,"Floor mate, Friend, Classmate",71,43,34,55,48,49,49,50,89,78,15,81,92,83,44,1,4 +63,60,96,93,88,54,0,N/A,16,83,71,50,49,51,63,21,78,92,66,21,87,97,98,2,4 +63,61,50,47,47,40,0,N/A,23,21,32,49,45,50,45,18,58,32,47,56,56,95,88,3,4 +63,64,3,2,1,49,0,,5,3,0,55,35,44,7,4,13,51,49,22,38,2,2,4,4 +63,68,47,69,63,50,2,,36,54,55,66,55,43,55,27,63,50,49,37,80,72,53,5,4 +63,65,76,70,86,74,2,,32,43,59,61,61,49,51,45,65,62,57,77,97,94,89,6,4 +63,69,36,46,53,54,52,floormates,49,51,48,65,61,51,53,11,75,45,48,8,71,63,64,7,4 +63,66,77,100,97,44,3,Sits behind me in class and she asks a lot of great questions.,0,68,50,51,48,51,64,25,84,54,55,8,83,100,100,8,4 +63,62,57,66,52,24,0,,19,51,45,50,48,50,50,7,71,49,53,3,73,95,87,9,4 +64,67,79,71,100,45,0,,51,54,55,80,0,69,83,43,96,56,43,47,89,39,42,1,4 +64,68,43,42,28,60,0,,32,10,37,46,0,15,40,42,43,54,40,37,56,60,45,2,4 +64,63,23,42,28,54,0,,44,7,12,28,0,36,46,46,51,52,41,36,54,40,47,3,4 +64,65,85,85,72,50,0,,42,45,46,58,0,57,74,39,73,53,58,44,77,62,48,4,4 +64,69,57,61,42,48,0,,53,14,15,47,0,64,68,52,63,54,58,43,86,53,53,5,4 +64,62,60,57,90,57,0,,54,60,58,81,0,40,61,48,56,53,57,45,65,68,68,6,4 +64,70,73,68,54,48,0,,53,44,42,47,0,79,73,45,95,55,61,45,89,78,54,7,4 +64,66,65,72,46,55,0,,53,47,37,34,0,66,68,26,65,54,58,36,84,74,56,8,4 +64,61,93,96,46,51,0,,56,38,46,37,0,64,70,43,77,65,60,43,92,75,58,9,4 +64,60,65,61,39,52,0,,53,41,36,53,0,46,71,45,64,60,64,45,90,55,55,10,4 +65,67,85,85,66,50,6,,12,62,63,73,0,89,82,26,27,66,65,32,94,72,51,1,4 +65,63,76,66,62,76,0,,18,34,36,55,12,72,71,77,71,72,36,36,74,50,58,2,4 +65,64,75,71,72,60,0,,30,63,65,76,26,75,76,23,76,79,77,15,79,51,51,3,4 +65,69,59,63,61,27,0,,14,37,36,51,9,94,82,42,71,62,64,32,79,61,81,4,4 +65,68,67,55,57,51,0,,10,57,37,70,11,57,58,39,67,57,64,35,63,45,51,5,4 +65,62,66,68,71,54,0,,23,60,60,61,6,70,65,55,78,63,63,39,75,55,56,6,4 +65,66,63,41,40,51,0,,29,39,26,56,11,31,40,54,60,60,36,30,60,51,51,7,4 +65,61,69,68,69,51,0,,18,62,39,69,18,31,67,28,75,60,63,33,72,51,51,8,4 +65,70,72,71,85,63,0,,32,63,66,66,14,68,74,60,73,69,73,60,72,59,60,9,4 +65,60,71,81,84,47,0,,26,60,40,55,3,100,90,61,75,65,67,54,83,57,73,10,4 +66,67,100,100,100,51,56,"floormate last year, in same class this term, in same extracurricular activity",68,89,44,62,0,64,60,54,100,56,61,56,100,88,89,1,4 +66,69,78,90,95,62,58,classmate,65,28,38,65,0,65,100,31,86,56,56,55,100,71,63,2,4 +66,68,54,44,43,50,0,did not know them,19,24,18,53,0,52,53,18,52,39,41,11,53,42,38,3,4 +66,65,84,70,65,54,5,did not know,32,41,40,100,0,57,55,44,78,57,61,37,71,50,50,4,4 +66,64,64,63,42,63,0,did not know,22,38,38,100,0,100,82,67,58,69,60,44,71,45,31,5,4 +66,70,43,54,62,50,0,did not know them,45,11,59,100,0,59,100,80,66,58,56,60,57,44,45,6,4 +66,60,45,47,54,48,0,did not know them,11,39,46,100,0,42,61,40,40,42,55,42,56,20,15,7,4 +66,63,99,100,100,83,0,did not know them,59,83,100,100,0,29,61,36,100,81,68,89,100,100,100,8,4 +66,62,62,60,76,51,64,freshman floor mate,64,55,33,100,0,31,56,43,83,61,62,36,74,61,61,9,4 +66,61,42,34,16,20,0,did not know her,28,24,27,100,0,35,31,67,44,46,38,0,29,11,0,10,4 +67,66,93,94,94,72,85,"Freshman floor mates, on EMS together (she is ranked higher than me in EMS)",61,60,54,43,16,41,63,24,64,71,63,33,74,49,49,1,4 +67,64,74,83,82,54,0,,27,70,84,83,11,87,83,20,50,76,70,34,65,27,22,2,4 +67,65,96,93,96,39,14,Had a class together,64,61,89,77,6,62,92,51,78,70,69,39,88,65,51,3,4 +67,61,100,100,100,51,35,in class together,51,98,84,84,2,82,100,49,90,89,93,38,98,62,51,4,4 +67,70,72,72,56,50,43,in class together,59,44,55,65,0,46,67,60,82,56,57,43,66,51,51,5,4 +68,61,75,66,56,34,0,,36,54,43,52,0,85,65,28,64,52,49,55,81,63,56,1,4 +68,70,64,66,82,56,20,We met at Dimensions and spent a few hours together in a group.,38,61,73,56,0,40,66,75,78,53,60,90,85,68,51,2,4 +68,64,29,28,48,59,0,,5,25,53,76,0,12,10,15,38,43,32,0,48,18,5,3,4 +68,63,45,52,53,55,0,,11,61,53,51,0,11,45,31,46,55,43,6,56,54,54,4,4 +68,62,59,65,67,32,0,,22,11,59,53,0,46,58,16,56,54,51,22,65,52,51,5,4 +68,69,65,75,100,52,0,,31,36,100,64,0,33,85,42,86,46,52,27,100,52,51,6,4 +68,66,56,55,18,47,0,,28,27,40,51,0,13,52,33,58,53,47,12,72,52,53,7,4 +68,65,40,55,58,42,0,,15,45,57,59,0,33,59,13,45,44,44,25,36,51,51,8,4 +68,60,73,78,83,53,0,,25,53,73,52,0,72,68,0,58,63,57,0,62,53,52,9,4 +69,65,93,95,92,45,0,,42,60,61,66,6,86,97,45,98,79,93,46,100,65,60,1,4 +69,66,85,98,99,52,77,We are classmates in PSYC 6. ,93,79,65,52,0,78,89,64,97,88,88,63,100,79,64,2,4 +69,64,82,67,67,55,0,,58,64,58,73,5,79,73,58,94,85,76,55,98,56,45,3,4 +69,68,94,92,96,59,0,,57,76,90,61,0,84,94,64,94,87,90,62,96,73,69,4,4 +69,62,72,69,71,50,0,,54,57,58,47,0,79,76,40,96,73,71,42,97,61,58,5,4 +69,70,97,96,100,56,75,She is my floormate. ,86,67,75,68,12,93,96,45,95,92,91,47,100,89,73,6,4 +69,61,74,77,87,46,0,,57,64,74,68,0,84,83,47,96,90,88,47,97,57,57,7,4 +69,60,94,90,91,56,55,We met through mutual friends. ,75,73,78,58,0,79,95,56,98,86,92,46,98,95,93,8,4 +69,63,88,90,92,54,67,We are floormates. ,80,66,74,69,0,87,87,46,96,90,87,40,98,75,78,9,4 +70,68,100,100,100,58,6,From dimensions but we had not talked since,22,49,48,76,15,51,100,47,85,68,63,59,88,90,56,1,4 +70,63,84,73,100,17,98,Floor mates,87,80,84,78,5,85,86,87,55,22,72,30,100,50,43,2,4 +70,62,72,100,100,72,0,,26,67,67,88,0,79,94,43,83,67,76,58,88,67,38,3,4 +70,60,97,87,88,21,56,friend from class,51,74,74,77,7,66,78,45,86,74,63,24,85,67,67,4,4 +70,61,70,81,72,39,0,,32,65,75,68,12,72,90,48,73,76,89,56,77,59,54,5,4 +70,64,82,75,65,39,0,,21,56,46,58,0,61,84,43,66,76,80,19,59,51,37,6,4 +70,65,91,100,100,51,0,,19,82,91,81,0,68,94,33,76,83,100,34,87,95,62,7,4 +70,69,87,82,75,70,50,floormate,58,61,84,87,0,79,97,36,73,90,86,45,84,73,51,8,4 +70,66,65,77,82,66,0,,22,65,78,69,0,66,78,36,64,72,88,54,67,52,52,9,4 +70,67,92,84,92,48,2,took a test in the same room,12,80,86,84,6,91,100,31,73,83,85,54,99,92,59,10,4 +80,89,65,59,35,47,0,,19,26,26,37,0,26,35,23,59,61,45,21,88,62,62,1,5 +80,81,91,92,88,57,54,"One of my best friends is from the same hometown as my partner is so we have met and talked before and although we do not hang out, we get along well.",60,47,84,81,0,62,97,32,97,85,75,60,96,81,75,2,5 +80,83,94,94,66,46,61,Had a class with him and am from roughly the same area.,64,45,45,35,0,56,92,54,92,65,92,58,96,79,82,3,5 +80,87,97,98,81,65,14,Briefly talked to him at the frats before.,40,90,88,65,0,95,100,66,100,75,82,73,100,88,65,4,5 +80,82,72,69,56,51,0,,20,54,40,54,0,44,60,34,76,58,46,45,73,58,50,5,5 +80,88,95,95,81,51,0,,41,66,67,58,0,61,91,63,100,70,81,47,100,67,66,6,5 +80,90,95,93,78,29,40,I've gone biking with him before.,45,72,45,60,0,93,85,68,100,65,75,53,95,74,69,7,5 +80,85,88,90,69,53,0,,35,61,62,65,0,64,89,42,92,73,73,43,91,64,56,8,5 +80,84,71,68,32,53,0,,28,29,28,46,0,44,62,38,66,68,61,45,71,50,51,9,5 +80,86,92,85,82,45,0,,35,59,71,79,0,85,91,55,98,66,80,43,87,57,56,10,5 +81,89,89,91,79,71,0,,51,73,56,51,0,44,59,53,57,63,58,52,92,67,69,1,5 +81,82,79,80,81,51,0,,46,60,53,51,0,54,70,31,66,47,55,20,100,43,41,2,5 +81,80,100,100,100,51,50,Friend of a friend,59,59,61,54,0,56,100,51,100,54,69,30,100,51,51,3,5 +81,90,100,100,100,51,0,,52,72,52,51,0,100,100,61,100,58,100,51,100,75,77,4,5 +81,83,100,100,100,51,97,Friend and former teammate,98,81,51,51,0,96,100,97,100,99,100,98,100,51,75,5,5 +81,85,100,100,100,51,99,Friend and went to grade school in same state,51,99,97,51,0,99,100,98,100,99,100,99,100,51,51,6,5 +81,87,100,100,100,51,0,,51,69,70,51,0,100,100,51,100,100,100,51,100,77,75,7,5 +81,84,100,100,100,51,0,,51,51,45,51,0,51,100,52,51,100,100,51,51,51,51,8,5 +81,86,100,100,100,51,0,,51,100,100,51,0,100,100,51,100,100,100,51,100,100,100,9,5 +81,88,100,100,100,51,0,,51,51,51,51,0,100,100,51,100,100,100,51,100,51,51,10,5 +82,89,79,75,65,54,6,"Didn't know personally, recognized from Psych 1 class.",63,75,43,31,0,43,65,69,95,72,36,64,82,82,79,1,5 +82,86,69,58,73,53,0,,51,46,63,52,0,89,69,35,64,55,35,58,42,56,39,2,5 +82,81,61,67,71,60,0,,41,70,63,7,0,39,55,56,67,60,33,40,57,35,25,3,5 +82,87,82,79,84,52,0,,59,92,86,10,0,73,73,76,81,63,35,64,89,74,68,4,5 +82,80,65,52,71,67,0,,21,45,61,41,0,73,68,70,71,72,33,42,57,40,35,5,5 +82,83,63,61,68,85,63,"Knew of him, have several mutual friends",56,59,78,8,0,62,62,65,63,66,40,59,63,73,73,6,5 +82,90,74,73,71,36,0,,56,67,67,19,3,71,76,68,70,64,42,40,76,56,65,7,5 +82,84,65,43,44,35,0,,41,32,41,12,0,41,38,58,58,39,36,36,38,27,23,8,5 +82,88,75,76,69,62,24,Knew of him from Gov 3 class last term.,44,60,62,38,22,67,62,59,66,67,34,68,59,64,59,9,5 +82,85,67,65,70,73,0,,55,67,63,7,5,66,53,67,61,60,38,63,63,40,50,10,5 +83,80,62,75,80,59,51,"Writing 5 Classmates, we peer-edited each other's assignments",60,68,62,77,0,67,81,36,67,70,55,43,75,62,51,1,5 +83,86,60,69,39,58,0,,28,71,27,58,0,36,58,58,53,65,59,38,54,55,42,2,5 +83,81,97,100,100,68,81,"2 classes together this term, friends as result of close mutual friend, ultimate frisbee",91,82,75,77,0,85,100,27,100,84,84,43,100,90,90,3,5 +83,82,96,98,92,44,0,,57,66,77,51,0,77,92,60,85,91,92,44,98,83,85,4,5 +83,87,78,92,87,45,0,,26,57,80,58,0,85,97,26,78,61,68,39,95,79,75,5,5 +83,88,88,94,77,83,0,,18,57,68,54,0,95,96,4,85,98,94,44,66,70,77,6,5 +83,90,60,73,74,51,3,,40,76,77,56,0,69,82,54,75,54,60,45,85,56,50,7,5 +83,84,34,33,55,61,0,,34,11,20,28,0,39,37,61,67,66,52,16,52,32,37,8,5 +83,85,99,99,100,47,37,"Mutual friends, have sat at meals together once or twice",63,95,90,60,0,85,99,59,96,96,93,54,100,82,83,9,5 +83,89,64,59,88,57,51,From our last conversation in the lab that didnt get recorded,66,46,56,48,0,17,40,61,89,47,36,43,95,36,41,10,5 +84,89,92,100,72,29,0,,27,75,29,59,0,100,69,8,82,25,30,24,67,73,78,1,5 +84,87,71,17,16,10,0,,3,48,22,63,25,75,15,0,60,15,15,16,66,28,23,2,5 +84,90,75,67,72,41,0,,23,78,25,33,0,78,58,4,73,32,32,30,56,37,51,3,5 +84,83,40,31,4,51,0,,5,8,16,5,0,68,24,36,33,66,31,23,26,23,25,4,5 +84,82,59,26,28,62,0,,22,33,30,21,5,64,29,32,45,60,27,15,63,28,38,5,5 +84,85,31,63,71,38,0,,30,59,29,50,21,37,59,22,36,34,42,14,64,30,35,6,5 +84,86,34,32,68,51,51,Hung out one time,30,25,19,100,100,72,39,60,50,39,35,27,57,38,41,7,5 +84,81,58,39,49,51,0,,18,30,21,65,33,63,36,17,51,34,28,8,49,27,35,8,5 +84,80,30,17,0,63,0,,6,0,0,50,33,86,22,12,37,50,12,35,41,21,25,9,5 +84,88,100,100,100,37,0,,38,58,61,62,3,43,100,35,100,68,67,30,100,69,64,10,5 +85,89,91,92,91,51,4,Had seen him around before but never talked,58,63,63,72,14,67,90,37,79,69,55,32,82,64,64,1,5 +85,88,58,64,44,50,0,,51,53,18,60,18,68,58,25,52,58,54,20,63,51,51,2,5 +85,81,80,85,83,64,62,"Met him during Orientation week and have mutual friends, have talked a few times",69,66,66,68,16,75,80,30,80,69,69,25,89,71,70,3,5 +85,83,96,94,95,73,53,Mutual friends,53,78,76,53,13,68,89,6,73,70,72,18,79,73,74,4,5 +85,84,61,65,54,63,0,,25,60,60,61,14,66,59,20,58,61,57,11,59,42,40,5,5 +85,90,83,82,74,50,0,,63,62,62,63,11,83,83,19,85,66,67,28,77,63,64,6,5 +85,86,65,82,72,43,0,,32,62,62,82,18,85,85,62,80,62,65,22,46,61,49,7,5 +85,80,77,75,64,50,0,,41,75,64,67,22,37,70,26,55,62,60,22,51,62,50,8,5 +85,87,90,89,75,51,0,,38,72,74,72,21,79,79,23,93,69,78,25,80,69,62,9,5 +85,82,84,80,76,53,0,,31,75,60,77,13,55,72,24,51,62,65,17,61,71,59,10,5 +86,82,74,34,37,44,0,,40,46,47,37,0,54,42,22,60,67,56,40,69,70,63,1,5 +86,90,86,81,73,50,0,,64,65,68,30,0,54,65,39,85,73,81,43,86,74,77,2,5 +86,87,87,91,90,80,0,,21,35,71,57,0,68,78,26,98,70,76,19,97,95,80,3,5 +86,83,53,27,28,68,0,,19,39,31,30,0,36,38,26,45,56,36,33,67,32,60,4,5 +86,88,45,88,89,61,0,,54,82,67,65,0,73,65,33,81,65,43,38,79,82,64,5,5 +86,84,37,27,18,51,20,friend of a friend who I am not friends with anymore,36,18,15,25,0,35,28,47,45,39,35,31,34,34,37,6,5 +86,85,59,68,55,48,0,,40,44,59,40,0,59,65,45,72,60,63,37,85,62,82,7,5 +86,81,72,88,83,65,0,,41,80,70,56,0,69,72,57,79,69,70,49,90,85,68,8,5 +86,89,70,69,45,42,46,failed conversation for this study,57,36,39,40,0,46,43,68,86,60,40,42,71,59,84,9,5 +86,80,69,73,73,74,0,,44,61,61,54,0,65,68,41,65,78,63,39,78,62,65,10,5 +87,84,79,83,73,63,0,,26,52,50,52,0,56,66,42,59,57,52,37,82,58,25,1,5 +87,86,89,86,63,37,0,,51,47,55,79,0,66,77,59,65,38,41,25,75,62,58,2,5 +87,90,76,84,71,42,0,,60,61,62,36,0,63,75,60,68,65,64,36,85,74,71,3,5 +87,82,100,94,86,68,0,,66,77,78,56,0,41,82,63,72,72,66,37,100,80,76,4,5 +87,80,70,74,77,64,16,"I may have met my partner once before, but not in a very personal setting",65,64,61,59,0,57,70,63,65,66,61,24,76,52,54,5,5 +87,88,61,59,58,29,0,,59,37,41,58,0,41,70,84,82,70,65,17,89,75,78,6,5 +87,83,57,57,60,57,0,,34,44,44,43,0,44,65,30,70,56,62,61,79,79,63,7,5 +87,81,100,100,100,77,0,,40,65,64,59,0,62,87,35,77,70,61,25,100,97,73,8,5 +87,89,81,78,62,31,0,,65,38,25,40,0,65,80,63,79,77,78,53,76,51,51,9,5 +87,85,75,77,63,51,0,,64,62,62,43,0,56,80,63,86,65,66,20,84,52,56,10,5 +88,85,70,78,79,44,0,,4,65,51,48,32,81,82,62,82,59,63,65,91,57,60,1,5 +88,90,71,72,78,32,0,,14,81,81,54,27,92,82,73,76,63,66,82,78,48,54,2,5 +88,86,100,100,93,59,24,He was in my favorite class at Dartmouth,59,96,95,78,51,94,92,73,93,81,81,82,84,85,84,3,5 +88,87,100,100,95,77,0,,56,80,77,81,38,81,88,86,88,100,81,83,84,78,71,4,5 +88,80,91,85,78,70,0,,53,65,55,68,49,49,88,77,34,95,61,45,83,53,53,5,5 +88,83,92,86,82,26,0,,51,69,75,63,48,89,91,70,93,61,73,34,94,55,65,6,5 +88,89,75,69,62,55,0,,53,28,61,49,48,84,77,75,85,74,66,60,89,62,48,7,5 +88,82,38,43,35,52,0,,55,31,33,37,34,57,54,77,58,34,29,42,39,19,40,8,5 +88,81,81,81,78,74,0,,53,55,65,54,49,77,97,73,35,90,74,78,90,69,70,9,5 +88,84,83,87,92,55,0,,63,76,77,78,51,75,94,67,80,82,76,46,89,81,55,10,5 +89,80,25,60,59,58,0,,25,32,32,72,25,76,58,33,44,60,46,58,61,56,50,1,5 +89,81,59,58,54,55,0,,16,58,59,59,10,43,60,35,64,58,52,57,70,56,52,2,5 +89,82,95,88,87,54,0,,43,80,84,67,9,74,67,55,78,63,59,45,95,65,54,3,5 +89,84,74,79,71,74,0,,10,53,61,54,9,53,74,15,62,80,66,12,77,60,53,4,5 +89,85,82,85,95,71,0,,35,62,63,61,5,72,81,13,80,69,68,31,97,70,59,5,5 +89,90,85,83,82,50,7,"Saw them in Psych class, never spoke",34,58,59,55,7,67,73,41,74,77,65,47,95,61,55,6,5 +89,87,83,79,91,65,0,,22,52,61,76,5,79,78,14,80,73,72,29,90,59,53,7,5 +89,83,56,66,52,51,25,Was previously in a study with them,46,42,45,47,2,57,57,33,53,53,55,17,63,47,46,8,5 +89,88,55,59,47,50,0,,35,40,47,66,6,65,61,57,62,56,52,42,84,57,52,9,5 +89,86,68,80,67,53,22,Was in a study with them before,40,55,54,91,7,75,74,57,86,57,60,52,83,57,52,10,5 +90,81,89,88,86,70,0,,43,35,65,63,0,86,83,40,71,74,41,37,91,87,57,1,5 +90,86,89,88,93,51,0,,53,74,71,72,0,85,85,51,52,70,77,58,83,87,82,2,5 +90,87,67,67,57,60,0,,40,65,61,41,0,71,74,39,59,61,61,33,69,71,60,3,5 +90,88,55,63,53,70,0,,51,43,44,43,0,37,42,64,52,57,53,42,44,40,40,4,5 +90,89,77,68,58,50,21,,55,35,39,37,0,58,64,45,66,57,54,42,65,58,65,5,5 +90,84,55,57,57,50,0,,53,33,55,45,0,56,35,42,38,65,64,44,56,49,49,6,5 +90,80,65,66,67,67,40,,59,59,60,59,0,55,44,38,61,70,40,45,55,57,55,7,5 +90,82,62,61,58,63,0,,35,40,57,47,0,68,67,45,63,61,56,43,64,54,46,8,5 +90,83,46,47,48,58,0,,37,36,43,19,0,57,43,43,41,54,45,46,54,55,54,9,5 +90,85,20,32,20,56,0,,36,43,30,29,0,44,14,18,29,41,40,40,37,28,32,10,5 +100,101,80,75,79,51,0,,15,55,54,25,0,34,71,22,79,33,57,24,58,48,49,1,6 +100,103,77,57,68,41,0,,13,58,76,15,0,35,54,6,75,61,32,17,76,35,47,2,6 +100,108,47,59,41,45,0,,34,35,44,0,0,69,42,51,70,61,45,52,77,15,31,3,6 +100,106,26,32,26,65,0,,10,26,28,0,0,25,34,24,33,56,40,31,47,15,18,4,6 +100,107,97,83,87,53,0,,44,82,73,0,0,92,78,79,74,76,75,72,81,87,85,5,6 +100,110,81,82,61,43,0,,36,35,57,0,0,73,72,61,75,66,63,39,69,80,76,6,6 +100,104,90,90,89,54,0,,39,56,38,0,0,81,89,81,86,78,64,46,74,74,75,7,6 +100,109,47,30,21,21,0,,14,37,30,0,0,29,42,16,100,59,31,9,67,9,44,8,6 +100,102,78,67,78,65,0,,36,41,71,0,0,34,72,74,73,77,64,77,87,63,64,9,6 +100,105,64,28,34,95,0,,2,86,36,0,0,9,20,24,29,81,78,72,58,11,27,10,6 +101,106,31,36,32,54,0,,32,29,44,37,0,11,29,45,34,61,36,39,53,39,42,1,6 +101,110,96,97,96,51,86,FRIEND,90,86,80,65,20,42,68,58,83,61,63,37,90,82,72,2,6 +101,108,88,70,72,51,0,,26,39,60,54,18,83,73,36,66,31,62,30,65,61,39,3,6 +101,107,98,96,93,51,24,Met and talked during orientation,62,86,74,51,9,62,86,54,78,59,65,44,80,86,80,5,6 +101,103,55,66,63,59,0,,24,40,72,58,12,13,59,41,43,39,39,23,69,51,51,6,6 +101,100,73,66,87,58,0,,25,40,82,51,10,21,71,36,74,35,44,19,73,50,52,7,6 +101,102,34,58,48,63,7,First aid class,30,41,36,41,12,32,43,33,40,40,42,27,41,41,30,8,6 +101,105,61,74,74,57,0,,40,88,78,66,13,33,64,18,47,41,47,19,70,44,42,9,6 +101,109,13,29,4,12,4,Same class,19,5,6,10,0,0,8,14,4,12,11,0,2,1,4,10,6 +102,104,62,67,50,50,15,met briefly during DOC Trips,39,49,36,67,0,75,61,24,49,39,62,24,73,50,50,1,6 +102,103,38,66,59,59,0,,43,60,61,56,0,32,36,10,42,49,42,29,61,52,53,2,6 +102,110,82,74,68,50,0,,51,56,36,69,0,71,61,39,49,30,50,22,58,61,56,3,6 +102,108,45,59,50,50,0,,37,41,41,53,0,68,51,59,49,61,49,57,74,50,50,4,6 +102,107,71,69,73,50,50,He is in some of the clubs I am in.,51,72,50,78,0,100,72,34,58,35,61,29,62,64,62,5,6 +102,106,69,54,69,50,0,,31,50,50,38,0,38,57,38,62,70,53,61,51,48,50,6,6 +102,109,80,80,65,38,0,,50,50,38,37,0,74,86,59,71,41,62,37,74,66,65,7,6 +102,101,51,40,41,51,0,,39,39,50,39,0,40,36,38,49,59,49,40,60,33,38,8,6 +102,105,37,65,45,50,13,I see him in my PSYC 6 class,36,39,35,59,0,37,49,19,49,39,42,29,54,37,37,9,6 +102,100,65,64,54,49,0,,23,35,38,60,0,35,58,57,60,58,59,40,62,60,60,10,6 +103,102,85,95,68,65,0,,49,67,62,63,0,14,67,24,72,42,43,16,96,73,65,1,6 +103,110,98,93,60,59,0,,57,25,65,75,0,73,87,15,66,64,55,70,81,62,59,2,6 +103,106,64,84,74,48,0,,61,64,56,53,0,14,60,23,43,73,39,18,65,69,56,3,6 +103,104,42,56,45,50,0,,25,18,26,64,0,92,67,35,57,29,44,20,73,50,46,4,6 +103,107,99,100,79,38,0,,56,69,41,71,0,90,98,26,81,28,60,29,85,76,66,5,6 +103,108,26,41,29,51,0,,15,28,30,49,0,62,37,23,59,21,22,15,43,41,40,6,6 +103,109,94,99,83,25,0,,69,76,76,54,0,18,92,31,42,16,45,32,68,93,65,7,6 +103,101,95,87,71,64,0,,68,69,68,54,0,31,70,61,73,59,56,42,86,59,59,8,6 +103,105,12,28,54,80,0,,20,38,60,63,0,25,33,12,38,31,15,42,55,27,30,9,6 +103,100,98,86,60,52,0,,57,64,72,60,0,58,75,38,64,37,58,41,95,55,55,10,6 +104,106,34,70,63,62,0,,40,53,29,36,0,36,48,65,65,57,45,35,61,46,48,1,6 +104,102,60,65,70,68,5,Same trip section,36,36,62,46,0,53,63,20,65,59,55,11,70,69,69,2,6 +104,109,68,89,59,12,0,,42,7,4,18,0,4,93,70,55,38,44,21,92,85,65,3,6 +104,103,59,61,54,67,0,,45,36,47,46,0,55,61,16,55,67,60,75,68,65,69,5,6 +104,110,88,93,90,57,0,,58,53,82,58,0,76,77,41,76,75,67,38,95,93,91,6,6 +104,107,90,90,92,49,0,,53,49,56,53,0,78,82,34,85,81,75,38,83,88,90,7,6 +104,100,78,78,74,51,0,,45,87,67,66,0,78,62,32,70,69,59,28,97,96,95,8,6 +104,108,73,78,80,51,3,I met him through this study when we were supposed to do our conversation the first time,52,37,64,52,0,69,69,15,76,66,69,21,69,72,73,9,6 +104,105,18,22,29,85,0,,16,10,11,53,0,14,14,12,12,61,48,10,11,11,11,10,6 +105,108,31,43,60,36,0,,33,30,33,67,0,60,42,7,37,29,34,16,39,31,37,1,6 +105,109,69,63,57,56,0,,71,53,72,6,7,0,64,54,55,55,52,7,55,58,51,2,6 +105,103,26,60,58,51,0,,34,54,43,42,0,34,46,18,56,62,41,20,45,26,24,3,6 +105,101,56,82,83,59,0,,77,79,79,49,0,35,72,43,64,59,57,25,58,54,35,4,6 +105,106,95,99,92,61,0,,79,72,65,45,0,19,87,56,83,55,58,34,73,72,60,5,6 +105,102,78,73,79,53,0,,54,55,58,63,0,59,65,28,60,61,65,7,69,63,62,6,6 +105,104,53,65,65,41,0,,54,53,61,44,0,64,83,16,63,42,53,17,60,43,49,7,6 +105,107,82,80,79,26,69,Floormates,73,62,67,76,59,90,65,26,75,29,45,21,65,61,59,8,6 +105,110,70,78,72,58,0,,58,63,60,80,41,61,60,29,75,42,55,22,72,69,43,9,6 +105,100,69,62,64,59,0,,44,57,57,68,0,55,65,21,63,56,43,18,64,45,44,10,6 +106,104,71,90,86,50,0,,53,80,60,65,0,51,81,53,62,76,49,62,65,56,55,1,6 +106,101,50,39,73,51,0,,59,38,64,66,0,29,46,40,51,44,43,59,44,45,58,2,6 +106,103,73,77,81,53,0,,58,36,71,71,0,17,77,50,66,60,61,54,68,66,71,3,6 +106,108,65,66,55,65,0,,51,37,34,57,0,70,58,42,59,55,55,62,60,62,59,4,6 +106,110,100,100,88,87,0,,65,58,51,79,0,100,75,40,67,74,73,74,73,83,85,5,6 +106,107,100,100,100,63,0,,74,64,66,67,0,45,93,61,100,96,94,54,100,88,89,6,6 +106,102,58,69,76,35,0,,57,65,66,65,0,26,50,65,51,65,52,41,62,45,56,7,6 +106,105,65,81,69,67,1,,51,59,47,67,0,56,67,66,70,55,59,60,76,72,75,8,6 +106,100,33,36,37,59,0,,37,62,42,55,0,47,36,28,38,32,43,47,62,43,45,9,6 +106,109,100,100,100,40,51,We are fellow PMHAs for the Dartmouth cohort of the nationwide effort Project LETS.,79,85,59,57,0,14,82,38,100,77,100,33,100,100,100,10,6 +107,101,92,86,87,59,32,"I have met my partner through meetings with my house identity at Dartmouth, CPR and First Aid classes, and random interactions.",35,58,57,21,0,64,81,52,75,100,60,71,85,72,67,1,6 +107,103,61,35,32,65,0,,17,40,14,29,7,28,32,25,24,31,21,22,34,38,45,2,6 +107,102,98,96,86,55,25,We took a First Aid class together and interact in random passings.,42,69,60,16,0,48,75,50,81,51,45,53,88,69,80,3,6 +107,104,75,76,39,51,0,,18,42,56,15,3,41,41,36,55,22,23,28,62,34,41,4,6 +107,106,71,70,64,72,0,,24,59,29,5,0,37,66,65,64,55,41,71,64,37,31,5,6 +107,109,89,91,74,32,0,,51,76,50,15,0,58,100,82,73,43,42,75,75,67,73,6,6 +107,100,77,80,75,51,0,,19,57,62,68,33,71,78,33,66,39,37,55,60,56,40,7,6 +107,108,73,73,71,50,0,,16,30,46,71,22,61,56,58,60,58,62,59,64,50,49,8,6 +107,110,96,99,88,49,0,,26,77,77,86,75,90,100,63,97,96,96,69,88,73,48,9,6 +107,105,49,41,82,81,50,He is my floormate.,53,39,34,61,3,22,38,25,22,67,43,35,59,42,43,10,6 +108,109,60,75,26,42,0,,33,24,0,30,0,0,58,78,79,63,81,31,86,59,64,1,6 +108,101,77,82,61,73,0,,50,37,29,41,0,62,69,69,78,84,71,63,85,60,60,2,6 +108,106,32,56,15,60,0,,30,12,3,33,0,32,46,61,63,65,61,40,77,57,36,3,6 +108,103,65,68,40,60,0,,32,41,29,32,0,62,75,41,59,60,55,25,60,42,41,4,6 +108,105,63,64,55,54,0,,38,39,45,75,1,60,62,21,62,58,36,18,48,36,38,5,6 +108,102,77,88,38,54,0,,69,43,33,37,0,68,74,72,64,64,78,35,65,37,40,6,6 +108,110,95,92,79,64,0,,58,59,77,77,0,96,77,36,91,81,65,57,85,75,68,7,6 +108,100,89,68,41,60,0,,59,39,72,61,0,79,80,32,80,68,80,39,85,71,80,8,6 +108,107,78,77,60,69,0,,34,61,66,61,0,96,91,32,76,63,61,34,76,61,77,9,6 +108,104,91,87,74,62,8,saw him before a session,60,68,77,83,0,71,86,63,79,75,73,39,73,66,63,10,6 +109,108,79,49,49,40,10,same stats class last winter,28,18,19,83,13,92,51,52,52,53,32,32,45,51,51,1,6 +109,104,76,62,51,79,0,,25,50,48,66,0,41,53,26,44,68,51,48,52,50,50,2,6 +109,105,64,73,66,91,0,,21,35,49,72,0,50,51,9,52,87,53,54,57,32,31,3,6 +109,103,97,91,72,76,0,,25,77,80,68,0,4,79,17,60,89,86,21,87,77,69,4,6 +109,110,100,100,100,64,3,same psych class,31,71,73,89,0,71,76,36,89,81,85,30,79,76,78,5,6 +109,102,85,86,78,74,4,same neuro class,34,69,59,72,0,54,76,22,61,82,76,51,72,61,62,6,6 +109,107,85,77,63,87,0,,18,56,42,73,0,70,76,23,60,79,60,62,67,51,51,7,6 +109,106,79,78,0,52,35,i used to be in the same club as him,36,0,0,23,0,50,100,21,37,82,67,31,50,49,26,8,6 +109,100,70,64,58,55,10,same classes,34,49,50,78,0,60,68,10,61,61,63,23,56,34,34,9,6 +109,101,22,19,0,100,0,,1,0,0,22,0,5,0,4,10,50,23,51,51,6,6,10,6 +110,101,92,89,90,56,82,friend ,84,89,89,7,0,71,72,41,99,80,80,27,92,68,75,1,6 +110,103,61,59,62,57,0,,9,19,28,25,0,21,41,8,11,60,62,11,31,49,11,2,6 +110,102,30,29,11,70,0,,4,5,10,13,0,11,12,10,0,64,61,11,28,13,9,3,6 +110,109,84,84,82,33,0,,24,77,75,9,0,64,78,30,63,66,63,10,77,70,65,4,6 +110,104,74,65,63,44,0,,6,30,64,17,0,57,57,19,52,55,44,5,27,35,33,5,6 +110,106,65,60,63,61,0,,16,58,59,8,0,31,59,5,57,60,55,38,61,36,34,6,6 +110,108,64,59,55,57,0,,8,57,55,33,0,59,59,18,12,58,59,19,57,15,21,7,6 +110,107,72,68,63,61,0,,12,56,60,5,0,61,66,15,24,62,59,10,58,24,14,8,6 +110,100,61,58,56,60,0,,6,20,33,25,0,38,55,10,13,59,37,0,60,26,12,9,6 +110,105,0,0,0,75,0,,0,5,0,22,0,0,0,0,0,62,33,19,0,0,0,10,6 \ No newline at end of file diff --git a/Data/post_convo_survey/strangers/data_common_questions.csv b/Data/post_convo_survey/strangers/data_common_questions.csv new file mode 100644 index 0000000..2f355e8 --- /dev/null +++ b/Data/post_convo_survey/strangers/data_common_questions.csv @@ -0,0 +1,645 @@ +subID,partnerID,convo_flow,convo_enjoy,friends,speak,knew_before,knew_before_text,know_now,common,similar,attractive,attracted_to,extraverted,fun,disclosed,comfortable_self,convo_order,round_robin_num,dyad +1,2,66,58,63,10,58,We're both sophomores in the same sorority,69,44,58,74,45,70,62,57,57,1,1,14 +1,4,100,87,94,67,0,,65,58,37,55,49,36,60,55,88,2,1,34 +1,3,88,74,79,65,0,,71,58,72,88,53,57,70,37,62,3,1,23 +1,5,96,93,100,80,78,freshman friends,74,82,88,63,52,61,73,79,92,4,1,42 +1,6,90,87,89,44,66,sorority,68,28,27,64,53,90,65,92,61,5,1,54 +1,10,81,68,84,68,0,,68,32,60,62,45,31,65,66,70,6,1,97 +1,7,94,77,63,28,0,,56,13,67,54,44,73,69,85,87,7,1,65 +1,11,82,79,64,60,0,,58,33,61,57,38,41,62,29,64,8,1,108 +1,9,75,63,62,22,0,,79,63,70,60,46,46,46,76,86,9,1,86 +1,8,92,97,100,41,0,,87,40,78,60,56,100,89,77,72,10,1,79 +2,6,77,80,80,62,62,She's in my sorority and in my neuroscience class.,65,65,41,70,70,74,74,61,74,1,1,51 +2,8,67,81,66,82,0,,20,34,34,34,27,34,70,62,70,2,1,70 +2,7,77,72,78,59,0,,16,60,71,75,76,37,72,59,71,3,1,61 +2,10,36,61,64,73,0,,10,67,58,41,37,40,54,35,22,4,1,93 +2,1,91,91,91,77,58,Same sorority as me.,65,42,58,44,30,41,71,41,79,5,1,14 +2,3,36,17,23,64,0,,8,19,21,57,57,33,20,11,19,6,1,22 +2,4,73,84,80,50,0,,24,64,81,40,36,34,72,60,78,7,1,35 +2,11,59,38,39,61,0,,9,59,39,24,20,38,56,42,64,8,1,106 +2,5,98,83,83,68,71,one of my good friends; in my sorority,71,59,59,41,27,61,80,60,76,9,1,46 +2,9,77,76,67,78,8,my friend's trip leader,22,65,63,44,27,19,78,65,78,10,1,84 +3,6,80,85,70,38,0,,34,51,48,61,27,66,64,65,58,1,1,50 +3,4,81,96,73,47,3,,39,59,66,64,25,50,80,50,69,2,1,33 +3,2,65,69,72,42,10,,36,55,42,60,45,61,70,60,70,3,1,22 +3,1,64,72,39,49,17,,34,41,39,41,27,61,51,48,80,4,1,23 +3,8,72,78,74,47,15,,37,47,43,48,25,52,54,45,63,5,1,75 +3,11,52,57,72,58,1,,23,41,44,41,9,44,46,31,71,6,1,105 +3,5,59,80,64,52,6,,28,63,53,46,13,46,60,32,65,7,1,47 +3,7,60,65,68,52,17,,51,60,58,56,21,54,73,57,75,8,1,67 +3,9,77,70,71,53,13,,33,55,55,59,14,48,68,36,66,9,1,89 +3,10,63,61,76,53,8,,27,59,56,41,12,54,58,35,77,10,1,99 +4,10,82,88,86,65,25,Classmate in big lecture course,63,87,70,71,5,84,80,49,83,1,1,94 +4,11,79,69,53,45,0,,52,77,36,56,4,55,48,53,86,2,1,103 +4,5,68,59,44,43,0,,45,31,35,53,1,79,71,40,83,3,1,41 +4,3,74,69,67,54,0,,53,44,52,78,3,39,56,11,86,4,1,33 +4,1,100,100,100,50,0,,60,87,82,95,2,96,100,58,100,5,1,34 +4,2,100,100,100,47,4,I've seen her in my neuro class,54,93,94,100,4,98,100,63,97,6,1,35 +4,8,92,88,62,46,0,,52,63,33,78,5,71,44,53,95,7,1,76 +4,6,95,86,47,46,7,She sits behind me in neuro class,45,73,30,75,6,70,58,77,95,8,1,57 +4,7,98,95,83,56,0,,53,90,86,92,4,39,87,72,89,9,1,64 +4,9,93,96,72,46,0,,54,76,65,77,5,70,62,38,87,10,1,85 +5,11,83,84,84,50,0,,51,79,79,73,0,26,81,27,100,1,1,104 +5,4,72,68,59,51,0,,33,66,41,42,0,18,66,28,69,2,1,41 +5,1,99,98,100,51,93,"friends since early freshman year, same sorority, same psych class",95,100,90,91,0,100,100,53,100,3,1,42 +5,10,98,97,97,85,0,,50,72,72,72,0,23,74,19,79,4,1,96 +5,6,100,100,100,34,78,same sorority,71,85,71,94,0,31,68,82,98,5,1,55 +5,8,89,85,69,34,0,,33,32,45,77,0,23,69,27,81,6,1,78 +5,2,80,76,79,51,90,same sorority,90,72,67,89,0,52,63,66,88,7,1,46 +5,3,60,60,63,62,0,,24,50,51,95,0,28,68,25,63,8,1,47 +5,9,76,86,83,51,15,"took a class together, hadn't really spoken before",50,77,77,67,0,38,73,66,97,9,1,88 +5,7,81,90,89,51,0,,56,66,59,49,0,35,70,51,83,10,1,68 +6,3,66,63,56,58,0,,28,29,60,59,36,71,72,51,100,1,1,50 +6,2,100,80,80,65,71,Sorority sisters,51,65,78,70,51,76,100,75,100,2,1,51 +6,9,53,60,50,50,0,,26,30,43,27,0,51,51,35,76,3,1,83 +6,10,50,50,22,50,1,Study group,24,28,37,7,0,52,52,46,100,4,1,95 +6,1,87,68,80,49,52,Sorority sister,63,52,61,46,7,81,69,50,100,5,1,54 +6,5,67,100,92,49,51,Sorority sister,62,48,56,42,0,75,75,58,100,6,1,55 +6,8,68,56,50,67,0,,72,34,67,18,0,76,45,47,100,7,1,77 +6,4,31,18,6,35,0,,12,17,35,0,0,47,17,32,100,8,1,57 +6,7,15,7,19,51,0,,26,27,6,9,0,51,27,39,45,9,1,69 +6,11,100,78,72,60,0,,41,38,68,49,0,81,70,32,49,10,1,109 +7,8,100,100,100,71,6,"I'd seen her before, but we had never spoken. ",24,63,65,62,0,74,76,54,100,1,1,71 +7,2,100,100,84,50,0,,12,61,36,77,0,100,78,52,100,2,1,61 +7,11,61,52,11,51,0,,15,29,0,17,0,64,58,0,79,3,1,107 +7,10,100,100,100,51,0,,45,69,100,75,0,84,100,5,100,4,1,98 +7,4,100,82,58,37,0,,33,54,39,43,0,75,71,48,100,5,1,64 +7,1,100,100,100,51,0,,52,100,100,69,0,100,100,29,100,6,1,65 +7,9,100,85,51,51,0,,26,24,34,0,0,54,71,27,100,7,1,87 +7,3,100,100,100,51,0,,47,66,70,52,0,76,76,34,100,8,1,67 +7,5,100,100,100,51,5,,36,100,100,53,0,82,94,39,100,9,1,68 +7,6,82,60,21,45,0,,19,27,27,68,0,58,27,32,78,10,1,69 +8,2,90,89,58,32,0,,19,31,24,76,0,86,100,5,100,1,1,70 +8,7,90,88,87,58,0,,57,87,78,28,0,41,89,56,95,2,1,71 +8,9,99,97,94,50,0,,54,38,59,58,0,78,90,43,96,3,1,80 +8,10,96,96,75,55,0,,60,78,66,54,0,62,88,42,89,4,1,91 +8,11,87,74,31,61,23,I taught her in PE skating however never really talked to her. ,52,29,21,25,0,73,71,29,62,5,1,102 +8,3,88,89,68,52,0,,23,37,26,69,0,34,62,15,71,6,1,75 +8,4,72,42,9,55,0,,32,18,15,21,0,25,38,24,44,7,1,76 +8,6,81,71,4,52,0,,38,24,22,21,0,68,62,55,65,8,1,77 +8,5,28,31,18,95,0,,23,20,20,17,0,33,38,33,25,9,1,78 +8,1,100,100,90,69,0,,31,32,77,59,0,97,100,12,97,10,1,79 +9,8,93,90,84,64,0,,59,38,60,58,4,75,77,61,84,1,1,80 +9,11,84,78,67,66,0,,59,71,36,61,1,57,66,33,64,2,1,101 +9,10,85,77,80,69,0,,62,71,38,66,0,74,68,39,74,3,1,92 +9,6,43,55,44,54,0,,39,57,40,69,0,43,37,39,64,4,1,83 +9,2,53,46,59,55,67,She is friends with my trippees,40,57,39,62,2,59,60,28,34,5,1,84 +9,4,77,65,60,60,1,,57,62,38,59,0,62,69,57,67,6,1,85 +9,1,85,79,78,65,10,,64,57,63,71,1,61,69,70,77,7,1,86 +9,7,58,56,57,67,1,,65,56,38,57,0,41,61,40,59,8,1,87 +9,5,73,78,65,71,29,We had a class together last Spring,63,72,57,61,0,61,68,57,73,9,1,88 +9,3,65,40,40,68,1,,44,39,29,57,0,26,61,33,78,10,1,89 +10,11,75,76,52,58,0,,51,71,67,35,0,61,64,57,81,1,1,100 +10,8,95,97,98,51,0,,51,73,73,62,0,67,100,50,100,2,1,91 +10,9,36,61,53,20,0,,35,28,22,49,0,34,55,56,55,3,1,92 +10,2,93,92,84,36,0,,49,53,37,90,0,60,79,51,97,4,1,93 +10,4,56,75,79,39,23,"Mutual friend, Same class",56,42,39,78,0,38,61,69,60,5,1,94 +10,6,49,52,44,28,43,We are in the same clas,49,36,37,72,0,55,49,46,20,6,1,95 +10,5,59,72,72,51,0,,59,61,48,60,0,76,74,56,58,7,1,96 +10,1,53,47,24,49,0,,44,21,0,49,0,60,37,20,21,8,1,97 +10,7,100,100,100,51,0,,69,59,76,78,0,58,100,76,100,9,1,98 +10,3,68,68,55,31,0,,35,21,26,86,0,74,75,73,60,10,1,99 +11,10,100,100,72,44,0,,25,86,78,58,49,60,61,74,100,1,1,100 +11,9,100,83,83,46,0,,17,77,56,71,52,74,100,72,100,2,1,101 +11,8,100,99,77,51,7,One of my PE class instructers,24,55,47,70,50,63,100,58,100,3,1,102 +11,4,82,88,86,52,0,,62,74,80,75,51,57,84,16,100,4,1,103 +11,5,87,89,74,57,0,"We were Link Up Buddies but I accidentally never replied to her emails, so we knew each other in name only",18,70,61,77,51,71,79,67,84,5,1,104 +11,3,84,90,86,59,0,,4,42,60,72,50,63,92,32,93,6,1,105 +11,2,91,93,93,46,0,,9,65,62,76,50,78,95,23,100,7,1,106 +11,7,92,99,95,51,0,,8,60,65,71,52,66,100,7,100,8,1,107 +11,1,100,100,100,50,0,,35,71,82,72,50,80,100,36,96,9,1,108 +11,6,100,99,84,40,0,,36,64,51,69,51,80,95,39,100,10,1,109 +20,21,60,61,62,55,52,We have a class together.,56,45,52,72,0,56,58,54,54,1,2,121 +20,25,100,100,100,51,0,,64,100,76,66,0,80,100,83,71,2,2,157 +20,26,53,53,51,52,0,,45,46,47,70,45,38,42,55,54,3,2,172 +20,28,79,67,57,37,0,,64,67,53,39,0,75,76,52,85,4,2,190 +20,22,85,78,74,53,0,,68,58,62,67,0,80,78,57,67,5,2,133 +20,29,66,67,58,32,0,,70,43,57,36,0,63,64,53,68,6,2,205 +20,30,100,80,66,31,0,,70,70,67,52,0,81,73,35,72,7,2,212 +20,24,53,54,47,36,0,,65,35,44,35,0,56,53,47,59,8,2,156 +21,27,92,57,25,43,9,Seeing in class,29,55,27,26,0,54,70,54,85,1,2,177 +21,22,89,83,76,68,0,,41,34,67,73,1,45,61,9,86,2,2,130 +21,28,95,87,65,64,1,Briefly seen on Trips,39,35,59,54,0,62,63,66,87,3,2,188 +21,20,90,91,95,42,38,Small class together this term,54,46,67,90,11,84,87,52,95,4,2,121 +21,23,89,96,96,50,33,Classes together,45,66,66,60,2,58,64,53,83,5,2,138 +21,25,86,99,97,49,20,Classes together,37,65,69,71,9,64,75,32,91,6,2,160 +21,26,26,26,23,55,0,,10,19,53,69,1,62,10,0,39,7,2,171 +21,24,73,60,20,43,31,Previous attempt at this study,42,8,5,3,0,38,46,4,44,8,2,152 +21,29,100,97,99,52,0,,49,59,19,52,2,19,75,58,91,9,2,200 +21,30,83,64,61,45,1,,41,56,61,79,2,84,77,87,68,10,2,209 +22,29,76,68,52,49,0,,18,55,56,75,13,52,69,45,69,1,2,196 +22,26,63,62,62,50,0,,50,63,64,76,4,78,79,57,67,2,2,167 +22,21,69,68,66,51,0,,51,64,64,82,6,93,76,60,73,3,2,130 +22,28,100,100,75,42,0,,65,29,61,63,0,95,96,54,95,4,2,189 +22,24,74,63,38,46,0,,50,26,27,48,0,50,52,48,82,5,2,155 +22,20,100,96,95,51,0,,67,83,88,75,3,95,97,66,96,6,2,133 +22,23,96,97,98,42,0,,66,89,91,72,0,90,91,80,91,7,2,142 +22,25,98,84,87,50,0,,57,91,90,66,0,74,77,61,96,8,2,164 +22,30,100,98,99,50,51,Friend of a friend; live in same building,79,95,95,81,0,87,97,72,97,9,2,211 +22,27,26,24,21,63,0,,35,27,22,48,0,28,30,41,21,10,2,184 +23,21,92,69,61,51,13,Classmate,30,59,59,66,0,83,65,38,92,1,2,138 +23,24,20,14,13,70,0,,7,9,9,16,0,15,13,23,0,2,2,151 +23,25,80,71,71,51,0,,51,67,68,71,0,72,78,24,82,3,2,158 +23,29,87,80,69,50,0,,51,67,65,61,0,56,63,27,84,4,2,199 +23,22,89,84,69,51,0,,51,72,75,89,0,88,77,19,75,5,2,142 +23,27,63,65,51,51,0,,51,53,48,49,0,42,57,33,57,6,2,180 +23,30,85,70,61,48,0,,52,59,59,59,0,87,67,70,65,7,2,214 +23,28,56,47,37,74,0,,29,36,37,51,0,41,50,37,36,8,2,194 +23,26,59,51,57,60,0,,33,46,55,85,0,64,60,32,54,9,2,176 +24,29,100,100,86,60,23,"I met her once or twice during our first-year orientation week. We are also both in the same LLC (the Thought Project), so we've had a few short conversations through that.",35,66,35,76,58,35,91,78,91,1,2,197 +24,30,84,68,56,70,0,,23,55,27,60,52,39,65,16,91,2,2,206 +24,28,99,99,99,51,0,,33,66,31,59,51,99,98,86,92,3,2,187 +24,27,66,58,43,59,0,,60,37,38,60,53,36,61,35,59,4,2,179 +24,23,70,68,53,55,0,,58,58,29,55,56,62,59,53,56,5,2,151 +24,21,68,72,59,54,25,We had had a conversation before through this study.,71,39,37,59,62,69,69,55,55,6,2,152 +24,25,72,72,64,40,0,,62,69,68,58,58,41,59,45,60,7,2,161 +24,26,75,76,65,61,0,,40,62,56,57,58,61,60,63,56,8,2,173 +24,22,65,69,71,57,0,,57,67,71,55,55,61,64,59,58,9,2,155 +24,20,82,81,70,63,0,,32,69,40,64,62,80,80,16,60,10,2,156 +25,20,88,99,95,63,0,,35,76,65,70,62,82,91,76,80,1,2,157 +25,23,89,74,88,63,0,,25,18,66,30,5,74,83,27,90,2,2,158 +25,26,13,22,65,41,36,We went to high school and lived in the same dorm,40,18,35,71,65,70,85,39,27,3,2,170 +25,21,85,82,67,70,0,,27,77,80,39,9,69,82,16,89,4,2,160 +25,24,5,7,0,72,0,,8,3,0,0,0,19,15,4,5,5,2,161 +25,28,87,100,100,64,0,,66,100,79,62,62,83,97,31,79,6,2,192 +25,29,26,9,3,82,0,,18,19,5,8,4,16,27,9,22,7,2,204 +25,22,74,87,89,41,0,,23,66,70,87,73,76,87,24,71,8,2,164 +25,27,34,34,17,69,0,,16,24,19,12,0,35,17,37,30,9,2,183 +25,30,80,85,81,31,0,,68,33,50,65,42,94,91,94,83,10,2,213 +26,22,94,97,97,49,0,,11,86,90,92,0,100,100,33,67,1,2,167 +26,29,100,100,100,50,0,,45,75,62,78,3,23,85,77,99,2,2,198 +26,27,100,100,94,50,0,,59,100,75,69,2,63,97,94,100,3,2,178 +26,25,100,100,100,42,92,"Same boarding school, lived in same dorm",98,100,95,92,4,95,100,100,100,4,2,170 +26,21,62,89,84,44,0,,59,58,77,87,23,41,80,44,97,5,2,171 +26,20,100,100,100,28,0,,79,92,95,92,5,100,100,44,97,6,2,172 +26,24,100,100,94,34,0,,75,79,32,52,1,57,98,68,100,7,2,173 +26,28,99,99,93,11,0,,85,9,45,64,2,95,99,94,97,8,2,193 +26,30,86,90,88,44,0,,76,71,76,76,2,100,91,14,91,9,2,215 +26,23,98,100,98,43,0,,81,87,95,88,1,100,100,30,96,10,2,176 +27,21,78,79,75,65,11,,32,74,70,57,23,64,65,57,62,1,2,177 +27,26,82,86,82,63,18,,68,90,84,72,22,67,75,56,67,2,2,178 +27,24,62,69,61,61,9,,69,64,70,56,6,60,87,64,76,3,2,179 +27,23,75,73,74,72,10,,68,65,76,65,35,63,66,64,71,4,2,180 +27,30,65,66,58,66,23,,60,66,62,42,42,59,70,55,62,5,2,210 +27,29,70,69,69,71,60,mutual friends,65,68,73,63,34,66,74,63,70,6,2,203 +27,25,58,64,57,68,31,,55,60,63,69,39,61,63,59,66,7,2,183 +27,22,78,80,78,61,17,,77,67,70,69,42,64,85,67,67,8,2,184 +27,28,59,61,79,71,22,,76,68,67,57,36,64,69,62,68,9,2,195 +28,30,95,95,96,31,0,,20,58,70,91,0,100,100,5,90,1,2,207 +28,24,97,100,100,56,0,,15,61,60,82,0,69,100,23,92,2,2,187 +28,21,94,94,100,47,2,,20,38,64,66,0,100,100,15,80,3,2,188 +28,22,100,100,100,90,0,,5,8,63,82,0,100,100,38,100,4,2,189 +28,20,85,100,100,84,0,,8,77,63,98,0,100,100,0,97,5,2,190 +28,29,100,100,100,51,34,We are in two of the same clubs,30,52,62,74,0,24,85,30,100,6,2,202 +28,25,99,95,100,38,0,,16,67,65,77,0,95,93,2,84,7,2,192 +28,26,55,53,59,55,0,,10,13,37,75,0,84,75,4,80,8,2,193 +28,23,31,41,35,47,0,,3,16,35,67,0,66,54,9,57,9,2,194 +28,27,70,89,94,56,6,,24,59,58,85,0,45,97,26,79,10,2,195 +29,22,92,86,81,73,0,,44,78,80,76,0,45,82,62,82,1,2,196 +29,24,62,78,57,31,45,"Met during orientation, sit near each other in psych. ",69,72,63,68,0,31,67,85,43,2,2,197 +29,26,32,45,45,82,0,,30,52,52,76,0,86,68,13,40,3,2,198 +29,23,87,88,84,50,0,,63,86,77,75,0,88,90,60,74,4,2,199 +29,21,84,87,85,35,10,Have just seen her around,73,70,39,76,0,88,89,97,71,5,2,200 +29,30,86,90,83,57,0,,66,73,83,75,0,46,87,65,86,6,2,208 +29,28,91,99,96,26,30,Growing Change center for service program,70,87,89,74,0,84,88,81,79,7,2,202 +29,27,81,82,83,53,46,"My friend's roommate, we met a couple of times",60,87,86,77,0,46,82,63,68,8,2,203 +29,25,87,89,92,20,0,,53,75,73,82,0,91,90,64,70,9,2,204 +29,20,87,94,85,80,0,,65,81,82,83,0,81,91,63,86,10,2,205 +30,24,72,59,25,43,0,,12,17,17,33,0,61,43,70,36,1,2,206 +30,28,78,59,44,55,0,,23,42,32,54,0,61,57,35,58,2,2,207 +30,29,67,59,23,64,0,,26,32,32,39,0,33,47,32,47,3,2,208 +30,21,69,68,68,54,0,,28,57,40,55,0,57,58,59,64,4,2,209 +30,27,68,62,22,60,0,,14,20,20,15,0,59,29,41,67,5,2,210 +30,22,67,69,64,59,39,mutual friends,40,61,60,90,0,69,71,31,72,6,2,211 +30,20,70,73,78,67,0,,29,68,69,73,0,64,72,26,75,7,2,212 +30,25,74,75,79,67,0,,24,86,86,87,0,75,69,19,77,8,2,213 +30,23,79,77,70,71,0,,20,78,78,78,0,58,62,26,69,9,2,214 +30,26,72,65,61,67,0,,15,58,41,78,0,67,58,27,64,10,2,215 +40,49,90,83,66,31,0,,28,35,54,32,0,41,58,45,63,1,3,307 +40,42,58,53,42,38,0,,23,25,26,27,0,66,57,39,53,2,3,237 +40,48,37,55,65,55,0,,23,24,50,66,0,59,41,14,65,3,3,296 +40,44,78,64,56,53,6,Met once quickly,14,31,50,15,0,43,59,36,59,4,3,259 +40,41,37,45,43,55,0,,22,32,29,23,0,27,36,29,43,5,3,232 +40,47,23,20,4,32,0,,24,0,0,16,0,7,18,60,37,6,3,289 +40,45,67,70,73,45,80,Friend,81,69,68,63,0,61,63,41,75,7,3,272 +40,46,27,16,5,24,0,,17,14,9,20,0,14,17,15,59,8,3,282 +40,43,93,91,92,55,30,Friend,56,85,85,55,0,85,86,61,71,9,3,254 +40,50,75,65,55,38,0,,22,42,43,20,0,44,57,44,73,10,3,325 +41,49,77,76,75,29,0,,21,60,59,48,3,85,93,73,87,1,3,306 +41,46,37,55,59,78,0,,26,35,35,20,0,15,35,21,83,2,3,279 +41,45,70,69,70,51,14,Met once before,44,51,51,52,0,82,80,49,75,3,3,269 +41,50,39,66,67,50,0,,12,29,35,50,0,34,64,26,68,4,3,323 +41,44,73,74,74,50,0,,37,81,42,24,0,30,60,66,84,5,3,260 +41,43,82,64,72,61,0,,24,51,51,53,0,65,64,47,80,6,3,249 +41,40,60,40,50,51,0,,38,31,39,48,0,65,64,51,87,7,3,232 +41,47,45,70,51,41,0,,36,51,26,13,0,18,35,26,75,8,3,286 +41,42,60,68,74,52,0,,29,30,55,4,0,41,62,38,82,9,3,243 +41,48,36,35,42,65,0,,25,32,35,58,0,64,39,25,68,10,3,304 +42,50,85,79,51,67,0,,14,13,5,48,0,56,62,27,79,1,3,316 +42,40,51,63,62,67,0,,26,29,38,67,0,63,61,28,63,2,3,237 +42,44,62,71,57,60,0,,24,8,23,25,0,59,65,66,70,3,3,257 +42,49,88,80,72,55,0,,27,62,62,46,0,74,75,41,79,4,3,310 +42,43,82,73,68,67,0,,26,31,44,40,0,66,70,34,74,5,3,250 +42,48,63,71,64,65,0,,26,64,55,74,0,68,61,44,75,6,3,300 +42,47,56,58,29,44,0,,24,39,17,19,0,42,43,65,63,7,3,291 +42,41,69,60,37,52,3,"In my study group, but barely",24,34,45,22,0,55,55,59,69,8,3,243 +42,45,58,55,51,60,0,,16,31,32,63,0,62,51,56,65,9,3,274 +42,46,22,45,20,46,0,,29,9,4,11,0,6,19,56,42,10,3,285 +43,50,82,66,10,67,0,,10,40,33,9,0,72,57,60,64,1,3,319 +43,45,100,100,100,51,68,Friends,85,100,90,64,0,100,100,54,100,2,3,268 +43,49,91,87,50,35,0,,5,57,59,47,0,93,87,10,78,3,3,309 +43,41,77,57,20,58,0,,11,34,12,25,0,34,31,58,88,4,3,249 +43,42,91,76,56,33,0,,18,61,58,45,0,78,80,70,93,5,3,250 +43,44,79,69,56,65,0,,21,27,23,10,0,54,71,38,83,6,3,261 +43,46,13,3,0,85,0,,3,0,0,0,0,0,8,44,16,7,3,280 +43,48,81,86,69,50,0,,12,58,62,70,0,89,79,55,86,8,3,302 +43,40,96,92,93,48,14,Met him a couple times through friends,30,82,76,67,0,91,82,66,90,9,3,254 +43,47,59,29,0,54,0,,10,10,10,10,0,15,15,6,41,10,3,293 +44,50,95,94,91,56,43,PSYCHOLOGY CLASS AND WE PLAYED POOL TOGETHER ONCE,62,69,67,23,83,44,78,61,71,1,3,317 +44,42,71,71,44,27,0,,66,42,44,53,19,65,81,55,62,2,3,257 +44,49,79,80,98,23,3,,71,65,60,63,29,73,88,68,65,3,3,308 +44,40,62,52,40,50,31,We met during an event at orientation,55,55,53,47,15,62,60,64,57,4,3,259 +44,41,70,68,64,56,0,,62,65,67,54,19,45,65,47,69,5,3,260 +44,43,59,60,58,52,0,,59,44,47,57,16,59,59,53,72,6,3,261 +44,45,69,63,60,52,0,,64,58,56,54,12,63,65,54,68,7,3,275 +44,46,18,16,20,79,0,,58,30,31,26,20,12,17,37,33,8,3,284 +44,47,60,55,22,19,0,,75,42,40,10,2,3,56,63,54,9,3,294 +44,48,93,87,87,42,0,,64,40,57,55,20,91,90,56,80,10,3,305 +45,46,73,60,35,27,0,,51,7,9,22,0,36,59,52,73,1,3,276 +45,50,98,89,79,50,53,Acquaintences,67,71,70,44,0,91,83,40,87,2,3,320 +45,43,100,88,78,43,85,friends,86,85,68,73,0,100,85,54,93,3,3,268 +45,41,86,76,67,50,24,had met once before,49,69,65,49,0,78,77,71,86,4,3,269 +45,48,76,68,62,52,0,,30,40,52,86,12,72,66,51,69,5,3,298 +45,47,92,86,69,40,0,,51,69,35,32,0,41,62,65,77,6,3,288 +45,40,84,82,89,50,80,friends,85,79,78,85,3,77,83,61,97,7,3,272 +45,49,84,77,71,11,0,,41,64,57,62,0,100,81,57,77,8,3,312 +45,42,69,65,49,33,0,,42,25,39,62,0,74,72,72,75,9,3,274 +45,44,77,71,64,62,0,,51,66,60,27,0,75,73,60,75,10,3,275 +46,45,89,74,44,66,0,,58,21,36,63,18,62,62,49,74,1,3,276 +46,50,51,51,37,46,0,,53,29,55,53,14,61,56,54,58,2,3,318 +46,48,42,47,28,68,0,,25,19,31,64,11,56,43,41,53,3,3,297 +46,41,69,56,52,41,0,,47,35,55,47,11,58,56,49,68,4,3,279 +46,43,33,36,20,44,0,,35,18,34,63,4,62,30,36,38,5,3,280 +46,47,40,58,58,49,0,,54,71,62,27,1,38,44,55,40,6,3,287 +46,40,42,52,55,62,0,,34,46,47,63,3,46,46,41,54,7,3,282 +46,49,55,58,45,82,0,,11,37,51,58,3,44,57,13,60,8,3,313 +46,44,33,38,32,52,0,,31,17,37,55,1,53,37,38,18,9,3,284 +46,42,43,54,29,52,0,,26,26,39,63,3,55,52,45,57,10,3,285 +47,41,85,68,79,63,0,,58,57,36,77,0,59,72,57,72,1,3,286 +47,46,42,40,44,59,15,,55,24,28,59,7,36,43,59,56,2,3,287 +47,45,68,69,62,43,10,,65,57,43,66,43,73,73,73,78,3,3,288 +47,40,59,58,56,57,3,,67,39,38,64,18,64,56,56,66,4,3,289 +47,50,78,78,77,48,16,,73,62,55,76,19,73,75,58,81,5,3,324 +47,42,55,55,57,58,6,,56,38,33,60,8,62,59,76,42,6,3,291 +47,49,75,76,72,45,9,,68,57,58,59,9,76,74,76,83,7,3,314 +47,43,65,66,57,61,6,,62,25,25,57,3,59,61,80,58,8,3,293 +47,44,68,81,62,44,6,,71,64,43,72,4,74,71,71,67,9,3,294 +47,48,61,60,60,91,2,,63,43,40,77,18,65,64,35,70,10,3,303 +48,40,37,36,61,49,0,,19,46,36,59,0,73,56,0,4,1,3,296 +48,46,63,65,27,7,0,,20,0,10,13,0,12,55,6,63,2,3,297 +48,45,100,74,85,51,0,,51,83,79,85,17,73,74,16,80,3,3,298 +48,50,100,100,93,54,0,,72,41,41,41,0,69,100,69,100,4,3,322 +48,42,93,70,75,0,0,,61,77,70,71,0,92,85,74,73,5,3,300 +48,49,100,100,100,49,76,"On football team together, taking class together, in same fraternity",80,92,73,29,0,78,95,23,83,6,3,315 +48,43,98,97,93,36,14,"In class together, friend of a friend",47,66,31,68,9,92,71,12,84,7,3,302 +48,47,80,63,67,6,0,,26,30,0,0,0,13,66,67,83,8,3,303 +48,41,78,86,63,51,0,,35,26,31,26,0,65,67,19,82,9,3,304 +48,44,90,100,100,63,0,,61,33,34,9,0,20,74,61,100,10,3,305 +49,41,92,87,55,51,0,,10,67,67,52,0,37,66,38,87,1,3,306 +49,40,42,57,44,68,0,,31,81,31,51,0,55,57,64,46,2,3,307 +49,44,93,100,83,87,0,,50,79,68,49,0,56,100,32,100,3,3,308 +49,43,72,81,97,76,0,,40,77,89,49,0,79,93,15,84,4,3,309 +49,42,100,100,90,51,0,,48,84,100,50,0,72,100,30,100,5,3,310 +49,50,89,82,61,97,0,,25,56,42,50,0,44,100,21,93,6,3,321 +49,45,64,86,82,95,3,,14,85,80,50,0,63,83,14,95,7,3,312 +49,46,87,84,46,39,0,,30,41,34,49,0,31,69,41,74,8,3,313 +49,47,57,63,34,55,0,,43,61,40,50,0,28,62,75,86,9,3,314 +49,48,93,100,100,72,71,He's my football teammate and friend,76,90,90,50,0,85,100,68,100,10,3,315 +50,42,100,89,81,32,0,,47,59,51,54,0,62,85,62,100,1,3,316 +50,44,100,95,91,52,14,We are both students of the same psychology class,64,65,55,57,0,76,87,57,94,2,3,317 +50,46,42,36,28,72,0,,33,31,31,17,0,0,34,20,41,3,3,318 +50,43,100,100,100,51,0,,63,67,68,69,6,100,100,61,100,4,3,319 +50,45,100,100,100,51,63,Orientation friends,97,94,100,100,18,100,100,63,100,5,3,320 +50,49,100,92,94,51,0,,72,64,70,57,0,100,83,64,100,6,3,321 +50,48,100,100,100,52,0,,100,100,100,100,17,100,100,88,100,7,3,322 +50,41,62,56,60,59,0,,54,56,55,43,0,61,62,56,59,8,3,323 +50,47,78,64,69,65,0,,50,53,52,48,0,40,48,31,74,9,3,324 +50,40,100,91,84,51,0,,70,77,80,63,11,86,84,67,100,10,3,325 +60,63,97,100,100,63,0,,51,77,44,63,0,81,99,57,100,1,4,355 +60,62,53,59,57,58,0,,46,43,39,43,0,13,34,48,46,2,4,346 +60,70,100,100,100,71,78,Classmates and both part of FYSEP Program,71,79,75,72,0,57,78,100,100,3,4,419 +60,68,98,89,100,54,0,,50,60,61,88,0,63,73,28,100,4,4,406 +60,66,92,78,91,87,0,,29,79,63,36,0,34,42,12,100,5,4,389 +60,64,67,70,15,52,0,,7,0,12,100,0,99,73,14,93,6,4,372 +60,69,59,76,100,37,34,We had already been introduced to each other.,43,17,24,100,0,59,69,0,92,7,4,414 +60,61,100,100,100,32,0,,51,91,100,11,0,100,100,75,100,8,4,343 +60,65,100,100,100,66,0,,70,32,100,100,0,70,100,68,89,9,4,382 +61,68,83,76,87,50,0,,21,78,41,79,0,38,51,46,78,1,4,398 +61,63,41,40,41,51,0,,69,40,27,37,0,52,44,71,61,2,4,356 +61,67,100,100,100,50,12,We're in the same study session.,64,63,82,83,0,100,100,66,100,3,4,396 +61,62,44,35,29,65,0,,37,26,28,30,0,37,37,36,40,4,4,350 +61,70,57,38,38,60,1,I saw her once at a study session.,50,36,37,29,0,61,62,67,60,5,4,420 +61,65,40,35,23,57,0,,31,33,33,39,0,40,38,41,42,6,4,380 +61,69,60,51,50,50,0,,45,41,41,61,0,50,50,37,60,7,4,413 +61,64,76,75,81,50,0,,65,68,65,79,0,71,73,59,84,8,4,371 +61,60,88,77,78,56,0,,63,50,51,47,0,68,77,76,81,9,4,343 +61,66,51,27,18,65,0,,29,25,23,27,0,34,36,36,44,10,4,392 +62,70,76,77,72,36,0,,34,61,68,68,8,28,64,27,77,1,4,418 +62,60,63,64,58,43,0,,28,63,57,68,5,44,65,39,68,2,4,346 +62,68,63,64,64,51,0,,29,37,62,75,3,43,66,24,77,3,4,402 +62,69,43,44,50,61,0,,25,50,48,62,1,34,51,22,77,4,4,411 +62,64,54,68,51,51,0,,30,59,40,76,0,62,61,36,75,5,4,368 +62,61,71,67,60,43,0,,41,50,67,73,0,38,70,37,82,6,4,350 +62,65,67,71,66,43,0,,34,68,51,74,0,69,68,30,77,7,4,378 +62,63,81,76,60,64,0,,51,51,64,65,0,31,67,30,79,8,4,362 +62,66,74,75,75,44,51,Freshman floormate,55,66,65,64,0,51,72,21,98,9,4,391 +63,70,89,48,82,100,88,"Floor mate, Friend, Classmate",71,43,34,55,48,49,49,50,92,1,4,417 +63,60,96,93,88,54,0,,16,83,71,50,49,51,63,21,87,2,4,355 +63,61,50,47,47,40,0,,23,21,32,49,45,50,45,18,56,3,4,356 +63,64,3,2,1,49,0,,5,3,0,55,35,44,7,4,38,4,4,365 +63,68,47,69,63,50,2,,36,54,55,66,55,43,55,27,80,5,4,401 +63,65,76,70,86,74,2,,32,43,59,61,61,49,51,45,97,6,4,374 +63,69,36,46,53,54,52,floormates,49,51,48,65,61,51,53,11,71,7,4,415 +63,66,77,100,97,44,3,Sits behind me in class and she asks a lot of great questions.,0,68,50,51,48,51,64,25,83,8,4,390 +63,62,57,66,52,24,0,,19,51,45,50,48,50,50,7,73,9,4,362 +64,67,79,71,100,45,0,,51,54,55,80,0,69,83,43,89,1,4,394 +64,68,43,42,28,60,0,,32,10,37,46,0,15,40,42,56,2,4,400 +64,63,23,42,28,54,0,,44,7,12,28,0,36,46,46,54,3,4,365 +64,65,85,85,72,50,0,,42,45,46,58,0,57,74,39,77,4,4,375 +64,69,57,61,42,48,0,,53,14,15,47,0,64,68,52,86,5,4,409 +64,62,60,57,90,57,0,,54,60,58,81,0,40,61,48,65,6,4,368 +64,70,73,68,54,48,0,,53,44,42,47,0,79,73,45,89,7,4,421 +64,66,65,72,46,55,0,,53,47,37,34,0,66,68,26,84,8,4,387 +64,61,93,96,46,51,0,,56,38,46,37,0,64,70,43,92,9,4,371 +64,60,65,61,39,52,0,,53,41,36,53,0,46,71,45,90,10,4,372 +65,67,85,85,66,50,6,,12,62,63,73,0,89,82,26,94,1,4,395 +65,63,76,66,62,76,0,,18,34,36,55,12,72,71,77,74,2,4,374 +65,64,75,71,72,60,0,,30,63,65,76,26,75,76,23,79,3,4,375 +65,69,59,63,61,27,0,,14,37,36,51,9,94,82,42,79,4,4,407 +65,68,67,55,57,51,0,,10,57,37,70,11,57,58,39,63,5,4,405 +65,62,66,68,71,54,0,,23,60,60,61,6,70,65,55,75,6,4,378 +65,66,63,41,40,51,0,,29,39,26,56,11,31,40,54,60,7,4,386 +65,61,69,68,69,51,0,,18,62,39,69,18,31,67,28,72,8,4,380 +65,70,72,71,85,63,0,,32,63,66,66,14,68,74,60,72,9,4,422 +65,60,71,81,84,47,0,,26,60,40,55,3,100,90,61,83,10,4,382 +66,67,100,100,100,51,56,"floormate last year, in same class this term, in same extracurricular activity",68,89,44,62,0,64,60,54,100,1,4,393 +66,69,78,90,95,62,58,classmate,65,28,38,65,0,65,100,31,100,2,4,408 +66,68,54,44,43,50,0,did not know them,19,24,18,53,0,52,53,18,53,3,4,404 +66,65,84,70,65,54,5,did not know,32,41,40,100,0,57,55,44,71,4,4,386 +66,64,64,63,42,63,0,did not know,22,38,38,100,0,100,82,67,71,5,4,387 +66,70,43,54,62,50,0,did not know them,45,11,59,100,0,59,100,80,57,6,4,424 +66,60,45,47,54,48,0,did not know them,11,39,46,100,0,42,61,40,56,7,4,389 +66,63,99,100,100,83,0,did not know them,59,83,100,100,0,29,61,36,100,8,4,390 +66,62,62,60,76,51,64,freshman floor mate,64,55,33,100,0,31,56,43,74,9,4,391 +66,61,42,34,16,20,0,did not know her,28,24,27,100,0,35,31,67,29,10,4,392 +67,66,93,94,94,72,85,"Freshman floor mates, on EMS together (she is ranked higher than me in EMS)",61,60,54,43,16,41,63,24,74,1,4,393 +67,64,74,83,82,54,0,,27,70,84,83,11,87,83,20,65,2,4,394 +67,65,96,93,96,39,14,Had a class together,64,61,89,77,6,62,92,51,88,3,4,395 +67,61,100,100,100,51,35,in class together,51,98,84,84,2,82,100,49,98,4,4,396 +67,70,72,72,56,50,43,in class together,59,44,55,65,0,46,67,60,66,5,4,425 +68,61,75,66,56,34,0,,36,54,43,52,0,85,65,28,81,1,4,398 +68,70,64,66,82,56,20,We met at Dimensions and spent a few hours together in a group.,38,61,73,56,0,40,66,75,85,2,4,416 +68,64,29,28,48,59,0,,5,25,53,76,0,12,10,15,48,3,4,400 +68,63,45,52,53,55,0,,11,61,53,51,0,11,45,31,56,4,4,401 +68,62,59,65,67,32,0,,22,11,59,53,0,46,58,16,65,5,4,402 +68,69,65,75,100,52,0,,31,36,100,64,0,33,85,42,100,6,4,410 +68,66,56,55,18,47,0,,28,27,40,51,0,13,52,33,72,7,4,404 +68,65,40,55,58,42,0,,15,45,57,59,0,33,59,13,36,8,4,405 +68,60,73,78,83,53,0,,25,53,73,52,0,72,68,0,62,9,4,406 +69,65,93,95,92,45,0,,42,60,61,66,6,86,97,45,100,1,4,407 +69,66,85,98,99,52,77,We are classmates in PSYC 6. ,93,79,65,52,0,78,89,64,100,2,4,408 +69,64,82,67,67,55,0,,58,64,58,73,5,79,73,58,98,3,4,409 +69,68,94,92,96,59,0,,57,76,90,61,0,84,94,64,96,4,4,410 +69,62,72,69,71,50,0,,54,57,58,47,0,79,76,40,97,5,4,411 +69,70,97,96,100,56,75,She is my floormate. ,86,67,75,68,12,93,96,45,100,6,4,423 +69,61,74,77,87,46,0,,57,64,74,68,0,84,83,47,97,7,4,413 +69,60,94,90,91,56,55,We met through mutual friends. ,75,73,78,58,0,79,95,56,98,8,4,414 +69,63,88,90,92,54,67,We are floormates. ,80,66,74,69,0,87,87,46,98,9,4,415 +70,68,100,100,100,58,6,From dimensions but we had not talked since,22,49,48,76,15,51,100,47,88,1,4,416 +70,63,84,73,100,17,98,Floor mates,87,80,84,78,5,85,86,87,100,2,4,417 +70,62,72,100,100,72,0,,26,67,67,88,0,79,94,43,88,3,4,418 +70,60,97,87,88,21,56,friend from class,51,74,74,77,7,66,78,45,85,4,4,419 +70,61,70,81,72,39,0,,32,65,75,68,12,72,90,48,77,5,4,420 +70,64,82,75,65,39,0,,21,56,46,58,0,61,84,43,59,6,4,421 +70,65,91,100,100,51,0,,19,82,91,81,0,68,94,33,87,7,4,422 +70,69,87,82,75,70,50,floormate,58,61,84,87,0,79,97,36,84,8,4,423 +70,66,65,77,82,66,0,,22,65,78,69,0,66,78,36,67,9,4,424 +70,67,92,84,92,48,2,took a test in the same room,12,80,86,84,6,91,100,31,99,10,4,425 +80,89,65,59,35,47,0,,19,26,26,37,0,26,35,23,88,1,5,516 +80,81,91,92,88,57,54,"One of my best friends is from the same hometown as my partner is so we have met and talked before and although we do not hang out, we get along well.",60,47,84,81,0,62,97,32,96,2,5,438 +80,83,94,94,66,46,61,Had a class with him and am from roughly the same area.,64,45,45,35,0,56,92,54,96,3,5,456 +80,87,97,98,81,65,14,Briefly talked to him at the frats before.,40,90,88,65,0,95,100,66,100,4,5,500 +80,82,72,69,56,51,0,,20,54,40,54,0,44,60,34,73,5,5,450 +80,88,95,95,81,51,0,,41,66,67,58,0,61,91,63,100,6,5,510 +80,90,95,93,78,29,40,I've gone biking with him before.,45,72,45,60,0,93,85,68,95,7,5,532 +80,85,88,90,69,53,0,,35,61,62,65,0,64,89,42,91,8,5,483 +80,84,71,68,32,53,0,,28,29,28,46,0,44,62,38,71,9,5,474 +80,86,92,85,82,45,0,,35,59,71,79,0,85,91,55,87,10,5,495 +81,89,89,91,79,71,0,,51,73,56,51,0,44,59,53,92,1,5,517 +81,82,79,80,81,51,0,,46,60,53,51,0,54,70,31,100,2,5,448 +81,80,100,100,100,51,50,Friend of a friend,59,59,61,54,0,56,100,51,100,3,5,438 +81,90,100,100,100,51,0,,52,72,52,51,0,100,100,61,100,4,5,526 +81,83,100,100,100,51,97,Friend and former teammate,98,81,51,51,0,96,100,97,100,5,5,458 +81,85,100,100,100,51,99,Friend and went to grade school in same state,51,99,97,51,0,99,100,98,100,6,5,478 +81,87,100,100,100,51,0,,51,69,70,51,0,100,100,51,100,7,5,503 +81,84,100,100,100,51,0,,51,51,45,51,0,51,100,52,51,8,5,473 +81,86,100,100,100,51,0,,51,100,100,51,0,100,100,51,100,9,5,493 +81,88,100,100,100,51,0,,51,51,51,51,0,100,100,51,100,10,5,514 +82,89,79,75,65,54,6,"Didn't know personally, recognized from Psych 1 class.",63,75,43,31,0,43,65,69,82,1,5,518 +82,86,69,58,73,53,0,,51,46,63,52,0,89,69,35,42,2,5,486 +82,81,61,67,71,60,0,,41,70,63,7,0,39,55,56,57,3,5,448 +82,87,82,79,84,52,0,,59,92,86,10,0,73,73,76,89,4,5,499 +82,80,65,52,71,67,0,,21,45,61,41,0,73,68,70,57,5,5,450 +82,83,63,61,68,85,63,"Knew of him, have several mutual friends",56,59,78,8,0,62,62,65,63,6,5,459 +82,90,74,73,71,36,0,,56,67,67,19,3,71,76,68,76,7,5,533 +82,84,65,43,44,35,0,,41,32,41,12,0,41,38,58,38,8,5,470 +82,88,75,76,69,62,24,Knew of him from Gov 3 class last term.,44,60,62,38,22,67,62,59,59,9,5,513 +82,85,67,65,70,73,0,,55,67,63,7,5,66,53,67,63,10,5,485 +83,80,62,75,80,59,51,"Writing 5 Classmates, we peer-edited each other's assignments",60,68,62,77,0,67,81,36,75,1,5,456 +83,86,60,69,39,58,0,,28,71,27,58,0,36,58,58,54,2,5,489 +83,81,97,100,100,68,81,"2 classes together this term, friends as result of close mutual friend, ultimate frisbee",91,82,75,77,0,85,100,27,100,3,5,458 +83,82,96,98,92,44,0,,57,66,77,51,0,77,92,60,98,4,5,459 +83,87,78,92,87,45,0,,26,57,80,58,0,85,97,26,95,5,5,502 +83,88,88,94,77,83,0,,18,57,68,54,0,95,96,4,66,6,5,511 +83,90,60,73,74,51,3,,40,76,77,56,0,69,82,54,85,7,5,534 +83,84,34,33,55,61,0,,34,11,20,28,0,39,37,61,52,8,5,469 +83,85,99,99,100,47,37,"Mutual friends, have sat at meals together once or twice",63,95,90,60,0,85,99,59,100,9,5,479 +83,89,64,59,88,57,51,From our last conversation in the lab that didnt get recorded,66,46,56,48,0,17,40,61,95,10,5,523 +84,89,92,100,72,29,0,,27,75,29,59,0,100,69,8,67,1,5,519 +84,87,71,17,16,10,0,,3,48,22,63,25,75,15,0,66,2,5,496 +84,90,75,67,72,41,0,,23,78,25,33,0,78,58,4,56,3,5,531 +84,83,40,31,4,51,0,,5,8,16,5,0,68,24,36,26,4,5,469 +84,82,59,26,28,62,0,,22,33,30,21,5,64,29,32,63,5,5,470 +84,85,31,63,71,38,0,,30,59,29,50,21,37,59,22,64,6,5,480 +84,86,34,32,68,51,51,Hung out one time,30,25,19,100,100,72,39,60,57,7,5,491 +84,81,58,39,49,51,0,,18,30,21,65,33,63,36,17,49,8,5,473 +84,80,30,17,0,63,0,,6,0,0,50,33,86,22,12,41,9,5,474 +84,88,100,100,100,37,0,,38,58,61,62,3,43,100,35,100,10,5,515 +85,89,91,92,91,51,4,Had seen him around before but never talked,58,63,63,72,14,67,90,37,82,1,5,520 +85,88,58,64,44,50,0,,51,53,18,60,18,68,58,25,63,2,5,506 +85,81,80,85,83,64,62,"Met him during Orientation week and have mutual friends, have talked a few times",69,66,66,68,16,75,80,30,89,3,5,478 +85,83,96,94,95,73,53,Mutual friends,53,78,76,53,13,68,89,6,79,4,5,479 +85,84,61,65,54,63,0,,25,60,60,61,14,66,59,20,59,5,5,480 +85,90,83,82,74,50,0,,63,62,62,63,11,83,83,19,77,6,5,535 +85,86,65,82,72,43,0,,32,62,62,82,18,85,85,62,46,7,5,492 +85,80,77,75,64,50,0,,41,75,64,67,22,37,70,26,51,8,5,483 +85,87,90,89,75,51,0,,38,72,74,72,21,79,79,23,80,9,5,505 +85,82,84,80,76,53,0,,31,75,60,77,13,55,72,24,61,10,5,485 +86,82,74,34,37,44,0,,40,46,47,37,0,54,42,22,69,1,5,486 +86,90,86,81,73,50,0,,64,65,68,30,0,54,65,39,86,2,5,527 +86,87,87,91,90,80,0,,21,35,71,57,0,68,78,26,97,3,5,497 +86,83,53,27,28,68,0,,19,39,31,30,0,36,38,26,67,4,5,489 +86,88,45,88,89,61,0,,54,82,67,65,0,73,65,33,79,5,5,508 +86,84,37,27,18,51,20,friend of a friend who I am not friends with anymore,36,18,15,25,0,35,28,47,34,6,5,491 +86,85,59,68,55,48,0,,40,44,59,40,0,59,65,45,85,7,5,492 +86,81,72,88,83,65,0,,41,80,70,56,0,69,72,57,90,8,5,493 +86,89,70,69,45,42,46,failed conversation for this study,57,36,39,40,0,46,43,68,71,9,5,525 +86,80,69,73,73,74,0,,44,61,61,54,0,65,68,41,78,10,5,495 +87,84,79,83,73,63,0,,26,52,50,52,0,56,66,42,82,1,5,496 +87,86,89,86,63,37,0,,51,47,55,79,0,66,77,59,75,2,5,497 +87,90,76,84,71,42,0,,60,61,62,36,0,63,75,60,85,3,5,528 +87,82,100,94,86,68,0,,66,77,78,56,0,41,82,63,100,4,5,499 +87,80,70,74,77,64,16,"I may have met my partner once before, but not in a very personal setting",65,64,61,59,0,57,70,63,76,5,5,500 +87,88,61,59,58,29,0,,59,37,41,58,0,41,70,84,89,6,5,509 +87,83,57,57,60,57,0,,34,44,44,43,0,44,65,30,79,7,5,502 +87,81,100,100,100,77,0,,40,65,64,59,0,62,87,35,100,8,5,503 +87,89,81,78,62,31,0,,65,38,25,40,0,65,80,63,76,9,5,522 +87,85,75,77,63,51,0,,64,62,62,43,0,56,80,63,84,10,5,505 +88,85,70,78,79,44,0,,4,65,51,48,32,81,82,62,91,1,5,506 +88,90,71,72,78,32,0,,14,81,81,54,27,92,82,73,78,2,5,529 +88,86,100,100,93,59,24,He was in my favorite class at Dartmouth,59,96,95,78,51,94,92,73,84,3,5,508 +88,87,100,100,95,77,0,,56,80,77,81,38,81,88,86,84,4,5,509 +88,80,91,85,78,70,0,,53,65,55,68,49,49,88,77,83,5,5,510 +88,83,92,86,82,26,0,,51,69,75,63,48,89,91,70,94,6,5,511 +88,89,75,69,62,55,0,,53,28,61,49,48,84,77,75,89,7,5,524 +88,82,38,43,35,52,0,,55,31,33,37,34,57,54,77,39,8,5,513 +88,81,81,81,78,74,0,,53,55,65,54,49,77,97,73,90,9,5,514 +88,84,83,87,92,55,0,,63,76,77,78,51,75,94,67,89,10,5,515 +89,80,25,60,59,58,0,,25,32,32,72,25,76,58,33,61,1,5,516 +89,81,59,58,54,55,0,,16,58,59,59,10,43,60,35,70,2,5,517 +89,82,95,88,87,54,0,,43,80,84,67,9,74,67,55,95,3,5,518 +89,84,74,79,71,74,0,,10,53,61,54,9,53,74,15,77,4,5,519 +89,85,82,85,95,71,0,,35,62,63,61,5,72,81,13,97,5,5,520 +89,90,85,83,82,50,7,"Saw them in Psych class, never spoke",34,58,59,55,7,67,73,41,95,6,5,530 +89,87,83,79,91,65,0,,22,52,61,76,5,79,78,14,90,7,5,522 +89,83,56,66,52,51,25,Was previously in a study with them,46,42,45,47,2,57,57,33,63,8,5,523 +89,88,55,59,47,50,0,,35,40,47,66,6,65,61,57,84,9,5,524 +89,86,68,80,67,53,22,Was in a study with them before,40,55,54,91,7,75,74,57,83,10,5,525 +90,81,89,88,86,70,0,,43,35,65,63,0,86,83,40,91,1,5,526 +90,86,89,88,93,51,0,,53,74,71,72,0,85,85,51,83,2,5,527 +90,87,67,67,57,60,0,,40,65,61,41,0,71,74,39,69,3,5,528 +90,88,55,63,53,70,0,,51,43,44,43,0,37,42,64,44,4,5,529 +90,89,77,68,58,50,21,,55,35,39,37,0,58,64,45,65,5,5,530 +90,84,55,57,57,50,0,,53,33,55,45,0,56,35,42,56,6,5,531 +90,80,65,66,67,67,40,,59,59,60,59,0,55,44,38,55,7,5,532 +90,82,62,61,58,63,0,,35,40,57,47,0,68,67,45,64,8,5,533 +90,83,46,47,48,58,0,,37,36,43,19,0,57,43,43,54,9,5,534 +90,85,20,32,20,56,0,,36,43,30,29,0,44,14,18,37,10,5,535 +100,101,80,75,79,51,0,,15,55,54,25,0,34,71,22,58,1,6,552 +100,103,77,57,68,41,0,,13,58,76,15,0,35,54,6,76,2,6,575 +100,108,47,59,41,45,0,,34,35,44,0,0,69,42,51,77,3,6,623 +100,106,26,32,26,65,0,,10,26,28,0,0,25,34,24,47,4,6,604 +100,107,97,83,87,53,0,,44,82,73,0,0,92,78,79,81,5,6,612 +100,110,81,82,61,43,0,,36,35,57,0,0,73,72,61,69,6,6,644 +100,104,90,90,89,54,0,,39,56,38,0,0,81,89,81,74,7,6,583 +100,109,47,30,21,21,0,,14,37,30,0,0,29,42,16,67,8,6,634 +100,102,78,67,78,65,0,,36,41,71,0,0,34,72,74,87,9,6,565 +100,105,64,28,34,95,0,,2,86,36,0,0,9,20,24,58,10,6,595 +101,106,31,36,32,54,0,,32,29,44,37,0,11,29,45,53,1,6,597 +101,110,96,97,96,51,86,FRIEND,90,86,80,65,20,42,68,58,90,2,6,636 +101,108,88,70,72,51,0,,26,39,60,54,18,83,73,36,65,3,6,617 +101,107,98,96,93,51,24,Met and talked during orientation,62,86,74,51,9,62,86,54,80,5,6,606 +101,103,55,66,63,59,0,,24,40,72,58,12,13,59,41,69,6,6,573 +101,100,73,66,87,58,0,,25,40,82,51,10,21,71,36,73,7,6,552 +101,102,34,58,48,63,7,First aid class,30,41,36,41,12,32,43,33,41,8,6,563 +101,105,61,74,74,57,0,,40,88,78,66,13,33,64,18,70,9,6,589 +101,109,13,29,4,12,4,Same class,19,5,6,10,0,0,8,14,2,10,6,635 +102,104,62,67,50,50,15,met briefly during DOC Trips,39,49,36,67,0,75,61,24,73,1,6,577 +102,103,38,66,59,59,0,,43,60,61,56,0,32,36,10,61,2,6,566 +102,110,82,74,68,50,0,,51,56,36,69,0,71,61,39,58,3,6,638 +102,108,45,59,50,50,0,,37,41,41,53,0,68,51,59,74,4,6,621 +102,107,71,69,73,50,50,He is in some of the clubs I am in.,51,72,50,78,0,100,72,34,62,5,6,608 +102,106,69,54,69,50,0,,31,50,50,38,0,38,57,38,51,6,6,602 +102,109,80,80,65,38,0,,50,50,38,37,0,74,86,59,74,7,6,631 +102,101,51,40,41,51,0,,39,39,50,39,0,40,36,38,60,8,6,563 +102,105,37,65,45,50,13,I see him in my PSYC 6 class,36,39,35,59,0,37,49,19,54,9,6,591 +102,100,65,64,54,49,0,,23,35,38,60,0,35,58,57,62,10,6,565 +103,102,85,95,68,65,0,,49,67,62,63,0,14,67,24,96,1,6,566 +103,110,98,93,60,59,0,,57,25,65,75,0,73,87,15,81,2,6,637 +103,106,64,84,74,48,0,,61,64,56,53,0,14,60,23,65,3,6,598 +103,104,42,56,45,50,0,,25,18,26,64,0,92,67,35,73,4,6,580 +103,107,99,100,79,38,0,,56,69,41,71,0,90,98,26,85,5,6,607 +103,108,26,41,29,51,0,,15,28,30,49,0,62,37,23,43,6,6,619 +103,109,94,99,83,25,0,,69,76,76,54,0,18,92,31,68,7,6,629 +103,101,95,87,71,64,0,,68,69,68,54,0,31,70,61,86,8,6,573 +103,105,12,28,54,80,0,,20,38,60,63,0,25,33,12,55,9,6,588 +103,100,98,86,60,52,0,,57,64,72,60,0,58,75,38,95,10,6,575 +104,106,34,70,63,62,0,,40,53,29,36,0,36,48,65,61,1,6,596 +104,102,60,65,70,68,5,Same trip section,36,36,62,46,0,53,63,20,70,2,6,577 +104,109,68,89,59,12,0,,42,7,4,18,0,4,93,70,92,3,6,627 +104,103,59,61,54,67,0,,45,36,47,46,0,55,61,16,68,5,6,580 +104,110,88,93,90,57,0,,58,53,82,58,0,76,77,41,95,6,6,640 +104,107,90,90,92,49,0,,53,49,56,53,0,78,82,34,83,7,6,609 +104,100,78,78,74,51,0,,45,87,67,66,0,78,62,32,97,8,6,583 +104,108,73,78,80,51,3,I met him through this study when we were supposed to do our conversation the first time,52,37,64,52,0,69,69,15,69,9,6,625 +104,105,18,22,29,85,0,,16,10,11,53,0,14,14,12,11,10,6,592 +105,108,31,43,60,36,0,,33,30,33,67,0,60,42,7,39,1,6,620 +105,109,69,63,57,56,0,,71,53,72,6,7,0,64,54,55,2,6,628 +105,103,26,60,58,51,0,,34,54,43,42,0,34,46,18,45,3,6,588 +105,101,56,82,83,59,0,,77,79,79,49,0,35,72,43,58,4,6,589 +105,106,95,99,92,61,0,,79,72,65,45,0,19,87,56,73,5,6,603 +105,102,78,73,79,53,0,,54,55,58,63,0,59,65,28,69,6,6,591 +105,104,53,65,65,41,0,,54,53,61,44,0,64,83,16,60,7,6,592 +105,107,82,80,79,26,69,Floormates,73,62,67,76,59,90,65,26,65,8,6,615 +105,110,70,78,72,58,0,,58,63,60,80,41,61,60,29,72,9,6,645 +105,100,69,62,64,59,0,,44,57,57,68,0,55,65,21,64,10,6,595 +106,104,71,90,86,50,0,,53,80,60,65,0,51,81,53,65,1,6,596 +106,101,50,39,73,51,0,,59,38,64,66,0,29,46,40,44,2,6,597 +106,103,73,77,81,53,0,,58,36,71,71,0,17,77,50,68,3,6,598 +106,108,65,66,55,65,0,,51,37,34,57,0,70,58,42,60,4,6,618 +106,110,100,100,88,87,0,,65,58,51,79,0,100,75,40,73,5,6,641 +106,107,100,100,100,63,0,,74,64,66,67,0,45,93,61,100,6,6,610 +106,102,58,69,76,35,0,,57,65,66,65,0,26,50,65,62,7,6,602 +106,105,65,81,69,67,1,,51,59,47,67,0,56,67,66,76,8,6,603 +106,100,33,36,37,59,0,,37,62,42,55,0,47,36,28,62,9,6,604 +106,109,100,100,100,40,51,We are fellow PMHAs for the Dartmouth cohort of the nationwide effort Project LETS.,79,85,59,57,0,14,82,38,100,10,6,633 +107,101,92,86,87,59,32,"I have met my partner through meetings with my house identity at Dartmouth, CPR and First Aid classes, and random interactions.",35,58,57,21,0,64,81,52,85,1,6,606 +107,103,61,35,32,65,0,,17,40,14,29,7,28,32,25,34,2,6,607 +107,102,98,96,86,55,25,We took a First Aid class together and interact in random passings.,42,69,60,16,0,48,75,50,88,3,6,608 +107,104,75,76,39,51,0,,18,42,56,15,3,41,41,36,62,4,6,609 +107,106,71,70,64,72,0,,24,59,29,5,0,37,66,65,64,5,6,610 +107,109,89,91,74,32,0,,51,76,50,15,0,58,100,82,75,6,6,632 +107,100,77,80,75,51,0,,19,57,62,68,33,71,78,33,60,7,6,612 +107,108,73,73,71,50,0,,16,30,46,71,22,61,56,58,64,8,6,624 +107,110,96,99,88,49,0,,26,77,77,86,75,90,100,63,88,9,6,643 +107,105,49,41,82,81,50,He is my floormate.,53,39,34,61,3,22,38,25,59,10,6,615 +108,109,60,75,26,42,0,,33,24,0,30,0,0,58,78,86,1,6,626 +108,101,77,82,61,73,0,,50,37,29,41,0,62,69,69,85,2,6,617 +108,106,32,56,15,60,0,,30,12,3,33,0,32,46,61,77,3,6,618 +108,103,65,68,40,60,0,,32,41,29,32,0,62,75,41,60,4,6,619 +108,105,63,64,55,54,0,,38,39,45,75,1,60,62,21,48,5,6,620 +108,102,77,88,38,54,0,,69,43,33,37,0,68,74,72,65,6,6,621 +108,110,95,92,79,64,0,,58,59,77,77,0,96,77,36,85,7,6,642 +108,100,89,68,41,60,0,,59,39,72,61,0,79,80,32,85,8,6,623 +108,107,78,77,60,69,0,,34,61,66,61,0,96,91,32,76,9,6,624 +108,104,91,87,74,62,8,saw him before a session,60,68,77,83,0,71,86,63,73,10,6,625 +109,108,79,49,49,40,10,same stats class last winter,28,18,19,83,13,92,51,52,45,1,6,626 +109,104,76,62,51,79,0,,25,50,48,66,0,41,53,26,52,2,6,627 +109,105,64,73,66,91,0,,21,35,49,72,0,50,51,9,57,3,6,628 +109,103,97,91,72,76,0,,25,77,80,68,0,4,79,17,87,4,6,629 +109,110,100,100,100,64,3,same psych class,31,71,73,89,0,71,76,36,79,5,6,639 +109,102,85,86,78,74,4,same neuro class,34,69,59,72,0,54,76,22,72,6,6,631 +109,107,85,77,63,87,0,,18,56,42,73,0,70,76,23,67,7,6,632 +109,106,79,78,0,52,35,i used to be in the same club as him,36,0,0,23,0,50,100,21,50,8,6,633 +109,100,70,64,58,55,10,same classes,34,49,50,78,0,60,68,10,56,9,6,634 +109,101,22,19,0,100,0,,1,0,0,22,0,5,0,4,51,10,6,635 +110,101,92,89,90,56,82,friend ,84,89,89,7,0,71,72,41,92,1,6,636 +110,103,61,59,62,57,0,,9,19,28,25,0,21,41,8,31,2,6,637 +110,102,30,29,11,70,0,,4,5,10,13,0,11,12,10,28,3,6,638 +110,109,84,84,82,33,0,,24,77,75,9,0,64,78,30,77,4,6,639 +110,104,74,65,63,44,0,,6,30,64,17,0,57,57,19,27,5,6,640 +110,106,65,60,63,61,0,,16,58,59,8,0,31,59,5,61,6,6,641 +110,108,64,59,55,57,0,,8,57,55,33,0,59,59,18,57,7,6,642 +110,107,72,68,63,61,0,,12,56,60,5,0,61,66,15,58,8,6,643 +110,100,61,58,56,60,0,,6,20,33,25,0,38,55,10,60,9,6,644 +110,105,0,0,0,75,0,,0,5,0,22,0,0,0,0,0,10,6,645 \ No newline at end of file diff --git a/Data/subject_id_key.csv b/Data/subject_id_key.csv new file mode 100644 index 0000000..b299ab8 --- /dev/null +++ b/Data/subject_id_key.csv @@ -0,0 +1,388 @@ +video_name,S1,S2 +1_10,1,10 +1_11,1,11 +1_2,1,2 +1_3,1,3 +1_4,1,4 +1_5,1,5 +1_6,1,6 +1_7,1,7 +1_8,1,8 +1_9,1,9 +10_2,10,2 +10_4,10,4 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the gaps in conversation more than friends -> Emma M. Templeton, Luke J. Chang, Elizabeth A. Reynolds, Marie D. Cone LeBeaumont and Thalia P. Wheatley +> Long gaps between turns are awkward for strangers but not for friends +> Emma M. Templeton, Luke J. Chang, Elizabeth A. Reynolds, Marie D. 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